Enabled Wireless Sensor Networks Research Articles

Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN

SummaryInternet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering.

Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness.

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks

Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks

The high-speed data routing in the Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs) is vital in improving network performance. Existing routing protocols face difficulties dealing with frequent node relocations, energy efficiency optimization, scalability, and dynamic network environments. The inadequacy of conventional routing algorithms in handling the issues provided by frequent node relocations, energy efficiency optimization, scalability, and dynamic network settings in IoT-enabled WSNs is identified as the problem. Traditional protocols are inflexible in the face of node relocations, whereas machine learning-based techniques frequently miss energy efficiency factors. Reinforcement learning-based techniques presume static topologies and the potential of federated learning in WSN routing is largely untapped. The goal is to provide adaptive and energy-efficient routing while considering message overhead, temporal complexity, data sum rate, communication delay, and scalability. The research adopts federated deep reinforcement learning (FDRL) to address these limitations for routing the high-speed data packets in IoT-enabled WSNs. The proposed research technique uses FDRL, which enables distributed decision-making and adaptive routing in dynamic network situations. The fundamental instant data load is separated into cluster pairs in the proposed technique, each with one strong and one weak sensor node. This split enables optimal network load balancing and resource use. The FDRL architecture is used to disperse the learning process across numerous nodes or access points, allowing for localized decision-making while taking advantage of global coordination. The simulations are conducted to validate the effectiveness of the FDRL Routing in terms of packet loss, latency, energy efficiency, and scalability. The results show that the proposed federated DRL-based intelligent data routing approach outperforms traditional protocols and other current routing strategies.

Multiple Concurrent Slotframe Scheduling for Wireless Power Transfer-Enabled Wireless Sensor Networks

This paper presents a multiple concurrent slotframe scheduling (MCSS) protocol for wireless power transfer (WPT)-enabled wireless sensor networks. The MCSS supports a cluster-tree network topology composed of heterogeneous devices, including hybrid access points (HAPs) serving as power transmitting units and sensor nodes serving as power receiving units as well as various types of traffic, such as power, data, and control messages (CMs). To this end, MCSS defines three types of time-slotted channel hopping (TSCH) concurrent slotframes: the CM slotframe, HAP slotframe, and WPT slotframe. These slotframes are used for CM traffic, inter-cluster traffic, and intra-cluster traffic, respectively. In MCSS, the length of each TSCH concurrent slotframe is set to be mutually prime to minimize the overlap between cells allocated in the slotframes, and its transmission priority is determined according to the characteristics of transmitted traffic. In addition, MCSS determines the WPT slotframe length, considering the minimum number of power and data cells required for energy harvesting and data transmission of sensor nodes and the number of overprovisioned cells needed to compensate for overlap between cells. The simulation results demonstrated that MCSS outperforms the legacy TSCH medium access control protocol and TSCH multiple slotframe scheduling (TMSS) for the average end-to-end delay, aggregate throughput, and average harvested energy.

An Optimized Genetic Algorithm for Cluster Head Election Based on Movable Sinks and Adjustable Sensing Ranges in IoT-Based HWSNs

Internet of Things (IoT)-enabled wireless sensor network (WSN) permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. However, the common IoT devices usually have very limited battery power, which is not frequently rechargeable. Thus, an energy-efficient mechanism is required to operate IoT-enabled WSN. To address the limited power shortcoming of IoT-enabled WSN, we propose an optimized genetic algorithm (GA) for cluster head (CH) election (OptGACHE). The CH election using GA incorporates four different criteria, namely: 1) node density; 2) distance; 3) energy; and 4) heterogeneous node’s capability for the development of fitness function. These criteria help in optimizing intracluster distance, systematic utilization of node’s energy in the cluster, reducing hop count, and promoting selection of highly capable nodes for CHs. The proposed movable sink strategy shortens the length of communication distance between sink and CH and also diminishes the hotspot problem. Furthermore, the incorporated dynamic sensing range adjustment minimizes the overlapping of sensing range of CH along with cutting down transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, namely, network’s remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

UAV-Enabled Data Collection for Wireless Sensor Networks With Distributed Beamforming

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV’s trajectory design and the GNs’ transmit power allocation over time, subject to the UAV’s flight speed constraints and the GNs’ individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV’s flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV’s flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV’s flight speed constraints are ignored.

Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks

The Internet of Things (IoT) paradigm allows the integration of cyber and physical worlds and other emerging technologies. IoT-enabled wireless sensor networks (WSNs) are rapidly gaining interest due to their ability to aggregate sensing data and transmit it towards the central or intermediate repositories, such as computational clouds and fogs. This paper presents an efficient multi-hop routing protocol (EMRP) for efficient data dissemination in IoT-enabled WSNs where hierarchy-based energy-efficient routing is involved. It considers a rank-based next-hop selection mechanism. For each device, it considers the residual energy to choose the route for data exchange. We extracted the residual energy at each node and evaluated it based on the connection degree to validate the maximum rank. It allowed us to identify the time slots for measuring the lifetime of the network. We also considered the battery expiry time of the first node to identify the network expiry time. We validated our work through extensive simulations using Network Simulator. We also implemented TCL scripts and C language code to configure low-power sensing devices, cluster heads and sink nodes. We extracted results from the trace files by utilizing AWK scripts. Results demonstrate that the proposed EMRP outperforms the existing related schemes in terms of the average lifetime, packet delivery ratio, time-slots, communication lost, communication area, first node expiry, number of alive nodes and residual energy.

