Linkage and Connectivity Control in Wireless Sensor Network: A New Mechanism

Document Type : Original Article

Authors

Department of Electrical and Computer Engineering, Naein Branch, Islamic Azad University, Naein, Iran

Abstract

Recent developments in electronics and wireless communication play a leading role in manufacturing sensors with reduced power consumption that have wireless connectivity and limited processing capabilities. Due to the limitation of battery in sensor nodes, one of the main challenges in this type of network is energy consumption, which is directly related to the lifetime of the network.  Another important issue is to keep nodes connected in the network during data transmission. For these purposes, a connectivity control system is required. By improving the tree growth algorithm in the network graph, an optimal graph using a suitable path for data transmission in the network is designed. Connectivity control significantly improved system performance in terms of network power consumption and lifetime.   In this paper, a new algorithm for connectivity and linkage control, based on sequential mode is presented, which has achieved a significant improvement compared to an ordinary algorithm. The outcomes of the proposed algorithm on the selected model show 56% improvement in the remaining battery charge. In addition, the end-to-end delay was reduced by 0.5 m seconds in the network.

Keywords

References

  • Shakeri, N. Ardalani, and P. Derakhshan, “Minimization of Outage Probability using Joint Channel and Power Assignment in Dual and Multi Hop Cognitive Radio Ad Hoc Networks”, Majlesi Journal of Electrical Engineering, vol. 14, no. 1, pp. 71-76, 2021.
  • Zhang,  W. Cai, “AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Protocols, and Techniques”, Journal of Sensors, vol. 2022, 2022.
  • Derakhshan “Joint Resource Allocation and Spectral Radiometry in Non-Stationary Cognitive Radio Networks”, Int. J. of Innovative Research in Elec. and Comm.,vol. 4, no. 2, pp. 18-25, 2017
  • Alablani,  M. Alenazi, “EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities, Sensors, vol. 20, no. 24, p. 7191, 2020.
  • Huanan, X. Suping, and W. Jiannan, “Security, and application of wireless sensor network”, Procedia Comput. Sci., vol. 183, pp. 486–492, 2021.
  • Nasri, S. Mnasri, and T. Val, “3D node deployment strategies prediction in wireless sensors network”, Int. J. Electron, vol. 107, no. 7, pp.808–838, 2020.
  • Shakeri, N. Ardalani, and P. Derakhshan-Barjoei, “Improvement of Network Throughput by Providing CAODV-Based Routing Algorithm in Cognitive Radio Ad Hoc Networks”, Wireless Personal Communications, vol. 113, no. 2, pp. 893-903, 2021.
  • Priyadarshi, B. Gupta, and A. Anurag, “Wireless Sensor Networks Deployment: A Result Oriented Analysis”, Wirel. Pers. Commun., vol. 113, no. 2, pp. 843–866, 2020.
  • Xia, Y. Hong, M. S. Khan, X. Wen, and H. Du, “Sensor Deployment Method Based on Faiw-DPSO in DASNs”, IEEE Access, vol. 8, pp. 78403–78416, 2020.
  • Han, H. Byun, B. Yang, J. H. Kim, and T. H. Lee, “Optimization of Sensor Nodes Deployment Based on An Improved Differential Evolution Algorithm for Coverage Area Maximization”, Proceedings of the IEEE 4th Advanced Inf. Tech., Elec. and Automation Control Conf., Chengdu, China, IEEE, 20–22 December, pp. 250–254, 2019.
  • S. Panag,  J. S. Dhillon, “Maximal coverage hybrid search algorithm for deployment in wireless sensor networks”, Wirel. Netw., vol. 25, pp. 637–652, 2019.
  • Wang, L. Tian, W. Wu, L. Lin, Z. Li, and Y. Tong, “A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks”, Wireless Communications and Mobile Computing, 2022, https://doi.org/10.1155/2022/7732989.
  • He, G. Mujica, J. Portilla, T. Riesgo, “Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length”, Journal of Heuristics, vol. 21, no. 2, pp. 257–300, 2015.
  • Le Berre, M. Rebai, F. Hnaien, H. Snoussi, “A Specific Heuristic Dedicated to a Coverage/Tracking Bi-objective Problem for Wireless Sensor Deployment,” Wirel. Pers. Commun., vol. 84, no. 3, pp. 2187–2213, 2015.
  • Khalesian, M. R. Delavar, “Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach,” Eng. Appl. Artif. Intell. vol. 53, pp.126–139, 2016.
  • Moh’d Alia, A. Al-Ajouri, “Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm,” IEEE Sens. Journal, vol. 17, no. 3, pp. 882–896, 2017.
  • Yarinezhad, S. N. Hashemi, “A sensor deployment approach for target coverage problem in wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., pp.1–16, 2020.
  • Gorgbandi, R. Brangi, “ Anomalous Cluster Heads and Nodes in Wireless Sensor Networks” International Journal of Web Research (IJWR), vol. 5, no. 1, pp. 66-73, 2022.
  • Surendran, S. Vijayan, “Distributed Computation of Connected Dominating Set for Multi-Hop Wireless Networks,” Procedia Computer Science, vol. 63, pp. 482-487, 2015.
  • S. Nimisha, R. Ramalakshmi, “Energy efficient Connected Dominating Set construction using Ant Colony Optimization technique in Wireless Sensor Network,” Int. Conf. on Innovations in info. Embedded and Comm. Sys. (ICIIECS), Coimbatore, India, IEEE, 2015, pp. 1-5.
  • Mohajer, M. H. Hajimobini, A. Mirzaei, and E. Noori, “Trusted-CDS Based Intrusion Detection System in Wireless Sensor Network (TC-IDS),” Open Access Library Journal, vol. 1 no.7, pp. 1-10, 2014.
  • More, V. Raisinghani, “A node failure and battery-aware coverage protocol for wireless sensor networks,” Computers & Electrical Engineering, vol. 64, pp. 200-219, 2017.
  • Lipiński, “Routing Algorithm for Maximizing Lifetime of Wireless Sensor Network for Broadcast Transmission”, Wireless Personal Communications, vol. 101, vo. 1, pp.251–268, 2018.
  • Shi, Y. Cheng, J. Shao, Q. Liu, and W. X. Zheng, “Locating Link Failures in WSNs via Cluster Consensus and Graph Decomposition”, IEEE/ACM Transactions on Networking, vol. 30, no. 5, pp. 2304-2314, 2022.
  • G. Chen, Y. Lin, Y. J. Gong, Z. H. Zhan, and J. Zhang, “Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based Estimation of Distribution Algorithm”, IEEE Transactions on Cybernetics, vol. 51, no. 11, pp. 5433-5444, 2021.

