Next Article in Journal
Socioeconomic Productive Capacity and Renewable Energy Development: Empirical Insights from BRICS
Next Article in Special Issue
The Adoption and Use of Smart Assistants in Residential Homes: The Matching Hypothesis
Previous Article in Journal
Construction and Optimization of an Ecological Network in the Comprehensive Land Consolidation Project of a Small Rural Town in Southeast China
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

The Integration of WoT and Edge Computing: Issues and Challenges

Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan
Department of Computer Science and Information Technology, Peshawar Campus, University of Engineering and Technology, Peshawar 25000, Pakistan
Department of Computer Science, City University of New York, New York, NY 10019, USA
College of Computing & IT, University of Doha for Science and Technology, Doha 24449, Qatar
Author to whom correspondence should be addressed.
Sustainability 2023, 15(7), 5983;
Submission received: 6 February 2023 / Revised: 15 March 2023 / Accepted: 16 March 2023 / Published: 30 March 2023


The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.

1. Introduction

The internet has grown to be a crucial medium for online communication nowadays. On the other hand, small embedded servers are now readily available thanks to decades of research into the technology of a small web server. Web services in particular have shown to be crucial for developing interactive applications on the internet nowadays. Smart components with embedded web servers are readily added into an existing website as web resources. Reusing current web standards and technology to connect the online and offline worlds is only logical. In light of this, a study application views IoT as part of the Web of Things (WoT). With the help of smart devices, existing web technologies may be improved and repurposed to provide new apps and services, flexibility, personalization, and high productivity. In brief, WoT allows the devices to speak the same language in order to be able to communicate and interact with one another, which is distinct from the standard IoT approach that assigns the device an ordinary IP address and permits the device to connect to the internet and work online more freely.
A collection of web services may be located, labeled, and utilized according to the WoT idea. By expanding web-only services from the network world to the online world and worldwide services, it therefore broadens the spectrum of traditional web services. Additionally, WoT is actually an ecosystem of services that aims to arrange various service kinds in a helpful manner to make them more intelligent and human-centered. The internet has grown to be a crucial medium for online communication nowadays.
Consumers may now choose from a variety of smart devices, including voice-activated smart speakers, smart plugs, smart lighting, smart sensors, smart door locks, and more. IoT and smart cities are mutually inseparable since the idea of a smart city is built on the integration of IoT technologies. Data about numerous facets of residents’ life can be collected via IoT devices such as sensors and cameras.
For IoT applications (smart transportation, smart grid, smart city, etc.), a more delicate scenario develops, meaning that extremely quick response times are not negotiable and should not be satisfied with conventional cloud computing-based services.
A summarized previous effort, current work, and a fully integrated approach to the IoT and edge were presented [1,2,3,4,5,6,7]. On an edge network, near to the user, edge computing combines computation and data storage [8,9,10,11,12,13,14,15]. When we compare edge computing and cloud computing, edge computing will transfer data and computation to network edge which can considerably reduce the latency in message transfers [12]. The greater amount of site visitors can be lowered since the threshold compute node areas are in the direction of end users. Redirecting visitors from overloaded nodes to free nodes can improve response times for WoT applications in comparison to similar cloud computing services. The success of the Internet of Things and cloud services needs edge computing. In edge computing, data computation occurs partially at the network edge which reduces burden on the cloud. Edge computing is used to reduce latency, battery life problems, bandwidth costs, privacy and security issues [8]. Security and privacy are very important concerns for sustainable development of smart homes and residential communities. Over-the-counter, computing may also send calculations and overhead communication from limited battery nodes or power to nearby nodes with significant electrical resources. By doing this, the limited battery nodes lifespan can be increased, hence extending the WoT community’s existence.
The paper is divided into multiple sections: we briefly go through the fundamental ideas of edge computing and the Web of Things (WoT) and talk about how the two technologies could be combined in Section 2. The integration of the web of things and edge computing are discussed in Section 3; we have presented an edge computing based WoT architecture as well. In Section 4, the challenges and advantages of WoT and edge computing have been outlined. In Section 5, future challenges have been discussed. In Section 6, the conclusion of the paper has been presented.

2. Review of WoT and Edge Computing

In this part, the fundamental ideas of edge computing and the Web of Things (WoT) have been explored and discussed including how the two technologies could be combined. Further, working of different authors on Web of Things is discussed in Table 1.

2.1. Web of Things

The use of web-based technologies to access information and document services gave rise to the Web of Things (WoT). Every physical thing in World of Warcraft has a digital equivalent known as a “Virtual Object” or “Web Object” [16]. These items may be accessed through the HTTP (Hypertext Transfer Protocol) protocol by using the RESTful API, which is based on the REST (Representational State Transfer) property. An OWL-based semantic specification, a REST API to access its features and operations, and an HTML or JSON representation of the web object are all possible.
There are three ways how online material is incorporated into the web: first, directly hosted on a hardware; second, embedded; and third, webserver. An embedded device with only 200 bytes of RAM and 7 KB of code may operate the web server with clever configuration [17]. A web server integrated into a gateway device or a cloud service can be used to control the physical content of non-convertible goods. The gateway device in these applications transforms traffic to HTTP into a virtual object identification. Figure 1 depicts these three referral techniques. The Index contains a summary of online integrated technology authorization [18]. Applications in WoT communicate with objects using the RESTful API and the HTTP standard protocol.
As a result, the document is simple to retrieve, usable in online applications, and compatible with already existing web resources [17]. By highlighting sensory abilities and reviving the global open market, it encourages the development of cyber-physical services and added value [19]. WoT effectively transforms the outside world into a collection of online software resources.
Table 1. Important information of papers along with citation of papers on Web of Things.
Table 1. Important information of papers along with citation of papers on Web of Things.
Author NameWorking of AuthorsCitations of Paper
(Baraković, S., et al., 2020)Examine the connections and interactions between quality of life (QoL), quality of experience (QoE), perceptions of safety, and the variables affecting those perceptions.[20]
(Niwarlangga, A.C. and M.Z.C. Candra. 2020)It focuses on creating a framework for Semantic Web of Things applications that maximizes productivity while creating Semantic Web of Things applications.[21]
(Datta, S.K. and C. Bonnet. 2018)A list of WoT components that provide communication between two IoT systems.[22]
(Kamilaris, A., et al., 2017)Create an ecosystem for urban computing that blends the network of things idea with big data analytics and event processing in order to realize the goal of creating smarter cities that provide their citizens with real-time information about the city.[23]
(Younan, M., S. Khattab, and R. Bahgat, 2021)Provide an overview of wireless sensor networks (WSNs), the Internet of Things, and its future paradigm (WoT) with a discussion of key elements, major layers, major challenges, and annotation formats[24]
(Cimmino, A., M. Poveda-Villalón, and R. García-Castro, 2020)It provides a mechanism based on SPARQL queries to transparently discover and access IoT devices that publish heterogeneous data.[25]
(Premkumar, M., et al., 2022)To identify malicious behaviors in WoT, such as normal, botnet, brute force, DoS/DDoS, infiltration, port scans, and web assaults, an intrusion detection system based on deep belief networks is used.[26]
(Parwej, F., N. Akhtar, and Y. Perwej, 2019)Provide a thorough overview of the Web of Things, including its architecture, open platform, devices that make it possible, security measures it takes, and use cases.[27]
(Sciullo, L., et al., 2019)Authors proposed a WoT Store, a novel platform for managing and easing the deployment of Things and applications on the W3C WoT.[28]
(Vanden Hautte, S., et al., 2020)Demonstrate a dynamic dashboard that fixes these problems. As long as a RESTful Web of Things compatible gateway is available, sensors, visualizations, and aggregations may be automatically found.[29]

2.2. Integration Pattern for Connecting Things to Web

2.2.1. Direct Integration

The straightforward model is a straightforward integration paradigm in which the gadget is directly exposed into the Web of Things API, which is beneficial for reasonably capable devices with TCP/IP and HTTP capabilities and direct internet connectivity (e.g., Wi-Fi cameras). For home network devices that might need to employ NAT or TCP tunneling to disconnect firewalls, this pattern might be challenging. It also exposes the gadget to risks to its security on the internet as shown in Figure 2.

2.2.2. Gateway Integration

For devices having applications that can utilize the HTTP server directly and use the gateway to integrate them with the web, the gateway integration paradigm can be helpful. Figure 3 shows that this pattern was beneficial for devices using PAN network technologies, such as Bluetooth or ZigBee offline, or devices with low power. All varieties of IoT devices that are already connected to the web may be integrated via the gateway.

2.2.3. Cloud Integration

In the cloud integration paradigm, a cloud server serving as a distant gateway is given access to the Web of Things API, and the device converses with the server in the background via a separate protocol. This strategy is helpful for several devices that need to be integrated centrally throughout a wide region as shown in Figure 4.

2.3. Edge Computing

The management, processing, and distribution of data from billions of devices worldwide is changing as a result of edge computing. End-to-End computing systems are still being propelled by the explosive expansion of Internet of Things (IoT) devices and new applications that demand real-time processing capability. End-to-End computing systems can create or support real-time applications such as video processing and analytics, driverless cars, artificial intelligence, and robots more quickly thanks to quick network technologies such as 5G wireless.
At the most fundamental level, using computers eliminates the reliance on location and puts counting and data storage closer to the devices where it is gathered. This makes sure that data, especially real-time data, is free of latency problems that could affect the performance of an application. Additionally, by lowering the quantity of data that has to be processed in a central or cloud-based environment, organizations may save money by processing locally. In response to the growing number of Internet of Things (IoT) devices that connect to the internet in order to access the cloud and recover data, edge computing can be very helpful. Additionally, a lot of IoT devices produce a lot of data when they are in use.
In Table 2, we have mentioned several works related to the Internet of Things and the Web of Things. Mostly, authors have found that interoperability due to heterogeneity is the biggest problem for IoT and WoT environments, which can be solved mainly by using different standards. In our opinion, there is a need to develop more open standards for IoT and WoT. Additionally, there is a need to find out more solutions for interoperability in these environments due to the expected extensive use of IoT in the upcoming future. Furthermore, there is a need to work more on interoperability issues where edge computing is used as less research has been published on edge computing.
Edge computing ia used by many people these days. We have mentioned the most significant works related to Edge Computing in In Table 3.

2.3.1. Edge Computing Architecture

Using computer resources outside of traditional and cloud data centers, edge computing is a technology that allows work to be completed near to where data is produced and where actionable analytics can be carried out deeper. Engineers can develop applications by utilizing and controlling computing resources situated in distant locations, such as factories, shops, warehouses, hotels, distribution centers, or automobiles because it substantially reduces latencies, lowers demands on network bandwidth, increases privacy of sensitive information, and enables operations even when networks are disrupted.
More nodes could be needed to transfer application functionality to the edge. The following are some of the essential elements of the edge ecosystem.

