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J. Sens. Actuator Netw., Volume 10, Issue 1 (March 2021) – 22 articles

Cover Story (view full-size image): Olive trees often suffer from various diseases which are predictable and curable if they are caught in early stages, making early identification of infestation very crucial. This identification is usually done through manual labor, a task which is impractical and difficult, as early stages cannot be easily perceived with the naked eye. In the MyOlivGroveCoach project, we sought to build a system which uses multispectral unmanned aerial vehicle imagery to help to monitor olive groves through the autonomous and automatic processing of the multispectral images using both computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, to facilitate the prediction of Verticillium wilt spread, and to implement a decision support system that guides the farmer/agronomist in a timely manner. View this paper
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Article
Magnetoresistive Sensors and Piezoresistive Accelerometers for Vibration Measurements: A Comparative Study
J. Sens. Actuator Netw. 2021, 10(1), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010022 - 12 Mar 2021
Viewed by 658
Abstract
This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable [...] Read more.
This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications. Full article
(This article belongs to the Special Issue Advanced Instrumentation for Power Converter Applications)
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Article
Optimising Performance for NB-IoT UE Devices through Data Driven Models
J. Sens. Actuator Netw. 2021, 10(1), 21; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010021 - 05 Mar 2021
Viewed by 698
Abstract
This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to [...] Read more.
This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation. Full article
(This article belongs to the Special Issue Machine Learning in IoT Networking and Communications)
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Article
A Model-Based Approach for Adaptable Middleware Evolution in WSN Platforms
J. Sens. Actuator Netw. 2021, 10(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010020 - 04 Mar 2021
Viewed by 490
Abstract
Advances in technology call for a parallel evolution in the software. New techniques are needed to support this dynamism, to track and guide its evolution process. This applies especially in the field of embedded systems, and certainly in Wireless Sensor Networks (WSNs), where [...] Read more.
Advances in technology call for a parallel evolution in the software. New techniques are needed to support this dynamism, to track and guide its evolution process. This applies especially in the field of embedded systems, and certainly in Wireless Sensor Networks (WSNs), where hardware platforms and software environments change very quickly. Commonly, operating systems play a key role in the development process of any application. The most used operating system in WSNs is TinyOS, currently at its TinyOS 2.1.2 version. The evolution from TinyOS 1.x and TinyOS 2.x made the applications developed on TinyOS 1.x obsolete. In other words, these applications are not compatible out-of-the-box with TinyOS 2.x and require a porting action. In this paper, we discuss on the porting of embedded system (i.e., Wireless Sensor Networks) applications in response to operating systems’ evolution. In particular, using a model-based approach, we report the porting we did of Agilla, a Mobile-Agent Middleware (MAMW) for WSNs, on TinyOS 2.x, which we refer to as Agilla 2. We also provide a comparative analysis about the characteristics of Agilla 2 versus Agilla. The proposed Agilla 2 is compatible with TinyOS 2.x, has full capabilities and provides new features, as shown by the maintainability and performance measurement presented in this paper. An additional valuable result is the architectural modeling of Agilla and Agilla 2, missing before, which extends its documentation and improves its maintainability. Full article
(This article belongs to the Special Issue QoS in Wireless Sensor/Actuator Networks)
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Article
Distributed Architecture to Enhance Systems Protection against Unauthorized Activity via USB Devices
J. Sens. Actuator Netw. 2021, 10(1), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010019 - 02 Mar 2021
Viewed by 602
Abstract
Cyberattacks exploiting Universal Serial Bus (USB) interfaces may have a high impact on individual and corporate systems. The BadUSB is an attack where a USB device’s firmware is spoofed and, once mounted, allows attackers to execute a set of malicious actions in a [...] Read more.
Cyberattacks exploiting Universal Serial Bus (USB) interfaces may have a high impact on individual and corporate systems. The BadUSB is an attack where a USB device’s firmware is spoofed and, once mounted, allows attackers to execute a set of malicious actions in a target system. The countermeasures against this type of attack can be grouped into two strategies: phyiscal blocking of USB ports and software blocking. This paper proposes a distributed architecture that uses software blocking to enhance system protection against BadUSB attacks. This architecture is composed of multiple agents and external databases, and it is designed for personal or corporate computers using Microsoft Windows Operating System. When a USB device is connected, the agent inspects the device, provides filtered information about its functionality and presents a threat assessment to the user, based on all previous user choices stored in external databases. By providing valuable information to the user, and also threat assessments from multiple users, the proposed distributed architecture improves system protection. Full article
(This article belongs to the Special Issue Security Threats and Countermeasures in Cyber-Physical Systems)
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Article
Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study
J. Sens. Actuator Netw. 2021, 10(1), 18; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010018 - 24 Feb 2021
Viewed by 581
Abstract
Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, [...] Read more.
Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias. Full article
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Article
Resource Management and Model Personalization for Federated Learning over Wireless Edge Networks
J. Sens. Actuator Netw. 2021, 10(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010017 - 23 Feb 2021
Viewed by 562
Abstract
Client and Internet of Things devices are increasingly equipped with the ability to sense, process, and communicate data with high efficiency. This is resulting in a major shift in machine learning (ML) computation at the network edge. Distributed learning approaches such as federated [...] Read more.
Client and Internet of Things devices are increasingly equipped with the ability to sense, process, and communicate data with high efficiency. This is resulting in a major shift in machine learning (ML) computation at the network edge. Distributed learning approaches such as federated learning that move ML training to end devices have emerged, promising lower latency and bandwidth costs and enhanced privacy of end users’ data. However, new challenges that arise from the heterogeneous nature of the devices’ communication rates, compute capabilities, and the limited observability of the training data at each device must be addressed. All these factors can significantly affect the training performance in terms of overall accuracy, model fairness, and convergence time. We present compute-communication and data importance-aware resource management schemes optimizing these metrics and evaluate the training performance on benchmark datasets. We also develop a federated meta-learning solution, based on task similarity, that serves as a sample efficient initialization for federated learning, as well as improves model personalization and generalization across non-IID (independent, identically distributed) data. We present experimental results on benchmark federated learning datasets to highlight the performance gains of the proposed methods in comparison to the well-known federated averaging algorithm and its variants. Full article
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Article
Factor Optimization for the Design of Indoor Positioning Systems Using a Probability-Based Algorithm
J. Sens. Actuator Netw. 2021, 10(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010016 - 19 Feb 2021
Cited by 1 | Viewed by 639
Abstract
Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise [...] Read more.
Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise useful information, such as the received signal strength (RSS), that is processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing the arrangement of APs over the environment using an enhanced probability-based algorithm. From the assumption that a log-distance path loss model can reasonably describe, on average, the distribution of RSS throughout the environment, we build a simulation framework to analyze the impact, on the accuracy, of the main factors that constitute the positioning algorithm, such as the number of reference points (RPs) and the number of samples of RSS collected per test point. To demonstrate the applicability of the proposed solution, a real-world testbed dataset is used for validation. The obtained results for accuracy show that the trends verified via simulation strongly correlate to the verified in the dataset processing when allied with an optimal configuration of APs. This indicates our method is capable of providing an optimal factor combination—through early simulations—for the design of more efficient IPSs that rely on a probability-based positioning algorithm. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Article
Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves
J. Sens. Actuator Netw. 2021, 10(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010015 - 18 Feb 2021
Viewed by 1103
Abstract
The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) [...] Read more.
The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) and the application of beneficial microorganisms (Micosat F BS WP) restore health in infected trees. However, for their efficient implementation the above methodologies require the marking of trees in the early stages of infestation—a task that is impractical with traditional means (manual labor) but also very difficult, as early stages are difficult to perceive with the naked eye. In this paper, we present the results of the My Olive Grove Coach (MyOGC) project, which used multispectral imaging from unmanned aerial vehicles to develop an olive grove monitoring system based on the autonomous and automatic processing of the multispectral images using computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, help in the prediction of Verticillium wilt spread and implement a decision support system that guides the farmer/agronomist. Full article
(This article belongs to the Special Issue Secure, Efficient Cyber-Physical Systems and Wireless Sensors)
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Review
CNA Tactics and Techniques: A Structure Proposal
J. Sens. Actuator Netw. 2021, 10(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010014 - 10 Feb 2021
Viewed by 679
Abstract
Destructive and control operations are today a major threat for cyber physical systems. These operations, known as Computer Network Attack (CNA), and usually linked to state-sponsored actors, are much less analyzed than Computer Network Exploitation activities (CNE), those related to intelligence gathering. While [...] Read more.
