Abstract
In the past few years, research and development in Wireless Sensor networks (WSNs) have gained momentum due to its numerous applications in agriculture, industrial manufacturing, military surveillance, environmental monitoring, consumer electronics, medical & healthcare, disaster recovery operations etc. Dynamic WSNs offer a robust blend of distributed sensing, computing and communication. Dynamic sensor networks are characterized by large scale deployment, dynamic and unstructured topology, power limitations, less memory and limited computational capabilities. Sensor nodes deployed in real-time environment’s for sensing data have power-limitations which hampers the overall performance of WSNs. So, the only obvious solution is to propose an energy efficient routing protocol to optimize WSN real-time performance. Different specialists have proposed various directing conventions for WSNs dependent on Fuzzy Logic, Genetic Algorithms, Meta-Heuristics, and other improvement strategies. However, every solution suggested till date has its advantages and limitations. In this paper, our primary objective is to utilize Swarm-Intelligence based approach i.e. “Ant Colony Optimization (ACO)”, for routing protocol development. Ant colony optimization (ACO) based approach gives optimal solution in terms of efficient routing path determination, energy efficiency and delivering high performance in terms of packet delivery and throughput. In this paper, we propose a novel energy efficient ACO based multipath routing protocol for WSN i.e. IEEMARP (Improvised Energy Efficient Multipath ACO based Routing Protocol). The proposed protocol works in three phases (Neighbor Discovery via Link Knowledge, Packet Transmission via exponentially weighted moving average method and ACKR packet delivery for assuring end-to-end delivery. To validate the performance of the protocol proposed, extensive simulations were conducted using NS-2.35-allinone simulator on diverse parameters like (PDR), throughput, routing overhead, energy consumption and end-to-end delay. In addition to this, the performance of protocol is compared with traditional routing protocols like Basic ACO, DSDV and DSR and other ACO based WSN protocols like ACEAMR, AntChain, EMCBR, IACR, AntHQSeN, FACOR and ANTALG. Simulation based results, clearly states that as compared to Basic ACO, DSDV and DSR, the performance of WSN network is improvised to around 10% in all performance metrics via IEEMARP routing protocol. And as compared to ACEAMR, AntChain, EMCBR and IACR, IEEMARP performs 20% better in overall functionality and almost 10–12% better as compared to AntHQSeN, FACOR, ANTLAG routing protocols in varied WSN scenarios. It is also observed that IEEMARP protocol is highly efficient in TCP packet transmission from source to destination node.
Similar content being viewed by others
References
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422
Amiri E, Keshavarz H, Alizadeh M, Zamani M, Khodadadi T (2014) Energy efficient routing in wireless sensor networks based on fuzzy ant colony optimization. Int J Distribut Sensor Netw 10(7):768936
Bansal, J. C., Singh, P. K., & Pal, N. R.. Evolutionary and Swarm Intelligence Algorithms
Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2(4):353–373
Camilo T, Carreto C, Silva JS, Boavida F (2006) An energy-efficient ant-based routing algorithm for wireless sensor networks. In: International workshop on ant Colony optimization and swarm intelligence. Springer, Berlin Heidelberg, pp 49–59
Deepika D, Anand N (2013) Complete scenario of routing protocols, security leaks and attacks in MANETs. J Proc IJARCSEE 3(10)
Ding N, Liu PX (2005). A centralized approach to energy-efficient protocols for wireless sensor networks. In Mechatronics and Automation, 2005 IEEE International Conference (Vol. 3, pp. 1636–1641). IEEE.
Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278
Dorigo M, Di Caro G (1999). Ant colony optimization: a new meta-heuristic. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 2, pp. 1470–1477). IEEE.
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics. Springer, US, pp 227–263
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39
Doss S, Nayyar A, Suseendran G, Tanwar S, Khanna A, Thong PH (2018) APD-JFAD: accurate prevention and detection of jelly fish attack in MANET. Ieee Acc 6:56954–56965
Gunes M, Sorges U, Bouazizi I (2002). ARA-the ant-colony based routing algorithm for MANETs. In Parallel Processing Workshops, 2002. Proceedings. International Conference on (pp. 79–85). IEEE.
Gupta A, Gupta M, Nayyar A (2014). Improved energy efficiency and reduced delay using self-knowledge with SCHP in wireless sensor networks. international journal of advanced research in computer science and management studies, 2(8)
Gupta A, Gupta M, Nayyar A (2014). Approaches for Combating Delay and Achieving Optimal Path Efficiency in Wireless Sensor Networks
Hans S, Nayyar A (2014). A review of de-facto MAC standard: IEEE 802.11 DCF. In 2014 Fourth International Conference on Advanced Computing & Communication Technologies (pp. 372–376). IEEE
Hassanien AE, Emary E (2018). Swarm intelligence: principles, advances, and applications. CRC Press
Hussein O, Saadawi T (2003). Ant routing algorithm for mobile ad-hoc networks (ARAMA). In Performance, Computing, and Communications Conference, 2003. Conference Proceedings of the 2003 IEEE International (pp. 281–290). IEEE.
