Coverage Problem In Wireless Sensor Networks Research Articles

ECAH: A New Energy-Aware Coverage Method for Wireless Sensor Networks using Artificial Bee Colony and Harmony Search

Wireless Sensor Networks (WSNs) offer diverse applications in the research and commercial fields, such as military applications, medical science, waste management, home automation, habitat monitoring, and environmental observation. WSNs are generally composed of a large number of low-cost, low-power, and multifunctional sensor nodes that sense, process, and communicate data. These nodes are connected by a wireless medium, allowing them to collect and share data with each other. To achieve network coverage in a WSN, a few to thousands of tiny and low-power sensor nodes should be placed in an interconnected manner. Over the last decade, deploying sensor nodes in a WSN to cover a large area has received much attention. Coverage, regarded as an NP-hard problem, is an essential parameter for WSNs that determines how the deployed sensor nodes handle each point of interest. Various algorithms have been proposed to tackle this problem. However, they often come with a trade-off between energy efficiency and coverage rate. Moreover, the scalability of the algorithms needs to be considered for large-scale networks. This paper proposes a novel energy-aware method combining Artificial Bee Colony (ABC) and Harmony Search (HS) algorithms to address the coverage problem in WSN, called ECAH. The proposed ECAH algorithm has been tested with various network scenarios and compared with other existing algorithms. The results show that ECAH outperforms the existing methods in terms of network lifetime, coverage rate, and energy consumption. Additionally, the proposed algorithm is also more robust and efficient as it can adjust to dynamic network environment changes, making it suitable for various network scenarios.

Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

In this paper, we propose a new metaheuristic algorithm based on Lévy flight called Lévy flight distribution (LFD) for solving real optimization problems. The LFD algorithm is inspired from the Lévy flight random walk for exploring unknown large search spaces (e.g., wireless sensor networks (WSNs). To assess the performance of the LFD algorithm, various optimization test bed problems are considered, namely the congress on evolutionary computation (CEC) 2017 suite and three engineering optimization problems: tension/compression spring, the welded beam, and pressure vessel. The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and Harris Hawks Optimization (HHO) algorithm. Furthermore, the performance of the LFD algorithm is tested on other different optimization problems of unknown large search spaces such as the area coverage problem in WSNs. The LFD algorithm shows high performance in providing a good deployment schema than energy-efficient connected dominating set (EECDS), A3, and CDS-Rule K topology construction algorithms for solving the area coverage problem in WSNs. Eventually, the LFD algorithm performs successfully achieving a high coverage rate up to 43.16 %, while the A3, EECDS, and CDS-Rule K algorithms achieve low coverage rates up to 40 % based on network sizes used in the simulation experiments. Also, the LFD algorithm succeeded in providing a better deployment schema than A3, EECDS, and CDS-Rule K algorithms and enhancing the detection capability of WSNs by minimizing the overlap between sensor nodes and maximizing the coverage rate. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/76103-lfd.

Genetic algorithm‐based meta‐heuristic for target coverage problem

In wireless sensor networks (WSNs), network lifetime and energy consumption are two important parameters which directly impacts each other. In order to enhance the global network lifetime, one should need to utilise the available sensors' energy in an optimise way. There are several approaches discussed in the literature to maximise the network lifetime for well-known target coverage problem in WSN. The target coverage problem is presented as a maximum network lifetime problem (MLP) and solved heuristically using various approaches. In this study, the authors propose a genetic algorithm (GA)-based meta-heuristic to solve the above said MLP. The GA is a non-linear optimisation solution method which is proven to be better as compared to the column generation or approximation schemes.

Full Coverage Deployment in Wireless Sensor Network

In wireless sensor network very less work has been done on deployment. As we know in random deployment we can’t achieve the maximum coverage in communication which result in short life time of the battery. So in this paper we discuss few benefit of the Uniform deployment in which we can achieve the full coverage with uniformity. In this paper we can achieve the maximum efficiency and increase lifetime time of the sensors which result in the long battery life which is the back bone of any Wireless Sensor Network for Remote Sensing like in Oceanographic, Glacier study and Metrological Department for weather study which are nearly inaccessible regions. Wireless sensor networks (WSNs) are composed of large number of sensor nodes that have capability of sensing, data processing and communication functionalities. The nodes are usually accoutered with power-strained batteries, which are often difficult, extravagant and even impossible to be restored once the nodes are deployed. Therefore energy aliveness becomes the key research challenge for sensor network protocols. The energy consumed by a node depends on its state. Each node could be in one of four states: transmit, receive, idle (when the node keeps listening to the medium even when no messages are being transmitted) and finally sleep state (where the radio module is switched off: no communication is possible). Recent research showed that accurate energy savings can be achieved by scheduling node’s activities in high-density WSNs. Specifically, some nodes are scheduled to sleep whereas the remaining ones provide continuous monitoring. The main issue here is how to minimize the number of active nodes in order to maximize the network lifetime. Sensing coverage is also a significant issue for sensor networks and is viewed as one of the critical measures of the supervision quality provided by a WSN. Sensor deployment is important in WSNs, as it not only determines the cost for creating the network but also affects how well a region is supervised by sensors. One frequently used method to decrease the undesirable energy consumption is to implement an energy-efficient configuration protocol where sensors operate under a well-defined activating schedule, that is, some unnecessary sensors switch to off mode or low-power listening mode. The coverage problem in WSNs is how one can be sure that the deployment of active sensors provides the necessary coverage level and how to determine the minimum number of active sensors required and their locations in the interested area to ensure desirable coverage. Regarding sensors activating or deployment control problem, some work have been investigated how some sensors can be arranged to go to sleeping modes to elongate the network lifetime while maintaining desirable coverage of sensing field. On the other hand how these active sensors can be selected to maintain the desirable coverage of sensing field. And provide a near-optimal grid-based sensor placement algorithm to achieve the complete coverage. In this method, the interested area is divided into grid points uniformly. If one grid point can be detected by at least one sensor, the grid point is covered. The coverage of the area is defined as the ratio of detected grid points to the total

