Abstract
In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged network lifetime. The proposed algorithm, divides the Region of Interest into virtual zones, each having some designated cluster head nodes. In the entire process, a node can either be a part of a cluster or it may remain as an independent entity. A non-cluster member transmits its data to next hop node using IRP-Intelligent Routing Process (based on the trade-off between the residual energy of itself as well as its neighbor, and the required energy to transmit packets to its neighbor). If on the transmission path, some cluster member is elected as a next hop, it rejects IRP and transmits the packets to cluster head, which later forwards them to sink (adopting multihop communication among cluster heads). Routing is not solely performed using clusters, rather they aid the overall routing process, hence this protocol is named as Cluster Aided Multipath Routing (CAMP). CAMP has been compared with various sensor network routing protocols, viz., LEACH, PEGASIS, DIRECT TRANSMISSION, CEED, and CBMR. It is found that the proposed algorithm outperformed them in network lifetime, energy consumption and coverage ratio.
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Notes
In this article, sink and BS are interchangeably used.
Among the two schemes, it greedily selects that approach which results in less energy consumption.
In this article, sink and BS are interchangeably used.
The time from the start of the network operation to the death of the first node in the network.
Set of nodes in which no node is the immediate neighbor any other node.
Nodes latch themselves to CH based on RSSI value of the CH or on the basis of distance to CH [37].
Nomenclature of all the symbols are tabulated in Table 1.
Neighbors are those nodes which lie in the communication range of a given node.
Ideally 5–6% of the total nodes must be designated as \(Total\_CH\)s [26].
This model incorporates both reception and transmission energy expended by the sensor node for communication. Nodes which performs data aggregation constitutes the \(D_{agg}\) set.
Each node is assigned to a single zone only.
The nearest node to the sink is more than \(d_0\) distance apart.
Indeed the test cases are not exhaustive, but for our simulated scenarios, 4 zones suffice. We will further look into the formulation of the optimal number of zones in our future work.
References
Ammari, H. M., & Das, S. K. (2009). Fault tolerance measures for large-scale wireless sensor networks. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 4(1), 2.
Jurcik, P., Kouba, A., Severino, R., Alves, M., & Tovar, E. (2010). Dimensioning and worst-case analysis of cluster-tree sensor networks. ACM Transactions on Sensor Networks (TOSN), 7(2), 14.
Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192–219.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.
Amjad, M., Sharif, M., Afzal, M. K., & Kim, S. W. (2016). TinyOS-new trends, comparative views, and supported sensing applications: A review. IEEE Sensors Journal, 16(9), 2865–2889.
Marinagi, C., Belsis, P., & Skourlas, C. (2013). New directions for pervasive computing in logistics. Procedia-Social and Behavioral Sciences, 73, 495–502.
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.
Akhtar, F., & Rehmani, M. H. (2015). Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review. Renewable and Sustainable Energy Reviews, 45, 769–784.
Bellman, R. (1958). On a routing problem. Quarterly of Applied Mathematics, 16(1), 87–90.
Chen, Y., & Zhao, Q. (2005). On the lifetime of wireless sensor networks. IEEE Communications Letters, 9(11), 976–978.
Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 15(2), 551–591.
Jin, R. C., Gao, T., Song, J. Y., Zou, J. Y., & Wang, L. D. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks, 19(8), 1851–1866.
Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.
Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (pp. 56–67).
Heinzelman, W. R., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In Proceedings of the 5th annual ACM/IEEE International Conference on Mobile Computing and Networking (pp. 174–185).
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000 (pp. 1–10).
Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Aerospace Conference Proceedings IEEE (pp. 1125–1130).
Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of the 15th International Parallel and Distributed Processing Symposium (IPDPS) (Vol. 1, pp. 189–195).
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.
Meng, X., Shi, X., Wang, Z., Wu, S., & Li, C. (2016). A grid-based reliable routing protocol for wireless sensor networks with randomly distributed clusters. Ad Hoc Networks, 51, 47–61.
Kim, H. Y. (2016). An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks. Cluster Computing, 19(1), 279–283.
Snigdh, I., & Gupta, N. (2016). Quality of service metrics in wireless sensor networks: A survey. Journal of The Institution of Engineers (India), 97(1), 91–96.
