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Published in: Wireless Networks 5/2019

21-02-2018

CAMP: cluster aided multi-path routing protocol for wireless sensor networks

Authors: Mohit Sajwan, Devashish Gosain, Ajay K. Sharma

Published in: Wireless Networks | Issue 5/2019

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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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Footnotes
1
In this article, sink and BS are interchangeably used.
 
2
Among the two schemes, it greedily selects that approach which results in less energy consumption.
 
3
In this article, sink and BS are interchangeably used.
 
4
The time from the start of the network operation to the death of the first node in the network.
 
5
Set of nodes in which no node is the immediate neighbor any other node.
 
6
Nodes latch themselves to CH based on RSSI value of the CH or on the basis of distance to CH [37].
 
7
Nomenclature of all the symbols are tabulated in Table 1.
 
8
Neighbors are those nodes which lie in the communication range of a given node.
 
9
Ideally 5–6% of the total nodes must be designated as \(Total\_CH\)s [26].
 
10
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.
 
11
Each node is assigned to a single zone only.
 
12
The nearest node to the sink is more than \(d_0\) distance apart.
 
13
For simulations corresponding to Figs. 11, 12, 13, 14, 15, and 16 sink is placed at the center of the field.
 
14
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.
 
Literature
1.
go back to reference 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. 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.
2.
go back to reference 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.CrossRef 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.CrossRef
3.
go back to reference 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.CrossRef 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.CrossRef
4.
go back to reference Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef
5.
go back to reference 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.CrossRef 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.CrossRef
6.
go back to reference Marinagi, C., Belsis, P., & Skourlas, C. (2013). New directions for pervasive computing in logistics. Procedia-Social and Behavioral Sciences, 73, 495–502.CrossRef Marinagi, C., Belsis, P., & Skourlas, C. (2013). New directions for pervasive computing in logistics. Procedia-Social and Behavioral Sciences, 73, 495–502.CrossRef
7.
go back to reference Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.MATHCrossRef Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.MATHCrossRef
8.
go back to reference 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.CrossRef 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.CrossRef
10.
go back to reference Chen, Y., & Zhao, Q. (2005). On the lifetime of wireless sensor networks. IEEE Communications Letters, 9(11), 976–978.CrossRef Chen, Y., & Zhao, Q. (2005). On the lifetime of wireless sensor networks. IEEE Communications Letters, 9(11), 976–978.CrossRef
11.
go back to reference 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.CrossRef 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.CrossRef
12.
go back to reference 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.CrossRef 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.CrossRef
13.
go back to reference Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.CrossRef Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.CrossRef
14.
go back to reference 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). 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).
15.
go back to reference 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., 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).
16.
go back to reference 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). 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).
17.
go back to reference Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Aerospace Conference Proceedings IEEE (pp. 1125–1130). Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Aerospace Conference Proceedings IEEE (pp. 1125–1130).
18.
go back to reference 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). 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).
19.
go back to reference 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.CrossRef 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.CrossRef
20.
go back to reference 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.CrossRef 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.CrossRef
21.
go back to reference Kim, H. Y. (2016). An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks. Cluster Computing, 19(1), 279–283.CrossRef Kim, H. Y. (2016). An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks. Cluster Computing, 19(1), 279–283.CrossRef
22.
go back to reference 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.CrossRef 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.CrossRef
23.
go back to reference 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.CrossRef 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.CrossRef
24.
go back to reference Sharma, S., & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review, 45(2), 14–20.CrossRef Sharma, S., & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review, 45(2), 14–20.CrossRef
25.
go back to reference Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In Communications IEEE International Conference (Vol. 6, pp. 3646–3651). Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In Communications IEEE International Conference (Vol. 6, pp. 3646–3651).
26.
go back to reference 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.CrossRef 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.CrossRef
27.
go back to reference 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. 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.
28.
go back to reference 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.CrossRef 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.CrossRef
29.
go back to reference Yi, D., & Yang, H. (2016). HEERA delay-aware and energy-efficient routing protocol for wireless sensor networks. Computer Networks, 104, 155–173.CrossRef Yi, D., & Yang, H. (2016). HEERA delay-aware and energy-efficient routing protocol for wireless sensor networks. Computer Networks, 104, 155–173.CrossRef
30.
go back to reference 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.CrossRef 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.CrossRef
31.
go back to reference 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.CrossRef 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.CrossRef
32.
go back to reference 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.CrossRef 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.CrossRef
33.
go back to reference 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.CrossRef 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.CrossRef
34.
go back to reference 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.CrossRef 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.CrossRef
35.
go back to reference 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.CrossRef 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.CrossRef
36.
go back to reference 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). 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).
37.
go back to reference 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.CrossRef 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.CrossRef
38.
go back to reference Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (APS). In Global Telecommunications Conference GLOBECOM’01 IEEE (Vol. 5, pp. 2926–2931). Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (APS). In Global Telecommunications Conference GLOBECOM’01 IEEE (Vol. 5, pp. 2926–2931).
39.
go back to reference Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley.CrossRef Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley.CrossRef
40.
go back to reference 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). 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).
41.
go back to reference 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.CrossRef 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.CrossRef
42.
go back to reference Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.CrossRef Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.CrossRef
43.
go back to reference 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.CrossRef 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.CrossRef
44.
go back to reference 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.CrossRef 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.CrossRef
45.
go back to reference 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. 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.
46.
go back to reference 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.CrossRef 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.CrossRef
48.
go back to reference 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.CrossRef 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.CrossRef
49.
go back to reference 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.CrossRef 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.CrossRef
Metadata
Title
CAMP: cluster aided multi-path routing protocol for wireless sensor networks
Authors
Mohit Sajwan
Devashish Gosain
Ajay K. Sharma
Publication date
21-02-2018
Publisher
Springer US
Published in
Wireless Networks / Issue 5/2019
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-018-1689-0

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