2.3.2. Cloud

It can be a repository for applications, and it could be a public or private cloud. These clouds host and distribute software necessary to organize and control the numerous edge zones. These cloud workloads will communicate with edge workloads, local workloads, and device workloads. All the data those other nodes require may come from and be stored in the cloud.

2.3.3. Edge Device

A tablet is a unique gadget with a built-in computer. Peripherals such as small manufacturing machinery, ATMs, smart cameras, or autos might be the subject of interesting research. This gadget often has constrained computational capabilities owing to financial reasons. End-to-end devices frequently have 1 or 2 cores, 128 MB of RAM, and maybe 1 GB of permanent local memory ARM or x86 class CPUs. Although they have great power, peripherals are the same as they are now.

2.3.4. Edge Node

Any device or edge server that can utilize a computer is referred to as an edge node. On Android gadgets, including Android tablets and Android telephones, we put in force aspect. IoT edge nodes perform three responsibilities: they get hold of statistics from IoT nodes, perform semantic analysis, and supply data to cloud servers. For communique, it uses Socket and the MQTT (MQ Telemetry Transport) purchaser, and for semantic reasoning, it makes use of the Jena framework. There are two working modes for it: the first mode entails the brink nodes storing the ontology domestically; the second mode entails the brink nodes using an HTTP request to obtain the ontology from the remote Ontology Repository Server, which may additionally transmit any or all of the ontology. Figure 5 depicts the brink nodes’ procedure. These nodes first wait for RDF (Resource Description Framework) facts from IoT nodes, then use the nearby repository to seek up the ontology or make an HTTP name to the semantic server. They then perform the analysis and simultaneously transfer the statistics to the cloud server.

2.3.5. Edge Cluster/Server

An edge server is a computer which is situated in a distant office, such as a factory, retail space, hotel, or some financial institution. Typically, industrial PCs or rack computers are used to construct terminals and servers. End-to-end servers with 8, 16, or more processing cores, 16 GB of memory, and hundreds of GB of local storage have been very typical. Business operations and shared resources are supported by an end-to-end cluster or server.

2.3.6. Edge Gateway

An edge gateway is often an edge cluster or server that offers services for network tasks such protocol compilation, termination networking, tunneling, security systems, or wireless connections in addition to being able to handle corporate duties and shared services. Edge ports are frequently distinct from peripheral devices, even if certain edge devices can perform limited network functions or act as gateways. IoT sensors are static devices without built-in or computer memory that gather and transmit data to the edge or cloud. This makes it impossible to ship containers.

2.3.7. Edge Computing Implementation

Edge computing has concentrated on creating edge computational models in order to make use of the aforementioned structure. The first two patterns generally resolve the following:
  • Hierarchical Model: The edge structure is divided into categories, defining resources and functions based on distance, considering that edge/cloudlet servers might be utilized by end users in various locations. As a result, the hierarchical model must specify the edge computing network’s topology. Numerous studies on the hierarchical model have been conducted. In [48] for instance, a phased paradigm using Mobile edge computing (MEC) servers and cloud infrastructure was developed. Because the MEC enables them to meet their computational and storage demands, mobile users in this model may access the services they have requested. Tong, L., Y. Li, and W. Gao in [49] has proposed a cloud-based methodology that may be utilized to deliver the required loads for mobile users. In this concept, the terminal servers are utilized in conjunction with a regional edge cloud that is constructed as a tree configuration and used cloudlet servers at the network edge. The computing capabilities of peripheral servers may be reconstructed to handle heavy loads by adopting this design procedure [50].
  • Software Defined Model: Additionally, maintaining IoT and edge computing will be quite challenging given that hundreds of apps with millions of users and end devices have been involved. IT management complexity may be successfully addressed by Software Defined Networking (SDN) [51,52,53,54]. The SDN model has been the subject of several research projects. For example, in [53], the capabilities of MEC systems with software-defined systems provide a suitable software model. Costs related to management and administration might be decreased in this way as mentioned by Du, P. and A. Nakao in [54]. In order to merge the capabilities of MEC systems with software-defined systems, a special software model should be proposed. Management and administrative costs might be decreased in this way. Authors in [55] presented a cutting-edge operating system that strengthens network and service platforms by utilizing freely accessible open-source technologies. Salman, O., et al. in [56] planned to combine three novel ideas: Network Functions Virtualization, Software Defined Networking (SDN), and MEC. The system may then be scaled up to support IoT deployments anywhere while achieving the highest MEC performance on the mobile network. Lin, T., et al. in [57] offer the creation of intelligent applications within the Software Defined Virtual infrastructure of the smart edge frameworks, which may be utilized to facilitate the creation of a broad variety of distributed network resources and applications.

3. Integration of Wot and Edge Computing

We have discussed WoT integration and edge computing capabilities in this section. We compared the functionality of the WoT and the edge computing in order to demonstrate how edge computing enhances WoT performance. We have also concentrated on transmission, storage, and computation aspects.

3.1. Overview

The Thing Description (TD) proxy format, which is based on JSON, must be handled and parsed by the user. The information model and the format’s output in JSON-LD compliant can be processed using either JSON libraries or a JSON-LD processor. In addition to facilitating semantic processing, such as interpreting RDF triples, anticipating semantics, and completing tasks based on ontological terms, the usage of JSON-LD processors in TD processing can provide greater freedom. TD is an example (i.e., it discusses each item, not just certain sorts of objects) and goes beyond the standard (web) language. There may be other representations of objects such as HTML-based user interfaces, just business visualizations, or non-web presentations in closed programs. However, at least one TD presentation is necessary in order to qualify.
Interacting with a particular object using WoT Thing Description, which is a standardized and understandable machine format, enables interaction between all the different IoT platforms, such as biological systems. A Thing directory that manages the list of available objects and often keeps track of their TD representation can also help with this. A Web of Things can be created by connecting the definitions of things in WoT with other objects and other online resources. To reflect the Network-to-Network interface, or WoT Interface of a Thing, items must be handled in the network system components using a software stack.
Nothing may also serve as a synopsis of the apparent business. Providing a single WoT Object Description with an integrated set of visual business capabilities is one design choice. When a composition is quite complicated, its TD may relate to Sub Things that are arranged hierarchically inside the composition. Only broad information and possibly all-encompassing capabilities have been present in the main TD, which serves as an entrance point. This makes it possible to gather together some elements of More Complex Things. Figure 6 interprets the Data Analysis and Monitoring using IoT and edge computing technology. In addition to hierarchical structures, constraints also apply to broad connections between objects and other resources. Link types describe how items are connected, such as a switch that controls a light or a room that is motion-activated.
In general, Web linkages between items allow people and machines to browse the Web of Mediators, which can serve as proxies for objects, with the Mediator having a definition for a WoT Thing that is similar to the original Thing but points to the WoT Interface made available by the Mediator. Mediators can design a new item in the Multiple Items offered or enhance existing items with additional skills, thus creating a real company. Since Mediators have WoT Thing definitions and a WoT interface, they seem to consumers as Objects, and as a consequence, they will not be shown in Objects in the form of a horizontal device such as the Web. Identity in WoT object descriptions should allow for several TDs to represent the same distinctive item or specific visible business. With the customer, items can be grouped together to enable Thing-to-Thing communication.
Usually, the conductor can be included into the software component, which also utilizes the Object behavior. This may use a Thing to specify the customer conductor configuration. IoT sensors are collecting big data that are expected to grow exponentially every year. Using edge computing allows businesses to simplify and accelerate analytics and obtain the right information at the right time, as shown in Figure 6. All IoT application ranges, including the tool level, the limitation level, and the cloud level, adhere to WoT standards. This encourages common place connections with APIs in all relevant ranges and enables a variety of integration models, such as cloud computing, or more specifically, a connected cloud, as well as Thing-to-Thing, Thing-to-Gateway, Thing-to-Cloud, and Gateway-to-Cloud. In IoT applications, PC structures for two or more carrier providers.
Figure 7 represents the edge computing-based WoT architecture. An embedded device with sensors and actuators that connects the mobile business, where Things are housed, however, was not required by WoT; it can be on an IoT device directly, an edge device such as a gateway, or the cloud. A typical deployment project is one in which local networks cannot be accessed through the internet because of a firewall or IPv4 Network Address Translation (NAT) devices. WoT allows for intermediaries between Things and Consumers in order to address this problem.

3.2. WoT Performance Demands

3.2.1. Transmission

The sum of the transmission time and the processing time may be used to determine the overall reaction time. IoT devices often continually produce vast volumes of data, but they only have a few computational uses [58]. In fact, considerable network delays are undesirable and cannot satisfy Quality of Service (QoS) standards. Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication are two specific examples. Response times should also be very fast when it comes to issues with public safety and requirements for first responders. For real-time data collecting and analysis applications, edge computing provides conventional cloud components that offer several dispersed computer nodes close to end users [4]. In addition, edge computation nodes have a sufficient processing capability to meet IoT requirements. Therefore, IoT application requirements can employ edge computing’s quick transmission time rather than the delays in typical cloud services such as Amazon Cloud or Google Cloud.

3.2.2. Storage

IoT was a fantastic source of data, as it was not already a significant component of large-scale data generation. IoT must thus load huge data at the edge or into cloud storage. However, faster upload times are an advantage of edge storage uploads [59]. It was a challenge to guarantee the integrity, information protection, anonymization, denial, and renewal of the original data since edge nodes work for many businesses [60,61]. Additionally, edge nodes’ storage capacity is constrained and cannot be compared to cloud data centers’ huge and long-term storage capacities. The complexity of data management is further increased by the usage of several edge nodes, which have been integrated when data has been required.