A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

SummaryUnmanned aerial vehicle (UAV)‐enabled wireless sensor networks (WSNs) have evolved as the significant paradigms to communicate the information among the devices in today's scenario instantly. These technologies support for developing smart crop spraying, smart infrastructure, aerial photography and videography, emergency services, etc. in providing smart solutions. Therefore, there is a dire need to develop an energy‐efficient solution for data gathering through UAVs. In this paper, an optimum weighted probability function is proposed by considering residual node energy, node spacing, and average energy of networks. This function helps in optimizing the clustering process for three‐tier heterogeneity by minimizing the energy of the proposed network. The proposed methods achieve 29.45% and 52.48% increment in the network lifespan as compared to the existing algorithm. The reason for such a gigantic enhancement in the network lifespan is the selection of the highest capable node as a cluster head using the proposed function.

An Energy-Efficient Modified Metaheuristic Inspired Algorithm for Disaster Management System Using WSNs

In recent trends, Internet of Things (IoT) enabled Wireless Sensor Network (WSN) is widely used for various applications like emergency medical services, fire detection, flood control, etc. for disaster management in the smart city. Commonly, sensor devices are equipped with limited battery but they consumed a lot of battery in the communication for such scenarios. Therefore, practical comprehension of such networks is the challenging task as data collection consuming tremendous energy due to the shortcomings of the existing methods i.e., cluster overlapping, elect lower energy nodes as cluster-head (CH), formed highly dense clusters and hotspot problem in the network, and large communication distance. To overcome the above-discussed issues, an integrated modified Genetic Algorithm (GA) for CH election in WSNs is proposed to maximizing the network lifetime referred to as ModifyGA. The energy-efficiency of ModifyGA is increased by incorporating dynamic sensing range and criteria’s used for developing fitness function. The ModifyGA satisfies various constraints for optimizing intra-cluster distance, systematic utilization of node’s energy in the cluster, reducing hop-count and promoting selection of highly capable nodes for CHs. The simulation analysis of ModifyGA is carried-out to operate with a single static sink, multiple static and movable sinks to have an impartial comparative analysis.

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications.

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

Anti-Jamming 3D Trajectory Design for UAV-Enabled Wireless Sensor Networks Under Probabilistic LoS Channel

This paper investigates the anti-jamming three-dimensional (3D) trajectory design to safeguard the legitimate communications of unmanned aerial vehicle (UAV)-enabled wireless sensor networks (WSNs). Specifically, a UAV is dispatched to collect data over multiple ground sensors (GSs) in the presence of a malicious ground jammer. Under the practical probabilistic line-of-sight (LoS) channel model, we aim to maximize the minimum (average) expected rate among GSs over a finite flight period, by jointly optimizing the GS transmission scheduling, the UAV horizontal and vertical trajectories. To make the formulated non-convex problem tractable, we provide an achievable lower bound for the expected rate, based on which an efficient iterative algorithm is proposed to solve it suboptimally by exploiting the block coordinate descent and successive convex approximation techniques. Numerical results show the significant rate improvement of the proposed joint design and provide new insights into the anti-jamming 3D trajectory design under the probabilistic LoS model, as compared to the conventional two-dimensional (2D) UAV trajectory or under LoS channel models.

A Receiver Initiated Low Delay MAC Protocol for Wake-Up Radio Enabled Wireless Sensor Networks

In this paper, we propose a broadcast-based receiver-initiated low-delay wake-up radio (RI-LD-WuR) Medium Access Control (MAC) protocol for wireless sensor networks. RI-LD-WuR MAC improves delay and energy-efficiency by reducing the packet collisions. We reduce the packet collisions by reducing the contention during channel access. We design a new cycle structure and divide the nodes into K groups to decrease the contention during channel access. We propose an algorithm to determine the value of K that provides the lowest delay. To evaluate the performance, we develop a discrete-time Markov-chain (DTMC) model and drive expressions for packet delivery ratio, average energy consumption per transmitted data packet, and delay. Numerical results demonstrate the superiority of the RI-LD-WuR MAC protocol.

