Wireless Sensor Network Deployment Problem Research Articles

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Wireless Sensor Deployment Based on Multiobjective Adaptive Fish Migration Optimization

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive‐based repository and crowding degree‐selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO.

VLMOO: A framework for benchmarking Variable-length Multiobjective Optimization problems with WSN focus

<h2>Abstract</h2> Wireless Sensor Network (WSN) management has several NP-hard optimization problems with multiobjective space and variable solution space nature. Such problems are considered challenging and require a carefully developed and well-understood algorithm in behavior and performance. This article presents an open-source benchmarking framework that enables comparison within supporting multiobjective optimization metrics and networking measures. As the first of its type, the framework is designated as Variable-length Multiobjective Optimization (VLMOO). It includes three sets of mathematical benchmarking problems, WSN deployment problem with multiobjective, and variable-length nature. It is extensible and flexible in enabling interfacing with other algorithms with rich visualization graphs generation for the metrics.

Behaviour-based grey wolf optimiser for a wireless sensor network deployment problem

Behaviour-based grey wolf optimiser for a wireless sensor network deployment problem

Behaviour-based grey wolf optimiser for a wireless sensor network deployment problem

Behaviour-based grey wolf optimiser for a wireless sensor network deployment problem

Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot

In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.

Multi-Objective Wireless Sensor Network Deployment Problem with Cooperative Distance-Based Sensing Coverage

This paper investigates the multi-objective problem of deploying wireless sensor networks with cooperative distance-based sensing coverage. This problem considers deploying a number of sensor nodes to cove multiple target points on a deployment area. Based on the locations of target points and the sensor nodes with their own inner and outer coverage radii, the distance-based sensing coverages of target nodes by sensor nodes are divided into three categories: full coverage (i.e., within the inner coverage radius), no coverage (i.e., outside the outer coverage radius), and partial coverage (i.e., between the inner and outer radii). Furthermore, this paper additionally considers the cooperative sensing coverage in which the sensing coverage of a target point is provided by more than one sensor node. The decision of sensor deployment in this paper is to select sensor nodes from potential sensor node positions so as to simultaneously maximize the collective sensing coverage of all target points and minimize the total distances between each target point and the selected sensor node(s). This paper first formulates this problem as a multi-objective optimization model, and then develops a solution procedure to determine the best non-dominated solution set for the problem model. Numerical experiments for the concerned problem by the proposed solution approach are demonstrated.

A multi-group dragonfly algorithm for application in wireless sensor network deployment problem

Metaheuristic algorithm is a popular research field. In recent years, a host of optimisers have been presented. Dragonfly algorithm (DA) mimicking the behaviour of a dragonfly performs markedly competitive to some optimisation problems. However, the DA sometimes does not perform well when encountering some complicated problems, easily falls into the local optimum, and premature convergence. To overcome the deficiencies of the canonical DA, this paper presents an enhanced version of DA, namely the multi-group dragonfly algorithm (MDA). The proposed MDA with three communication strategies applies effectively the multi-group trick to improve the diversity of the population. To verify the performance of the proposed MDA, it is evaluated by different benchmarks including unimodal functions, multimodal functions, hybrid, and composed functions. The experimental data confirm that the MDA performs better than the DA. Besides, the MDA is also applied in the wireless sensor network deployment problem, the simulation results appear that the MDA can obtain a more ideal sensor node distribution.

A multi-group dragonfly algorithm for application in wireless sensor network deployment problem

A multi-group dragonfly algorithm for application in wireless sensor network deployment problem

A sensor deployment approach for target coverage problem in wireless sensor networks

Sensor deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Studies on the coverage problem are concentrated on deploying sensors to cover determined areas or a set of determined points (targets). On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Therefore, prolonging the network lifetime must be taken into account in designing a solution for a WSN that covers determined points. The problem of determining the location of sensor nodes such that the targets are covered and the network lifetime is maximized is called the sensor deployment problem. So far, various approaches, such as heuristics and approximation algorithms, have been used to solve the sensor deployment problem in WSNs. In this paper, we use two improved versions of the Particle Swarm Optimization (PSO) algorithm to solve the sensor deployment problem. The first one is cooperative PSO and the second one is improved cooperative PSO using fuzzy logic. The simulation results show that the algorithms proposed in this paper solve the target coverage problem with a longer network lifetime compared to the other similar algorithms.

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

Wireless sensor networks (WSNs) play a prominent role in the world of computer networks. WSNs rely on deployment as a basic requirement and an effective factor on the basic network services. In deployment, creating a balance between conflicting optimisation factors, e.g. connectivity and coverage, is a challenging and sophisticated issue, so that deployment turns into an NP-complete problem. The majority of existing researches has attempted to tackle this problem by applying classic single-objective metaheuristic algorithms in 2D small-scale uniform environments. In this study, a new hybrid multi-objective optimisation algorithm, which is constructed by the combination of multi-objective bee algorithms and Levy flight (LF) random walk is proposed to deal with the deployment problem in WSNs. For this purpose, two of the most important criteria, connectivity and coverage, have been considered as objectives. A series of experiments are carried out in large-scale non-uniform 3D environments, despite the fact that most of the present methods are applicable in small-scale uniform 2D environments. This study completely takes into account the stochastic behaviour of swarms, something that other papers do not consider. The evaluation results show that the multi-objective LF bee algorithm, in most cases, surpasses NSGAII, IBEA and SPEA2 algorithms.

