Field Of Wireless Sensor Networks Research Articles

Routing Methods For Data Transmission In Large Networks: A Review

A wireless sensor network (WSN) is a network consisting of miniaturized smart sensors communicating the information gathered or collected from a monitored environment via a wireless link. The sensors are capable of sensing the events within their environment, process the data, and transmit the data to the base station (BS). The entire processing of data and subsequent transmission to BS requires high energy consumption. The operation of WSN is limited by repeated dead nodes, which results in energy depletion. Hence, to prolong the life-span of the network, several routing protocols have been developed. However, the effectiveness of these protocols has not been well examined in terms of increasing node population for a given WSN field or area. Therefore, in this work the effect of increasing node density on cluster based energy-efficient routing protocols in wireless sensor network was analysed. The implemented routing protocols were Low Energy Adaptive Clustering Hierarchy LEACH, stable election protocol (SEP), and zone-SEP (Z-SEP). The dimension of the WSN was 100 × 100 square metre area with varying number of sensor nodes: 50, 60, 70, 80, 90, and 100. The results of the simulation conducted in MATLAB revealed that increasing node density resulted in increased alive node and throughput (measures in terms of transmitted number of packets). On the contrary, the protocol with the best performance was ZSEP.

Advancing precision agriculture through multi-objective optimization using butterfly algorithm

Wireless Sensor Networks (WSNs) are transforming precision agriculture by enabling seamless monitoring and control of key factors such as temperature, humidity, solar radiation, soil moisture, and various dissolved compounds. This technology enhances efficiency and productivity while reducing costs. However, optimizing coverage area and energy efficiency in precision agriculture WSNs presents significant challenges. To address these challenges, our work focuses on developing innovative solutions inspired by advanced algorithms and state-of-the-art techniques for WSNs. Our primary objective is to improve area coverage and reduce energy consumption in precision agriculture WSNs. We are developing algorithms that can adapt to diverse agricultural landscapes. Through simulations, we aim to evaluate the performance and impact of our novel algorithm on precision agriculture applications. These simulations will provide valuable insights into the effectiveness of our algorithm in enhancing coverage area and energy efficiency in WSNs. Furthermore, our research aims to contribute to the broader field of WSNs by providing a detailed analysis of the challenges and opportunities in optimizing coverage area and energy efficiency in agricultural settings. By leveraging advanced algorithms and techniques, we aim to enhance the capabilities of WSNs in precision agriculture, leading to more sustainable and efficient farming practices.

An Energy-Efficient Learning Automata and Cluster-Based Routing Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs), which may be used for a broad variety of applications, have recently emerged as a prominent data collection paradigm. The fundamental concerns in wireless sensor networks are the efficient use of energy and the reliable delivery of data, both of which are largely determined by the rate at which packets are dropped. When developing an energy-efficient routing protocol, one of the most important steps is selecting a node to act as a successor node in a routing path. The application of learning automata theory to guide the routing decisions made by the sensors in a WSN has recently been the subject of research in the field of WSNs, where it has been shown to have several advantages. In this paper, a learning automata-based PSO relay selection scheme for energyefficient relay selection and reliable data delivery is proposed. The network is clustered using the LEACH protocol. The random number in the traditional LEACH protocol will be stabilized with the sensor node energy level for CH stability. Every sensor node in the network estimates the best possible routes to the sink node using the PSO algorithm. Instead of retransmissions, here we introduce learning automata for successor node selection during packet loss. The proposed learning automata calculate the next node’s selection probability in a routing path using multi-objective parameters like communication cost, residual energy, distance from BS, buffer size, and previous selection probability. Performance evaluation clearly showed that the proposed approach decreases energy usage, transmission delays, and data transfers while extending network lifetime. According to the experimental results, the proposed scheme can improve energy efficiency by 21.68%, delay by 31%, PDR by 87%, routing overhead by 0.5%, and throughput by 18.76% as compared to existing techniques like O-LEACH (Optimized Low Energy Adaptive Clustering Hierarchy Protocol) and EEPC (enhanced energy proficient clustering).

Load balancing clustering and routing for IoT‐enabled wireless sensor networks

AbstractInternet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.

