Recently, the Internet of Things (IoT) had emerged very rapidly and became the most important technology in today’s era. In an IoT-based environment, every physical object is connected to the ...internet. It has many applications in the fields of home automation, healthcare, military, weather forecasting, and industrial monitoring. Wireless Sensor Networks (WSNs) acting as a backbone of any IoT-based system where IoT-enabled sensor nodes are deployed to collect real-time data from the monitoring environment. IoT-enabled WSNs require well-designed network architecture so that the overall lifetime of the network increases since the nodes are battery-operated. The overall performance of the architecture depends on how the data is transmitted from the source nodes to the Base Station efficiently and effectively by minimizing the data losses. Static Sinks and Mobile Sinks are used for data gathering in IoT-enabled WSNs. But the performance of Mobile Sink (MS) based data gathering approaches are more efficient as compared to Static Sink based data gathering approaches. However, MS-based data gathering approaches have several drawbacks and limitations. Therefore, a detailed study of the existing state-of-the-art MS-based data gathering approaches can help further development in this direction. In this paper, the working of the sensor network is explained with its application in various fields. It also discusses the various path selection algorithm used for the MS to gather the information in the shortest possible time by minimizing the energy dissipation of each node to enhance the overall lifetime of the whole network. Some other factors also affect the path planning for a MS which is discussed and explained in the paper.
Recently, the Internet of Things (IoT) topology has used to collect physical, physiological, vital signs of patients in consumer-centric e-health or consumer' wellness care services. In such ...healthcare systems, varieties of medical sensors are attached to the patients to collect vital signs from those who are under observation. The data gathering process in IoT-enabled Wireless Sensor Network (WSN) suffers from the congestion problem. The effect of this translates on missing packets, a decrease of reliability and throughput degradation in IoT-enabled WSN. This article proposes a distributed congestion control algorithm for IoT-enabled WSNs to effectively resolve the congestion for healthcare applications. The proposed scheme alleviates congestion by a priority-based data routing strategy. Furthermore, this article presents a priority queue based scheduling scheme for better reliability. We analyze the properties of the proposed congestion control mechanism mathematically and validate its performance through extensive simulation and real-life experiments. The application of this work can be used to an early warning system in detecting abnormal heart rate, blood pressure, ECG, EMG in the hospital/home care environment to the state-of-art diagnosis.
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•An energy efficient fault detection and data routing scheme (EFDR) is proposed for sensor nodes hardware.•Cellular automata (CA) rules have been employed for sensor node and network ...management.•The proposed scheme reuses the faulty sensor nodes in order to improve the network performances.•The proposed method uses vector based fault detection model for sensor circuit fault identification.•The performance of the EFDR is better in terms of network coverage, network life time and energy consumption.
In a wireless sensor network (WSN), random occurrences of faulty nodes degrade the quality of service of the network. In this paper, we propose an efficient fault detection and routing (EFDR) scheme to manage a large size WSN. The faulty nodes are detected by neighbour node’s temporal and spatial correlation of sensing information and heart beat message passed by the cluster head. In EFDR scheme, three linear cellular automata (CA) are used to manage transmitter circuit/ battery condition/microcontroller fault, receiver circuit fault and sensor circuit fault representation. On the other hand, L-system rules based data routing scheme is proposed to determine optimal routing path between cluster head and base station. The proposed EFDR technique is capable of detecting and managing the faulty nodes in an efficient manner. The simulation results show 86% improvement in the rate of energy loss compared to an existing algorithm.
Safety is a major concern for Industrial 4.0 where different physical parameters are monitored for avoiding uncertain events in the industry. In industries, natural calamities like fire and leakage ...of harmful gases can cause huge damage to life and property. An Industrial Internet of Things (IIoT) is used to monitor such natural calamities and take timely prompt actions. However, sensors in the IIoT are vulnerable to failures due to energy depletion and hardware malfunctioning. It significantly reduces the reliability of the network. This article proposes an intelligent fault-tolerant scheme where different faults within the wireless sensor network-assisted IIoT such as node fault and link fault are detected and tolerated in a timely manner. It significantly improves the reliability of the network. Extensive simulations show the out-performance of the proposed scheme in terms of average packet delivery, energy consumption, throughput, network lifetime, communication delay, and recovery speed.
Recent years have seen an explosion in the demand for the Internet of Things (IoT). IoT makes any physical object smart by providing sensing capability. The physical objects are embedded with sensors ...that form a network of sensors. However, sensors are vulnerable to faults due to energy depletion, software failures, and hardware failures. The existing fault detection schemes put huge computational overhead on the battery-limited and low computational capacity sensor nodes that are subject to the premature death of the sensor nodes. Also, they suffer from poor fault detection accuracy and huge false alarm rate that significantly reduce the overall performance of the networks. In this paper, an energy-aware intelligent fault detection scheme is proposed for IoT-enabled wireless sensor networks that significantly improve fault detection accuracy and reduce false alarm rate. A novel 3-Tier hard fault detection mechanism is used for detecting hardware unit faults of the sensor nodes. Furthermore, an optimized deep learning mechanism is used for various soft fault detection that prevents premature death of sensor nodes. The paper mathematically analyses the proposed scheme in terms of energy consumption, time complexity, and message complexity. Extensive simulations show the enhanced performance of the proposed scheme compared with the state-of-the-art algorithms in terms of fault detection accuracy, false alarm rate, false-positive rate, energy consumption, and network lifetime.