 

 Pouya Derakhshan-Barjoei: He obtained B.Sc. degree in the field of power-electrical engineering and M. Sc. degree in the field of communication-electrical engineering, in 2002 and 2004, respectively; he received his Ph.D. degree in telecommunication-electrical engineering at the Islamic Azad University, Science and Research Branch, Tehran, Iran, in 2011. His Ph.D. research is focused on cognitive radio networks. During his ,study he worked on digital communication, Microstrip antenna, RADAR signal detection, Machine vision, Fuzzy logic, and optical communication and various aspects of communications especially in cognitive networking in medical and military areas. He has taught several courses in communications, electronics, and computer engineering. He has supervised over 100 bachelor and postgraduate students in electrical and computer engineering. Dr. Derakhshan has been involved in several researches and teaching cooperation programs at University. He is also a pioneer researcher in his field and reviewer and TPC member of several leading conferences and journals. He is a member of the young researchers and elits club of IAU University.

 Ahmad Yousofi received his B.Sc. degree from the Islamic Azad University, Qazvin branch, Iran, and his M.Sc. degree from the Amirkabir University of Technology (Tehran Polytechnic), Iran, both in the field of computer engineering in 2002 and 2007, respectively.  He hold a PhD degree in the field of computer engineering since 2018 from Islamic Azad University Science and Research Branch, Tehran, Iran. Now, he is a faculty member at IAU, Iran. His research interests are wireless networks, cognitive radio networks, parallel processing and web research.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

International Journal of Web Research
Volume 5, Issue 2
Summer-Autumn
December 2022
Pages 70-76

Files

Share

How to cite

Statistics