Mid Square Hash Bezier Secured Routing for IoT Enabled Wireless Sensor Networks

The swift uptake of sensors and the increasing demand of Wireless Sensor Network (WSN) applications and services create unmatched appeals for routing data packets on WSN infrastructure. In the era of Internet of Things (IoT), an immense number of sensing devices collect numerous sensor data over time for an extensive span of fields. For IoT-enabled devices routing through WSN, sensor nodes transmit confidential data to the gateway nodes via public channels. In such an environment, secured routing remains a serious issue that has to be addressed. To address this issue, in this work, a robust method using Mid Square Hash-based Bezier Authentication (MSH-BA) is designed to secure the routing for IoT-enabled WSNs. First, a Mid Square Hashing Data Collection algorithm is proposed to minimize the energy consumption through unique key generation. To minimize the running time of IoT sensor network, discrete registration is performed between user and gateway nodes. Finally, the proof of correctness of mutual authentication is performed using the Bezier Authentication method via progressive distance, therefore ensuring packet delivery ratio. The robust authentication method is analyzed comprehensively and compared against the similar authentication methods and the results showed that it is efficient and robust than earlier methods.

Energy Efficiency Optimization in SWIPT Enabled WSNs for Smart Agriculture

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers’ pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

A collaborative caching strategy for content-centric enabled wireless sensor networks

The Content-Centric Networking (CCN) is an efficient traffic handling technology by accessing content with its name instead of its physical location and achieving in-network caching. Indeed, CCN caching ensures high content availability, network traffic reduction, and low retrieval latency which reduces congestion and improves end-to-end delay. Moreover, it could greatly improve the efficiency of content delivery in Wireless Sensor Networks (WSNs). In this article, we propose to exploit this feature to enable CCN in WSN environments. The CCN architecture enables the content caching on each sensor-node in WSN and several research studies have been devoted to the caching management issue in such a context. However, caching the content on all the nodes is not a good strategy in terms of resource utilization. It is, therefore, necessary to determine where to cache and how to handle it in order to optimize the resources while realizing a high-interest satisfaction rate. Thus, our objective is to study existing caching strategies and propose a novel one that takes into account the node centrality and its distance from the source of the content. Through extensive simulations, we examine the performance of our scheme under different configurations and demonstrate how it outperforms the traditional caching strategies such as LCE (Leave Copy Everywhere) and LCD (Leave Copy Down).

Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs’ locations and the probabilistic LoS channel model is known a priori , while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs’ individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.

Behavioral Based Uncertainty Handling in Energy Depletion Reduction (BUEDR) through Alleviation of Holes in an IoT Enabled Wireless Sensor Networks

In current era the usage of Internet is gradually improvising from Internet of People to an Internet of Things (IoT). Through the gateway, Wireless Sensors Networks (WSN) are integrating things to the Internet. But there are two major elements which affect the performance of the network, the presence of void hole and energy hole. Due to the unavailability of forwarded nodes, a particular region may suffer from void hole. In addition, if the load of data is not correctly balanced among the sensor nodes, then energy hole occurs. To overcome these issues, the proposed work uses a threefold behavioral based uncertainty handling transmission policy. In the first fold, the area of WSN is divided using voronoi diagram, which resolves the geometric relationships between sensors and to distribute transmission uniformly over the network area. The second fold alleviates energy hole vagueness in an adversarial environment by gathering information about the node denoted by intuitionistic fuzzy domain based cluster head selection to enhance energy dissipation. The third fold is optimal route selection for which fuzzy fish swarm optimization is adopted. From the simulation results, it is observed that the energy consumption achieves its superiority by hole alleviation and thus maximizes the network life time.

Logical Tree Based Secure Rekeying Management for Smart Devices Groups in IoT Enabled WSN

With the rapid growth in a huge number of devices connecting online, Internet of Things (IoT) is rapidly growing and getting interested of researchers. IoT enabled wireless sensor network (WSN) plays a significant role to collect sensing data and transmit to central repositories. Moreover, multicasting ensures efficient group communication for disseminating the same query or command to all smart devices to perform mobile service computing. It is applicable in the smart home, healthcare, smart cities, and smart industries for monitoring and control. To secure such sensitive information exchange, we have considered a secure group communication scenario where logical trees are maintained for each group. The main problem is unnecessary rekeying when a smart device frequently joining or leaving the network. It causes computation, communication, and energy overheads. To overcome the excessive rekeying problem, we have presented a logical tree-based secure mobility management scheme (LT-SMM) using mobile service computing in IoT. It includes the group deployment phase where smart devices securely setup a group by registering with group heads for future secure information exchange. We have presented group deployment, mobile node joining and mobile node migration protocols. Moreover, we have used chaotic map based one-way hash functions to ensure message integrity. To validate our work, extensive simulations are performed using NS 2.35. TCL code is used to configure smart devices, deploy logical tree, messaging. C language is used for algorithm implementation and messaging backend coding. The results verify the supremacy of our scheme as compared to existing tree based schemes in terms of computation, communication, and energy consumption.

Trajectory Design for Distributed Estimation in UAV-Enabled Wireless Sensor Network

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is nonconvex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves a significant performance gain in terms of the number of SNs whose data are successfully collected, as compared with other benchmark schemes.