3.2.3. Computation

Numerous IoT devices have limited computing and power capabilities, making it impossible to execute complicated computations locally. The majority of the time, IoT devices merely gather data and transmit it to robust Computing Nodes for processing and analysis. Measuring the edge computing capability was a challenging task because edge nodes’ processing power was constrained.
However, IoT needs can be completely addressed even with low processing power requirements for IoT devices, particularly in real-time applications with edge nodes. Edge nodes also lower IoT device power consumption by loading computational tasks. That developed an issue for IoT. Thing Descriptions’ (TDs) capacity to offer an abstract interface was one of the Web of Things’ most potent characteristics. When device capabilities, device suppliers, or new computer capabilities are made available, this abstraction might not change. Affordances may or may not be equivalent to the physical entity described by the same TD. A software simulation of a TD-compliant device is referred to as a Virtual Object.
The majority of the time, these inputs will be related to data streams that, when analyzed by Artificial Intelligence (AI), will enable that program to imitate the characteristics, operations, and events that would typically be given by a real-world physical device. In a straightforward scenario, software may decipher data from a new door sensor product and imitate the behaviors, characteristics, and events that the older device supported. With this feature, consuming software may protect itself against the assault of bringing new devices into the ecosystem and remain unmodified. The original item description will still serve as the interface definition in the consuming program. If the situation is more complicated, software can handle the data stream to simulate a real device.
These Virtual Objects make it possible to upgrade the capturing hardware, in this example, a video camera, without having to fully rebuild the program that was designed to use the Thing’s original description. As seen in Figure 8, dataflow may also be utilized to allow fresh descriptions of objects alongside older ones and to imitate several Virtual Entities. It will be much easier for device owners to maintain their software and hardware if they can utilize existing Thing Descriptions as an abstraction for Virtual Objects.
  • Use software emulation to provide support for earlier descriptions of objects on modern devices.
  • Offer innovative, potent multipurpose gadgets that can handle a variety of Things’ Descriptions.
  • Let older and newer versions of devices coexist in a device.
  • Protect current software from modifications.

4. Challenges and Advantages of Edge Computing and WoT

The Web of Things and edge computing have several challenges and advantages. These challenges and advantages of WoT and edge computing have been covered in this section.

4.1. Challenges of Web of Things

4.1.1. Search and Discovery of Smart Things

The search and discovery of Smart Things is a significant issue confronting the World Wide Web, examining a huge number of web pages. As a result, the great notion of locating Things becomes extremely impractical when searching by reading HTML pages with links. Because objects are made up of location-based information, often switch between different contexts, and have wordy HTML submissions, searching for them is far more difficult than searching for documents. Compared to typical web pages, the key is smaller. Recent advancements in semantic description, such as Micro formats, which may be integrated with HTML presentations, will undoubtedly assist to improve the perception of the services offered by Web Objects [62,63].
It might be necessary to inform any person or organization of the existence, capabilities, and details of the desired web service. For instance, in order to negotiate a shared objective to establish a new group in accordance with specific requirements, objects need to know the identities of smart objects and web services in their environment. To WoT, search engines are crucial. According to [62], there are primarily two methods for developing a search engine. The push approach involves pushing active sensor results to the search engine, which then uses the information to address queries proactively. In the smart object-based crowdsourcing environment, this approach cannot scale. It can only be applied to systems with a few devices.
As an alternative, the pull approach has the search engine send pertinent data to the sensor only after receiving the user’s query. Although this approach can be scaled up, it is valued for its precision and promptness. Here, our attention is on the latter. More crowdfunding than ever before is being supported by the web and smart object integration. The internet offers a lot of the web-based services and information found in the physical world. More services may be beneficial and convenient for users, but WoT is not a search engine’s worst nightmare. With the massive amount of web content created every day and the mass search of Web2.0, search engines are not an easy task, let alone the mass search of Web3.0 created by trillions of smart objects. A search engine that enables looking for physical world services that meet specific criteria will also be the main service for WoT. Static or slowly changing content written by humans rules the conventional web. WoT’s content is constantly evolving because smart items are generated automatically [64].
As a result, searching for frequently changing content will be supported by the WoT search engine. This is the main issue because the search engine is built on the premise that most web content changes gradually and that updating its index infrequently is sufficient. With many devices in the physical world operating at a frequency of minutes or seconds, this is not possible for WoT. However, some web resources or services are only useful at specific times.
Future installations will also be produced dynamically depending on the situation. Web services for resources might need to be dynamically searched for and retrieved in real time. The dynamics of WoT, which are brought about by features such as the movement and connectivity of intelligent objects, make this issue even more challenging. Real-time information searching and real-time web service discovery are supported by the WoT search engine. On this subject, some ground-breaking work has been performed. The web infrastructure that already exists can be used to support the publishing of sensor and entity data, as demonstrated by Ostermaier, B., et al. [65]. The authors examine and improve current methods (such as Wang, H., et al. [66], Tan, C.C., et al. [67], Yap, K.-K., V. Srinivasan, and M. Motani [68], etc.).
In their work [68], the authors proposed an architecture and system capable of rapidly searching physical objects. They noted that emerging technology trends such as smart textiles, smart paints, RFID tags, smart dust, and HP’s Memory Spot suggest that a broad range of physical objects will soon be equipped with small devices having fundamental communication and processing capabilities.

4.1.2. Data Inconsistency

The current state of the IoT generally demonstrates that a large number of devices are producing real-time data through their interactions. Following the conversion to WoT, a justification for processing the supplied data has been explained. It provides a set of three IoT devices to demonstrate the overall scenario. Every IoT device produces data, which Reasoner will have access to. IoT Device 1, the first of these devices, produces static data, but IoT Devices 2 and 3 provide data that are incompatible. The IoT network protocol gathers all of this data, which are then analyzed by a suitable device, such as a smartphone, computer, or router, and then converted and incorporated into WoT.
After being updated by an RDF identifier, the data produced by the IoT device will be converted to the Semantic Web. Then, Reasoner may receive this data, which is already provided as triplets in accordance with Semantic Web standards. In the end, Reasoner provides consumers with results via a website or another web-based means. The facts, however, are not always accurate; frequently, they display negativity. When portrayed as being challenging to grasp, incompatible data is described as data that is delivered in an ambiguous context.
The weight of this information on the choice may change significantly as a result of this ambiguity, which might provide contradictory or inconsistent results. Contradicting information arises when two separate sources give conflicting information, yet one of them is essential. That was forced into an unstoppable pattern that is unable to provide a clear sequence for various data sources as a result of the proliferation of IoT applications. As a result, managing the contradictory data produced by various IoT sources can be the largest problem.
The right conversion protocol that enables Reasoners to analyze this data is currently absent, although there are instances where Reasoners can function with fixed data from a single source if it goes over the full network of IoT devices. Otherwise, it can result in inconsistent results for the user or possibly harm the machine on which it is installed. Therefore, it is crucial for end users to fix data errors in WoT [69].

4.1.3. Security

The development of proper authorization procedures is required to prevent data leakage or poor performance on shared WoT resources since security is a key concern for the IoT and WoT domains [64]. However, it is crucial to make a distinction between platform access control and object access control. In regard to the first function, WoT uses contemporary web technology to authenticate users when they enter the platform; content visibility can also be automatically changed [28].
The latter now only has two visible levels (public or private); however, it is possible to identify which subscription policy has the strongest characters. The access policy for the Thing, in contrast, is specified in its TD. The current version of the W3C WoT has established a number of authorization models, including token-based models, for restricted access to the topic. Data encryption is crucial, but security best practices and robust user, device, service, and application authentication mechanisms were also required.

4.1.4. IoT Protocol

There is a significant gap between IoT transactions and advances that continue to emphasize the need for covert layers that freely interact with core IoT technologies to ease the curve and improve service development boost overall sustainability and developer learning. The numerous device and version suppliers that are available can be helped by the abstraction layers. IoT devices are in danger when security issues are discovered since they are frequently hacked and may not be able to change their software [70]. Utilizing device ports that are software upgradeable, as opposed to working on hardware-delayed devices, can help achieve this goal and offer superior security. In truth, gateways can make use of device drivers, but this raises the issue of which drivers will be necessary for each individual device and will need the usage of common APIs by drivers to meet IoT standards.

4.1.5. Identity Verification

As part of end-to-end security and trust management, identity verification is crucial for devices, users, apps, and services. It cannot be presumed that WoT users are real or capable of authenticating, unlike typical web application users. As a result, developing techniques for validating metadata, such as the existence of data, the position of a certain sensor, and many other identifiers, will be necessary for trust management to know your customer’s needs. Well-known brands, stringent testing procedures, and even the availability of a big number of individuals in terms of reputation management can serve as the foundation for this trust.
In [71], the authors offer a threat analysis for IP packet fragmentation attacks against the 6LoWPAN adaption layer and suggest a mitigation method that uses time stamps and dots appended to fragment IDs to thwart such attacks. For instance, to ensure present and future compatibility with other web-based software applications, Chen, G., et al. [72] depend on open data portability protocols such as OData, oEmbed, OpenID17, and OAuth18. Only specific groups are permitted to use certain online services. An authentication proxy between users and smart objects, the Social Access Controller (SAC) prototype, is implemented by Guinard et al. [73]. Access control between objects cannot be handled by UN-based approaches, which can only handle access control between persons and things. It is anticipated that a universal yet dispersed access control system would allow for interdependent behavior while protecting the owners’ privacy.

4.1.6. Resilience

We will need to focus on resilience as the usage of WoT based applications grows, and services will need to keep up with demand. Hardware or software malfunctions, such as relevant updates, may prevent or cause some mistakes. Services must be designed to withstand breakdowns, including the capability of implementing the proper corrective actions when sensory reading has been acknowledged to be impractical. Resilience may also be put to the test by cyber threats from adversarial nations. To combat this, considerable attention will need to be paid to fixing security weaknesses, diligent observation of fraudulent activity, and the implementation of strong countermeasures such as security devices.

4.1.7. Things and Avatars

Usually, more data than the raw data sensor can offer was needed for applications and services. Data must also be contextualized within the context of other sources. Regulatory systems must also contextually translate their acts into actions for the contractor. WoT must be able to simulate the actual world at various production levels, allowing for free, competitive marketplaces for services at each level. Because items in World of Warcraft are not just linked gadgets, items may have one or more avatars or presentations. Access control and data management regulations, as well as ownership, detailed descriptions, and services, are all features of avatars. The avatar can also be accessible via web technologies and has a URI. Avatars assist in developing software and services that mix data from several sources with varying degrees of output.

4.1.8. Smart Searches

Intelligent goal-based searches will have more chances to open service standards. Additionally, the procedure begins as it usually does when the user enters a search string. Using an easy-to-use interface tailored to a particular aim, search engines utilize six-way rules to identify targets, extract pertinent search parameters, and lead them to registered services. These specialized services may make further service requests as necessary, but in order to appear on the search engine results page, they must be able to deliver results in under a second. Be aware that the user can access a particularly challenging question in the results by clicking on a link or symbol. This enables users to receive time-consuming chores as out-of-band alerts.

4.1.9. Legal Implications

Legal implications might be the customer paying for services that are free or inexpensive. Since the contract is set up between the service provider and the customer for premium services, it can be reversed. There could be disagreement on the test technique with regard to trust and reference. As WoT raises the likelihood of accidents, the insurance sector may be in disarray.