Enhancing Security in Wireless Sensor Networks With Key Management Techniques

In several fields, including environmental monitoring and healthcare, wireless sensor networks (WSNs) have emerged as a vital technology. Security is a critical concern for WSNs, as these networks are often deployed in hostile environments where attackers may attemptto compromise the integrity and confidentiality of data. Key management is a key component of WSN security because it is employed to create and maintain secure channels for communication between nodes. This paper provides an overview of key management techniques for WSNs, including symmetric and asymmetric key distribution, hash-based key distribution, and threshold cryptography. We also discuss the strengths and weaknesses of each technique, as well as their suitability for different types of WSN applications. Finally, we identify key research challenges in the field of WSNs' key management, including the development of lightweight and energy-efficient key management protocols and the integration of key management with other security mechanisms such as intrusion detection and secure routing.

A multi objective optimization in wireless sensor network using enhanced NSGA-3 algorithm

Wireless sensor network (WSN) is one of the most challenging study areas in the field of information technology. Among the many challenges posed by WSN, optimization in the presence of multiple conflicting objectives is one of the most major challenges. This paper discusses the previous research work in the field of MOO WSN. This research proposes a new algorithm, Enhanced NSGA-III which is a reference based with new dynamic weight-based cluster scheduling algorithm. ENSGA uses multi-parent order crossover (MPOX) to enhance fresh child to produce the best Pareto Fronts (PF). Total number of sensor nodes, network coverage and energy consumption are considered as the three conflicting objectives for the optimization study. ENSGA exhibits promising results in comparison to the NSGA-II and NSGA-III. The possible future work is also discussed in the paper.

Leader election of dynamic wireless intelligent control machine in sensor network distributed processing

Strategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span, will convey a lot of data to the sink until the sensor nodes die. This paper has proposed modeling the WSN protocol by utilizing the classification and clustering of sensor nodes using the chimpanzee leader election optimization (CLEO) method. The CLEO algorithm is a meta-heuristic algorithm inspired by a social model that applies the pattern of leader selection to the chimpanzee community. The optimization method has the advantage of getting a fast fitness value which is one of the critical factors in utilizing meta-heuristic models in the WSN field. The closest sensor nodes at a certain distance to the sink are marked as classification, while other sensor nodes are marked as clusters. The clustering of sensor nodes utilizes a cluster function using the CLEO algorithm. This paper also carried out the selection of group leaders to obtain an energy efficiency model from WSN-CLEO. The results of the proposed method, WSN-CLEO, have been compared to other protocol models.

A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization

The technology of wireless sensor networks (WSNs) is developing rapidly, and it has been applied in diverse fields, such as medicine, environmental control, climate prediction, monitoring, etc. Location is one of the critical fields in WSNs. Time difference of arrival (TDOA) has been widely used to locate targets because it has a simple model, and it is easy to implement. Aiming at the problems of large deviation and low accuracy of the nonlinear equation solution for TDOA, many metaheuristic algorithms have been proposed to address the problems. By analyzing the available literature, it can be seen that the swarm intelligence metaheuristic has achieved remarkable results in this domain. The aim of this paper is to achieve further improvements in solving the localization problem by TDOA. To achieve this goal, we proposed a hybrid bald eagle search (HBES) algorithm, which can improve the performance of the bald eagle search (BES) algorithm by using strategies such as chaotic mapping, Lévy flight, and opposition-based learning. To evaluate the performance of HBES, we compared HBES with particle swarm algorithm, butterfly optimization algorithm, COOT algorithm, Grey Wolf algorithm, and sine cosine algorithm based on 23 test functions. The comparison results show that the proposed algorithm has better search performance than other reputable metaheuristic algorithms. Additionally, the HBES algorithm was used to solve the TDOA location problem by simulating the deployment of different quantities of base stations in a noise situation. The results show that the proposed method can obtain more consistent and precise locations of unknown target nodes in the TDOA localization than that of others.

Development of Energy Efficient and Optimized Coverage Area Network Configuration to Achieve Reliable WSN Network Using Meta-Heuristic Approaches

Energy optimization and coverage area optimization of wireless sensor networks (WSN) are two major challenges to accomplish reliability optimization in the field of WSN. Reliability optimization in the field of WSN is directly connected to the performance and efficiency and consistency of the network. In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. They have configured an optimized route/path using ant colony optimization (ACO) algorithm and deployed static WSN nodes. After configuring an efficient network, if we can maximize the coverage area, then we can ensure that the network is a reliable network. For coverage area optimization, they used a hybrid differential evolution-quantum behaved particle swarm optimization (DE-QPSO) algorithm. The result has been compared with existing literature, and the authors found good results applying those meta-heuristic and hybrid algorithms.

