Energy-efficient topology to enhance the wireless sensor network lifetime using connectivity control

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

[1]  Arputharaj Kannan,et al.  An Energy Efficient Routing Algorithm for WSNs Using Intelligent Fuzzy Rules in Precision Agriculture , 2020, Wireless Personal Communications.

[2]  Huan Zhao,et al.  Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink , 2015, Appl. Soft Comput..

[3]  Jinkui Hou,et al.  Mobile-Service Based Approach for Topology Control of Wireless Sensor Networks , 2018, Wirel. Pers. Commun..

[4]  Haibo Zhang,et al.  Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks , 2009, IEEE Transactions on Parallel and Distributed Systems.

[5]  Ibrahim Korpeoglu,et al.  Power efficient data gathering and aggregation in wireless sensor networks , 2003, SGMD.

[6]  Hongke Zhang,et al.  Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks , 2011, IEEE Trans. Mob. Comput..

[7]  Wei Liu,et al.  Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks , 2019, Sensors.

[8]  Yasuo Tan,et al.  Optimal Energy Balanced Data Gathering in Wireless Sensor Networks , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[9]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[10]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[11]  Jemal H. Abawajy,et al.  Learning automaton based topology control protocol for extending wireless sensor networks lifetime , 2018, J. Netw. Comput. Appl..

[12]  Mohammadreza Eslaminejad,et al.  An energy-aware and load balanced distributed geographic routing algorithm for wireless sensor networks with dynamic hole , 2020, Wirel. Networks.

[13]  Ramin Yarinezhad,et al.  Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure , 2019, Ad Hoc Networks.

[14]  Weifa Liang,et al.  Online Data Gathering for Maximizing Network Lifetime in Sensor Networks , 2007, IEEE Transactions on Mobile Computing.

[15]  Pierre Hansen,et al.  Variable neighborhood search: basics and variants , 2017, EURO J. Comput. Optim..

[16]  Bai Chen,et al.  Topology control game algorithm based on Markov lifetime prediction model for wireless sensor network , 2018, Ad Hoc Networks.

[17]  Roberto López-Valcarce,et al.  Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach , 2015, J. Netw. Comput. Appl..

[18]  Bin Li,et al.  Particle swarm optimization based clustering algorithm with mobile sink for WSNs , 2017, Future Gener. Comput. Syst..

[19]  Halil Yetgin,et al.  A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks , 2017, IEEE Communications Surveys & Tutorials.

[20]  Cunqing Hua,et al.  Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks , 2008, IEEE/ACM Transactions on Networking.

[21]  Parham Hadikhani,et al.  An adaptive hybrid algorithm for social networks to choose groups with independent members , 2019, Evol. Intell..

[22]  Chen Peng,et al.  Evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks , 2020, Int. J. Distributed Sens. Networks.