4.2. Advantages of Web of Things

4.2.1. Current Open Ecosystem

There are many of ways to use already-developed languages such as JavaScript, data input techniques such as Efficient XML Interchange (EXI) and JSON, metadata and data formatting tools such as those created by Open Data group, as well as protocols such as HTTP and the Web. These were only a few secrets: for instance, utilizing driver’s devices on gateways that employ the IoT protocol, JavaScript may be used directly to interface with IoT sensors and actuators in the browser, whether they are in the cloud or at the network’s edge. These devices operate the forum and show web settings. This can be performed with ease on smartphones and tablets, and operating systems such as iOS and Android already offer native APIs for Bluetooth or Near-Field Communication (NFC) access to onboard sensors and proximity devices. In the future, this work may combine APIs with different protocols, such as MQTT (MQ Telemetry Transport) and CoAP (Constraint Application Protocol). Other devices nearby can be notified via Low Power Bluetooth [74].

4.2.2. Home Hubs

Another alternative to hosting services are home hubs. Nowadays, most individuals have a hub at home that serves as a Wi-Fi hotspot for high-speed internet access. It is likely that these gadgets will evolve into platforms for offering IoT home gadget services. These centers must include open standards that allow consumers to install service with any provider they choose if they are to have the best chance of success. Portal devices that can be placed close to the home will also be necessary to access IoT devices outside of the hub.
A key component of a smart home is the ability of residential facilities and household appliances to communicate with one another thanks to the “ECHONET Lite” protocol. An HTTP message interpreter can be used by WoT clients to connect to the ECHONET Lite Web API application. Supporting transparent communication between WoT consumers and non-WoT devices that use the HTTP protocol as a transport protocol, such as the ECHONET Lite Web API device, may be desired for the WoT standard as shown in Figure 9.
  • Configuration by the user of the device before starting to use the service
    The user of the device logs in to the server of the “Household Management Service Provider” with which the user has a contract.
    The user specifies the lighting, air conditioning, and security sensor operating modes for when the user is away from home, when the user returns home, and when the specified time has elapsed after the user returned home.
  • When the device user leaves home
    The user of the device accesses the server of the “Home Management Service Provider” using a smartphone and informs the server that the user is about to leave the home.
    The server updates the operating modes of the lighting, air conditioning, and security sensor according to the configuration entered by the user during the time when the user is away from home.
    The server reads the operating modes of the lighting, air conditioning, and security sensor and informs the user’s smartphone about these operating modes.

4.2.3. Cloud Platforms

Platforms built on the cloud may be planned to expand flexibly as platform loads increase. One illustration is the Cloud Foundry-based open-source design forum and service naming which can allow using Node-RED and be transmitted to the naming cloud through this forum. Service owners may create security guidelines that take access control, static flow analysis, and robust monitoring into consideration.

4.2.4. Standard Vocabularies and Repository

A standard method for identifying the links supplied by services and the websites are necessary in the free market for services. A helpful analogy for organizing Linux packages is that of a tree, where each package contains its own name, version number, and declaration of the names and spacing of the other packages it depends on. However, how can you promote the reuse of metadata and data words and support current repositories that developers can browse and search, where they are encouraged to post brand-new phrases.

4.2.5. Monetizing

It goes without saying that generating a positive environment such as this requires monetizing a service. This will require vendor-neutral open standards for web-scale ecosystems. The Web Payments Interest Group, which the W3C has created, aims to eliminate web requests from payment systems. In reality, this can take the form of one-time fees, recurring charges, charges for individual usage, or non-discriminatory procedures for providing particular users or groups of users accesses to a particular service. The basis for legally enforceable agreements between service providers and customers may be found in contract law, which is essentially the same across the board. These agreements can apply to policies that provide data owners choice over how the data is used and for what reasons in addition to payment systems.

4.2.6. Cyber Physical Systems

In order to accomplish common system objectives such as regulating traffic on city highways, keeping a comfortable climate in huge buildings, and monitoring close to the smart grid for power supply, the network-physical system really controls the sensor and actuator lock. To fulfil the demands for low latency and tight connection across several actuators, control of such systems must be extended to the edge of the network and represented at multiple output levels. As a result, protocols will have to be modified to account for latency and vibration requirements, and it is possible that the service layer will have to transfer QoS requirements to the network layer. Furthermore, as latency may be less important than process intensity, different output levels may have different needs.

4.3. Challenges of Edge Computing

4.3.1. Bandwidth

High bandwidth can speed up transmission times from a latency standpoint, especially for huge data such as videos [75,76]. We can set up access to wireless bandwidth with short-term transmission so that we can transfer data to the edge. On the other hand, operating on the edge can dramatically reduce latency compared to working in the cloud if the workload can be managed there. Additionally, the bandwidth between the edge and the cloud is preserved. As an illustration, practically all data in a smart home setting may be handled at the doorstep using Wi-Fi or other high-speed communication techniques. Additionally, the shorter transmission increases the transmission’s dependability. However, even if the transmission distance cannot be decreased since the edge can meet the computing need, the data is processed sooner at the edges, and the download data size is significantly decreased.
To considerably minimize the quantity of data in the case of smart cities, it has been advised to analyze photographs before downloading. Particularly when using the carrier’s data infrastructure, it helps conserve the user’s bandwidth. Globally speaking, bandwidth is preserved in both scenarios and is available for use by other domains to upload and download data. Therefore, we must determine if a high-bandwidth connection is required and what speeds are appropriate given the constraint.
Additionally, in order to avoid concurrency and latency, there were some needs to consider the available computing capacity and bandwidth consumption in order to precisely calculate the workload allocation for each layer.
Users write their own code and deploy it on the cloud while using cloud computing. Where computing will take place in the cloud must be decided by the cloud provider. Users have little to no understanding of how the program functions. One benefit of cloud computing is that the user can see right through the infrastructure. Many calculations can be carried out at the edge rather than a central cloud, as described in edge computing [77].

4.3.2. Latency

One of the most crucial performance testing measures, particularly for collaborative apps and services, is latency [78,79,80,81]. Advanced computational capabilities are offered by cloud servers. They are capable of completing complicated tasks quickly, including speech recognition and picture processing. The computation time is not, however, constrained by the delay. Real-time application/interaction behavior can be significantly impacted by long-term WAN latency [81,82].
Workload should be properly minimized at a neighboring layer with adequate processing power at the network’s edge in order to avoid delays. Instead of uploading all the pictures, for instance, in the case of a smart city, phones can be utilized to process the local photographs first and transmit the information that might be lost back to the cloud. The front edge will be processed considerably more quickly due to the sheer volume of photos and their size. A close-up strategy might not, however, always be the best choice. In order to locate the appropriate layer, there is a need to consider instructions on how to utilize the application. It would be preferable to upload the image to a nearby door or tiny center if the user is playing a game because the phone counting gadget is already in use.

4.3.3. Energy

Batteries are a crucial component of every network. Edge loading work might be viewed as a technique for the final seam construction [83,84] with therefore more energy-efficient to relax all of the burden (or a portion of it) at the edges rather than depending on it, given a particular workload.
The right balance between the computer’s power use and transmission capacity is crucial. Generally speaking, it must first consider the workload’s advantages. If there is no network signal [85], transmission capacity will also depend on the size of the data and the available bandwidth. Only when the airpath is shorter than utilizing the local computer do we prefer to employ edge computing. However, the overall power consumption should equal the total cost of energy for each layer employed if we are interested in the edge compute process as a whole rather than simply the endpoints [81,86].
A local and transmission cost calculator may be used to estimate the power consumption of each layer for the final point layer. The precise assignment plan can alter in this situation. For instance, the workload is continually being uploaded to the top tier because the local data center layer is busy. Multi-step transmission can greatly improve performance as compared to terminal computing, but it also consumes more power.

4.3.4. Cost

From the perspective of service providers, such as YouTube, Amazon, and others, edge computing allows them to operate with the least amount of latency and power, which can enhance their performance and user experience. They may therefore handle the same unit of labor while making more money.
For instance, it may position a popular film on the edge of the building layer based on the preferences of numerous residents. You might be released from this task and given an extremely challenging one on the edge of the city floor. The overall sum may be raised. The investment made by the service provider was equal to what it costs to build and maintain each layer’s materials. Providers may impose fees on customers depending on location data in order to maximize the value of each layer’s location data. To ensure service provider profitability and user approval, new cost models need to be updated.

4.4. Advantages of Edge Computing

4.4.1. Distributed Computing

A distributed computer in a distant data center might utilize the application just as much as one in a central data center; hence, edge infrastructure must constantly be accurately assessed.

4.4.2. Security and Accessibility

Companies may integrate technological and physical security by integrating with single-premise applications in the data center, creating a virtual barrier around resources. Edge computing transforms the security landscape by demanding the same network and mobile security models on remote servers in order to track location and traffic patterns. Given that using a computer may need access to a very large number of devices, IT teams will need to clearly map user access privileges.

4.4.3. Backup

The data generating environment is often what drives the end-to-end computing approach. When selecting how to secure these assets, network bandwidth needs will be just as crucial as storage media concerns since it might not be practical to create a backup across the network.

4.4.4. Data Accumulation

Data is a crucial corporate asset, and if it is not collected and managed properly, it could eventually pose new problems and potentially lead to debt. Both data access and storage are crucial, and both require network integration as a component of the data lifecycle.

4.4.5. Control and Management

Although edge zones in an organization, private cloud, or even public cloud might be flexible, managers and controllers must adhere to the same procedures and guidelines wherever they can. The new orchestration tools should enable enterprises to manage apps without hindrance, regardless of location.

4.4.6. Scale

The scope of everything the IT team performs expands as more highly connected devices are added to the edge. In the end, edge computing is increasingly used across all computer domains, including computation, networking, storage, management, security, authorization, and more. When putting apps on the network, business owners need to be aware of this edge: measure the impact of edge on anything IT-related, not only as additional distant gear.

