An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks
S.S.Aravinth1, J.Senthilkumar2, V.Mohanraj3, Y.Suresh,4
1Mr.S.S.Aravinth*, AP, CSE, Dhirajlal Gandhi College of Technology, Salem.
2Dr.J.Senthilkumar, Prof, IT, Sona College of Technology, Salem. 3Dr.V.Mohanraj, Prof, IT, Sona College of Technology, Salem. 4Dr.Y.Suresh, Prof, IT, Sona College of Technology, Salem.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1764-1771 | Volume-9 Issue-1, November 2019. | Retrieval Number: L35521081219/2019©BEIESP | DOI: 10.35940/ijitee.A3912.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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, Prims Algorithm, and Improved Artificial Bee Colony (IABC).
Scope of the Article: Wireless Sensor Network