IoT

Since the inception of the Internet of Things (IoT), we have adopted centralized architecture for decades. With the vastly growing number of IoT devices and gateways, this architecture struggles to cope with the high demands of state-of-the-art IoT services, which require scalable and responsive infrastructure. In response, decentralization becomes a considerable interest among IoT adopters. Following a similar trajectory, this paper introduces an IoT architecture re-work that enables three spheres of IoT workflows (i.e., computing, storage, and networking) to be run in a distributed manner. In particular, we employ the blockchain and smart contract to provide a secure computing platform. The distributed storage network maintains the saving of IoT raw data and application data. The software-defined networking (SDN) controllers and SDN switches exist in the architecture to provide connectivity across multiple IoT domains. We envision all of those services in the form of separate yet integrated peer-to-peer (P2P) overlay networks, which IoT actors such as IoT domain owners, IoT users, Internet Service Provider (ISP), and government can cultivate. We also present several IoT workflow examples showing how IoT developers can adapt to this new proposed architecture. Based on the presented workflows, the IoT computing can be performed in a trusted and privacy-preserving manner, the IoT storage can be made robust and verifiable, and finally, we can react to the network events automatically and quickly. Our discussions in this paper can be beneficial for many people ranging from academia, industries, and investors that are interested in the future of IoT in general. Full article

The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale. Full article

In today’s Industrial Internet of Things (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities and the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions for these attacks, as they have been proposed in the most recent literature. Full article

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network. Full article

Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%. Full article

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually. Full article

The latest quantum computers have the ability to solve incredibly complex classical cryptography equations particularly to decode the secret encrypted keys and making the network vulnerable to hacking. They can solve complex mathematical problems almost instantaneously compared to the billions of years of computation needed by traditional computing machines. Researchers advocate the development of novel strategies to include data encryption in the post-quantum era. Lattices have been widely used in cryptography, somewhat peculiarly, and these algorithms have been used in both; (a) cryptoanalysis by using lattice approximation to break cryptosystems; and (b) cryptography by using computationally hard lattice problems (non-deterministic polynomial time hardness) to construct stable cryptographic functions. Most of the dominant features of lattice-based cryptography (LBC), which holds it ahead in the post-quantum league, include resistance to quantum attack vectors, high concurrent performance, parallelism, security under worst-case intractability assumptions, and solutions to long-standing open problems in cryptography. While these methods offer possible security for classical cryptosytems in theory and experimentation, their implementation in energy-restricted Internet-of-Things (IoT) devices requires careful study of regular lattice-based implantation and its simplification in lightweight lattice-based cryptography (LW-LBC). This streamlined post-quantum algorithm is ideal for levelled IoT device security. The key aim of this survey was to provide the scientific community with comprehensive information on elementary mathematical facts, as well as to address real-time implementation, hardware architecture, open problems, attack vectors, and the significance for the IoT networks. Full article

The IoT is transforming the ordinary physical objects around us into an ecosystem of information that will enrich our lives. The key to this ecosystem is the cooperation among the devices, where things look for other things to provide composite services for the benefit of human beings. However, cooperation among nodes can only arise when nodes trust the information received by any other peer in the system. Previous efforts on trust were concentrated on proposing models and algorithms to manage the level of trustworthiness. In this paper, we focus on modelling the interaction between trustor and trustee in the IoT and on proposing guidelines to efficiently design trust management models. Simulations show the impacts of the proposed guidelines on a simple trust model. Full article

The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges. Full article

A low-cost “Internet of Things” (IoT) tide gauge network was developed to provide real-time and “delayed mode” sea-level data to support monitoring of spatial and temporal coastal morphological changes. It is based on the Arduino Sigfox MKR 1200 micro-controller platform with a Measurement Specialties pressure sensor (MS5837). Experiments at two sites colocated with established tide gauges show that these inexpensive pressure sensors can make accurate sea-level measurements. While these pressure sensors are capable of ~1 cm accuracy, as with other comparable gauges, the effect of significant wave activity can distort the overall sea-level measurements. Various off-the-shelf hardware and software configurations were tested to provide complementary data as part of a localized network and to overcome operational constraints, such as lack of suitable infrastructure for mounting the tide gauges and for exposed beach locations. Full article

Security presents itself as one of the biggest threats to the enabling and the deployment of the Internet of Things (IoT). Security challenges are evident in light of recent cybersecurity attacks that targeted major internet service providers and crippled a significant portion of the entire Internet by taking advantage of faulty and ill-protected embedded devices. Many of these devices reside at home networks with user-administrators who are not familiar with network security best practices, making them easy targets for the attackers. Therefore, security solutions are needed to navigate the insecure and untrusted public networks by automating protections through affordable and accessible first-hand network information sharing. This paper proposes and implements a proof of concept (PoC) to secure Internet Service Providers (ISPs), home networks, and home-based IoT devices using blockchain technologies. The results obtained support the idea of a distributed cyber threat intelligence data sharing network capable of protecting various stakeholders. Full article