5. Future Challenges

The integration of the Internet of Things (IoT) and edge computing presents several current and future challenges, including:
  • Data Management: Handling the massive amounts of data generated by IoT devices and ensuring their timely and efficient processing at the edge is a major challenge [86].
  • Security: Ensuring the security of IoT devices and the data they generate is a critical challenge, as these devices are often vulnerable to hacking and cyber-attacks [87,88,89]. Another problem is that the integration of smart things into the standard Internet introduces additional security challenges because the majority of Internet technologies and communication protocols were not designed to support Internet of Things [90].
  • Interoperability: Ensuring interoperability between different IoT devices and edge computing systems is a challenge, as it requires standardization and common protocols [91].
  • Scalability: Scaling edge computing systems to meet the demands of growing IoT networks is a challenge, as it requires efficient resource utilization and effective management [92].
  • Latency: Reducing latency in IoT networks and edge computing systems is a challenge, as it requires efficient data processing and communication [93].
  • Energy Efficiency: Ensuring energy efficiency in IoT devices and edge computing systems is a challenge, as it requires energy-saving algorithms and strategies [94].
  • Integration with Cloud Computing: Integrating edge computing with cloud computing is a challenge, as it requires seamless communication and coordination between these two systems [77,95,96].
  • Cost Effectiveness: Ensuring cost-effectiveness in IoT and edge computing systems is a challenge, as it requires efficient resource utilization and effective cost management [97].
  • Searching resources in WoT is also a big challenge, especially dynamic searching and intent based searching [63]. Many physical objects (Things) are connected to the internet and are accessible through a web interface, and efficient searching of Things is required as there is a significant increase in IoT devices [98]. Searching of suitable Things from billions of device is also difficult because of multiple Things performing the same functionality, but some devices are near to the user or free at some time which can respond more accurately and efficiently which depends upon the selection of the right Thing [99].
These challenges highlight the need for ongoing research and development in the fields of WoT and edge computing to overcome these obstacles and advance the integration of these technologies.
Implementing edge in WoT presents open research challenges. There is heterogeneity in the computing and communication technologies used in WoT-based edge computing. Communication technologies may be heterogeneous in terms of data transfer rate, transmission distance, and bandwidth, whereas computing platforms may have a variety of operating systems and hardware architectures.
The need to create software solutions that can be applied in various environments is one of the challenges of edge computing. Because different applications are deployed in edge devices, this problem is very important.
In order to facilitate the execution of workloads concurrently on various levels of hardware, programmers should create a programming model for edge nodes that is supported by task-level and data-level parallelism. Use a language that supports hardware heterogeneity as a second factor. Different devices and sensors connect to and communicate with the edge server and each other in this heterogeneous environment using different communication protocols. Due to the unique interfaces on these devices, particular communication protocols are needed.
In order to facilitate communication between these heterogeneous devices in the WoT, standard protocols and interfaces should be developed since different vendors produce various types of devices in the WoT. The quick development of new devices makes it difficult to develop standard protocols and interfaces in WoT.
The provision of resources and services at the hardware and software levels anywhere, at any time for the WoT subscription is included in the availability in the WoT-based edge computing environment. Due to the unique interfaces on these devices, particular communication protocols are needed.
In order to facilitate communication between these heterogeneous devices in the WoT, standard protocols and interfaces should be developed since different vendors produce various types of devices in the WoT. The quick development of new devices makes it difficult to develop standard protocols and interfaces in the WoT. We have discussed most significant open challenges and guidelines for future in Table 4.
Ensuring the availability of resources and services for WoT-based edge computing environments, anytime and anywhere, is a crucial aspect. Availability, which encompasses the mean time between failures, the probability of failure, and the mean time to recovery, is essential in providing hardware and software resources and services to WoT subscribers. However, addressing the challenge of maintaining availability for the increasing WoT population is a complex research area.
One way to improve availability is to enhance the mean time between failures while reducing both the mean time to recovery and the probability of failure. In the context of the Web of Things (WoT), numerous devices capable of generating data are connected through a network and send vast quantities of raw data to the edge device.
For edge devices, it is computationally challenging to analyze such large data. Risks to security are also connected to this. It is advisable to carry out pre-processing of data at the gateway level to eliminate noise/low quality data, detect events, and ensure privacy protection. A higher layer will receive the processed data to provide additional services in the future.
However, this process is not without its share of issues. To ensure privacy and security, applications operating on edge devices should not have access to this raw data. Therefore, during data pre-processing, data details should be eliminated. Hiding the specifics of the captured data, however, might affect how usable the data is. The amount of raw data that should be filtered is another issue, as many applications cannot produce accurate results using such data.
Because data does not cross the network, edge computing has a positive impact on cybersecurity. The network is, however, exposed by the extremely dynamic environment at the network edge. The fact that different devices are interconnected in WoT creates a variety of security risks.
The data presented to these applications should be in an encrypted or concealed format, as numerous edge applications execute there. Otherwise, anyone can access the open data and use them for illicit purposes. For instance, if a home is wired to the Internet of Things, personal information such as health records can be taken.
The issue here is how to maintain the service without jeopardizing privacy. The raw data should not be visible to applications running on edge devices. Before it reaches the edge device, personal information can be removed.
However, edge nodes should be equipped with security measures that are more robust. High latency will be impacted by a crowded network, weak signal, and a slow router. Additionally, new techniques for gauging latency are needed. Based on data size, evaluate the effectiveness of storing RDF data in various forms. If the RDF data has a different structure, finding the same format has a different level of efficiency.
Consequently, a more intricate measurement could be carried out using the RDF data structure. To use a large data set for our scalability experiments, consider the future exponential growth of WoT. Everything around us, including a refrigerator, an oven, a bed, a table, and even a pencil, will eventually become intelligent WoT devices.
Future studies may examine the best way to assign tasks and the performance-enhancing criteria. For the sake of optimization, it was also necessary to determine how to divide the RDF data, rules, and ontology. The edge computing paradigm’s parallel execution of reasoning tasks will speed up processing and enhance performance.
The correlation between transfer speed, reasoning speed, and storage speed determines the degree of improvement. The performance improvement might eventually peak at steady state if the relationship between these rates is fixed. Performance will also be improved by adding more edge nodes. Future research should focus on finding the best way to divide up RDF rules and data.

6. Conclusions and Future Remarks

IoT is the next significant internet potential. It is more about the connection or interaction between intelligent things (i.e., things and people). Furthermore, all intelligent entities must be able to communicate freely. Thus, WoT has emerged as the dominant trend supporting the growth of IoT. Web of Things applications are an essential part in residential smart homes and smart communities. They provide necessary services which are required for increasing sustainable development of smart residential communities and for reducing their resource consumption. In this paper, WoT and edge computing integration have been discussed. It provides an overview of the functionality of WoT and edge computing as well as the impact of edge computing (i.e., storage, transmission, and computational aspects) to enhance WoT applications overall performance. Moreover, edge computing has the capabilities to handle real-time massive communications and computation. In contrast, cloud computing approaches have also been reviewed. Edge computing has the potential to lower down the transmission latency between end users and edge servers, leading to better response times for real-time WoT-based applications compared to cloud computing.
WoT will be certainly essential to people’s lives in the future as smart cities and communities are all based on it; ; however, WoT faces several challenges (i.e., search and discovery of smart things, data inconsistency, security and identity verification, scalability, interoperability, etc.) that have been argued. Additionally, the battery life of restricted nodes can be increased by decreasing the cost of moving data and moving computers, as well as by enhancing communication between nodes with limited battery capacity, and nodes with crucial power increase the lifespan of the overall IoT systems. The bulk of future IoT applications may be implemented using web technologies, which are widely used and provide all the flexibility and functionality required, including real-time messaging, security, interoperability, and discovery.

Author Contributions

Conceptualization, T.A. and Q.H.; methodology, A.A., M.A.O. and W.K.; software, T.A. and Q.H.; validation, A.S.A.-S. and A.A.; formal analysis, T.A. and W.K.; investigation, M.A.O. and A.A.; resources, W.K.; data curation, M.A.O. and A.S.A.-S.; writing—original draft preparation, Q.H.; writing—review and editing, A.A., M.A.O. and W.K.; visualization, A.S.A.-S. and Q.H.; supervision, T.A. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors have no conflict of interest.


