A Cluster–based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN

  • Jangiti Siva Prashanth Academy of Scientific & Innovative Res.,CSIR-IICT campus
  • Satyanarayana V. Nandury Academy of Scientific and Innovative Res., CSIR-IICT Campus, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.


Envoy Node Identification (ENI) and Halting Location Identifier (HLI) algorithms have been developed to reduce the travel time of Mobile Element (ME) by determining Optimal Path(OP) in Wireless Sensor Networks. Data generated by cluster members will be aggregated at the Cluster Head (CH) identified by ENI for onward transmission to the ME and it likewise decides an ideal path for ME by interfacing all CH/Envoy Nodes (EN). In order to reduce the tour length (TL) further HLI determines finest number of Halting Locations that cover all ENs by taking transmission range of CH/ENs into consideration. Impact of ENI and HLI on energy consumption and travel time of ME have been examined through simulations.

Author Biography

Jangiti Siva Prashanth, Academy of Scientific & Innovative Res.,CSIR-IICT campus


[1] Agarwal, A.; Gupta, K.; Yadav, K.P. (2016). A novel energy efficiency protocol for WSN based on optimal chain routing, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, 368-373, 2016.

[2] Al-Tabbakh, S.M. (2017). Novel technique for data aggregation in wireless sensor networks, 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), IEEE, 1-8, 2017.

[3] Amarlingam, M.; Mishra, P. K.; Rajalakshmi, P.; Giluka, M.K.; Tamma, B.R. (2018). Energy efficient wireless sensor networks utilizing adaptive dictionary in compressed sensing, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), 383-388, 2018.

[4] Begum, B.A.; Satyanarayana, N.V. (2015). Composite interference mapping model for interference fault-free transmission in WSN, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 2118-2125,2015.

[5] Chaudhari, M.;Koleva, P.; Poulkov, V.; Deshpande, V. (2017). Energy efficient reliable data transmission in resource constrained ad-hoc communication networks, 2017 Global Wireless Summit (GWS), IEEE, 17-21, 2017.

[6] Chen, T.C.; Chen, T.S.; Wu, P.W. (2008). Data collection in wireless sensor networks assisted by mobile collector, 2008 1st IFIP Wireless Days, IEEE, 1-5, 2008.

[7] Chiu, K.-M.; Liu, J.-S. (2011). Robot routing using clustering-based parallel genetic algorithm with migration, 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology, IEEE, 42-49, 2011.

[8] Cirstea, C.; Davidescu, R.; Jianu, A. (2013. Optimum communication paths for mobile WSNs using genetic algorithms, 2013 36th International Conference on Telecommunications and Signal Processing (TSP), IEEE, 299-303, 2013.

[9] Devendra Rao, B.V.; Vasumathi, D.; Nandury, S. V. (2015). Exploiting Common Nodes in Overlapped Clusters for Path Optimization in Wireless Sensor Networks, Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Springer, 3, 209, 2015.

[10] Diaz, S.; Mendez, D. (2019). Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks, Revista Facultad de Ingeniería, 93, 57-69, 2019.

[11] He, L.; Pan, J.; Xu, J. (2012). A progressive approach to reducing data collection latency in wireless sensor networks with mobile elements, IEEE Transactions on Mobile Computing, IEEE, 12(7), 1308-1320, 2012.

[12] He, L.; Xu, J.; Yu, Y.; Li, M.; Zhao, W. (2009). Genetic algorithm based length reduction of a mobile BS path in WSNs, 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, IEEE, 797-802, 2009.

[13] Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks, Proceedings of the 33rd annual Hawaii international conference on system sciences, IEEE, 2, 1-10, 2000.

[14] Helsgaun, K. (2000). An effective implementation of the Lin-Kernighan traveling salesman heuristic, European Journal of Operational Research, 126(1), 106-130, 2000.

[15] Jothikumar, C.; Venkataraman, R. (2019). EODC: An Energy Optimized Dynamic Clustering Protocol for Wireless Sensor Networks using PSO Approach, International Journal of Computers Communications & Control, 14(2), 183-198, 2019.

[16] Kakde, K.R.; Kadam, M. (2017) Performance analysis of tree cluster based data gathering for WSNs, 2017 International Conference on Intelligent Computing and Control (I2C2), 1-5, 2017.

[17] Konstantopoulos, C.; Pantziou, G.; Gavalas, D.; Mpitziopoulos, A.; Mamalis, B. (2011). A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks, IEEE Transactions on Parallel and Distributed Systems, 23, 809-817, 2011.