On the performance of quick artificial bee colony algorithm for dynamicdeployment of wireless sensor networks

In recent years, the use of wireless sensor networks (WSNs) has increased and there have been significant improvements in this field. Especially with smarter, cheaper, and smaller sensor nodes, various kinds of information can be detected and collected in different environments and under different conditions. WSNs have thus been used in many applications such as military, surveillance, target tracking, home, medical, and environmental applications. As the popularity of WSNs increases, problems related to these networks are being realized. The dynamic deployment problem is one of the main challenges that have a direct effect on the performance of WSNs. In this study, a novel optimization technique named the quick artificial bee colony (qABC) algorithm was applied to the dynamic deployment problem of WSNs. qABC is a new version of the artificial bee colony algorithm (ABC) and it redefines the onlooker bee phase of ABC in a more detailed way. In order to see the performance of qABC on this problem, WSNs that include only mobile sensors or both stationary and mobile sensors were considered with binary and probabilistic detection models. Some experimental studies were conducted for tuning the colony size ($CS$) and neighborhood radius ($r$) parameters of the qABC algorithm, and the performance of the proposed method was compared with the standard ABC algorithm and some other recently introduced approaches including a parallel ABC, a cooperative parallel ABC, a version of ABC powered by a transition control mechanism (tlABC), and a parallel version of tlABC. Additionally, some CPU time analyses were provided for qABC and ABC considering different dimensions of the problem. Simulation results show that the qABC algorithm is an effective method that can be used for the dynamic deployment problem of WSNs, and it generally improves the convergence performance of the standard ABC on this problem when $r \geq 1$.

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the...

Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints

A wireless sensor network (WSN) deployment requires the identification of optimal network nodes (sensor and sink) positions in an area of interest, to ensure the best network performances (Senouci et al. in Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp 1---6, 43). The deployment process can be divided in two main parts: (1) WSN model construction, and (2) placement optimization. Few research works were interested by WSN deployment in indoor environment, even though, most of them consider the objectives (coverage, cost, connectivity) individually without considering the sensors and sink in the same time. This paper proposes a multi-objective deployment strategy (MODS), where all important objectives are integrated. The MODS uses the multi-objective evolutionary algorithms to get near optimal solution for WSN deployment problem. An original coding solution, integrating both network cost and nodes positions is proposed. A comparative study between two evolutionary strategies (classical GA, and NSGA-II) was performed to identify the use case of each one. Obtained results showed the interest of the proposed methodology.

An improved dynamic deployment method for wireless sensor network based on multi-swarm particle swarm optimization

Dynamic deployment methods for wireless sensor network (WSN) can improve the quality of service (QoS) of the network by adjusting positions of mobile nodes. In the dynamic deployment problem model of this paper, not only the coverage rate of WSN but also the moving distance of mobile nodes is taken into consideration. This kind of model can be abstracted into multi-objective optimization problem, and particle swarm optimization (PSO) is introduced to solve this problem. In this paper, combined with previous work, an improved dynamic deployment method is proposed based on multi-swarm PSO. Specifically, we propose a discrete PSO to calculate the distance of mobile solutions, and a multi-swarm PSO is designed to optimize network performance for enhancing the QoS of deployment which includes higher coverage rate and lower energy consumption of mobile nodes. Experimental results demonstrate that the proposed method has a good performance in solving the WSN deployment problem.

An efficient local improvement operator for the multi-objective wireless sensor network deployment problem

Wireless sensor network layout, also known as sensor node deployment, is a complex NP-complete optimization task that determines most of the functioning features of a wireless sensor network. Coverage, connectivity and lifetime (handled through its opposing parameter, power consumption), are three of the most important characteristics of the service, and are taken into consideration in this article within a multi-objective approach of the problem. Leveraging on the specific properties of the wireless sensor nodes and networks, the Proximity Avoidance Coverage-preserving Operator (PACO) for local improvement is presented, described and tested. The testbed consists of a set of state-of-the-art multi-objective optimization algorithms with different configurations, and problem instances of varying size. In all the scenarios, and more specially in the algorithmic settings that already produce high performance solutions, PACO has proven to be a robust enhancement to the raw optimization technique, without requiring additional computation, that easily scales through problem complexity.

Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints

In this paper, we address a wireless sensor network deployment problem. It is considered when the deployment field is characterized by a geographical irregularity of the monitored event. Each point in the deployment area requires a specific minimum guarantee of event detection probability. Our objective is to generate the best network topology while minimizing cost of deployment, ensuring quality of monitoring and network connectivity, and optimizing network lifetime. The problem is formulated as combinatorial optimization problem, which is NP-complete. Unfortunately, due to the large solution state space and the exponential computational complexity, the exact methods can be applied only in the case of small-scale problem. To overcome the complexity of an optimal resolution, we propose new scalable deployment heuristics based on artificial potential field and Tabu search metaheuristic, namely potential field deployment algorithm ( PFDA) and multi-objective deployment algorithm ( MODA). We compare our proposal to the related deployment strategies, the obtained results show that PFDA and MODA obtain the best performances.

A Tabu Search WSN Deployment Method for Monitoring Geographically Irregular Distributed Events.

In this paper, we address the Wireless Sensor Network (WSN) deployment issue. We assume that the observed area is characterized by the geographical irregularity of the sensed events. Formally, we consider that each point in the deployment area is associated a differentiated detection probability threshold, which must be satisfied by our deployment method. Our resulting WSN deployment problem is formulated as a Multi-Objectives Optimization problem, which seeks to reduce the gap between the generated events detection probabilities and the required thresholds while minimizing the number of deployed sensors. To overcome the computational complexity of an exact resolution, we propose an original pseudo-random approach based on the Tabu Search heuristic. Simulations show that our proposal achieves better performances than several other approaches proposed in the literature. In the last part of this paper, we generalize the deployment problem by including the wireless communication network connectivity constraint. Thus, we extend our proposal to ensure that the resulting WSN topology is connected even if a sensor communication range takes small values.