Weighted Boost Spearman Correlative Dual Cluster Head for Robust Transmission in Wsn

Wireless Sensor Networks (WSNs) are widely used in various applications, but they are often subject to environmental factors that result in poor network performance and packet loss. To address this challenge, correlation-based routing algorithms have been proposed. However, the performance of these algorithms degrades when the correlation between nodes is low. In this paper, we propose a Weighted Boost Spearman Correlative Dual Cluster Head (WBSCDCH) approach for robust transmission in WSNs. The proposed approach uses weighted boosting to assign higher transmission power to cluster heads with low correlation and utilizes dual cluster heads for efficient data collection and transmission. Our simulation results show that the proposed approach outperforms existing algorithms in terms of packet delivery ratio, energy consumption, and end-to-end delay. This paper contributes to the field of WSNs by providing a novel approach for improving the robustness of data transmission in WSNs.

Survey on fault tolerance‐based clustering evolution in WSN

On-going research on remote communication and sensing essentially made accessible the flexible technology called ‘wireless sensor network (WSN)’, which is independent, self-organising and self-healing in nature. The WSN comprises an extensive number of homogeneous or heterogeneous sensor nodes that may be deployed in a uniform or non-uniform manner in a targeted zone. The organisation of WSN can be stretched out from residential to a harsh and hostile environment. The manageability of WSN is very subjective to an efficient adaptation to the failure situations. In this line of thought, clustering has been proven as an efficient strategy for prolonging the sensor network lifetime and selecting the correct topological structure for the sensor network. The authors’ first present a survey of existing survey works from 2002 to 2019 and then present a tutorial on existing fault-tolerant protocols with a comparative analysis. The target audience of this work is novice researchers in the field of WSN to get a preliminary idea about clustering and its objective.

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.

Routing in Wireless Sensor Networks Using Optimization Techniques: A Survey

Over the past few decades, one of the important advancements in wireless communication is low cost and limited power devices known as wireless sensor networks (WSNs). Sensor nodes are used to transmit data but have limited amount of energy. As the transmission takes place, energy gets depleted. So energy consumption and network lifetime are the major challenges in a WSN. Much research has been done in the past years to determine an optimal path between source and destination nodes, which will result in maximizing energy conservation of a network. However, the challenge is to create a routing algorithm that takes into consideration the major issues of minimizing energy consumption and maximizing network lifetime. Various optimization techniques are available to determine a routing path between a source node and destination node. In this article, we look into the details of routing in WSN using different optimization techniques. This article provides us a comprehensive summary of the previous studies in field of WSN during the span of 2010–2019. The results provided in this article provide the future insight for researchers to fill in existing gaps in the WSN research field and to find new research trends in this area.

Numerous Clustering Techniques with Application and Limitations in Wireless Sensor Network

Wireless Sensor Network (WSN) is a very common area to work with different technology which is very useful for real time application. There are various application works with the help of WSN .some of internet of things, machine learning, big data etc. in WSN there are four basic component of WSN that is sensing unit, transceiver unit, processing unit, and storage unit. Energy is the important critical design issue in WSN. By applying clustering approach we can save the energy and attain load balancing of a network. Sensor nodes are viewed as homogeneous since the looks into in the field of WSNs have been advanced, however, a few nodes might be of various energy levels and it is draw out the lifetime of a WSN and its unwavering quality. Clustering methods are required so that sensor networks can work long time with different area. In this paper, we discussed the various clustering techniques those are helpful to maximize the lifespan of WSN.

Cluster-Tree Routing Based Entropy Scheme for Data Gathering in Wireless Sensor Networks

Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research since last few years. The major factor behind the research efforts in the field of WSNs is their vast range of applications, such as surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constraint devices; therefore, energy efficient routing protocol is the foremost concern. In this paper, a new Cluster-Tree routing scheme for gathering data (CTRS-DG) is proposed that composed of two layers: routing and aggregation and reconstruction. In aggregation and reconstruction layer, a dynamic and a self-organizing entropy based clustering algorithm for cluster head (CH) selection and cluster formation is proposed. Data is aggregated and compressed at CHs based on compressive sensing technique. In routing layer, a new proposed algorithm to form the routing tree as backbone of the network is proposed. The routing tree is used to forward the compressed data by CHs to the base station (BS). Finally, as a phase of aggregation and reconstruction layer, an effective CS reconstruction algorithm called Bee based signal reconstruction (BEBR) is proposed to improve the recovery process at the BS. BEBR utilizes the advantages of the greedy algorithm and Bees algorithm to find the optimal solution of reconstruction process. Simulation results reveal that the proposed scheme outperforms existing baseline algorithms in terms of stability period, network lifetime, and average normalized mean squared error for compressive sensing data reconstruction.