Gawade, R. D., & Nalbalwar, S. L. (2016). A centralized energy efficient distance based routing protocol for wireless sensor networks. Journal of Sensors, 2016, 1–8.
Sharma, S., & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review, 45(2), 14–20.
Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In Communications IEEE International Conference (Vol. 6, pp. 3646–3651).
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Loscri, V., Morabito, G., & Marano, S. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). IEEE Vehicular Technology Conference, 62(3), 1809–1813.
Ahmad, A., Javaid, N., Khan, Z. A., Qasim, U., & Alghamdi, T. A. (2014). \((ACH)^ 2\): Routing scheme to maximize lifetime and throughput of wireless sensor networks. IEEE Sensors Journal, 14(10), 3516–3532.
Yi, D., & Yang, H. (2016). HEERA delay-aware and energy-efficient routing protocol for wireless sensor networks. Computer Networks, 104, 155–173.
Huynh, T. T., Dinh-Duc, A. V., & Tran, C. H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580–588.
Sabet, M., & Naji, H. (2016). An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: A self-organized approach. Computers and Electrical Engineering, 56, 399–417.
Xia, H., Zhang, R. H., Yu, J., & Pan, Z. K. (2016). Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks. International Journal of Wireless Information Networks, 23(2), 141–150.
Anand Chatterjee, R., & Kumar, V. (2017). Energy-efficient routing protocol via chain formation in Gaussian distributed wireless sensor networks. International Journal of Electronics Letters, 5(4), 449–462.
Sivaraj, C., Alphonse, P. J. A., & Janakiraman, T. N. (2017). Independent neighbour set based clustering algorithm for routing in wireless sensor networks. Wireless Personal Communications, 96(4), 1–23.
Han, Z., Wu, J., Zhang, J., Liu, L., & Tian, K. (2014). A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Transactions on Nuclear Science, 61(2), 732–740.
Kim, K. T., Lyu, C. H., Moon, S. S., & Youn, H. Y. (2010). Tree-base clustering (TBC) for energy efficient wireless sensor networks. In IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2010 (pp. 680–685).
Zhu, H., & Alsharari, T. (2015). An improved RSSI-based positioning method using sector transmission model and distance optimization technique. International Journal of Distributed Sensor Networks, 11(9), 1–11.
Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (APS). In Global Telecommunications Conference GLOBECOM’01 IEEE (Vol. 5, pp. 2926–2931).
Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley.
Chang J. H., & Tassiulas, L. (2000). Energy conserving routing in wireless ad-hoc networks. In INFOCOM Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies Proceedings (Vol. 1, pp. 22–31).
Solaiman, B. (2016). Energy optimization in wireless sensor networks using a hybrid k-means pso clustering algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2679–2695.
Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.
Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.
Deniz, F., Bagci, H., Korpeoglu, I., & Yazc, A. (2016). An adaptive, energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks. Ad Hoc Networks, 44, 104–117.
Mittal, V., Gupta, S., & Choudhury, T. (2018). Comparative analysis of authentication and access control protocols against malicious attacks in wireless sensor networks. In Smart Computing and Informatics (pp. 255–262). Springer, Singapore.
Islam, K., Shen, W., & Wang, X. (2012). Wireless sensor network reliability and security in factory automation: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1243–1256.
Bukhari, S. H. R., Siraj, S., & Rehmani, M. H. (2016). NS-2 based simulation framework for cognitive radio sensor networks. Wireless Networks. https://doi.org/10.1007/s11276-016-1418-5.
Yildiz, H. U., Temiz, M., & Tavli, B. (2015). Impact of limiting hop count on the lifetime of wireless sensor networks. IEEE Communications Letters, 19(4), 569–572.
Amjad, M., Afzal, M. K., Umer, T., & Kim, B. S. (2017). QoS-aware and heterogeneously clustered routing protocol for wireless sensor networks. IEEE Access, 5, 10250–10262.
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Sajwan, M., Gosain, D. & Sharma, A.K. CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Netw 25, 2603–2620 (2019). https://doi.org/10.1007/s11276-018-1689-0
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DOI: https://doi.org/10.1007/s11276-018-1689-0