Destructive and control operations are today a major threat for cyber physical systems. These operations, known as Computer Network Attack (CNA), and usually linked to state-sponsored actors, are much less analyzed than Computer Network Exploitation activities (CNE), those related to intelligence gathering. While in CNE operations the main tactics and techniques are defined and well structured, in CNA there is a lack of such consensuated approaches. This situation hinders the modeling of threat actors, which prevents an accurate definition of control to identify and to neutralize malicious activities. In this paper, we propose the first global approach for CNA operations that can be used to map real-world activities. The proposal significantly reduces the amount of effort need to identify, analyze, and neutralize advanced threat actors targeting cyber physical systems. It follows a logical structure that can be easy to expand and adapt. Full article
(This article belongs to the Special Issue Security Threats and Countermeasures in Cyber-Physical Systems)
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Article
Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications
J. Sens. Actuator Netw. 2021, 10(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010013 - 08 Feb 2021
Cited by 1 | Viewed by 832
Abstract
Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart [...] Read more.
Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution. Full article
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Article
An Extended Reselling Protocol for Existing Anti-Counterfeiting Schemes
J. Sens. Actuator Netw. 2021, 10(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010012 - 01 Feb 2021
Viewed by 597
Abstract
Product counterfeiting is a continuous problem in industry. Recently, an anti-counterfeiting protocol to address this issue via radio-frequency identification (RFID) technology was proposed by researchers. Yet, the use case of reselling the same product has not been fully addressed which might cause serious [...] Read more.
Product counterfeiting is a continuous problem in industry. Recently, an anti-counterfeiting protocol to address this issue via radio-frequency identification (RFID) technology was proposed by researchers. Yet, the use case of reselling the same product has not been fully addressed which might cause serious problems for the exciting and proposed schemes and transactions. This paper proposes an extended RFID-based anti-counterfeiting protocol to address the use case of the original buyer reselling the same item to a second buyer. We will follow the proposed extended scheme with a formal security analysis to prove that the proposed protocol is secure and immune against most known security attacks. Full article
(This article belongs to the Special Issue Architectures and Protocols for Wireless Sensor and Actuator Networks)
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Article
Quality of Experience in 6G Networks: Outlook and Challenges
J. Sens. Actuator Netw. 2021, 10(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010011 - 01 Feb 2021
Viewed by 678
Abstract
In this paper, we discuss the critical characteristics of user experience in sixth generation (6G) cellular networks. We first describe cellular networks’ evolution through 5G and then discuss the enabling technologies and projected services in 6G networks. We note that these networks are [...] Read more.
In this paper, we discuss the critical characteristics of user experience in sixth generation (6G) cellular networks. We first describe cellular networks’ evolution through 5G and then discuss the enabling technologies and projected services in 6G networks. We note that these networks are markedly centered around expanded intelligence, end-to-end resource and topology synchronization, and the intrinsic support to low-latency, high-bandwidth communication. These capabilities make context-rich, cyberphysical user experiences viable. It thereby becomes necessary to define and identify the role of quality of experience in 6G networks, especially when it comes to network management. We elaborate on these expected challenges and allude to viable opportunities in emerging technologies. Full article
(This article belongs to the Special Issue 5G and Beyond towards Enhancing Our Future)
Article
RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection
J. Sens. Actuator Netw. 2021, 10(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010010 - 30 Jan 2021
Cited by 2 | Viewed by 949
Abstract
The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and [...] Read more.
The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and natural habitats, are commonly affected by shadowing due to obstructions caused by natural and man-made obstacles. RF signal attenuation from shadowing introduces uncharacteristically high errors in RSSI-based distance estimates, which result in large errors in RSSI-based localization schemes. This paper proposes the use of outlier detection methods for removing the effect of such disproportionately erroneous distance estimates in location estimation using RSSI. Three different localization schemes are proposed that apply outlier detection to effectively reduce localization errors in shadowed environments. Performance results of the proposed schemes are obtained using computer simulations and experimental tests. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Article
Improved Smart Pillow for Remote Health Care System
J. Sens. Actuator Netw. 2021, 10(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010009 - 28 Jan 2021
Viewed by 728
Abstract
The outbreak of novel coronavirus (COVID-19) resulted in the clinical decision that reduced direct contact is optimal, especially for senior citizens residing in nursing homes. A smart pillow adapted for the Remote Healthcare System is presented in this paper, whose core is a [...] Read more.