A new range-free localisation in wireless sensor networks using support vector machine

ABSTRACTLocation information of sensor nodes is of vital importance for most applications in wireless sensor networks (WSNs). This paper proposes a new range-free localisation algorithm using support vector machine (SVM) and polar coordinate system (PCS), LSVM-PCS. In LSVM-PCS, two sets of classes are first constructed based on sensor nodes’ polar coordinates. Using the boundaries of the defined classes, the operation region of WSN field is partitioned into a finite number of polar grids. Each sensor node can be localised into one of the polar grids by executing two localisation algorithms that are developed on the basis of SVM classification. The centre of the resident polar grid is then estimated as the location of the sensor node. In addition, a two-hop mass-spring optimisation (THMSO) is also proposed to further improve the localisation accuracy of LSVM-PCS. In THMSO, both neighbourhood information and non-neighbourhood information are used to refine the sensor node location. The results obtained verify that the proposed algorithm provides a significant improvement over existing localisation methods.

Heterogeneous and Multitask Wireless Sensor Networks—Algorithms, Applications, and Challenges

Unlike traditional homogeneous single-task wireless sensor networks (WSNs), heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous devices dedicated to solving different signal processing tasks. Despite their heterogenous nature and the fact that each device may solve a different task, the devices could still benefit from a collaboration between them to achieve a superior performance. However, the design of such heterogeneous WSNs is very challenging and requires the design of scalable algorithms that maximize the performance of the devices without transmitting their raw sensor signals in an uncontrolled fashion. Towards this goal, novel techniques are needed both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications in the field of heterogeneous and multitask WSNs with special focus on the signal processing aspects. Moreover, we provide a general overview of the existing algorithms for distributed node-specific estimation. Finally, we discuss the main challenges that have to be tackled for the design of heterogeneous multitask WSNs.

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks), which frequently affect Wireless Sensor Networks (WSNs). Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM) for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe), which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

A Survey on Successors of LEACH Protocol

Even after 16 years of existence, low energy adaptive clustering hierarchy (LEACH) protocol is still gaining the attention of the research community working in the area of wireless sensor network (WSN). This itself shows the importance of this protocol. Researchers have come up with various and diverse modifications of the LEACH protocol. Successors of LEACH protocol are now available from single hop to multi-hop scenarios. Extensive work has already been done related to LEACH and it is a good idea for a new research in the field of WSN to go through LEACH and its variants over the years. This paper surveys the variants of LEACH routing protocols proposed so far and discusses the enhancement and working of them. This survey classifies all the protocols in two sections, namely, single hop communication and multi-hop communication based on data transmission from the cluster head to the base station. A comparitive analysis using nine different parameters, such as energy efficiency, overhead, scalability complexity, and so on, has been provided in a chronological fashion. The article also discusses the strong and the weak points of each and every variants of LEACH. Finally the paper concludes with suggestions on future research domains in the area of WSN.

A Distributed TDMA Scheduling Algorithm Based on Energy-Topology Factor in Internet of Things

In Internet of Things, various things realize interconnection and intercommunication by the wireless sensor network (WSN). The time-division multiple-access (TDMA) slot scheduling algorithm for WSN is a crucial technology for improving network efficiency and prolonging network lifetime. Considering the industrial field of WSN, the problems of limited energy and changed topology restrict the development of the centralized resource allocation. Although some traditional distributed time slot allocation algorithms solve the network topology change to a certain extent, there are still some problems and challenges in terms of efficiency and energy consumption. In this paper, we propose a distributed TDMA slot scheduling algorithm based on energy and topology factor. By analyzing and summarizing the distributed randomized time slot assignment algorithm, it is found that energy and topology information of network nodes are very important for TDMA slot scheduling. Then the definition of energy-topology (E-T) factor is proposed, which is based on the influence of residual energy and topology on the time slot allocation. Moreover, the distributed TDMA slot scheduling algorithm based on E-T factor is proposed by taking the number of neighbors and residual energy of network nodes into consideration to reduce the execution time and energy consumption of algorithm. Finally, we simulate the proposed scheme to evaluate its performance. The experimental results indicate that the proposed algorithm can reduce the message complexity, time complexity, and energy consumption, and improve the efficiency of TDMA slot scheduling.