Showing papers on "Wireless sensor network published in 2022"

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Abstract: The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

Enhancement of Energy Efficiency and Network Lifetime Using Modified MPCT Algorithm in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol.

Structural health monitoring of civil engineering structures by using the internet of things: A review

Abstract: Structural health monitoring (SHM) and damage assessment of civil engineering infrastructure are complex tasks. Structural health and strength of structures are influenced by various factors, such as the material production stage, transportation, placement, workmanship, formwork removal, and concrete curing. Technological advancements and the widespread availability of Wi-Fi networks has resulted in SHM shifting from traditional wire-based methods to Internet of Things (IoT)-based real-time wireless sensors. Comprehensive structural health assessment can be performed through the efficient use of real-time test data on structures obtained from various types of IoT sensors, which monitor several health parameters of structures, available on cloud-based data storage systems. The sensor data may be subsequently used for various applications, such as forecasting masonry construction deterioration, predicting the early-stage compressive strength of concrete, forecasting the optimum time for the removal of formwork, vibration and curing quality control, crack detection in buildings, pothole detection on roads, determination of the construction quality, corrosion diagnosis, identification of various damage typologies and seismic vulnerability assessment. This review paper summarizes the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure. In addition, several case studies on real structures and laboratory investigations for monitoring the structural health of civil engineering constructions are discussed.

Fundamentals of Wireless Sensor Networks

Abstract: The continuous technological upgradations in the RF (radio frequency), processors, nanotechnology, and microelectromechanical systems (MEMS) domains have fostered the growth of wireless sensor networks (WSN), which in turn allowed to develop a wide range applications based on it, for instance, the technological breakthrough in the semiconductor industry stimulated to produce low-power, low-cost, and small-sized processors with high computational capacities. Speaking in more clear words, the miniaturization of sensing and computing devices enabled the development of tiny, low-cost, and low-power sensors, controllers, and actuators. Basically WSNs consist of a large number of tiny and low-cost sensor nodes that are networked via low-power wireless communication links. These networks allow to closely observe ambient environment of interest at an economical cost much lower than other possible technological solutions. Each sensor node in WSN has sensing, communication, and computation capabilities. By exploiting appropriate advanced mesh networking protocols, these nodes form a sea of connectivity that covers the physical environmental area under observation. In WSN the transmitting node opt out possible communication paths by hopping sensed data of interest from node to node toward its destination. Although the capability of single sensor node is minimal, the composition of hundreds or thousands of such nodes offers very high new technological possibilities for wide variety of applications. The power of WSN lies in the possibility of heavy deployment of large numbers of tiny nodes, which can assemble and configure on their own. Stating in simple words, these nodes have networking capability, which facilitates coordination, cooperation, and collaboration among them to meet the requirements of the underlying application. The WSN can also provide a robust service in hostile or inaccessible environments, wherein human intervention may be too dangerous or almost not possible. This new technology is exciting with unlimited potential for numerous application areas, including environmental, medical, military, transportation, entertainment, crisis management, disaster relief operations, homeland defense, and smart spaces. It is envisioned that in the near future the WSN will be an integral as well as essential aspect of our lives.

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Abstract: The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review

Abstract: The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented.

Lightweight and Anonymity-Preserving User Authentication Scheme for IoT-Based Healthcare

Abstract: Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) consist of a spatially distributed set of autonomous connected sensor nodes. The deployed sensor nodes are extensively used for sensing and monitoring for environmental surveillance, military operations, transportation monitoring, and healthcare monitoring. The sensor nodes in these networks have limited resources in terms of battery, storage, and processing. In some scenarios, the sensor nodes are deployed closer to the base station and responsible to forward their own and neighbor nodes’ data towards the base station and depleted energy. This issue is called a hotspot in the network. Hotspot issues mainly appear in those locations where traffic load is more on the sensor nodes. The dynamic and unequal clustering techniques have been used and mitigate the hotspot issues. However, with few benefits, these solutions have suffered from coverage overhead, network connection issues, unbalanced energy utilization among the sink nodes, and network stability issues. In this paper, a comprehensive review of various equal clustering, unequal clustering, and hybrid clustering approaches with their clustering attributes is presented to mitigate hotspot issues in heterogeneous WSNs by using various parameters such as cluster head selection, number of clusters, zone formation, transmission, and routing parameters. This review provides a detailed platform for new researchers to explore the new and novel solutions to solve the hotspot issues in these networks.

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Abstract: Abstract Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

Forest fire detection system using wireless sensor networks and machine learning

Abstract: Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Abstract: Reliable data collection techniques, whose aim is to ensure that sensed data are received successfully by a sink, are essential for applications in Underwater Wireless Sensor Networks (UWSNs). However, traditional data collection with Radio Frequency (RF) functions poorly in UWSNs due to peculiar features of underwater. Moreover, acoustic communication creates challenges for the reliability of data collection such as high bit error rate, packet collision and voids in routing. Furthermore, the deployment of Autonomous Underwater Vehicles (AUVs) in some scenarios changed the paradigm of data collection and introduced new issues that affect reliability such as inaccurate navigation and lengthy travel time. Consequently, numerous studies focus on the relative reliability of various currently available data collection in UWSNs. In this paper, we first review the problems specific to UWSNs and their impact on reliable data collection. It is followed by a discussion about characteristics, challenges, and features associated with the design of reliable techniques in UWSNs. Afterward, to provide readers with an overview of reliable data collection techniques in UWSNs, this paper categorizes them according to their ability to enhance reliability at all the key stages of data collection. In this categorization framework, the advantages and disadvantages of each technique have been in-depth discussed. Finally, several possible areas for further research are identified and discussed.

