An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks

Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

[1]  Bin Li,et al.  Multiple mobile sink‐based routing algorithm for data dissemination in wireless sensor networks , 2015, Concurr. Comput. Pract. Exp..

[2]  Dheeresh K. Mallick,et al.  Fuzzy logic based multihop topology control routing protocol in wireless sensor networks , 2018, Microsystem Technologies.

[3]  Seung Seob Lee,et al.  Equations of exposure time and X-ray mask absorber thickness in the LIGA process , 2001 .

[4]  Prasanta K. Jana,et al.  PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks , 2017, Soft Comput..

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

[6]  WangJin,et al.  Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks , 2017 .

[7]  Ruohan Meng.  A fusion steganographic algorithm based on Faster R-CNN , 2018 .

[8]  Sai Ji,et al.  Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks , 2017, The Journal of Supercomputing.

[9]  Sariga Arjunan,et al.  Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol , 2017, Applied Intelligence.

[10]  Xiaojiang Chen,et al.  VD-PSO: An efficient mobile sink routing algorithm in wireless sensor networks , 2017, Peer-to-Peer Netw. Appl..

[11]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[12]  Bin Li,et al.  Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks , 2015, IEEE Transactions on Consumer Electronics.

[13]  Bin Li,et al.  A Glowworm Swarm Optimization Based Clustering Algorithm with Mobile Sink Support for Wireless Sensor Networks , 2015 .

[14]  Feng Pan,et al.  Cluster-Based Routing for the Mobile Sink in Wireless Sensor Networks With Obstacles , 2016, IEEE Access.

[15]  S. Manesis,et al.  A Survey of Applications of Wireless Sensors and Wireless Sensor Networks , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[16]  Jau-Yang Chang,et al.  An Efficient Tree-Based Power Saving Scheme for Wireless Sensor Networks With Mobile Sink , 2016, IEEE Sensors Journal.

[17]  Sankaranarayanan Swamynathan,et al.  Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network , 2017, Journal of Communications and Networks.

[18]  Chien-Fu Cheng,et al.  Data Gathering in Wireless Sensor Networks: A Combine–TSP–Reduce Approach , 2016, IEEE Transactions on Vehicular Technology.

[19]  Muhammad Sher,et al.  An improved and provably secure privacy preserving authentication protocol for SIP , 2017, Peer-to-Peer Netw. Appl..

[20]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[21]  Cong Wang,et al.  An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks , 2016, IEEE Transactions on Mobile Computing.

[22]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[23]  Dimitrios D. Vergados,et al.  A survey on power control issues in wireless sensor networks , 2007, IEEE Communications Surveys & Tutorials.

[24]  Cem Ersoy,et al.  Wireless sensor networks for healthcare: A survey , 2010, Comput. Networks.

[25]  Sohrab Effati,et al.  On Maximizing the Lifetime of Wireless Sensor Networks in Event-Driven Applications With Mobile Sinks , 2015, IEEE Transactions on Vehicular Technology.

[26]  Yingwu Chen,et al.  Time Optimization of Multiple Knowledge Transfers in the Big Data Environment , 2018 .

[27]  Hongyi Wu,et al.  A Tree-Cluster-Based Data-Gathering Algorithm for Industrial WSNs With a Mobile Sink , 2015, IEEE Access.

[28]  Jong Hyuk Park,et al.  An improved ant colony optimization-based approach with mobile sink for wireless sensor networks , 2017, The Journal of Supercomputing.

[29]  Sungyoung Lee,et al.  A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs , 2017, Cluster Computing.

[30]  Xiangyang Luo,et al.  Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division , 2018 .

[31]  Huei-Wen Ferng,et al.  A low energy consumption routing protocol for mobile sensor networks with a path-constrained mobile sink , 2016, 2016 IEEE International Conference on Communications (ICC).