Open Access
Subscription or Fee Access
Design and Assessment of Energy Efficient Improved LEACH Protocol for Wireless Sensor Network
Abstract
The recognition of wireless sensor networks has increased enormously due to the enormous possibility of the sensor networks to connect the physical world with the virtual world. While these devices rely on battery power and may be placed in unfriendly environments replacing them becomes a tedious task. Therefore, improving the energy of these networks becomes essential. This work provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It presents a contrast between the different methods on the basis of the network lifetime. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for both homogeneous WSNs and heterogeneous WSNs that collectives the ideas of energy-efficient cluster-based routing and media access together with data aggregation to attain good performance in terms of system lifetime, latency, and application-perceived quality. Further, we modify one of the most prominent wireless sensor networks routing protocol LEACH as modified LEACH by in-traducing efficient cluster head replacement scheme and dual transmitting power levels. Our modified LEACH, in parallel with LEACH outperforms it developing metrics of cluster head formation, throughput and network life. Finally, a brief performance analysis of LEACH and Modified LEACH is undertaken considering metrics of throughput, network life and cluster head replacements.
References
S. Han, Z. Gong, W. Meng, Automatic precision control positioning for wireless sensor network. IEEE Sensors Journal 16(7), 2140–2150 (2016).
M.S. Elgamel, A. Dandoush, A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs. AD HOC Networks 33, 168–189 (2015).
M. Erazo-Rodas, M. Sandoval-Moreno, et al., Multiparametric monitoring in equatorial tomato greenhouses (III): environmental measurement dynamics. Sensors 18(8) (2018).
S. Singh, P. Kumar, J. Singh, A survey on successors of LEACH protocol. IEEE Access 5, 4298–4328 (2017).
S. Peng, Energy neutral directed diffusion for energy harvesting wireless sensor networks. Computer Communication. 63, 40–52 (2015).
X. Fu, G. Fortino, P. Pace, G. Aloi, W. Li, Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion (2019). https://doi.org/10.1016/j.inffus.2019.06.001
S. Soro, W.B. Heinzelman, Prolong
ing the lifetime of wireless sensor networks via unequal clustering. IEEE International Parallel and Distributed Processing Symposium 365 (2005)
F. Bagci, Energy-efficient communication protocol for wireless sensor networks. Ad Hoc and Sensor Wireless Networks 30(3–4), 301–322 (2016).
Y. Zhai, L. Xu, Ant colony algorithm research based on pheromone update strategy, 2015 7th Int. Conf. Intell. Human-Machine Syst. Cybern. 1(2), 38–41 (2015).
W. Ding, W. Fang, Target tracking by sequential random draft particle swarm optimization algorithm. IEEE Int Smart Cities Conf. 2018, 1–7 (2018).
N. Us Sama, K. Bt Zen, A. Ur Rahman, B. BiBi, A. Ur Rahman and I. A. Chesti, "Energy Efficient Least Edge Computation LEACH in Wireless sensor network," 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2020, pp. 1–6, doi: 10.1109/ICCIS49240.
9257649.
Haibo Liang1*, Shuo Yang1, Li Li1 and Jianchong Gao, Research on routing optimization of WSNs based on improved LEACH protocol, EURASIP Journal on Wireless Communications and Networking (2019) 2019:194.
Karan Agarwal, Kunal Agarwal and K. Muruganandam, 2018, Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol: Simulation and Analysis using MATLAB, International Conference on Comput
ing, Power and Communication Technologies (GUCON) Galgotias University, Greater Noida, UP, India. Sep 28–29, 2018.
T. Alhmiedat, Low power environ
mental monitoring system for ZigBee wireless sensor network. KSII Transaction on Internet and Information Systems 11(10), 4781–4803 (2017).
H. Liang, J. Zou, Z. Li, M. Junaid, Y. Lu, Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm. Futur Gener Comput Syst 95, 454–466 (2019).
H. Liang, An sand plug of fracturing intelligent early warning model embedded in remote monitoring system. IEEE Access 7, 47944–47954 (2019).
N. Thi, H. Thi, T. Binh, N. Xuan, An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Inf. Sci. (Ny). 488, 58–75 (2019).
