Journal of Sensor and Actuator Networks

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data. Full article

With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models. Full article

The Internet of Things (IoT) is changing the way consumers, businesses, and governments interact with the physical and cyber worlds. More often than not, IoT devices are designed for specific functional requirements or use cases without paying too much attention to security. Consequently, attackers usually compromise IoT devices with lax security to retrieve sensitive information such as encryption keys, user passwords, and sensitive URLs. Moreover, expanding IoT use cases and the exponential growth in connected smart devices significantly widen the attack surface. Despite efforts to deal with security problems, the security of IoT devices and the privacy of the data they collect and process are still areas of concern in research. Whenever vulnerabilities are discovered, device manufacturers are expected to release patches or new firmware to fix the vulnerabilities. There is a need to prioritize firmware attacks, because they enable the most high-impact threats that go beyond what is possible with traditional attacks. In IoT, delivering and deploying new firmware securely to affected devices remains a challenge. This study aims to develop a security model that employs Blockchain and the InterPlanentary File System (IPFS) to secure firmware transmission over a low data rate, constrained Long-Range Wide Area Network (LoRaWAN). The proposed security model ensures integrity, confidentiality, availability, and authentication and focuses on resource-constrained low-powered devices. To demonstrate the utility and applicability of the proposed model, a proof of concept was implemented and evaluated using low-powered devices. The experimental results show that the proposed model is feasible for constrained and low-powered LoRaWAN devices. Full article

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided. Full article

From a basic refrigerator to a self-driving car, emerging technologies are increasingly involving various facets of our daily lives. These bring together many regularly used devices, each with its own characteristics, to communicate and collaborate within the same system. Computer network experts regard this so-called structure as a heterogeneous network made up of several connected objects that do not speak the same language. Communication is therefore ensured by additional types of nodes, such as gateways or converters. In this case, we can detect an increased complexity and a decreased level of security. And thus, the need to adopt a common slang for these kinds of networks has been brought to life. In this work, we compare two different routing protocols: optimized link-state routing (OLSR) and the multipath heterogeneous ad hoc network OLSR (MHAR-OLSR). The latter is an OLSR extension with new functionalities: nodes identification, paths calculation, paths classification, and paths choice that we designed for heterogeneous ad hoc networks composed of MANET, VANET, and FANET devices; it ensures direct communication between these diverse components. We verify and explain all the elements of our solution using colored Petri nets. We also present a global evaluation of Packet Delivery Ratio (PDR), End-To-End Delay, and energy consumption as QoS measures with different numbers of nodes in a heterogeneous scenario. To do this, we use NS-3 and BonnMotion as a tool-set of simulation. Experimental results show improvement in performance when compared to the classical routing protocol. Full article

Pervasive sensing with Body Sensor Networks (BSNs) is a promising technology for continuous health monitoring. Since the sensor nodes are resource-limited, on-node processing and advertisement of digested information via BLE beacon is a promising technique that can enable a node gateway to communicate with more sensor nodes and extend the sensor node’s lifetime before requiring recharging. This study proposes a Dynamic Light-weight Symmetric (DLS) encryption algorithm designed and developed to address the challenges in data protection and real-time secure data transmission via message advertisement. The algorithm uses a unique temporal encryption key to encrypt each transmitting packet with a simple function such as XOR. With small additional overhead on computational resources, DLS can significantly enhance security over existing baseline encryption algorithms. To evaluate its performance, the algorithm was utilized on beacon data encryption over advertising channels. The experiments demonstrated the use of the DLS encryption algorithm on top of various light-weight symmetric encryption algorithms (i.e., TEA, XTEA, PRESENT) and a MD5 hash function. The experimental results show that DLS can achieve acceptable results for avalanche effect, key sensitivity, and randomness in ciphertexts with a marginal increase in the resource usage. The proposed DLS encryption algorithm is suitable for implementation at the application layer, is light and energy efficient, reduces/removes the need for secret key exchange between sensor nodes and the server, is applicable to dynamic message size, and also protects against attacks such as known plaintext attack, brute-force attack, replaying attack, and differential attack. Full article

This article addresses the problem of cloud distributed control systems development for mobile robots. The authors emphasize the lack of a design methodology to guide the process of the development in accordance with specific technical and economic requirements for the robot. On the analysis of various robots architectures, the set of the nine most significant parameters are identified to direct the development stage by stage. Based on those parameters, the design methodology is proposed to build a scalable three-level cloud distributed control system for a robot. The application of the methodology is demonstrated on the example of AnyWalker open source robotics platform. The developed methodology is also applied to two other walking robots illustrated in the article. Full article

Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible. Full article

The paper investigates retraining options and the performance of pre-trained Convolutional Neural Networks (CNNs) for sound classification. CNNs were initially designed for image classification and recognition, and, at a second phase, they extended towards sound classification. Transfer learning is a promising paradigm, retraining already trained networks upon different datasets. We selected three ‘Image’- and two ‘Sound’-trained CNNs, namely, GoogLeNet, SqueezeNet, ShuffleNet, VGGish, and YAMNet, and applied transfer learning. We explored the influence of key retraining parameters, including the optimizer, the mini-batch size, the learning rate, and the number of epochs, on the classification accuracy and the processing time needed in terms of sound preprocessing for the preparation of the scalograms and spectrograms as well as CNN training. The UrbanSound8K, ESC-10, and Air Compressor open sound datasets were employed. Using a two-fold criterion based on classification accuracy and time needed, we selected the ‘champion’ transfer-learning parameter combinations, discussed the consistency of the classification results, and explored possible benefits from fusing the classification estimations. The Sound CNNs achieved better classification accuracy, reaching an average of 96.4% for UrbanSound8K, 91.25% for ESC-10, and 100% for the Air Compressor dataset. Full article

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of $95\%$; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities. Full article

LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR. Full article

Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy. Full article

Sensor networks that collect data from the environment can be utilized in the development of context-aware applications, bringing into sight the need for data collection, management, and distribution. Boards with microcontrollers, such as Arduino and Raspberry Pi, have gained wide acceptance and are used mainly for educational and research purposes. Utilizing the information available via sensors connected to these platforms requires extended technical knowledge. In this work, we present a sensor management framework, SensoMan, that manages a collection of sensors spread in the environment connected to microcontroller boards. We present the framework’s architecture, a method for sensor data management, and a prototype system. Sensor data can also trigger the execution of actions on actuators. Thus, we further propose a rule engine as well as social connectivity following a scheme where sensors and their data can be shared among users. Our work shows that the creation of such a system is feasible and can use simple equipment (e.g., sensors, controller plugs) that can be replicated in other environments. The use of SensoMan is demonstrated via two scenarios that show its potential in combining simple tools that do not require an extended learning curve. A small-scale user study was also performed. Full article

The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB. Full article

An autonomous vehicle relies on sensors in order to perceive its surroundings. However, there are multiple causes that would hinder a sensor’s proper functioning, such as bad weather or lighting conditions. Studies have shown that rainfall and fog lead to a reduced visibility, which is one of the main causes of accidents. This work proposes the use of a drone in order to enhance the vehicle’s perception, making use of both embedded sensors and its advantageous 3D positioning. The environment perception and vehicle/Unmanned Aerial Vehicle (UAV) interactions are managed by a knowledge base in the form of an ontology, and logical rules are used in order to detect and infer the environmental context and UAV management. The model was tested and validated in a simulation made on Unity. Full article

Many industries, such as aeronautics construction are still equipped with legacy machines and are not keen to change old, however fully functional, equipment to new ones. Hence, an upgrade must be found to cope the legacy and fully functional machines to IoT technologies. This paper is a contribution to embrace those challenges in a new IoT architecture able to support the creation of solutions for Smart Industries. Internet of Things is increasing acceptance and the infrastructure for them is becoming available. This leads to an insurgence on investments and development of new dedicated IoT infrastructures. Industries need to adapt quickly to this constant technological evolution, implementing measures and connecting machines and robots at critical points to the Internet, instrumenting them using the concept of IoT, with the major goal of implementing a flexible, secure, easy to maintain and capable to evolve infrastructure, when legacy equipment is involved. The availability of machines and other critical assets directly affects the effectiveness of manufacturing operations. The architecture design offers security, flexibility, simplicity of implementation and maintenance, and is resilient to failures or attacks and technologically independent. Field tests are reported to evaluate key aspects of the proposed architecture. Full article

This work proposes an autonomous management system for distributed generation (DG) systems connected to the AC grid, using supervisory control theory (SCT). SCT is used to deal with discrete asynchronous events that modify the properties and operational conditions of these systems. The proposed management layer allows the smart inverters to interact with smart grid managers (SGMs), while guaranteeing operation compliance with the IEEE Standards. The implemented supervisor for the management layer is an automaton that performs the smart inverter manager (SIM) functions in the photovoltaic systems in discrete events. A DSP real-time verification was performed with Typhoon HIL 602+ to demonstrate the smart inverter’s operating dynamics connected to the grid. The results showed the fast response and robust operation of the smart inverter manager to the commands from the smart grid manager. Full article

This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food. Full article

This research aims to provide a high-level software framework for IoT-Edge-Cloud computational continuum-based applications with support for mobile IoT and DevOps integration utilizing the Edge computing paradigms. This is achieved by dividing the system in a modular fashion and providing a loosely coupled service and module descriptions for usage in the respective system layers for flexible and yet trustworthy implementation. The article describes the software architecture for a DevOps-enabled Edge computing solution in the IoT-Edge-Cloud computational continuum with the support for flexible and mobile IoT solutions. The proposed framework is validated on an intelligent transport system use case in the rolling stock domain and showcases the improvements gained by using the proposed IoT-Edge-Cloud continuum framework. Full article