Recently, two technologies have emerged to provide advanced programmability in Software-Defined Networking (SDN) environments, namely P4 and XDP. At the same time, the Internet of Things (IoT) represents a pillar of future 6G networks, which will be also sustained by SDN. In this regard, there is a need to analyze the suitability of P4 and XDP for IoT. In this article, we aim to compare both technologies to help future research efforts in the field. For this purpose, we evaluate both technologies by implementing diverse use cases, assessing their performance and providing a quick qualitative overview. All tests and design scenarios are publicly available in GitHub to guarantee replication and serve as initial steps for researchers that want to initiate in the field. Results illustrate that currently XDP is the best option for constrained IoT devices, showing lower latency times, half the CPU usage, and reduced memory in comparison with P4. However, development of P4 programs is more straightforward and the amount of code lines is more similar regardless of the scenario. Additionally, P4 has a lot of potential in IoT if a special effort is made to improve the most common software target, BMv2. Full article

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes. Full article

The Industrial Internet of Things (IIoT) has the potential to improve the production and business processes by enabling the extraction of valuable information from industrial processes. The mining industry, however, is rather traditional and somewhat slow to change due to infrastructural limitations in communication, data management, storage, and exchange of information. Most research efforts so far on applying IIoT in the mining industry focus on specific concerns such as ventilation monitoring, accident analysis, fleet and personnel management, tailing dam monitoring, and pre-alarm system while an overall IIoT architecture suitable for the general conditions in the mining industry is still missing. This article analyzes the current state of Information Technology in the mining sector and identifies a major challenge of vertical fragmentation due to the technological variety of various systems and devices offered by different vendors, preventing interoperability, data distribution, and the exchange of information securely between devices and systems. Based on guidelines and practices from the major IIoT standards, a high-level IIoT architecture suitable for the mining industry is then synthesized and presented, addressing the identified challenges and enabling smart mines by automation, interoperable systems, data distribution, and real-time visibility of the mining status. Remote controlling, data processing, and interoperability techniques of the architecture evolve all stages of mining from prospecting to reclamation. The adoption of such IIoT architecture in the mining industry offers safer mine site for workers, predictable mining operations, interoperable environment for both traditional and modern systems and devices, automation to reduce human intervention, and enables underground surveillance by converging operational technology (OT) and information technology (IT). Significant open research challenges and directions are also studied and identified in this paper, such as mobility management, scalability, virtualization at the IIoT edge, and digital twins. Full article

Coronavirus disease 2019 (COVID-19) has significantly impacted the entire world today and stalled off regular human activities in such an unprecedented way that it will have an unforgettable footprint on the history of mankind. Different countries have adopted numerous measures to build resilience against this life-threatening disease. However, the highly contagious nature of this pandemic has challenged the traditional healthcare and treatment practices. Thus, artificial intelligence (AI) and machine learning (ML) open up new mechanisms for effective healthcare during this pandemic. AI and ML can be useful for medicine development, designing efficient diagnosis strategies and producing predictions of the disease spread. These applications are highly dependent on real-time monitoring of the patients and effective coordination of the information, where the Internet of Things (IoT) plays a key role. IoT can also help with applications such as automated drug delivery, responding to patient queries, and tracking the causes of disease spread. This paper represents a comprehensive analysis of the potential AI, ML, and IoT technologies for defending against the COVID-19 pandemic. The existing and potential applications of AI, ML, and IoT, along with a detailed analysis of the enabling tools and techniques are outlined. A critical discussion on the risks and limitations of the aforementioned technologies are also included. Full article

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup. Full article

Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy. Full article

Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings several advantages in terms of the richness of available data, particularly for human activity recognition. Nevertheless, the infrastructure required to support large-scale mobile sensing requires an interoperable design, which is still hard to achieve today. This review paper contributes to raising awareness of challenges faced today by mobile sensing platforms that perform learning and behavior inference with respect to human routines: how current solutions perform activity recognition, which classification models they consider, and which types of behavior inferences can be seamlessly provided. The paper provides a set of guidelines that contribute to a better functional design of mobile sensing infrastructures, keeping scalability as well as interoperability in mind. Full article

Technological progress has recently led to the emergence of various technological resources and means that are improving specific aspects of society. An example of this can be found in the “internet of things” and “edge computing”. The present study aims at knowing and analyzing the scientific literature of the set of terms formed by “edge computing” and “internet of things”, called from now on ECIT. In order to carry out the research, a study has been developed, based on bibliometrics, by means of scientific mapping. In this case, different production indicators have been taken into account, as well as the structural and dynamic development of the terms and authors extracted from the publications through the programs Analyze Results, Creation Citation Report and SciMAT. The results indicate that the study theme “edge computing” and “internet of things” is of recent creation, given that its beginnings date back to 2014. Since then the level of production has been dizzying, increasing considerably in the past two years. It can be concluded that the field of study of ECIT is of recent creation, with a solid research base based on the “internet of things”. Furthermore, the themes “big data”, “energy” and “framework” can be considered as the future lines of research on ECIT. Full article