  1. Corcoran, P.; Datta, S.K. Mobile-edge computing and the internet of things for consumers: Extending cloud computing and services to the edge of the network. IEEE Consum. Electron. Mag. 2016, 5, 73–74. [Google Scholar] [CrossRef]
  2. Vallati, C.; Virdis, A.; Mingozzi, E.; Stea, G. Mobile-edge computing come home connecting things in future smart homes using LTE device-to-device communications. IEEE Consum. Electron. Mag. 2016, 5, 77–83. [Google Scholar] [CrossRef]
  3. Dastjerdi, A.V.; Buyya, R. Fog computing: Helping the Internet of Things realize its potential. Computer 2016, 49, 112–116. [Google Scholar] [CrossRef]
  4. Georgakopoulos, D.; Jayaraman, P.P.; Fazia, M.; Villari, M.; Ranjan, R. Internet of Things and edge cloud computing roadmap for manufacturing. IEEE Cloud Comput. 2016, 3, 66–73. [Google Scholar] [CrossRef]
  5. Jutila, M. An adaptive edge router enabling internet of things. IEEE Internet Things J. 2016, 3, 1061–1069. [Google Scholar] [CrossRef]
  6. Sabella, D.; Vaillant, A.; Kuure, P.; Rauschenbach, U.; Giust, F. Mobile-edge computing architecture: The role of MEC in the Internet of Things. IEEE Consum. Electron. Mag. 2016, 5, 84–91. [Google Scholar] [CrossRef]
  7. Chiang, M.; Zhang, T. Fog and IoT: An overview of research opportunities. IEEE Internet Things J. 2016, 3, 854–864. [Google Scholar] [CrossRef]
  8. Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016, 3, 637–646. [Google Scholar] [CrossRef]
  9. Frankston, B. Mobile-Edge Computing versus The Internet?: Looking beyond the literal meaning of MEC. IEEE Consum. Electron. Mag. 2016, 5, 75–76. [Google Scholar] [CrossRef]
  10. Garcia Lopez, P.; Montresor, A.; Epema, D.; Datta, A.; Higashino, T.; Iamnitchi, A.; Barcellos, M.; Felber, P.; Riviere, E. Edge-Centric Computing: Vision and Challenges. ACM SIGCOMM Comput. Commun. Rev. 2015, 45, 37–42. [Google Scholar] [CrossRef] [Green Version]
  11. Mao, Y.; You, C.; Zhang, J.; Huang, K.; Letaief, K.B. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutor. 2017, 19, 2322–2358. [Google Scholar] [CrossRef] [Green Version]
  12. Yu, W.; Liang, F.; He, X.; Hatcher, W.G.; Lu, C.; Lin, J.; Yang, X. A Survey on the Edge Computing for the Internet of Things. IEEE Access 2017, 6, 6900–6919. [Google Scholar]
  13. Li, H.; Shou, G.; Hu, Y.; Guo, Z. Mobile edge computing: Progress and challenges. In Proceedings of the 2016 4th IEEE International Conference on Mobile Cloud Computing, Services and Engineering (MobileCloud), Oxford, UK, 29 March–1 April 2016. [Google Scholar]
  14. Mach, P.; Becvar, Z. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 2017, 19, 1628–1656. [Google Scholar] [CrossRef] [Green Version]
  15. Hatcher, W.G.; Booz, J.; McGiff, J.; Lu, C.; Yu, W. Edge Computing Based Machine Learning Mobile Malware Detection. 2017. Available online: (accessed on 27 February 2023).
  16. Christophe, B.; Boussard, M.; Lu, M.; Pastor, A.; Toubiana, V. The web of things vision: Things as a service and interaction patterns. Bell Labs Tech. J. 2011, 16, 55–61. [Google Scholar] [CrossRef]
  17. Duquennoy, S.; Grimaud, G.; Vandewalle, J.-J. The web of things: Interconnecting devices with high usability and performance. In Proceedings of the 2009 International Conference on Embedded Software and Systems, Hangzhou, China, 25–27 May 2009; IEEE: Piscataway, NJ, USA, 2009. [Google Scholar]
  18. Ngu, A.H.; Gutierrez, M.; Metsis, V.; Nepal, S.; Sheng, Q.Z. IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J. 2016, 4, 1–20. [Google Scholar] [CrossRef]
  19. Stirbu, V. Towards a restful plug and play experience in the web of things. In Proceedings of the 2008 IEEE International Conference on Semantic Computing, Santa Monica, CA, USA, 4–7 August 2008; IEEE: Piscataway, NJ, USA, 2008. [Google Scholar]
  20. Baraković, S.; Baraković Husić, J.; Maraj, D.; Maraj, A.; Krejcar, O.; Maresova, P.; Melero, F.J. Quality of life, quality of experience, and security perception in web of things: An overview of research opportunities. Electronics 2020, 9, 700. [Google Scholar] [CrossRef]
  21. Niwarlangga, A.C.; Candra, M.Z.C. Application framework for semantic web of things. In Proceedings of the 2020 7th International Conference on Advance Informatics: Concepts, Theory and Applications (ICAICTA), Online, 8–9 September 2020; IEEE: Piscataway, NJ, USA, 2020. [Google Scholar]
  22. Datta, S.K.; Bonnet, C. Advances in web of things for IoT interoperability. In Proceedings of the 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Taichung, Taiwan, 19–21 May 2018; IEEE: Piscataway, NJ, USA, 2018. [Google Scholar]
  23. Kamilaris, A.; Pitsillides, A.; Prenafeta-Bold, F.X.; Ali, M.I. A Web of Things based eco-system for urban computing-towards smarter cities. In Proceedings of the 2017 24th International Conference on Telecommunications (ICT), Limassol, Cyprus, 3–5 May 2017; IEEE: Piscataway, NJ, USA, 2017. [Google Scholar]
  24. Younan, M.; Khattab, S.; Bahgat, R. From the wireless sensor networks (WSNs) to the Web of Things (WoT): An overview. J. Intell. Syst. Internet Things 2021, 4, 56–68. [Google Scholar] [CrossRef]
  25. Cimmino, A.; Poveda-Villalón, M.; García-Castro, R. eWoT: A semantic interoperability approach for heterogeneous iot ecosystems based on the web of things. Sensors 2020, 20, 822. [Google Scholar] [CrossRef] [Green Version]
  26. Premkumar, M.; Ashokkumar, S.R.; Mohanbabu, G.; Jeevanantham, V.; Jayakumar, S. Security behavior analysis in web of things smart environments using deep belief networks. Int. J. Intell. Netw. 2022, 3, 181–187. [Google Scholar] [CrossRef]
  27. Parwej, F.; Akhtar, N.; Perwej, Y. An empirical analysis of web of things (WOT). Int. J. Adv. Res. Comput. Sci. 2019, 10, 32–40. [Google Scholar] [CrossRef]
  28. Sciullo, L.; Gigli, L.; Trotta, A.; Di Felice, M. WoT Store: Managing resources and applications on the web of things. Internet Things 2020, 9, 100164. [Google Scholar] [CrossRef]
  29. Vanden Hautte, S.; Moens, P.; Van Herwegen, J.; De Paepe, D.; Steenwinckel, B.; Verstichel, S.; Ongenae, F.; Van Hoecke, S. A dynamic dashboarding application for fleet monitoring using semantic web of things technologies. Sensors 2020, 20, 1152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Pastor-Vargas, R.; Tobarra, L.; Robles-Gómez, A.; Martin, S.; Hernández, R.; Cano, J. A wot platform for supporting full-cycle iot solutions from edge to cloud infrastructures: A practical case. Sensors 2020, 20, 3770. [Google Scholar] [CrossRef]
  31. Marsh-Hunn, D.; Trilles, S.; González-Pérez, A.; Torres-Sospedra, J.; Ramos, F. A Comparative Study in the Standardization of IoT Devices Using Geospatial Web Standards. IEEE Sensors J. 2020, 21, 5512–5528. [Google Scholar] [CrossRef]
  32. Vresk, T.; Čavrak, I. Architecture of an interoperable IoT platform based on microservices. In Proceedings of the 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 30 May–3 June 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  33. Trilles, S.; Calia, A.; Belmonte, Ó.; Torres-Sospedra, J.; Montoliu, R.; Huerta, J. Deployment of an open sensorized platform in a smart city context. Futur. Gener. Comput. Syst. 2017, 76, 221–233. [Google Scholar] [CrossRef]
  34. Huang, C.Y.; Wu, C.H. Design and implement an interoperable Internet of Things application based on an extended OGC sensorthings API Standard. In Proceedings of the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 12–19 July 2016. [Google Scholar]
  35. Trilles, S.; Torres-Sospedra, J.; Belmonte, Ó.; Zarazaga-Soria, F.J.; González-Pérez, A.; Huerta, J. Development of an open sensorized platform in a smart agriculture context: A vineyard support system for monitoring mildew disease. Sustain. Comput. Inform. Syst. 2020, 28, 100309. [Google Scholar] [CrossRef]
  36. Kotsev, A.; Schleidt, K.; Liang, S.; Van der Schaaf, H.; Khalafbeigi, T.; Grellet, S.; Lutz, M.; Jirka, S. Extending INSPIRE to the Internet of Things through SensorThings API. Geosciences 2018, 8, 221. [Google Scholar] [CrossRef] [Green Version]
  37. Kotsev, A.; Minghini, M.; Tomas, R.; Cetl, V.; Lutz, M. From spatial data infrastructures to data spaces—A technological perspective on the evolution of European SDIs. ISPRS Int. J. Geo-Inf. 2020, 9, 176. [Google Scholar] [CrossRef] [Green Version]
  38. Granell, C.; Kamilaris, A.; Kotsev, A.; Ostermann, F.O.; Trilles, S. Internet of things. In Manual of Digital Earth; Springer Singapore: Singapore, 2020; pp. 387–423. [Google Scholar]
  39. Trilles, S.; Luján, A.; Belmonte, Ó.; Montoliu, R.; Torres-Sospedra, J.; Huerta, J. SEnviro: A sensorized platform proposal using open hardware and open standards. Sensors 2015, 15, 5555–5582. [Google Scholar] [CrossRef] [Green Version]
  40. Cao, K.; Liu, Y.; Meng, G.; Sun, Q. An overview on edge computing research. IEEE Access 2020, 8, 85714–85728. [Google Scholar] [CrossRef]
  41. Porambage, P.; Okwuibe, J.; Liyanage, M.; Ylianttila, M.; Taleb, T. Survey on multi-access edge computing for internet of things realization. IEEE Commun. Surv. Tutor. 2018, 20, 2961–2991. [Google Scholar] [CrossRef] [Green Version]
  42. Sha, K.; Yang, T.A.; Wei, W.; Davari, S. A survey of edge computing-based designs for IoT security. Digit. Commun. Netw. 2020, 6, 195–202. [Google Scholar] [CrossRef]
  43. Ahmed, E.; Rehmani, M.H. Mobile Edge Computing: Opportunities, Solutions, and Challenges; Elsevier: Amsterdam, The Netherlands, 2017; pp. 59–63. [Google Scholar]
  44. Pan, J.; McElhannon, J. Future edge cloud and edge computing for internet of things applications. IEEE Internet Things J. 2017, 5, 439–449. [Google Scholar] [CrossRef]
  45. Premsankar, G.; Di Francesco, M.; Taleb, T. Edge computing for the Internet of Things: A case study. IEEE Internet Things J. 2018, 5, 1275–1284. [Google Scholar] [CrossRef] [Green Version]
  46. Liu, Y.; Peng, M.; Shou, G.; Chen, Y.; Chen, S. Toward edge intelligence: Multiaccess edge computing for 5G and Internet of Things. IEEE Internet Things J. 2020, 7, 6722–6747. [Google Scholar] [CrossRef]
  47. Hamdan, S.; Ayyash, M.; Almajali, S. Edge-computing architectures for internet of things applications: A survey. Sensors 2020, 20, 6441. [Google Scholar] [CrossRef]
  48. Jararweh, Y.; Doulat, A.; AlQudah, O.; Ahmed, E.; Al-Ayyoub, M.; Benkhelifa, E. The future of mobile cloud computing: Integrating cloudlets and mobile edge computing. In Proceedings of the 2016 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece, 16–18 May 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  49. Tong, L.; Li, Y.; Gao, W. A hierarchical edge cloud architecture for mobile computing. In Proceedings of the IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 10–14 April 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  50. Ahmed, E.; Akhunzada, A.; Whaiduzzaman, M.; Gani, A.; Ab Hamid, S.H.; Buyya, R. Network-Centric Performance Analysis of Runtime Application Migration in Mobile Cloud Computing. Simul. Model. Pract. Theory 2015, 50, 42–56. [Google Scholar] [CrossRef]