The outbreak of novel coronavirus (COVID-19) resulted in the clinical decision that reduced direct contact is optimal, especially for senior citizens residing in nursing homes. A smart pillow adapted for the Remote Healthcare System is presented in this paper, whose core is a Bluetooth (BT) host equipped with temperature and pressure sensors. The measurement of Core Body Temperature (CBT) from the perspective of heat transfer is first analyzed, with two proven effective methods introduced—classical Zero-Heat-Flux (ZHF) and Dual-Heat-Flux (DHF)—then finally the similarities between the Smart Pillow and ZHF are demonstrated. A pressure pad is inserted inside the pillow to detect occupancy and the specific position of the head on the pillow that meets clinical diagnostic needs. Furthermore, a real-time proactive monitoring mode is enabled for urgent warnings, which forces the pillow to keep detecting and reporting data in a defined time duration but results in rapid battery drain of the pillow. In this way, the system can detect the CBT and in-bed situation of the inhabitant without being physically present to determine critical measurements. Utility of this system can be extended to elderly people living alone in regional or remote areas, such that medical help can be dispatched as soon as possible in case of medical emergency. Full article
(This article belongs to the Special Issue Sensor Networks and IoT in eHealth Applications)
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Article
MANET Performance Optimization Using Network-Based Criteria and Unmanned Aerial Vehicles
J. Sens. Actuator Netw. 2021, 10(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010008 - 28 Jan 2021
Viewed by 770
Abstract
In this contribution we consider the problem of optimal drone positioning for improving the operation of a mobile ad hoc network. We build upon our previous results devoted to the application of game-theoretic methods for computing optimal strategies. One specific problem that arises [...] Read more.
In this contribution we consider the problem of optimal drone positioning for improving the operation of a mobile ad hoc network. We build upon our previous results devoted to the application of game-theoretic methods for computing optimal strategies. One specific problem that arises in this context is that the optimal solution cannot be uniquely determined. In this case, one has to use some other criteria to choose the best (in some sense) of all optimal solutions. It is argued that centrality measures as well as node ranking can provide a good criterion for the selection of a unique solution. We showed that for two specific networks most criteria yielded the same solution, thus demonstrating good coherence in their predictions. Full article
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Article
Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning
J. Sens. Actuator Netw. 2021, 10(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010007 - 28 Jan 2021
Cited by 2 | Viewed by 1030
Abstract
Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by [...] Read more.
Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques. Full article
(This article belongs to the Special Issue Advanced Technologies for Smart Cities)
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Article
A Green Routing Protocol with Wireless Power Transfer for Internet of Things
J. Sens. Actuator Netw. 2021, 10(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010006 - 24 Jan 2021
Viewed by 835
Abstract
The usually constrained resources and lossy links scenarios of Internet of Things (IoT) applications require specific protocol suite, as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Due to its flexibility, RPL can support efficiently vertical applications such as environmental monitoring, [...] Read more.
The usually constrained resources and lossy links scenarios of Internet of Things (IoT) applications require specific protocol suite, as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Due to its flexibility, RPL can support efficiently vertical applications such as environmental monitoring, smart city and Industry 4.0. In this paper, we propose a new Objective Function (OF) for RPL based on a composite metric considering jointly the residual energy of a node (parent) together with the energy that a neighbor node (child) can transfer to the parent according to the Wireless Power Transfer (WPT) concept. Specifically, we consider simultaneous wireless information and power transfer (SWIPT) technique, which enables both the energy harvesting and information decoding from the same radio frequency (RF) signal, in order to influence the selection of the best path according to the proposed energy efficient metric in RPL. Performance evaluation on a realistic scenario pointed out a remarkable energy saving to prolong the network lifetime, by selecting the best path toward the sink node, with respect to the OFs usually considered in the literature. Full article
(This article belongs to the Special Issue Green Wireless Sensor Network)
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Article
Learning-Based Coordination Model for On-the-Fly Self-Composing Services Using Semantic Matching
J. Sens. Actuator Netw. 2021, 10(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010005 - 20 Jan 2021
Viewed by 608
Abstract
Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected [...] Read more.
Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage IoT, exploit devices capabilities (sensing, acting), the data they locally store at different time and geographic locations, and arise from the spontaneous interactions among those devices. Thanks to a learning-based coordination model, and without any pre-designed composition, reliable and pertinent spatial services dynamically and fully automatically arise from the self-composition of available services provided by connected devices. In this paper, we show how we extended our learning-based coordination model with semantic matching, enhancing syntactic self-composition with semantic reasoning. The implementation of our coordination model results in a learning-based semantic middleware. We validated our approach on various experiments: deployments of the middleware in various settings; instantiation of a specific scenario and various other case studies; experiments with hundreds of synthetic services; and specific experiments for setting up key learning parameters. We also show how the learning-based coordination model using semantic matching favours service composition, by exploiting three ontological constructions (is-a, isComposedOf, and equivalentTo), de facto removing the syntactic barrier preventing pertinent compositions to arise. Spatial services arise from the interactions of various objects, provide complex and highly adaptive services to users in seamless way, and are pertinent in a variety of domains such as smart cities or emergency situations. Full article
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Editorial
Acknowledgment to Reviewers of Journal of Sensor and Actuator Networks in 2020
J. Sens. Actuator Netw. 2021, 10(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010004 - 16 Jan 2021
Viewed by 552
Abstract
Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Journal of Sensor and Actuator Networks maintains its standards for the high quality of its published papers [...] Full article
Review
A Review of Techniques for Implementing Elliptic Curve Point Multiplication on Hardware
J. Sens. Actuator Netw. 2021, 10(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010003 - 31 Dec 2020
Cited by 5 | Viewed by 1127
Abstract
Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. [...] Read more.
Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. When compared to Rivest, Shamir and Adleman (RSA), for example, ECC can maintain security levels with a shorter key. Elliptic Curve Point Multiplication (ECPM) is the main function in ECC, and is the component with the highest hardware cost. Lots of ECPM implementations have been applied on hardware targeting the acceleration of its calculus. This article presents a systematic review of literature on ECPM implementations on both Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC). The obtained results show which methods and technologies have been used to implement ECPM on hardware and present some findings of the choices available to the hardware designers. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Article
A Bluetooth-Based Architecture for Contact Tracing in Healthcare Facilities
J. Sens. Actuator Netw. 2021, 10(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010002 - 31 Dec 2020
Viewed by 1132
Abstract
With the latest standard releases, Bluetooth technology is becoming more and more relevant for building and industrial automation. At the same time, Bluetooth is now becoming fundamental for contact tracing applications, to support monitoring and containment of the COVID-19 pandemic. Critical facilities such [...] Read more.
With the latest standard releases, Bluetooth technology is becoming more and more relevant for building and industrial automation. At the same time, Bluetooth is now becoming fundamental for contact tracing applications, to support monitoring and containment of the COVID-19 pandemic. Critical facilities such as nursing homes and hospitals have been severely exposed to the pandemic, but the currently available short-range wireless technology still faces the fundamental limits of proximity accuracy, battery lifetime, and privacy in those complex indoor environments. The aim of this paper is to investigate effective ways of building an architecture with heterogeneous devices to support contact tracing in critical scenarios such as healthcare facilities, while meeting the required level of accuracy and privacy. A framework based on standard Bluetooth mesh networking technology is proposed, and the research challenges are discussed. Full article
(This article belongs to the Special Issue Architectures and Protocols for Wireless Sensor and Actuator Networks)
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Article
Blockchain and IoMT against Physical Abuse: Bullying in Schools as a Case Study
J. Sens. Actuator Netw. 2021, 10(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10010001 - 29 Dec 2020
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Abstract
By law, schools are required to protect the well-being of students against problems such as on-campus bullying and physical abuse. In the UK, a report by the Office for Education (OfE) showed 17% of young people had been bullied during 2017–2018. This problem [...] Read more.
By law, schools are required to protect the well-being of students against problems such as on-campus bullying and physical abuse. In the UK, a report by the Office for Education (OfE) showed 17% of young people had been bullied during 2017–2018. This problem continues to prevail with consequences including depression, anxiety, suicidal thoughts, and eating disorders. Additionally, recent evidence suggests this type of victimisation could intensify existing health complications. This study investigates the opportunities provided by Internet of Medical Things (IoMT) data towards next-generation safeguarding. A new model is developed based on blockchain technology to enable real-time intervention triggered by IoMT data that can be used to detect stressful events, e.g., when bullying takes place. The model utilises private permissioned blockchain to manage IoMT data to achieve quicker and better decision-making while revolutionising aspects related to compliance, double-entry, confidentiality, and privacy. The feasibility of the model and the interaction between the sensors and the blockchain was simulated. To facilitate a close approximation of an actual IoMT environment, we clustered and decomposed existing medical sensors to their attributes, including their function, for a variety of scenarios. Then, we demonstrated the performance and capabilities of the emulator under different loads of sensor-generated data. We argue to the suitability of this emulator for schools and medical centres to conduct feasibility studies to address sensor data with disruptive data processing and management technologies. Full article
(This article belongs to the Special Issue Security Threats and Countermeasures in Cyber-Physical Systems)
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