A Wireless Sensor Network (WSN) is a major component of any Internet of Things (IoT)-based system. In WSNs, a Mobile Sink (MS) collects sensed data from deployed sensor nodes by visiting Rendezvous Points (RPs) in an energy-efficient manner. The presence of mobile obstacles in WSNs significantly reduces network performance by increasing data transmission delay and degrading overall operation. This paper proposes an Energy-Efficient Intelligent Obstacle Avoidance Data Routing Scheme (EEIOADRS) for IoT-enabled WSNs. It effectively identifies and avoids mobile obstructions with minimal message passing and low delay. A heuristic-based Minimum Spanning Tree algorithm is used to find an optimal path among all Grid Cell Heads (GCHs) for MS-based data gathering. Furthermore, Gain-Based Dynamic Ant Colony Optimization is used to construct an optimal mobile obstacle-free path for MS-based data collection. It significantly reduces data transmission delay and enhances overall network performance. Extensive simulations demonstrate that the proposed scheme significantly improves network performance. The proposed approach improves network lifetime by 50.84% relative to OMCSO, 42.34% relative to CSOBUG, and 39.83% relative to OASPP. Additionally, simulation results indicate that the proposed scheme enhances network throughput by 47.15% compared to OMCSO, 36.69% compared to CSOBUG, and 33.76% compared to OASPP.

Recently, the Internet of Things (IoT) has found widespread applications in diverse fields, including environmental monitoring, Industry 4.0, smart cities, and smart agriculture. In these applications, sensor nodes form Wireless Sensor Networks (WSNs) and collect data from the monitoring environment. Sensor nodes are vulnerable to various faults, including battery depletion and hardware malfunctions. These faulty nodes cut/partition the network into several isolated segments. Therefore, several non-faulty nodes become disconnected from the Base Station (BS)/Sink and are unable to transmit their data to the BS. It is subject to the early demise of the network. Network cuts also significantly degrade overall network performance. Once the network is divided into isolated segments, it is very difficult to detect and collect data from them. Therefore, this paper proposes a Mobile Data Collector (MDC)-based data-gathering approach for WSNs to collect data from isolated segments. This paper proposes a novel MDC-based network cut detection algorithm that identifies the formation of network cuts in WSNs. A network recovery algorithm is also proposed to enable data collection from the isolated segment. Furthermore, this paper proposes a Reinforcement learning Brain Storm Optimization (RLBSO) algorithm for optimal selection of Rendezvous Points (RPs) and optimal MDC path design. It significantly reduces data-gathering time across isolated network segments. The simulation and testbed results show that the proposed approach outperforms existing state-of-the-art approaches in terms of network lifetime, data collection ratio, energy consumption, and latency.

Nowadays, the Internet of Things (IoT) plays a significant role in the development of various real-life applications such as smart cities, healthcare, precision agriculture, and industrial automation. Wireless Sensor Networks (WSNs) are a major ingredient of these IoT-based applications. In WSNs, sensor nodes that are close to the Base Station (BS) relay more data packets compared to other nodes, which creates high energy consumption at nodes close to the BS. As a result, an energy imbalance is created among the sensor nodes. Therefore, sensor nodes close to BS die early as compared to the faraway sensor nodes. These early dead nodes drastically increase data collection delay within the network. Furthermore, the early death of the sensor nodes partitions the network into different isolated sub-networks/segments. The formation of isolated segments causes premature death of the network. This paper proposes a Deep Policy Dynamic Programming (DPDP) based intelligent data routing scheme for IoT-enabled WSNs. The proposed scheme identifies an optimal number of Cluster Heads (CHs) and forms clusters to reduce the energy consumption of the deployed sensor nodes and prevent the early death of sensor nodes. Furthermore, the proposed scheme identifies an optimal number of Rendezvous Points (RPs) and designs an optimal path for Mobile Sink (MS) based data collection. Optimal RP selection and path design algorithms prevent the premature death of the network and significantly improve the overall performance of the network. Extensive simulations and test-bed experiments are conducted to test the performance of the proposed scheme. The simulation and test-bed results show that the proposed scheme outperforms as compared to the existing state-of-the-art approaches in terms of network lifetime, network stability, data loss due to buffer overflow, residual energy, and delay.