  51. Wang, G.; Zhao, Y.; Huang, J.; Wang, W. The controller placement problem in software defined networking: A survey. IEEE Netw. 2017, 31, 21–27. [Google Scholar] [CrossRef]
  52. Zhu, D.; Yang, X.; Zhao, P.; Yu, W. Towards effective intra-flow network coding in software defined wireless mesh networks. In Proceedings of the 2015 24th International Conference on Computer Communication and Networks (ICCCN), Las Vegas, NV, USA, 3–6 August 2015; IEEE: Piscataway, NJ, USA, 2015. [Google Scholar]
  53. Jararweh, Y.; Doulat, A.; Darabseh, A.; Alsmirat, M.; Al-Ayyoub, M.; Benkhelifa, E. SDMEC: Software defined system for mobile edge computing. In Proceedings of the 2016 IEEE international conference on cloud engineering workshop (IC2EW), Berlin, Germany, 4–8 April 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  54. Du, P.; Nakao, A. Application specific mobile edge computing through network softwarization. In Proceedings of the 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), Pisa, Italy, 3–5 October 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  55. Manzalini, A.; Crespi, N. An edge operating system enabling anything-as-a-service. IEEE Commun. Mag. 2016, 54, 62–67. [Google Scholar] [CrossRef]
  56. Salman, O.; Elhajj, I.; Kayssi, A.; Chehab, A. Edge computing enabling the Internet of Things. In Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, Italy, 14–16 December 2015; pp. 603–608. [Google Scholar]
  57. Lin, T.; Park, B.; Bannazadeh, H.; Leon-Garcia, A. Demo abstract: End-to-end orchestration across SDI smart edges. In Proceedings of the 2016 IEEE/ACM Symposium on edge computing (SEC), Washington, DC, USA, 27–28 October 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
  58. Bonomi, F.; Milito, R.; Natarajan, P.; Zhu, J. Fog computing: A platform for internet of things and analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments; Springer: Berlin/Heidelberg, Germany, 2014; pp. 169–186. [Google Scholar]
  59. Jiang, H.; Shen, F.; Chen, S.; Li, K.-C.; Jeong, Y.-S. A secure and scalable storage system for aggregate data in IoT. Futur. Gener. Comput. Syst. 2015, 49, 133–141. [Google Scholar] [CrossRef]
  60. Hossain, M.M.; Fotouhi, M.; Hasan, R. Towards an analysis of security issues, challenges, and open problems in the internet of things. In Proceedings of the 2015 IEEE world congress on services, New York, NY, USA, 27 June–2 July 2015; IEEE: Piscataway, NJ, USA, 2015. [Google Scholar]
  61. Yang, X.; Wang, T.; Ren, X.; Yu, W. Survey on improving data utility in differentially private sequential data publishing. IEEE Trans. Big Data 2017, 7, 729–749. [Google Scholar] [CrossRef]
  62. Guinard, D.; Trifa, V.; Wilde, E. A resource oriented architecture for the web of things. In Proceedings of the 2010 Internet of Things (IOT), Tokyo, Japan, 29 November–1 December 2010; IEEE: Piscataway, NJ, USA, 2010. [Google Scholar]
  63. Faheem, M.R.; Anees, T.; Hussain, M. The Web of Things: Findability Taxonomy and Challenges. IEEE Access 2019, 7, 185028–185041. Available online: (accessed on 27 February 2023). [CrossRef]
  64. Sardar, R.; Anees, T. Web of Things: Security Challenges and Mechanisms. IEEE Access 2021, 9, 31695–31711. [Google Scholar] [CrossRef]
  65. Ostermaier, B.; Römer, K.; Mattern, F.; Fahrmair, M.; Kellerer, W. A real-time search engine for the web of things. In Proceedings of the 2010 Internet of Things (IOT), Tokyo, Japan, 29 November–1 December 2010; IEEE: Piscataway, NJ, USA, 2010. [Google Scholar]
  66. Wang, H.; Tan, C.C.; Li, Q. Snoogle: A search engine for pervasive environments. IEEE Trans. Parallel Distrib. Syst. 2009, 21, 1188–1202. [Google Scholar] [CrossRef] [Green Version]
  67. Tan, C.C.; Sheng, B.; Wang, H.; Li, Q. Microsearch: When search engines meet small devices. In Proceedings of the Pervasive Computing: 6th International Conference, Pervasive 2008, Sydney, Australia, 19–22 May 2008; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
  68. Yap, K.-K.; Srinivasan, V.; Motani, M. Max: Human-centric search of the physical world. In Proceedings of the 3rd international conference on Embedded networked sensor systems, San Diego, CA, USA, 2–4 November 2005; pp. 166–179. [Google Scholar]
  69. Blanco, J.M.; Ge, M.; Pitner, T. Modeling Inconsistent Data for Reasoners in Web of Things. Procedia Comput. Sci. 2021, 192, 1265–1273. [Google Scholar] [CrossRef]
  70. Schneier, B. The internet of things is wildly insecure-and often unpatchable. Schneier Secur. 2014, 6. Available online: (accessed on 27 February 2023).
  71. Kim, H. Protection against packet fragmentation attacks at 6LoWPAN adaptation layer. In Proceedings of the 2008 International conference on convergence and hybrid information technology, Daejeon, Republic of Korea, 28–30 August 2008; IEEE: Piscataway, NJ, USA, 2008. [Google Scholar]
  72. Chen, G.; Yau, N.; Hansen, M.; Estrin, D. Sharing Sensor Network Data; UCLA: Center for Embedded Network Sensing: Los Angeles, CA, USA, 2007. [Google Scholar]
  73. Guinard, D.; Trifa, V. Towards the web of things: Web mashups for embedded devices. In Proceedings of the Workshop on Mashups, Enterprise Mashups and Lightweight Composition on the Web (MEM 2009), Madrid, Spain, 20 April 2009. [Google Scholar]
  74. Lin, X.; Andrews, J.G.; Ghosh, A.; Ratasuk, A. An overview of 3GPP device-to-device proximity services. IEEE Commun. Mag. 2014, 52, 40–48. [Google Scholar] [CrossRef] [Green Version]
  75. Greenberg, A.; Hamilton, J.; Maltz, D.A.; Patel, P. The Cost of a Cloud: Research Problems in Data Center Networks; ACM: New York, NY, USA, 2008; pp. 68–73. [Google Scholar]
  76. Armbrust, M.; Fox, A.; Griffith, R.; Joseph, A.D.; Katz, R.; Konwinski, A.; Lee, G.; Patterson, D.; Rabkin, A.; Stoica, I. A view of cloud computing. Commun. ACM 2010, 53, 50–58. [Google Scholar] [CrossRef] [Green Version]
  77. Alam, Q.; Malik, S.U.; Akhunzada, A.; Choo, K.-K.R.; Tabbasum, S.; Alam, M. A Cross Tenant Access Control (CTAC) Model for Cloud Computing: Formal Specification and Verification. IEEE Trans. Inf. Forensics Secur. 2016, 12, 1259–1268. [Google Scholar] [CrossRef]
  78. Jackson, K.R.; Ramakrishnan, L.; Muriki, K.; Canon, S.; Cholia, S.; Shalf, J.; Wasserman, H.J.; Wright, N.J. Performance analysis of high performance computing applications on the amazon web services cloud. In Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, IN, USA, 30 November–3 December 2010; IEEE: Piscataway, NJ, USA, 2010. [Google Scholar]
  79. Li, A.; Yang, X.; Kandula, S.; Zhang, M. CloudCmp: Comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, Melbourne, Australia, 1–30 November 2010. [Google Scholar]
  80. Wadhwa, H.; Aron, R. TRAM: Technique for Resource Allocation and Management in Fog Computing Environment. J. Supercomput. 2022, 78, 667–690. [Google Scholar] [CrossRef]
  81. Hussain, S.M.; Wahid, A.; Shah, M.A.; Akhunzada, A.; Khan, F.; ul Amin, N.; Arshad, S.; Ali, I. Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers. In Recent Trends and Advances in Wireless and IoT-Enabled Networks; Springer International Publishing: Cham, Switzerland, 2019. [Google Scholar]
  82. Satyanarayanan, M. Mobile computing: The next decade. ACM SIGMOBILE Mob. Comput. Commun. Rev. 2011, 15, 2–10. [Google Scholar] [CrossRef]
  83. Miettinen, A.P.; Nurminen, J.K. Energy efficiency of mobile clients in cloud computing. In Proceedings of the 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10), Boston, MA, USA, 22–25 June 2010. [Google Scholar]
  84. Chun, B.-G.; Ihm, S.; Maniatis, P.; Naik, M.; Patti, A. Clonecloud: Elastic execution between mobile device and cloud. In Proceedings of the Sixth Conference on Computer Systems, Alzburg, Austria, 10–13 April 2011. [Google Scholar]
  85. Ding, N.; Wagner, D.; Chen, X.; Pathak, A.; Hu, Y.C.; Rice, A. Characterizing and modeling the impact of wireless signal strength on smartphone battery drain. ACM SIGMETRICS Perform. Eval. Rev. 2013, 41, 29–40. [Google Scholar] [CrossRef] [Green Version]
  86. Ma, M.; Wang, P.; Chu, C.-H. Data management for internet of things: Challenges, approaches and opportunities. In Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, Beijing, China, 20–23 August 2013; IEEE: Piscataway, NJ, USA, 2013. [Google Scholar]
  87. Toor, A.; ul Islam, S.; Sohail, N.; Akhunzada, A.; Boudjadar, J.; Khattak, H.A.; Din, I.U.; Rodrigues, J.J. Energy and Performance Aware Fog Computing: A Case of DVFS and Green Renewable Energy. Future Gener. Comput. Syst. 2019, 101, 1112–1121. [Google Scholar] [CrossRef]
  88. Khan, M.T.; Akhunzada, A.; Zeadally, S. Proactive Defense for Fog-to-Things Critical Infrastructure. IEEE Commun. Mag. 2022, 60, 44–49. [Google Scholar] [CrossRef]
  89. Khan, W.Z.; Rafique, W.; Haider, N.; Hakak, S.; Imran, M. Internet of Everything: Enabling Technologies, Applications, Security and Challenges. 2022. Available online: (accessed on 27 February 2023).
  90. Mosenia, A.; Jha, N.K. A comprehensive study of security of internet-of-things. IEEE Trans. Emerg. Top. Comput. 2016, 5, 586–602. [Google Scholar] [CrossRef]
  91. Noura, M.; Atiquzzaman, M.; Gaedke, M. Interoperability in internet of things: Taxonomies and open challenges. Mob. Netw. Appl. 2019, 24, 796–809. [Google Scholar] [CrossRef] [Green Version]
  92. Gupta, A.; Christie, R.; Manjula, R. Scalability in internet of things: Features, techniques and research challenges. Int. J. Comput. Intell. Res. 2017, 13, 1617–1627. [Google Scholar]
  93. Shukla, S.; Hassan, M.F.; Tran, D.C.; Akbar, R.; Paputungan, I.V.; Khan, M.K. Improving latency in Internet-of-Things and cloud computing for real-time data transmission: A systematic literature review (SLR). Clust. Comput. 2021, 1–24. [Google Scholar] [CrossRef]
  94. Liu, C.H.; Fan, J.; Branch, J.W.; Leung, K.K. Toward QoI and energy-efficiency in Internet-of-Things sensory environments. IEEE Trans. Emerg. Top. Comput. 2014, 2, 473–487. [Google Scholar] [CrossRef]
  95. Botta, A.; De Donato, W.; Persico, V.; Pescapé, A. Integration of cloud computing and internet of things: A survey. Futur. Gener. Comput. Syst. 2016, 56, 684–700. [Google Scholar] [CrossRef]
  96. Haghi Kashani, M.; Rahmani, A.M.; Jafari Navimipour, N. Quality of Service-Aware Approaches in Fog Computing. Int. J. Commun. Syst. 2020, 33, e4340. [Google Scholar] [CrossRef]
  97. Febrer, N.; Folkvord, F.; Lupiañez-Villanueva, F. Cost-effectiveness assessment of internet of things in smart cities. Front. Digit. Health 2021, 3, 662874. [Google Scholar] [CrossRef] [PubMed]