[18] Liao, W.-H.; Kuai, S.-C. (2017). An Energy-Efficient SDN-Based Data Collection Strategy for Wireless Sensor Networks, 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), 91-97, 2017.

[19] Liao, Y.; Qi, H.; Li, W. (2012). Load-balanced clustering algorithm with distributed selforganization for wireless sensor networks, IEEE Sensors Journal, 13, 1498-1506, 2012.

[20] Liu, J.-S.; Wu, S.-Y.; Chiu, K.-M. (2013). Path planning of a data mule in wireless sensor network using an improved implementation of clustering-based genetic algorithm, 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), 30-37, 2013.

[21] Misbahuddin, M.; Putri Ratna, A.A.; Sari, R.F. (2018). Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs, International Journal of Computers Communications & Control, 13(1), 99-116, 2018.

[22] Prashanth, J.S.; Nandury, S.V.(2015). Cluster-based rendezvous points selection for reducing tour length of mobile element in WSN, 2015 IEEE International Advance Computing Conference (IACC), 1230-1235, 2015.

[23] Restuccia, F.; Anastasi, G.; Conti, M.; Das, S.K. (2013). Analysis and optimization of a protocol for mobile element discovery in sensor networks, IEEE Transactions on Mobile Computing, 13(9),1942-1954, 2013.

[24] Rubel, M.D.S.I.; Kandil, N.; Hakem, N.; Zuyal, M.D.S.I. (2017). Clustering approach delay sensitive application in wireless sensor network (WSN), 2017 IEEE International Conference on Telecommunications and Photonics (ICTP), 82-86, 2017.

[25] Sen, S.; Chowdhury, C.; Neogy, S. (2016). Design of cluster-chain based WSN for energy efficiency, 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 150-154, 2016.

[26] Singh, V.K.; Kumar, R.; Sahana, S. (2017). To enhance the reliability and energy efficiency of WSN using new clustering approach, 2017 International Conference on Computing, Communication and Automation (ICCCA), 488-493, 2017.

[27] Venkataraman, G.; Emmanuel, S.; Thambipillai, S. (2005). DASCA: a degree and size based clustering approach for wireless sensor networks, 2005 2nd International Symposium on Wireless Communication Systems, 508-512, 2005.

[28] Venkataraman, G.; Emmanuel, S.; Thambipillai, S. (2008). Energy-efficient cluster-based scheme for failure management in sensor networks, IET communications, 2(4), 528-537, 2008.

[29] Vikram, G.R.; Krishna, A.V.N.; Chatrapati, K.S. (2017). Variable initial energy and unequal clustering (VEUC) based multicasting in WSN, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 82-86, 2017.

[30] Vinutha, C.B.; Nalini, N.; Veeresh, B.S. (2017). Energy efficient wireless sensor network using neural network based smart sampling and reliable routing protocol, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2081-2085, 2017.

[31] Welzl, E. (1991). Smallest enclosing disks (balls and ellipsoids), New results and new trends in computer science, 359-370, 1991.

[32] Xing, G.; Li, Mi.; Wang, T.; Jia, W.; Huang, J. (2011). Efficient rendezvous algorithms for mobility-enabled wireless sensor networks, IEEE Transactions on Mobile Computing, 11(1), 47-60, 2011.

[33] Xing, G.; Wang, T.; Jia, W.; Li, Mi. (2008). Rendezvous design algorithms for wireless sensor networks with a mobile base station, Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing, 231-240, 2008.

[34] Xing, G.; Wang, T.; Xie, Z.; Jia, W. (2008). Rendezvous planning in wireless sensor networks with mobile elements, IEEE Transactions on Mobile Computing, 7,1430-1443, 2008.

[35] Xu, Ji.; He, L.; Chen, Z.; Huang, G.; Yuan, T. (2008). Reducing the path length of a mobile BS in WSNs, 2008 International Seminar on Future BioMedical Information Engineering, 271-274, 2008.

[36] Xu, R.; Dai, H.; Wang, F.; Jia, Z. (2013). A convex hull based optimization to reduce the data delivery latency of the mobile elements in wireless sensor networks, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, IEEE, 2245-2252, 2013.
How to Cite
PRASHANTH, Jangiti Siva; NANDURY, Satyanarayana V.. A Cluster–based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 14, n. 6, p. 691-709, feb. 2020. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3630>. Date accessed: 19 jan. 2021. doi: https://doi.org/10.15837/ijccc.2019.6.3630.


Envoy Nodes, Halting Locations, travel time, latency