Abstract
In this paper, a proficient node deployment mechanism is proposed which covers all the target points using adjustable sensing ranges in grid-based wireless sensor networks. In the beginning, the sink and the target points are randomly deployed in a grid-based environment where locations are arbitrarily selected. At first, the ants are positioned at the sink location (grid point) from where the deployment of sensors starts. An ant can move in one of the four possible directions, i.e., {1 (left); 2 (right); 3 (top); 4 (bottom)} from the sink location depending on the maximum number of uncovered target points. In case there is no path to move forward, an existing sensor is selected as the next location corresponding to that next sensor point where a deployed sensor can cover maximum number of uncovered targets in one of the four possible directions. Our method deploys minimum number of sensors to cover all the targets with least number of iterations. It also provides full coverage and connectivity with minimum deployment cost.
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References
Abdollahzadeh S, Navimipour NJ (2016) Deployment strategies in the wireless sensor network: a comprehensive review. Comput Commun 91:1–16
Aznoli F, Navimipour NJ (2017) Deployment strategies in the wireless sensor networks: systematic literature review, classification, and current trends. Wirel Pers Commun 95(2):819–846
Chang CY, Chang HR (2008) Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks. Comput Netw 52(11):2189–2204
Deif DS, Gadallah Y (2014) Wireless sensor network deployment using a variable-length genetic algorithm. In: IEEE proceedings of wireless communications and networking conference (WCNC), pp 2450–2455
Dhillon SS, Chakrabarty K (2003) Sensor placement for effective coverage and surveillance in distributed sensor networks. In: Proceedings of the IEEE conference on wireless communications and networking, pp 1609–1614
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41
Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation and machine learning. Physica D 2:187–204
Guo X, Zhao C, Yang X, Sun C (2012) A deterministic sensor node deployment method with target coverage based on grid scan. Chin J Sens Actuators 25(1):104–109
Huang G, Chen D, Liu X (2014) A node deployment strategy for blindness avoiding in wireless sensor networks. IEEE Commun Lett 99:1–4
Kim DH, Abraham A, Cho JH (2007) A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf Sci 177:3918–3937
Kirkpatrick S, Gelatto CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Kumari S, Das AK, Wazid M, Li X, Wu F, Choo K-KR, Khan MK (2017) On the design of a secure user authentication and key agreement scheme for wireless sensor networks. Concurr Comput Pract Exp 29(23):e3930
Li D, Liu W, Cui L (2010) EasiDesign: an improved ant colony algorithm for sensor deployment in real sensor network system. In: IEEE Globecom 2010 proceedings, pp 1–5
Liao WH, Kuai SC, Lin MS (2015) An energy-efficient sensor deployment scheme for wireless sensor networks using ant colony optimization algorithm. Wirel Pers Commun 82:2135–2153
Lin FYS, Chiu PL (2005) Anear-optimal sensor placement algorithm to achieve complete coverage/discrimination in sensor networks. IEEE Commun Lett 9(1):43–45
Liu X (2012) Sensor deployment of wireless sensor networks based on ant colony optimization with three classes of ant transitions. IEEE Commun Lett 16(10):1604–1607
Liu X, He D (2014) Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. J Netw Comput Appl 39:310–318
Mouapi A, Hakem N, Delisle GY (2017) A new approach to design of RF energy harvesting system to enslave wireless sensor networks. ICT Express. https://doi.org/10.1016/j.icte.2017.11.002
Sendra S, Parra L, Lloret J, Khan S (2015) Systems and algorithms for wireless sensor networks based on animal and natural behavior. Int J Distrib Sens Netw 2015(3):625972. https://doi.org/10.1155/2015/625972
Singh S, Chand S, Kumar R, Kumar B (2013) Optimal sensors deployment for WSNs in a grid environment. IET Electron Lett 49(16):1040–1041
Singh S, Chand S, Kumar B (2014) Optimum deployment of sensors in WSNs. In: Proceedings of the 2nd IEEE international conference on information systems and computer networks (ISCON-2014), pp 113–117
Singh S, Chand S, Kumar B (2016) Multilevel heterogeneous network model for wireless sensor networks. Telecommun Syst Springer 64(2):259–277
Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks-technology, protocol, and application, 2nd edn. Wiley, London
Sun X, Zhang Y, Ren X, Chen K (2015) Optimization deployment of wireless sensor networks based on culture-ant colony algorithm. Appl Math Comput 250:58–70
Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process Mag 13(6):22–37
Xin H, Xiaolin G, Jian A (2010) A deterministic deployment approach of nodes in wireless sensor networks for target coverage. J Xi’an Jiaotong Univ 44(6):6–10
Younis M, Akkaya K (2008) Strategies and techniques for node placement in wireless sensor networks: a survey. J Ad Hoc Netw 6(4):621–655
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Singh, S. A Proficient Node Deployment Mechanism Using Adjustable Sensing Range in Wireless Sensor Networks. Iran J Sci Technol Trans Electr Eng 43 (Suppl 1), 191–199 (2019). https://doi.org/10.1007/s40998-018-0143-8
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DOI: https://doi.org/10.1007/s40998-018-0143-8