A Hybrid Genetic Algorithm for the Minimum Exposure Path Problem of Wireless Sensor Networks Based on a Numerical Functional Extreme Model

Miao, Yongwei; Wang, Yuping; Dai, Cai; Wang, Xiaoli

doi:10.1109/tvt.2015.2508504

Cited by 51 publications

(30 citation statements)

References 32 publications

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“…As this graph is an undirected network graph, . Consider the length of the edge be , the coordinate of node vi be denoted by , the weight could be computed as below [3][18]:…”

Section: Fig1proposed Methodology 11 Minimal Exposure Path (Mep) mentioning

confidence: 99%

“…A set of simulation experiments show that the newly developed optimization model with constraints and the hybrid genetic algorithm can help in efficiently solving the problem of proposed minimal exposure path. In [3] a new crossover operator is developed on the basis of the features of the sensor node coverage, and a novel local search approach is introduced, and an upside-down operator to elude from the local optima is designed. Combining all these, a Hybrid Genetic Algorithm (HGA) is introduced for the NFE model and its global convergence with probability one is shown to prove.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

Aravinth*,

Senthilkumar,

Mohanraj

et al. 2019

IJITEE

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The problem of exposure, which is associated with the quality of coverage, is a basic issue faced in wireless sensor networks. Search for the Minimum Exposure Path (MEP) is one among the crucial problems in Wireless Sensor Networks (WSNs). The exposure with respect to the MEP is one of the indicators used for measuring the coverage quality of a WSN, depicting the efficiency with which the monitoring of a mobile target is done all along the sensing field. The available hybrid technique does not have sufficient accuracy to get the MEP, which is too complicated, and is not suitable to networks having heterogeneous sensor nodes, or in large number, or an all-sensor field intensity function. In order to get over these problems, an Improved Artificial Bee Colony (IABC) model is introduced for the MEP problem associated with getting the optimal shortest path in this research work. The IABC model exhibits an extensive use of energy and data congestion will happen. In order to have an efficient resolution to the data congestion problem, Prim's algorithm has introduced the link level congestion scenario. Also for effective solving of this issue, in accordance with the features of the node, a semi Hidden Markov Model is developed to solve the problem of energy efficiency in WSN model. A number of experiments were carried out, and the results show that the proposed model and the designed IABC can help in increasing the solution accuracy and can be useful to not just the heterogeneous sensors' case but also the scenario in where there are a big number of sensor nodes and also an all-sensor field intensity function. Keywords: Minimal Exposure Problem (MEP), Wireless Sensor Network (WSN), Semi Hidden Markov Model, PrimsAlgorithm, and Improved Artificial Bee Colony (IABC).

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“…As this graph is an undirected network graph, . Consider the length of the edge be , the coordinate of node vi be denoted by , the weight could be computed as below [3][18]:…”

Section: Fig1proposed Methodology 11 Minimal Exposure Path (Mep) mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

Aravinth*,

Senthilkumar,

Mohanraj

et al. 2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Liu uses adaptive cell decomposition to transform the minimal exposure path problem into a discrete problem, and then designs an OMEPS algorithm to search for the obstacle-avoidance minimum exposure path in the grid-based network [38]. Accordingly, other various improved algorithms based on grid partition also have been proposed [39][40][41][42]. It is noted that, in grid-based approach, the larger the scale of the grid network within a fixed region is, the more accurate the solution to the MEP problem will be.…”

Section: Related Workmentioning

confidence: 99%

Target guiding self-avoiding random walk with intersection algorithm for minimum exposure path problem in wireless sensor networks

Yang

Jiang

Fang

et al. 2018

J Wireless Com Network

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To solve minimum exposure path (MEP) problem in wireless sensor networks more efficiently, this work proposes an algorithm called target guiding self-avoiding random walk with intersection (TGSARWI), which mimics the behavior of a group of random walkers that seek path to their destinations in a strange area. Target guiding leads random walkers move toward their end points, while self-avoiding prevents them from taking roundabout routes. Route intersections further accelerate the speed of seeking connected paths. Dijkstra algorithm (DA) is applied to solve MEP problem in a sub-network formed by multiple connected paths that walkers generate (called TGSARWI DA). Simulations show that the path exposure found by TGSARWI DA is very close to that by DA in the global network (Global DA), whereas the time complexity of computation is much lower. Compared with existing heuristic algorithms such as physarum optimization algorithm (POA), our algorithm shows higher generality and efficiency. This algorithm also exhibits good robustness to the fluctuations of parameters. Our algorithm could be very useful for the solution to MEP problem in fields with large-or highdensity sensors.

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“…To overcome this drawback few sensor networks utilize the base stations to provide inexhaustible power supply [10][11][12]. Generally, a base station is used to gather the data from the sensors to communicate with the central authority [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…(Worldwide Interoperability for Microwave Access) WiMAX has intended to present the maximum speed broadband wireless admittance used for the permanent base stations and the mobile stations [25]. Furthermore, the positioning of the base station can affect the network performance such as delay, throughput and energy consumption [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Base Station Positioning in Wireless Sensor Network to aid Cluster Head Selection Process

Shankar¹,

Jaisankar²

2017

IJIES

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Abstract:In this paper, we propose an (SAPSO) Self-Adaptive Particle Swarm Optimization algorithm to solve the base station positioning problem. This algorithm is used to minimize the distance between the base station and cluster head owing to maximizing the energy. Moreover, the proposed algorithm is compared with many other conventional algorithms such as Artificial Bee Colony-Dynamic Scout bee (ABC-DS) algorithm, Genetic algorithm (GA), Particle swarm optimization (PSO) and Firefly with Dual Update Process (FFDUP) algorithm. Also, empowering SAPSO helps to find the optimal location of the base station to conserve energy in a noteworthy manner to maximize the network lifetime and prolong network connectivity regarding minimizing distance, delay and maximizing the security, energy. Our simulation results demonstrate that the proposed SAPSO algorithm performs better than the other conventional algorithm.

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A Hybrid Genetic Algorithm for the Minimum Exposure Path Problem of Wireless Sensor Networks Based on a Numerical Functional Extreme Model

Cited by 51 publications

References 32 publications

An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

Target guiding self-avoiding random walk with intersection algorithm for minimum exposure path problem in wireless sensor networks

Base Station Positioning in Wireless Sensor Network to aid Cluster Head Selection Process

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