ZOGLO: A Scheme of Zoning and Data Gathering for Lifetime Optimization in Wireless Sensor Networks

Document Type : Persian Original Article

Authors

1 Computer Engineering Department, Faculty of Engineering, Vali-e-Asr University of Rafsanajn.

2 Computer Engineering Department, Faculty of Engineering, Vali-e-Asr University of Rafsanjan

Abstract

One of the main challenges of wireless sensor networks (WSNs), is unequal energy consumption of the nodes and early dead of the forwarder nodes around the base station because of the high load on these nodes. This matter causes a hole around the base station, thus, the communications between the alive nodes of the network and the base station are disrupted. In this paper, balancing the load and then energy consumption of the network nodes are followed. The aim is prolongation of the lifetime of all the nodes, particularly, the nodes around the base station to keep the communications between the network nodes and the base station until the end of lifetime of the nodes without any hole. To this purpose, a method for zoning the area of sensor networks is proposed. The distance of data transfer hop of each forwarder node is adjusted based on the amount of data to be transmitted by the forwarder. Hence, energy consumption of forwarders and thus their lifetimes are balanced. The proposed approach presents a solution for the challenge of short life of forwarders around the base station. Moreover, the dimensions of zones are calculated in such way that the communications between the sensors and the forwarder in each zone are performed in single hop manner. The approach balances the density of sensors of the created zones to uniform the coverage ratio in all the network area. The performance evaluations of the proposed scheme indicate that the scheme prolongs the lifetime of both forwarders and sensor nodes compared with the related works.

Keywords


   [1]      A.S. Ajith Kumar, K. Øvsthus and L.M. Kristensen, “An Industrial Perspective on Wireless Sensor Networks - A Survey of Requirements, Protocols, and Challenges,” IEEE Communications Surveys & Tutorials, Vol.16, No.3, pp. 1391-1412, 2014.
   [2]      J.A. Khan, H.K. Qureshi and A. Iqbal, “Energy management in Wireless Sensor Networks: A survey,” Computers & Electrical Engineering (Elsevier), Vol.41, pp. 159-176, 2015.
   [3]      Z. Fei, B. Li, S. Yang, C. Xing, H. Chen and L. Hanzo, “A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems,” IEEE Communications Surveys & Tutorials, Vol.19, No.1, pp. 550-586, 2017.
   [4]      T. Rault, A. Bouabdallah and Y. Challal, “Energy efficiency in wireless sensor networks: A top-down survey,” Computer Networks (Elsevier), Vol.67, pp. 104-122, 2014.
   [5]      A. Chamam, S. Pierre, “On the Planning of Wireless Sensor Networks: Energy-Efficient Clustering under the Joint Routing and Coverage Constraint,” IEEE Transactions on Mobile Computing, Vol.8, Issue 8, 2009.
   [6]      M. Abo-Zahhad, N. Sabor, S. Sasaki and S.M. Ahmed, “A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks,” Information Fusion (Elsevier), Vol.30, pp. 36-51, 2016.
   [7]      A. F. Jemal, R. H. Hussen, D. Y. Kim, Z. Li, T. Pei and Y. J. Choi, “Energy-efficient selection of cluster headers in wireless sensor networks,” International Journal of Distributed Sensor Networks, Vol. 14, No. 3, DOI: 10.1177/1550147718764642, 2018.
   [8]      A.K. Idrees, K. Deschinkel, M. Salomon and R. Couturier, “Coverage and Lifetime Optimization in Heterogeneous Energy Wireless Sensor Networks,” the Thirteenth International Conference on Networks (ICN), 2014.
   [9]      Z .Chen and H. Shen, “A grid-based reliable multi-hop routing protocol for energy-efficient wireless sensor networks,” International Journal of Distributed Sensor Networks, Vol. 14(3), DOI: 10.1177/1550147718765962, 2018.
[10]      D. Saha and A. Das, “Coverage Area Maximization by Heterogeneous Sensor Nodes with Minimum Displacement in Mobile Networks,” IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2015.
[11]      N. Tuah, M. Ismail and A.R. Haron, “Energy Consumption and Lifetime Analysis for Heterogeneous Wireless Sensor Networks,” the IEEE TENCON Spring Conference, 2013.
[12]      C. Han , Q. Lin, J. Guo, L. Sun,and Z. Tao, “A Clustering Algorithm for Heterogeneous Wireless Sensor Networks Based on Solar Energy Supply,” electronics journal (MDPI), Vol. 7, No. 103, DOI:10.3390/electronics7070103, 2018.
[13]      A. Sikandar and S. Kumar, “Energy Efficient Clustering in Heterogeneous Wireless Sensor Networks Using Degree Of Connectivity,” the International Journal of Computer Networks & Communications (IJCNC), Vol.7, No.2, pp. 19-31, 2015.
[14]      J.J. Liaw, C.W. Chou and C.Y. Dai, “The Lifetime Extension of Wireless Sensor Networks using Adaptive Energy Allocation by Distance,” International Journal of Distributed Sensor Networks, Vol.2013, pp. 1-8, 2013.
[15]      V. Gupta and R. Pandey, “An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks,” Engineering Science and Technology, Vol.19, Issue 2, pp. 1050-1058, 2016.
[16]      N. Mazumdar and H. Om, “Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks,” International journal of Communication systems, Vol. 31, Issue12, doi: 10.1002/dac.3709, 2018.
[17]      B. Baranidharan and B. Santhi, “DUCF: Distributed load balancing Unequal Clustering in wirelesssensor networks using Fuzzy approach,” Applied Soft Computing (Elsevier), Vol.40, pp. 495-506, 2016.
[18]      H. Bagci and A. Yazici, “An energy aware fuzzy approach to unequal clustering in wireless sensor networks,” Applied Soft Computing, Vol.13, No.4, pp. 1741-1749, 2013.
[19]      W.B. Heinzelman, A.P. Chandrakasan and H. Balakrish-nan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on wireless communications, Vol.1, Issue 4, 2002.