Artificial Intelligence in Underwater Digital Twins Sensor Networks

Abstract: The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs). This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ). In this work, three sets of experiments are performed in an area of 200 × 200 × 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information.

Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Abstract: Wireless Sensors Networks (WSN) is the self-configured Wireless Ad hoc Networks (WANET) for Internet of Things (IoT) which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes’ (MNs’) presence. Thus, to shun security threats, it is vital to find and isolate those MNs. Consequently, this work proffered a solution for detecting MNs in WSN utilizing every SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission (DT) by means of choosing the Cluster Head (CH) centred on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the Extended K-Means ( EKM) algorithm clusters the Trusted Nodes (TN), and the t-Distribution based Satin Bowerbird Optimization (t-DSBO) algorithm selects an individual CH for each cluster centred on those nodes’ residual energy. The data of that cluster is transmitted to the Base Station (BS) through the CH. The t-DSBO selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques.

Deep Learning Assisted Body Area Triboelectric Hydrogel Sensor Network for Infant Care

Abstract: Infants are physically vulnerable and cannot express their feelings. Continuous monitoring and measuring the biomechanical pressure to which an infant body is exposed remains critical to avoid infant injury and illness. Here, a body area sensor network comprising edible triboelectric hydrogel sensors for all‐around infant motion monitoring is reported. Each soft sensor holds a collection of compelling features of high signal‐to‐noise ratio of 23.1 dB, high sensitivity of 0.28 V kPa−1, and fast response time of 50 ms. With the assistance of deep learning algorithms, the body area sensor network can realize infant motion pattern identification and recognition with classification accuracy as high as 100%. Additionally, a customized user‐friendly cellphone application is developed to provide real‐time warning and one‐click guardian interaction. This self‐powered body area sensor network system provides a promising paradigm for reliable infant care in the era of the Internet of Things.

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Abstract: Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.

Node Replication Attack Detection in Distributed Wireless Sensor Networks

Abstract: Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs

Abstract: Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.

In-Sensor Computing: Materials, Devices, and Integration Technologies.

Abstract: The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.

A Survey on Mobility in Wireless Sensor Networks

Abstract: As Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) applications evolve, the need for robust protocols, capable to guarantee extended lifetime and high throughput, increases. Mobility of devices either in terms of mobile nodes or mobile sinks is a promising solution that can assist, for example, topology control and congestion mitigation. Such factors significantly contribute to the extension of the lifetime and the throughput of wireless ad-hoc networks. In this work we review and classify algorithms that introduce the characteristic of mobility in WSNs. We consider WSNs to be both a subset and a predecessor to IoT, and thus, consider that existing mobility solutions can be adapted for use in IoT. Finally, open problems and future directions are discussed that include wireless power transfer, network fault detection, and real-world/testbed evaluation of algorithms.

A survey on mobility in Wireless Sensor Networks

Abstract: As Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) applications evolve, the need for robust protocols, capable to guarantee extended lifetime and high throughput, increases. Mobility of devices either in terms of mobile nodes or mobile sinks is a promising solution that can assist, for example, topology control and congestion mitigation. Such factors significantly contribute to the extension of the lifetime and the throughput of wireless ad-hoc networks. In this work we review and classify algorithms that introduce the characteristic of mobility in WSNs. We consider WSNs to be both a subset and a predecessor to IoT, and thus, consider that existing mobility solutions can be adapted for use in IoT. Finally, open problems and future directions are discussed that include wireless power transfer, network fault detection, and real-world/testbed evaluation of algorithms.

False Data Injection Attacks Against Partial Sensor Measurements of Networked Control Systems

Abstract: This brief concerns the problem of false data injection (FDI) attacks against partial sensor measurements of a networked stochastic system. For a Kalman filter based output tracking control system with a residual-based anomaly detector, a partial FDI attack strategy is presented to deteriorate the system performance by injecting false signals into the feedback communication channel to tamper partial sensor measurements. The stealthiness condition of the attack as well as its impact on the closed-loop system is derived, which are quite different from those of the FDI attack against all sensor measurements given in the existing work. This may be helpful for guaranteeing the secure control of a networked system by protecting partial critical sensor measurements from FDI attacks. Two numerical examples are included to verify the theoretical results.

Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things

Abstract: : The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.

Understanding Node Capture Attacks in User Authentication Schemes for Wireless Sensor Networks

Abstract: Despite decades of intensive research, it is still challenging to design a practical multi-factor user authentication scheme for wireless sensor networks (WSNs). This is because protocol designers are confronted with a long-standing “security versus efficiency” dilemma: sensor nodes are lightweight devices with limited storage and computation capabilities, while the security requirements are demanding as WSNs are generally deployed for sensitive applications. Hundreds of proposals have been proposed, yet most of them have been found to be problematic, and the same mistakes are repeated again and again. Two of the most common security failures are regarding smart card loss attacks and node capture attacks. The former has been extensively investigated in the literature, while little attention has been given to understanding the node capture attacks. To alleviate this undesirable situation, this article takes a substantial step towards systematically exploring node capture attacks against multi-factor user authentication schemes for WSNs. We first investigate the various causes and consequences of node capture attacks, and classify them into ten different types in terms of the attack targets, adversary’s capabilities and vulnerabilities exploited. Then, we elaborate on each type of attack through examining 11 typical vulnerable protocols, and suggest corresponding countermeasures. Finally, we conduct a large-scale comparative measurement of 61 representative user authentication schemes for WSNs under our extended evaluation criteria. We believe that such a systematic understanding of node capture attacks would help design secure user authentication schemes for WSNs.