M. Ben Salah, A. Boulouz, Energy efficient clustering based on LEACH, IEEE International Conference on Engineering and MIS (ICEMIS), 2016.
Alka Singh, Shubhangi Rathkanthiwar, Sandeep Kakde, LEACH based-energy efficient routing protocol for wireless sensor networks, IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT, 2016.
S. Al-sodairi, R. Ouni, Sustainable computing: informatics and systems reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor net
works. Sustain. Comput. Informatics Syst. 20, 1–13 (2018).
G.K. Nigam, C. Dabas, ESO-LEACH: PSO based energy efficient clustering in LEACH. J. King Saud Univ.-Comput. Inf. Sci. 1, 4–11 (2018).
N. Mazumder, H. Om, Distribute fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks. International Journal of Communication Systems 31(12) (2018).
S. AS, O. Ridha, Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks. Sustainable Computing-Informatics and Systems 20, 1–13 (2018).
N.G. Palan, B.V. Barbadekar, S. Patil, Low energy adaptive clustering hierarchy (LEACH) protocol: a retrospective analysis. International Conference on Inventive Systems and Control (ICISC), 363–374 (2017, 2017).
K.A.Z. Ariffin, R.M. Mokhtar, A.H.A. Rahman, Performance analysis on LEACH protocol in wireless sensor network (WSN) under black hole attack. Advanced Science Letters 24(3), 1791–1794 (2018).
Darabkh, K.A., M.Z. El-Yabroudi, and A.H. ElMousa, BPA-CRP: A balanced power-aware clustering and routing protocol for wireless sensor networks. Ad Hoc Networks, 2019. 82: p. 155–171.
Fanian, F. and M.K. Rafsanjani, Cluster-based routing protocols in wireless sensor networks: A survey based on methodology. Journal of Network and Computer Applications, 2019.
Qu, Y., et al., A survey of routing protocols in WBAN for healthcare applications. Sensors, 2019. 19(7): p. 1638.
Zen, K. and A. Ur-Rahman, An Extensive Survey on Performance Comparison of Routing Protocols in Wireless Sensor Network. Journal of Applied Sciences, 2017. 17: p. 238–245.
Bhat, G. and A. Sreenivasan, Review on Energy Optimization and Cluster Based Routing Protocol in WSN. 2019.
Tomar, M.S. and P.K. Shukla, Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering with Hop Count Based Routing for Wireless Sensor Network. Multimedia Tools and Applications, 2019: p. 1–22.
Jan, B., et al., Energy efficient hierarchical clustering approaches in wireless sensor networks: A survey. Wireless Communications and Mobile Computing, 2017. 2017.
Wang, Q., et al., An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs. IEEE Sensors Journal, 2019. 19(10): p. 3950–3960.
Sama, N.U., et al., Energy-Aware Routing Hole Detection Algorithm in the Hierarchical Wireless Sensor Network. (IJACSA) International Journal of Advanced Computer Science and Applications, 2019. 10(3).
Sama, N.U., et al., Routing Hole Mitigation by Edge based Multi-Hop Cluster-based Routing Protocol in Wireless Sensor Network. International Journal of Computer Science and Network Security, 2019. 19(1): p. 253–260.
Mondal, S., S. Ghosh, and P. Dutta, Energy efficient data gathering in wireless sensor networks using rough fuzzy C-means and ACO, in Industry Interactive Innovations in Science, Engineering and Technology. 2018, Springer. p. 163–172.
Srinivas, M., Energy Efficiency in Load Balancing of Nodes Using Soft Computing Approach in WBAN, in Harmony Search and Nature Inspired Optimization Algorithms. 2019, Springer. p. 423–430.
Gharaei, N., et al., Energy-Efficient IntraCluster Routing Algorithm to Enhance the Coverage Time of Wireless Sensor Networks. IEEE Sensors Journal, 2019. 19(12): p. 4501–4508.
Lipare, A., D.R. Edla, and V. Kuppili, Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function. Applied Soft Computing, 2019. 84: p. 105706.
Zhao, X., et al., Energy-Efficient Routing Protocol for Wireless Sensor Networks Based on Improved Grey Wolf Optimizer. KSII Transactions on Internet and Information Systems, 2018. 12(6).
Refbacks
- There are currently no refbacks.