Kaur M, Nayyar A (2013) A comprehensive review of mobile adhoc networks (MANETS). Int J Emerg Trends Technol Comput Sci (IJETTCS) 2(6):196–210
Kaur M, Sarangal M, Nayyar A (2014) Simulation of jelly fish periodic attack in mobile ad hoc networks. Int J Comput Trends Technol (IJCTT) 15(1):20–22
Kaur M, Rani M, Nayyar A (2014). A novel defense mechanism via Genetic Algorithm for counterfeiting and combating Jelly Fish attack in Mobile Ad-Hoc Networks. In 2014 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence) (pp. 359–364). IEEE
Khan S, Pathan ASK, Alrajeh NA (2012). Wireless Sensor Networks: Current Status and Future Trends. CRC Press.
Kumar A, Nayyar A (2014) Energy efficient routing protocols for wireless sensor networks (WSNs) based on clustering. Int J Sci Eng Res (IJSER) 5(6):440–448
Kumar S, Dave M, Dahiya S (2014) ACO based QoS aware routing for wireless sensor networks with heterogeneous nodes. In: Emerging trends in computing and communication. Springer, New Delhi, pp 157–168
Kumar S, Nayyar A, Kumari R (2019) Arrhenius artificial bee Colony algorithm. In: International conference on innovative computing and communications. Springer, Singapore, pp 187–195
Maniezzo V, Carbonaro A (2002) Ant colony optimization: an overview. In: Essays and surveys in metaheuristics. Springer US, pp 469–492
Mirjalili S, Dong JS, Lewis A (2020) Ant Colony optimizer: theory, literature review, and application in AUV path planning. In: Nature-inspired optimizers. Springer, Cham, pp 7–21
Mohan BC, Baskaran R (2012) A survey: ant Colony optimization based recent research and implementation on several engineering domain. Expert Syst Appl 39(4):4618–4627
Mohanty, S. (2018). Swarm Intelligence Methods for Statistical Regression.
Mondal S, Ghosh S, Dutta P (2018) Energy efficient data gathering in wireless sensor networks using rough fuzzy C-means and ACO. In: Industry interactive innovations in science, engineering and technology. Springer, Singapore, pp 163–172
Nayyar A (2001) Cross-Layer System for Cluster Based Data Access in MANET’S. Special Issue of International Journal of Computer Science & Informatics (IJCSI), ISSN (PRINT)
Nayyar A (2012). Simulation based evaluation of reactive routing protocol for MANET. In 2012 Second International Conference on Advanced Computing & Communication Technologies (pp. 561–568). IEEE
Nayyar A (2012). Detecting Sequence Number Collector Problem in Black Hole Attacks in AODV Based Mobile Ad hoc Networks. International Journal of Advanced Research in Computer Engineering & Technology
Nayyar A (2013) Conceptual representation and survey of dynamic power management (DPM) in wireless sensor network. Int J Adv Res Comput Sci Software Eng 3(3)
Nayyar A (2017). Improvised Energy Efficient Routing Protocol based on Ant Colony Optimization (ACO) for Wireless Sensor Networks
Nayyar A, Balas VE (2019) Analysis of simulation tools for underwater sensor networks (UWSNs). In: International conference on innovative computing and communications. Springer, Singapore, pp 165–180
Nayyar A, Gupta A (2014) A comprehensive review of cluster-based energy efficient routing protocols in wireless sensor networks. IJRCCT 3(1):104–110
Nayyar A, Nguyen, N. G. (2018) Introduction to Swarm Intelligence. In Advances in Swarm Intelligence for Optimizing Problems in Computer Science (pp. 53–78). Chapman and Hall/CRC.
Nayyar A, Sharma S (2014) A survey on coverage and connectivity issues surrounding wireless sensor network. IJRCCT 3(1):111–118
Nayyar A, Singh R (2014) A comprehensive review of ant colony optimization (ACO) based energy-efficient routing protocols for wireless sensor networks. Int J Wireless Netw Broadband Technol (IJWNBT) 3(3):33–55
Nayyar A, Singh R (2015) A comprehensive review of simulation tools for wireless sensor networks (WSNs). J Wireless Netw Commun 5(1):19–47
Nayyar A, Singh R (2016) Ant Colony optimization—computational swarm intelligence technique. In: Computing for sustainable global development (INDIACom), 2016 3rd international conference on. IEEE, pp 1493–1499
Nayyar, A., & Singh, R. (2016). A comprehensive review of ant colony optimization (ACO) based energy-efficient routing protocols for wireless sensor networks.
Nayyar A, Singh R (2017) IEEMARP: improvised energy efficient multipath ant Colony optimization (ACO) routing protocol for wireless sensor networks. In: International conference on next generation computing technologies. Springer, Singapore, pp 3–24
Nayyar A, Singh R (2017) Simulation and performance comparison of ant Colony optimization (ACO) routing protocol with AODV, DSDV, DSR routing protocols of wireless sensor networks using NS-2 simulator. Am J Intel Syst 7(1):19–30
Nayyar A, Singh R (2017) Performance analysis of ACO based routing protocols-EMCBR, AntChain, IACR, ACO-EAMRA for wireless sensor networks (WSNs). Br J Math Comput Sci 20(6):1–18
Nayyar A, Singh R (2017) Ant Colony optimization (ACO) based routing protocols for wireless sensor networks (WSN): a survey. Int J Adv Comput Sci Appl (IJACSA) 8(2):148–155
Nayyar A, Puri V, Le DN (2016) A comprehensive review of semiconductor-type gas sensors for environmental monitoring. Rev Comput Eng Res 3(3):55–64
Nayyar A, Garg S, Gupta D, Khanna A (2018) Evolutionary computation: theory and algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. Chapman and Hall/CRC, pp 1–26
Nayyar A, Le DN, Nguyen NG (2018). Advances in Swarm Intelligence for Optimizing Problems in Computer Science. CRC Press.
Nayyar A, Puri V, Suseendran G (2019) Artificial bee Colony optimization—population-based meta-heuristic swarm intelligence technique. In: Data management, analytics and innovation. Springer, Singapore, pp 513–525
Patel M, Chandrasekaran R, Venkatesan S (2004) Efficient minimum-cost bandwidth-constrained routing in wireless sensor networks. In: International conference on wireless networks, pp 447–453
Peng S, Yang SX, Gregori S, Tian F (2008). An adaptive QoS and energy-aware routing algorithm for wireless sensor networks. In Information and Automation, 2008. ICIA 2008. International Conference on (pp. 578–583). IEEE
Potdar V, Sharif A, Chang E (2009). Wireless sensor networks: A survey. In Advanced Information Networking and Applications Workshops, 2009. WAINA'09. International Conference on (pp. 636–641). IEEE.
Rao SS, Singh V (1979) Optimization. IEEE Trans Syst, Man, Cybernet 9(8):447–447
Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48
Shah RC, Rabaey JM (2002). Energy aware routing for low energy ad hoc sensor networks. In Wireless Communications and Networking Conference, 2002. WCNC2002. 2002 IEEE (Vol. 1, pp. 350–355). IEEE.
Sharma N, Nayyar A (2014) A comprehensive review of cluster based energy efficient routing protocols for wireless sensor networks. Int J Appl Innov Eng Manag (IJAIEM) 3(1):441–453
Sharma S, Nayyar A (2014) Mint-route to avoid congestion in wireless sensor network. Int J Emerg Trends Technol Comput Sci 3:91–94
Sharma, S., Gupta, M., & Nayyar, A. (2014). Review of Routing Techniques Driving Wireless Sensor Networks.
Sharma N, Rani M, Nayyar A (2014) Performance comparison of distance and density based clustering algorithm (ddcsa) v/s normal technique to analyze power consumption and network lifetime of wireless sensor networks. Int J Curr Eng Technol 4:1503–1507
Sharma S, Kumar S, Nayyar A (2018) Logarithmic spiral based local search in artificial bee Colony algorithm. In: International conference on industrial networks and intelligent systems. Springer, Cham, pp 15–27
Singh G, Kumar N, Verma AK (2014) Antalg: an innovative aco based routing algorithm for manets. J Netw Comput Appl 45:151–167
Stützle T (2009) Ant colony optimization. In: International conference on evolutionary multi-criterion optimization. Springer, Berlin Heidelberg, pp 2–2
Sun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320
Suseendran G, Chandrasekaran E, Nayyar A (2019) Defending jellyfish attack in mobile ad hoc networks via novel fuzzy system rule. In: Data management, analytics and innovation. Springer, Singapore, pp 437–455
Xia, S., & Wu, S. (2009). Ant colony-based energy-aware multipath routing algorithm for wireless sensor networks. In Knowledge Acquisition and Modeling, 2009. KAM'09. Second International Symposium on (Vol. 3, pp. 198–201). IEEE.
Zou Z, Qian Y (2018) Wireless sensor network routing method based on improved ant colony algorithm. J Ambient Intell Humaniz Comput:1–8
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nayyar, A., Singh, R. IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks. Multimed Tools Appl 79, 35221–35252 (2020). https://doi.org/10.1007/s11042-019-7627-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7627-z