Recently, the Internet of Things (IoT) has played a vital role in emergency evacuation systems for smart buildings. Existing emergency evacuation systems do not consider future fire scenes, which leads to highly stretched paths or even trapping of individuals in the hazardous region. This article proposes a dynamic emergency evacuation system for shortest-safe path navigation (DESSN). The proposed work computes the shortest path for an individual toward a safe exit by considering the future spread of the fire region over time. The proposed approach creates a$fireMap$and a$routeMap$to show the fire spread and find a safe evacuation path. A modified Dijkstra algorithm is used to find the shortest path for a safe exit. This system is implemented using IoT-enabled WSN with sensor nodes, which are equipped with different types of sensors. Deployed sensor nodes communicate with the base station (BS) to plan every individual’s shortest and safe evacuation path. Sensor nodes are used to detect fire within the monitoring public infrastructure. Furthermore, BS is used to compute all the logical and arithmetical operations on real-time data. The proposed approach finds the shortest-safe path considering the future fire spread that enables quick evacuation of evacuees during an emergency. It also helps to avoid detours. Simulation results show that the proposed approach outperforms the existing state-of-the-art approaches.

Wireless Sensor Network (WSN) is one of the essential ingredients of any Internet of Things (IoT) based system. In WSNs, sensor nodes are responsible for collecting real-time data from the monitoring environment. Energy is one of the most significant resources in sensor nodes, which is used to sense and transmit data to the Base Station (BS). IoT-enabled WSN is a resource-constrained network. The collection of voluminous data from resource-constrained sensors creates several challenging issues within the network, such as poor network lifetime, message overhead, and data transmission delay. Furthermore, these sensors are vulnerable to fault due to deployment in harsh environments and natural calamities. It drastically reduces network lifetime as well as the overall performance of the network. Thus, IoT-enabled WSNs require an energy-efficient fault-tolerant data routing scheme to enhance network performance. This paper proposes a novel mobile sink-based fault-tolerance scheme with Q-learning to enhance network lifetime and overall performance of the networks. A genetic algorithm-based optimal cluster head selection mechanism is also proposed to improve energy efficiency and balance the energy consumption across the network. Extensive simulations and testbed experiments are performed to prove the out-performance of the proposed scheme in terms of network lifetime, average packet loss ratio, average energy consumption, data transmission latency, message overhead, and fairness index.

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.

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.

Wireless sensor networks (WSNs) play a vital role in the development of emergency evacuation systems. The main objective of the emergency evacuation system is to find a safe path for every individual by using the Internet of Things (IoT)-based WSNs, whenever an emergency arises. The major drawback of the existing approaches is that they ignore the clogging of individuals taking the same route to exit. Therefore, this article proposes an emergency evacuation system for clogging-free and shortest-safe path navigation (ECSSN) with IoT-enabled WSNs. The ECSSN algorithm detects the hazards, such as fire or harmful gases and humans, which creates an emergency in the indoor monitoring environment. Furthermore, several safety layers around the dangerous zones are created using a multisource breadth-first search (MBFS) algorithm. Therefore, the individuals near or far away from the hazardous region are directed along the clogging-free and shortest-safe path to the destination exit. The ECSSN algorithm also considers the dynamic emergency by taking and solving the current scenario’s input from sensor nodes after every fixed time interval. The proposed algorithm is compared with state-of-the-art algorithms in terms of execution time, average path stretch, and navigation overhead to prove the efficacy and competence of the ECSSN algorithm.

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.

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.

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.

The Internet of Things (IoT) is one of the fastest growing areas of research. Considering the IoT and healthcare simultaneously, classifying brain signals using smart IoT sensors is one of the standing nontrivial problems of literature. The issue is further exacerbated by noise in brain signals, and there is no efficient solution for classifying brain signals as seizorous or nonseizorous, yet. Moreover, research has mostly ignored the security and privacy aspect of this problem. Therefore, in this article, we try to bridge this gap and present a secure privacy-preserving technique for brain signal classification. We first transform a brain signal into an image. Subsequently, we apply transfer learning to solve the classification problem. To do that, we use the pretrained VGG-19 as a base model. In addition, we discuss a scheme to store images in a blockchain so as to make the overall architecture privacy aware. By conducting comprehensive numerical simulations on a supercomputer and using the famous TUH Abnormal EEG data set, we show the efficacy of the proposed work. The work presented here not only makes the storage of patient data secure and private but also outperforms all existing techniques in terms of classification accuracy.