Survey on Coverage Problems in Wireless Sensor Networks

The paper describes the concept of coverage problem in the wireless sensor networks (WSNs). The issue of quality of service is more closely related to the coverage problem in WSNs. The motive behin...

A New Approach for Area Coverage Problem in Wireless Sensor Networks with Hybrid Particle Swarm Optimization and Differential Evolution Algorithms

One of the most important and basic problems in Wireless Sensor Networks (WSNs) is the coverage problem. The coverage problem in WSNs causes the security environments is supervised by the existing sensors in the networks suitably. The importance of coverage in WSNs is so important that is one of the quality of service parameters. If the sensors do not suitably cover the physical environments they will not be enough efficient n supervision and controlling. The coverage in WSNs must be in a way that the energy of the sensors would be the least to increase the lifetime of the network. The other reasons which had increase the importance of the problem are the topologic changes of the network which are done by the damage or deletion of some of the sensors and in some cases the network must not lose its coverage. SO, in this paper we have hybrid the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms which are the Meta-Heuristic algorithms and have analyzed the area coverage problem in WSNs. Also a PSO algorithm is implemented to compare the efficiency of the hybrid model in the same situation. The results of the experiments show that the hybrid algorithm has made more increase in the lifetime of the network and more optimized use of the energy of the sensors by optimizing the coverage of the sensors in comparison to PSO.

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.

OPEN: an optimisation scheme of N-node coverage in wireless sensor networks

Deployment is an important issue in wireless sensor networks (WSNs). Most recent researches focus on the optimal deployment strategy to achieve full coverage in WSNs. However, the sensing devices in practical applications are finite, and cannot achieve full coverage in the whole monitored region. Moreover, the monitored objects distribute unevenly, and thus the monitored significance differs from region to region. How to deploy limited number of sensors efficiently has been to an important issue. In this study, we propose a new coverage problem in WSNs, named N-node coverage, to acquire the maximum information about monitored objects with N sensors. In this problem, the captured information is used as the new evaluation metric for network coverage. On the basis of the metric, we establish the N-node network coverage model, and prove that the N-node coverage problem is NP-hard. The authors propose a heuristic algorithm for deployment, named optimisation schEme of N-node (OPEN), to obtain the near-optimal solution of the problem. Experimental results show that the OPEN can effectively improve the coverage performance of the WSNs, and is able to provide practical guideline for deployment of WSNs with limited number of nodes.

Self organization of sensor networks for energy-efficient border coverage

Networking together hundreds or thousands of cheap sensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. As sensor nodes are typically battery operated, it is important to efficiently use the limited energy of the nodes to extend the lifetime of the wireless sensor network (WSN). One of the fundamental issues in WSNs is the coverage problem. In this paper, the border coverage problem in WSNs is rigorously analyzed. Most existing results related to the coverage problem in wireless sensor networks focused on planar networks; however, three dimensional (3D) modeling of the sensor network would reflect more accurately real-life situations. Unlike previous works in this area, we provide distributed algorithms that allow the selection and activation of an optimal border cover for both 2D and 3D regions of interest. We also provide self-healing algorithms as an optimization to our border coverage algorithms which allow the sensor network to adaptively reconfigure and repair itself in order to improve its own performance. Border coverage is crucial for optimizing sensor placement for intrusion detection and a number of other practical applications.

Energy-efficient connected-coverage in wireless sensor networks

This paper addresses the target coverage problem inWireless Sensor Networks (WSNs). Communication and sensing consume energy, therefore, efficient power management can extend network lifetime. In this paper, we consider a large number of sensors randomly deployed to monitor a number of targets. Each target may be redundantly covered by multiple sensors. To conserve energy, we organise sensors in sets activated sucessively. In this paper, we introduce the Connected Set Covers (CSC) problem that has as objective finding a maximum number of set covers such that each sensor node to be activated is connected to the Base Station (BS). A sensor can participate in multiple sensor sets, but the total energy spent in all sets is constrained by the initial energy reserves. We show that the CSC problem is NP-complete and we propose three solutions: an Integer Programming (IP)-based solution, a greedy approach and a distributed and localised heuristic. Simulation results that validate our approaches are also presented.