•Fuzzy logic based hardware status analysis of the deployed sensor nodes.•Faulty sensor nodes are reused to improve WSNs quality of services (QoS).•Data routing algorithm improves reusability of the ...faulty nodes.•Simulations verify the effectiveness of the approach with comparisons.
In a wireless sensor network (WSNs), probability of node failure rises with increase in number of sensor nodes within the network. The, quality of service (QoS) of WSNs is highly affected by the faulty sensor nodes. If faulty sensor nodes can be detected and reused for network operation, QoS of WSNs can be improved and will be sustainable throughout the monitoring period. The faulty nodes in the deployed WSN are crucial to detect due to its improvisational nature and invisibility of internal running status. Furthermore, most of the traditional fault detection methods in WSNs do not consider the uncertainties that are inherited in the WSN environment during the fault diagnosis period. Resulting traditional fault detection methods suffer from low detection accuracy and poor performance. To address these issues, we propose a fuzzy rule-based faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. In order to overcome uncertainties that are inherited in the WSN environment, a fuzzy logic based method is utilized. Fuzzy interface engine categorizes different nodes according to the chosen membership function and the defuzzifier generates a non-fuzzy control to retrieve the various types of nodes. In addition, we employed a routing scheme that reuses the retrieved faulty nodes during the data routing process. We performed extensive experiments on the proposed scheme using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm in terms of various important performance metrics.
In the Internet of Things (IoT)-based smart systems, wireless sensor networks (WSNs) play a vital role in physical object monitoring. It collects data by sensing the environment and sends the data to ...a central depository. Due to multihop data transmission, sensor nodes stationed near the sink have to relay a huge amount of data packets compared to the sensor nodes stationed far away from the sink. Hence, sensor nodes stationed near the sink consume more energy and die early. It leads to the premature death of the network. This article proposes a multiobjective gray-wolf-optimization-based intelligent data routing mechanism for WSNs that prevents premature death of the network. It significantly improves network lifetime performance. The proposed scheme divides the whole network into different optimal size clusters and selects optimal rendezvous points (RPs). Mobile sink visits each RP through the optimal path and collects data from the sensor nodes. Extensive simulation results show that the proposed scheme effectively prevents premature death of the network and improves the network performance compared to the state-of-the-art algorithms.
Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, ...wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.
Nowadays, the demand for the Industrial Internet of Things (IIoT) technology has increased immensely in various fields, such as the agriculture industry, smart mines, smart factories, healthcare ...industry, etc. Industrial wireless sensor networks (IWSNs) act as a backbone of any IIoT system by forming a network of heterogeneous sensors. In IWSNs, the fault occurrence probability is more due to continuous exposure to harsh environments. Furthermore, the presence of obstacles creates an extra burden in fault detection. In this article, the proposed scheme presents an optimal fault diagnostic point selection mechanism that significantly reduces fault detection latency and energy consumption. Multiple intelligent mobile fault detectors effectively avoid obstacles during the fault detection process that significantly improves fault detection accuracy (FDA). Extensive simulations and a testbed experiment demonstrate the effectiveness of the proposed scheme in terms of FDA, false alarm rate, false positive rate, F1-score, energy consumption, and network lifetime.
Wireless sensor networks (WSNs) have become one of the essential components of the Internet of Things (IoT). In any IoT application, different sensor-based devices gather data from physical objects ...and transmit the sensed information to the base station (BS). The BS analyzes this information depending on the sensor location. A high-performance intelligent WSNs is essential for any IoT-based application. In this article, high-performance intelligent WSNs are referred to as IoT-enabled WSNs. In IoT-enabled WSNs, fault occurrence probability is much more than traditional networks. Faulty nodes and broken links affect the reliability of the IoT-enabled WSNs. Various fault-tolerance algorithms enhance the network's reliability using multipath transmission, relay node placement, and backup node selection. However, these algorithms suffer from huge data transmission delays, packet overhead, and less detection accuracy. In this work, a multiobjective-deep reinforcement-learning (DRL)-based algorithm is proposed for fault tolerance in IoT-enabled WSNs. The main objective of this work is to detect the faulty nodes with high accuracy and less overhead. Furthermore, this work focuses on reliable data transmission after fault detection. Finally, a mobile sink (MS) is used for energy-efficient data gathering that significantly improves the network lifetime. Extensive simulations and theoretical analysis prove that the proposed algorithm outperformed as compared to the state-of-the-art algorithms in terms of fault detection accuracy (FDA), false alarm rate (FAR), false-positive rate (FPR), network lifetime, and throughput.