  98. Faheem, M.R.; Anees, T.; Hussain, M. Development of a Novel Ranking Mechanism and Search Engine in Web of Things; OSF: Galesburg, IL, USA, 2022. [Google Scholar]
  99. Faheem, M.R.; Anees, T.; Hussain, M. Keywords and Spatial Based Indexing for Searching the Things on Web. KSII Trans. Internet Inf. Syst. 2022, 16, 1489–1515. [Google Scholar]
  100. Ahmed, E.; Gani, A.; Sookhak, M.; Ab Hamid, S.H.; Xia, F. Application optimization in mobile cloud computing: Motivation, taxonomies, and open challenges. J. Netw. Comput. Appl. 2015, 52, 52–68. [Google Scholar] [CrossRef]
  101. Yang, R.; Yu, F.R.; Si, P.; Yang, Z.; Zhang, Y. Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Commun. Surv. Tutor. 2019, 21, 1508–1532. [Google Scholar] [CrossRef]
  102. Khan, W.Z.; Aalsalem, M.Y.; Khan, M.K.; Arshad, Q. Data and privacy: Getting consumers to trust products enabled by the Internet of Things. IEEE Commun. Surv. Tutor. 2019, 8, 35–38. [Google Scholar] [CrossRef]
  103. Hussein, N.; Nhlabatsi, A. Living in the Dark: MQTT-Based Exploitation of IoT Security Vulnerabilities in ZigBee Networks for Smart Lighting Control. IoT 2022, 3, 450–472. [Google Scholar] [CrossRef]
  104. Al-Roomi, M.; Al-Ebrahim, S.; Buqrais, S.; Ahmad, I. Cloud computing pricing models: A survey. Int. J. Grid Distrib. Comput. 2013, 6, 93–106. [Google Scholar] [CrossRef]
  105. Bertolli, C.; Buono, D.; Mencagli, G.; Torquati, M.; Vanneschi, M.; Mordacchini, M.; Nardini, F.M. Resource discovery support for time-critical adaptive applications. In Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, Caen, France, 28 June–2 July 2010. [Google Scholar]
  106. Kientopf, K.; Raza, S.; Lansing, S.; Güneş, M. Service Management Platform to Support Service Migrations for IoT Smart City Applications. In Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, 8–13 October 2017; pp. 1–5. [Google Scholar]
  107. Kirci, P. Ubiquitous and Cloud computing: Ubiquitous computing. In Resource Management and Efficiency in Cloud Computing Environments; IGI Global: Hershey, PA, USA, 2017; pp. 1–32. [Google Scholar]
  108. Ahmed, E.; Gani, A.; Khan, M.K.; Buyya, R.; Khan, S.U. Seamless application execution in mobile cloud computing: Motivation, taxonomy, and open challenges. J. Netw. Comput. Appl. 2015, 52, 154–172. [Google Scholar] [CrossRef]
  109. Li, X.; Liu, S.; Wu, F.; Kumari, S.; Rodrigues, J.J. Privacy preserving data aggregation scheme for mobile edge computing assisted IoT applications. IEEE Internet Things J. 2018, 6, 4755–4763. [Google Scholar] [CrossRef]
Figure 1. An Overview of WoT Model.
Figure 1. An Overview of WoT Model.
Sustainability 15 05983 g001
Figure 2. Direct Integration Pattern.
Figure 2. Direct Integration Pattern.
Sustainability 15 05983 g002
Figure 3. Gateway Integration.
Figure 3. Gateway Integration.
Sustainability 15 05983 g003
Figure 4. Cloud Integration.
Figure 4. Cloud Integration.
Sustainability 15 05983 g004
Figure 5. Workflow to Edge Node.
Figure 5. Workflow to Edge Node.
Sustainability 15 05983 g005
Figure 6. Data Analysis and Monitoring using IoT edge computing Technology.
Figure 6. Data Analysis and Monitoring using IoT edge computing Technology.
Sustainability 15 05983 g006
Figure 7. Edge computing-based WoT Architecture.
Figure 7. Edge computing-based WoT Architecture.
Sustainability 15 05983 g007
Figure 8. Edge Computing based WoT Architecture.
Figure 8. Edge Computing based WoT Architecture.
Sustainability 15 05983 g008
Figure 9. Home WoT System Architecture.
Figure 9. Home WoT System Architecture.
Sustainability 15 05983 g009
Table 2. Important information of papers along with citation of paper.
Table 2. Important information of papers along with citation of paper.
Author NameWork of AuthorCitation of Paper
(Pastor-Vargas, R., et al., 2020)Lab of Things at UNED (LoT@UNED), the proposed system, supports remote laboratories for the full development of IoT, including edge, fog, and cloud computing as well as communication protocols and cyber security.[30]
(Marsh-Hunn, D., et al., 2020)From a geospatial standpoint, it contrasts two open web standards (OGC Sensor Monitoring Service and SensorThings API).[31]
(Vresk, T. and I. Čavrak 2016)Outlines IoT-specific problems and suggests middleware architectures based on microservices for integrating a variety of IoT devices.[32]
(Trilles, S., et al., 2017)Gives a summary of the difficulties unique to the Internet of Things and suggests for a middleware architecture based on microservices that is intended to link a variety of IoT devices.[33]
(Huang, C. and C. Wu 2016)The goal of this project is to create a performance profile and include it into the SensorThings API, which was the industry standard.[34]
(Trilles, S., et al., 2020)Utilizing the IoT paradigm, edge computing, and the simplicity of end-to-end connectivity, SEnviro’s new platform version will be used to monitor the vineyard.[35]
(Kotsev, A., et al., 2018)Outlines our synthesis of the procedures that must be followed in order for the OGC SensorThings API standard to be regarded as a legal response to the responsibilities resulting from the INSPIRE Directive.[36]
(Kotsev, A., et al., 2020)Include developments relating to SDI in a wider discussion of the contemporary political and technical landscape.[37]
(Granell, C., et al., 2020)Explore synergies and trade-offs in building effective and sustainable collaboration between two-way infrastructures to automate multidisciplinary and increasingly complex problems, visualizations, and aggregates.[38]
(Trilles, S., et al., 2015)It offers a sensor platform based on the principles of the Internet of Things and the Web of Things. Wireless sensor nodes are built using open-source solutions, and communication relies on the HTTP/IP Internet protocol.[39]
Table 3. Work of different authors on edge computing.
Table 3. Work of different authors on edge computing.
Cao, K., et al. [40]2020Summarizes the idea of edge computing, compares it to cloud computing, and discusses the architecture, technology, security, and privacy of edge computing.
Porambage, P., et al. [41]2018Gives some insight into various other integration technologies in this field and talks about the technical aspects of enabling Multi Access edge computing (MEC) in the IoT.
Sha, K., et al. [42]2020Explores in-depth edge-based IoT security research initiatives in the context of firewalls, intrusion detection systems, authentication and authorization protocols, and privacy protection measures.
Ahmed, E. and M.H. Rehmani [43]2017Discusses the advantages of MEC and some significant research difficulties in the MEC environment.
Pan, J. and J. McElhannon [44]2017Explores the main rationale, state-of-the-art efforts, key technologies and research topics, and typical IoT applications taking advantage of the cloud.
Premsankar, G., M. Di Francesco, and T. Taleb. [45]2018Talks about the capabilities of the most advanced computing platforms available today and how the adoption of new technologies will affect the development of IoT applications in the future.
Liu, Y., et al. [46]2020It gives a general overview of the function that MEC plays in 5G and IoT, details the various IoT and 5G applications that support MEC, and outlines some exciting new directions for integrating MEC with 5G and IoT in the future.
Hamdan, S., M. Ayyash, and S. Almajali [47]2020Classifies several categories of Deep Computing Architectures for IoT (ECAs-IoT) such as data distribution, orchestration services, security, and big data. The report also evaluates each interior architecture and contrasts them based on a number of factors and explains the edge computing applications for the Internet of Things.
Table 4. Open Challenges and Guidelines.
Table 4. Open Challenges and Guidelines.
Heterogeneity(a) Different operating systems and hardware architectures,
(b) Can be heterogeneous with regard to data rate, transmission range, and bandwidth.
It should provide an edge node programming paradigm that is backed by task-level and data-level parallelism to make it easier to run workloads concurrently on various hardware levels. Using a language that enables hardware heterogeneity is a second thing to think about [100].
Standard Protocols and Interfaces(a) because of the rapid development of new devices.Standard protocols and interfaces should be developed to enable communication between these heterogeneous devices [101].
Trust(a) Lack of security and privacy-preserving mechanismsIt is possible to overcome difficulties in fostering customer confidence in edge computing systems by implementing influential aspects of consumer trust, such as security and privacy [102,103]
Pricing Models(a) High QoS requirements, (b) Inappropriate pricing models, (c) Service providers’ high cost.Dynamic pricing models may be created by considering three crucial elements, including resource availability, customer resource use frequency, and consumer resource usage duration [104].
Mobility(a) Intermitted connectivity due to mobility [3], (b) No accessibility of local resources, (c) Immature security policiesPeer-to-peer networks’ service discovery and wireless networks’ mobility management can serve as models [105,106].
Collaborations(a) Heterogeneous architectures [3], (b) Interoperability problems, (c) Data privacy issues, (d) Deficiencies in terms of load balancing.One can utilize ubiquitous systems’ interoperability and collaboration as a reference [107].
Fault tolerant deployment models(a) High availability, (b) Data integrity, (c) Fault torlerance [3], Disaster recovery.Machine learning may help provide low-cost fault tolerance through anomaly detection and predictive maintenance [108].
Security(a) Involvement of distributed data processing,Significant blockchain technology characteristics such as tamper-proof, redundant, and self-healing can reduce significant security risks. Quantum cryptography-based solutions may also be helpful [109].
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Anees, T.; Habib, Q.; Al-Shamayleh, A.S.; Khalil, W.; Obaidat, M.A.; Akhunzada, A. The Integration of WoT and Edge Computing: Issues and Challenges. Sustainability 2023, 15, 5983.

AMA Style

Anees T, Habib Q, Al-Shamayleh AS, Khalil W, Obaidat MA, Akhunzada A. The Integration of WoT and Edge Computing: Issues and Challenges. Sustainability. 2023; 15(7):5983.

Chicago/Turabian Style

Anees, Tayyaba, Qaiser Habib, Ahmad Sami Al-Shamayleh, Wajeeha Khalil, Muath A. Obaidat, and Adnan Akhunzada. 2023. "The Integration of WoT and Edge Computing: Issues and Challenges" Sustainability 15, no. 7: 5983.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop