A Review on Swarm Intelligence Based Routing Approaches
Keywords:wireless sensor networks, routing algorithms, ant colony optimization, swarm intelligence, energy efficiency
The principles of bio-inspired or swarm intelligence algorithms can be effectively used to achieve optimal solutions in routing for complex and dynamic wireless sensor networks or body area networks. As the name indicates, it is a field that is inspired by natural living beings like ants, bees, fishes, etc. Studies have proved that the routing protocols based on such bio-inspired methods perform better in terms of energy efficiency, reliability, adaptivity, scalability, and robustness. The general classification of routing protocols is classical-based and swarm-based routing protocols, where both the protocols were specifically categorized as data-centric, location-aware, hierarchical and network flow, and QoS aware protocols. In this paper, an evocative taxonomy and comparison of various swarm-based routing algorithms are presented. A brief discussion about the sensor network design and the major factors that influence the routing is also discussed. The comparative analysis of the selected swarm-based protocols is also done with respect to routing characteristics like query based, route selection, energy efficiency, and path selection. From the review, it is observed that the selection of a routing protocol is application dependent. This paper will be helpful to the researchers as a reference on bio-inspired algorithms for new protocol designs and also for the proper selection of routing protocols according to the type of applications.
W. R. Heinzelman, J. Kulik, and Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks,” Proc. ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99), ACM, August 1999, pp. 174-185.
G. Pau, “Power consumption reduction for wireless sensor networks using a fuzzy approach,” International Journal of Engineering and Technology Innovation, vol. 6, no. 1, pp. 55-67, January 2016.
N. Manju, S. Chand, and B. Kumar, “Improved-coverage preserving clustering protocol in wireless sensor network,” International Journal of Engineering and Technology Innovation, vol. 6, no. 1, pp. 16-29, January 2016.
K. Kharb and B. Sharma, “Reliable and congestion control protocols for wireless sensor networks,” International Journal of Engineering and Technology Innovation, vol. 6, pp. 68-78, January 2016.
S. Sofi and R. Naaz, “Securing Ad Hoc wireless sensor networks under byzantine attacks by implementing non-cryptographic methods”, Advances In Technology Innovation, vol. 2, no. 4, pp. 113-118, May 2017.
Y. Sasaki and T. Yokotani, “Performance evaluation of MQTT as a communication protocol for IoT and prototyping”, Advances In Technology Innovation, vol. 4, no. 1, pp. 21-29, January 2019.
H. Chi-Fu and C. Yi-Hong, “Message-efficient route planning based on comprehensive real-time traffic map in VANETs,” Proceedings of Engineering and Technology Innovation, vol.9, pp. 25-31, July 2018.
Y. Jahir, M. Atiquzzaman , H. Refai, A. Paranjothi, and P. G. LoPresti, “Routing protocols and architecture for Disaster Area Network: A survey,” Ad Hoc Networks, vol. 82, pp. 1-14, January 2019.
M. Zungeru, L. M. Ang, and K. P. Seng, “Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison,” Journal of Network and Computer Applications, vol. 35, no. 5, pp. 1508-1536, Sepember 2012.
J. Kulik, W. Heinzelman, and H. Balakrishnan, “Negotiation-based protocols for disseminating information in wireless sensor networks,” Wireless Networks, vol. 8, no. 2-3, pp. 169-185, March 2002.
C. Intanagonwiwat, R. Govindan, and Estrin D, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” Proc. 6th Annual International Conference on Mobile Computing and Networking, August 2000, pp. 56-67.
Y. Xu, J. Heidemann, and D. Estrin, “Geography-informed energy conservation for ad hoc routing,” Proc. 7th Annual International Conference on Mobile Computing and Networking (Mobicom 01), ACM, July 2001, pp. 70-84.
W. R. Heinzelman , A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” Proc. Hawaii International Conference on System Sciences, IEEE Press, January 2000, pp. 10.
W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, October 2002.
T. He, J. A. Stankovic, C. Lu, and T. Abdelzaher, “SPEED: A stateless protocol for real-time communication in sensor networks,” Proc. 23rd International Conference on Distributed Computing Systems, January 2003, pp. 46-55.
R. Schoonderwoerd, O. E. Holland, J. L. Bruten, and L. J. M. Rothkrantz, “Ant-based load balancing in telecommunications networks,” Adaptive Behavior, vol. 5, pp. 169-207, January 1997.
G. Di Caro and M. Dorigo, “AntNet: Distributed stigmergetic control for communications networks,” Journal of Artificial Intelligence Research, vol. 9, pp. 317-365, December 1998.
F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, March 2002.
J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: a survey,” IEEE Wireless Communications, vol. 11, pp. 6-28, December 2004.
M. Saleem, G. A. Di Caro, and M. Farooq, “Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions,” Information Sciences, vol. 181, no. 20, pp. 4597-4624, October 2011.
Zengin and S. Tuncel, “A survey on swarm intelligence based routing protocols in wireless sensor networks,” International Journal of Physical Sciences, vol. 5, pp. 2118-2126, November 2010.
T. Gui, C. Ma, F. Wang, and D. E. Wilkins, “Survey on swarm intelligence based routing protocols for wireless sensor networks: An extensive study,” Proc. IEEE International Conference on Industrial Technology, IEEE Press, May 2016, pp. 1944-1949.
N. Karaboga and M. B. Cetinkaya, “A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 19, pp. 175-190, February 2011.
Y. Yue, L. Cao, B. Hang, and Z. Luo, “A swarm intelligence algorithm for routing recovery strategy in wireless sensor networks with mobile sink,” IEEE Access, vol. 6, pp. 67434 – 67445, November 2018.
N. Srivastava and P. Raghav, “A review on swarm intelligence based routing algorithms in mobile adhoc network,” Proc. 8th IEEE International Conference on Computing, Communication and Networking Technologies (ICCCNT 17), IEEE Press, July 2017, pp. 1-7.
M. Mavrovouniotis, C. Li, and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithms and applications,” Swarm and Evolutionary Computation, vol. 33, pp. 1-17, April 2017.
A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, “Computing with the collective intelligence of honey bees–a survey,” Swarm and Evolutionary Computation, vol. 32, pp. 25-48, February 2017.
A. Nayyar and R. Singh, “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,” American Journal of Intelligent Systems, vol. 7, pp. 19-30, July 2017.
P. Kumari and S. K. Sahana, “Advances in intelligent systems and computing book series,” vol. 711, Singapore: Springer, 2019.
O. Zedadra et al., “Swarm intelligence-based algorithms within IoT-based systems: A review,” Journal of Parallel and Distributed Computing, vol. 122, pp. 173-187, August 2018.
Y. G. Yue and P. He, “A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy, challenges, and future directions,” Information Fusion, vol. 44, pp. 188-204, November 2018.
L. Cheng et al, “Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks,” Computer Networks, vol. 134, pp. 66-77, April 2018.
C. Y. Chong and S. P. Kumar, “Sensor networks: evolution, opportunities, and challenges,” Proc. IEEE, IEEE Press, vol. 91, no. 8, August 2003, pp. 1247-1256.
H. Grichi, O. Mosbahi, M. Khalgui, and Z. Li, “New power-oriented methodology for dynamic resizing and mobility of reconfigurable wireless sensor networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 7, pp. 1120-1130, July 2018.
K. M. Modieginyane, B. B. Letswamotse, R. Malekian, and A. M. Abu-Mahfouz, “Software defined wireless sensor networks application opportunities for efficient network management: A survey,” Computers & Electrical Engineering, vol. 66, pp. 274-287, February 2018.
S. Boubiche, D. E. Boubiche, A. Bilami, and H. Toral-Cruz, “Big data challenges and data aggregation strategies in wireless sensor networks,” IEEE Access, vol. 6, pp. 20558-20571, May 2018.
C. Yang, D. Puthal, S. P. Mohanty, and E. Kougianos, “Big-sensing-data curation for the cloud is coming: A promise of scalable cloud-data-center mitigation for next-generation IoT and wireless sensor networks,” IEEE Consumer Electronics Magazine, vol. 6, no. 4, pp. 48-56, October 2017.
K. Guleria and A. K. Verma, “Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks,” Wireless Networks. pp. 1-25, March 2018.
V. Bhanumathi and C. P. Sangeetha, “A guide for the selection of routing protocols in WBAN for healthcare applications,” Human-centric Computing and Information Sciences, vol. 7, pp. 1-19, August 2017.
L. Buttyan and J. P. Hubaux, “Enforcing service availability in mobile ad-hoc WANs,” Proc. ACM International symposium on Mobile ad hoc networking & computing, ACM, August 2000, pp. 87-96.
M. Roth and S. Wicker, “Termite: Ad-hoc networking with stigmergy,” Proc. IEEE Global Telecommunications Conference (GLOBECOM '03), IEEE Press, vol. 5, December 2003, pp. 2937-2941.
K. Li, C. E. Torres, K. Thomas, L. F. Rossi, and C. C. Shen, “Slime mold inspired routing protocols for wireless sensor networks,” Swarm Intelligence, vol. 5, no. 3-4, pp. 183-223, December 2011.
M. Saleem and M. Farooq, “Beesensor: a bee-inspired power aware routing protocol for wireless sensor networks,” Proc. Workshops on Applications of Evolutionary Computing (EvoWorkshops 07), Springer, vol. 4448, 2007, pp. 81-90.
X. Zhu, “Pheromone based energy aware directed diffusion algorithm for wireless sensor network,” Proc. International Conference on Intelligent Computing (ICIC 07), Springer, vol. 4681, August 2007, pp. 283-291.
W. Guo, W. Zhang, and G. Lu, “A comprehensive routing protocol in wireless sensor network based on ant colony algorithm,” Proc. Second International Conference on Networks Security Wireless Communications and Trusted Computing (NSWCTC 10), IEEE Press, vol. 1, April 2010, pp. 41-44.
Y. Zhang, L. D. Kuhn, and M. P. Fromherz, “Improvements on ant routing for sensor networks,” Proc. International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 04), Springer, September 2004, pp. 154-165.
S. Phoemphon, C. So-In, and D. T. Niyato, “A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization,” Applied Soft Computing, vol. 65, pp. 101-120, April 2018.
Y. Kiri, M. Sugano, and M. Murata, “Self-organized data-gathering scheme for multi-sink sensor networks inspired by swarm intelligence,” Proc. IEEE First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 07), IEEE Press, July 2007, pp. 161-172.
G. Wang, Y. Wang, and X. Tao, “An ant colony clustering routing algorithm for wireless sensor networks,” Proc. 3rd International Conference on Genetic and Evolutionary Computing, IEEE Press, October 2009, pp. 670-673.
M. Ziyadi, K. Yasami, and B. Abolhassani, “Adaptive clustering for energy efficient wireless sensor networks based on ant colony optimization,” Proc. Seventh Annual IEEE Communication Networks and Services Research Conference (CNSR 09), IEEE Press, May 2009, pp. 330-334.
L. Wang, R. Zhang, and S. Geng, “An energy-balanced ant-based routing protocol for wireless sensor networks,” Proc. 5th IEEE International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 09), IEEE Press, September 2009, pp. 1-4.
R. Xiu-li, L. Hong-wei, and W. Yu, “Multipath routing based on ant colony system in wireless sensor networks,” Proc. IEEE International Conference on Computer Science and Software Engineering, IEEE Press, vol. 3, December 2008, pp. 202-205.
N. Ding and P. X. Liu, “Data gathering communication in wireless sensor networks using ant colony optimization,” Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO 04), IEEE Press, August 2004, pp. 822-827.
C. Ramachandran, S. Misra, and M. S. Obaidat, “A probabilistic zonal approach for swarm‐inspired wildfire detection using sensor networks,” International Journal of Communication Systems, vol. 21, pp. 1047-1073, June 2008.
J. Yang, Y. Lin, W. Xiong, and B. Xu, “Ant colony-based multi-path routing algorithm for wireless sensor networks,” Proc. International Workshop on Intelligent Systems and Applications (ISA 09), IEEE Press, May 2009, pp. 1-4.
G. De-Min, Q. Huan-Yan, Y. Xiao-Yong, and W. Xiao-Nan, “Based on ant colony multicast trees of wireless sensor network routing research,” Journal of Iet-Wsn. Org, vol. 2, pp. 1-7, 2008.
X. Wang, Q. Li, N. Xiong, and Y. Pan, “Ant colony optimization-based location-aware routing for wireless sensor networks,” Proc. International Conference on Wireless Algorithms, Systems, and Applications (WASA 08), Springer , October 2008, pp. 109-120.
M. Paone, L. Paladina, M. Scarpa, and A. Puliafito, “A multi-sink swarm-based routing protocol for wireless sensor networks,” Proc. IEEE Symposium on Computers and Communications (ISCC 09), IEEE Press, July 2009, pp. 28-33.
W. M. Chen, C. S. Li, F. Y. Chiang, and H. C. Chao, “Jumping ant routing algorithm for sensor networks,” Computer Communications, vol. 30, no. 14-15, pp. 2892-2903, October 2007.
J. Bi, Z. Li, and R. Wang, “An ant colony optimization-based load balancing routing algorithm for wireless multimedia sensor networks,” Proc. 12th IEEE International Conference on Communication Technology (ICCT 10), IEEE Press, November 2010, pp. 584-587.
B. Singh and D. K. Lobiyal, “A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks,” Human-centric Computing and Information Sciences, vol. 2, pp. 13, December 2012.
D. R. Prasad, P. V. Naganjaneyulu, and K. S. Prasad, “A hybrid swarm optimization for energy efficient clustering in multi-hop wireless sensor network,” Wireless Personal Communications, vol. 94, no. 4, pp. 2459-2471, June 2017.
M. Shokouhifar and A. Jalali, “Optimized sugeno fuzzy clustering algorithm for wireless sensor networks,” Engineering Applications of Artificial Intelligence, vol. 60, pp. 16-25, April 2017.
S. Abba and J. A. Lee, “Bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architectures using Particle Swarm Optimization,” Microprocessors and Microsystems, vol. 51, pp.18-38, June 2017.
J. Wan, C. Ju, H. Ji, G. Youn, and J. U. Kim, “A particle swarm optimization and mutation operator based node deployment strategy for WSNs,” Proc. International Conference on Cloud Computing and Security (ICCCS 17), Springer, June 2017, pp. 430-437.
P. S. Mann and S. Singh, “Energy-efficient hierarchical routing for wireless sensor networks: a swarm intelligence approach,” Wireless Personal Communications, vol. 92, no. 2, pp. 785-805, January 2017.
V. Rosset, M. A. Paulo, J. G. Cespedes, and M. C. Nascimento, “Enhancing the reliability on data delivery and energy efficiency by combining swarm intelligence and community detection in large-scale WSNs,” Expert Systems with Applications, vol. 78, pp. 89-102, July 2017.
A. Verma and J. S. Prasad, “Performance enhancement by efficient ant colony routing algorithm based on swarm intelligence in wireless sensor networks,” International Journal of Wireless and Mobile Computing, vol. 12, pp. 232-238, January 2017.
A. Ali, Y. Ming, T. Si, S. Iram, and S. Chakraborty, “Enhancement of RWSN lifetime via firework clustering algorithm validated by ANN,” Information, vol. 9, pp. 1-13, March 2018.
T. Camilo, C. Carreto, J. Silva, and F. Boavida, “An energy-efficient ant-based routing algorithm for wireless sensor networks,” Proc. International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 06), Springer, September 2006, pp. 49-59.
Y. F. Wen, Y. Q. Chen, and M. Pan, “Adaptive ant-based routing in wireless sensor networks using energy* Delay metrics,” Journal of Zhejiang University-Science A, vol. 9, no. 4, pp. 531-538, April 2008.
R. Ghasem Aghaei, A. M. Rahman, M. A. Rahman, W. Gueaieb, and A. El Saddik, “Ant colony-based many-to-one sensory data routing in wireless sensor networks,” Proc. IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 08), IEEE Press, March 2008, pp. 1005-1010.
M. Saleem and M. Farooq, “A framework for empirical evaluation of nature inspired routing protocols for wireless sensor networks,” Proc. IEEE Congress on Evolutionary Computation (CEC 07), IEEE Press, September 2007, pp. 751-758.
V. Mahadevan and F. Chiang, “iACO: A bio-inspired power efficient routing scheme for sensor networks,” International Journal of Computer Theory and Engineering, vol. 2, no. 6, pp. 972-977, December 2010.
W. Cai, X. Jin, Y. Zhang, K. Chen, and R. Wang, “ACO based QoS routing algorithm for wireless sensor networks,” Proc. International Conference on Ubiquitous Intelligence and Computing (UIC 06), Springer, September 2006, pp. 419-428.
R. Misra and C. Mandal, “Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks,” Proc. IFIP IEEE International Conference on Wireless and Optical Communications Networks, IEEE Press, April 2006, pp. 1-5.
Y. Sun, H. Ma, L. Liu, and Y. E. Zheng, “ASAR: An ant-based service-aware routing algorithm for multimedia sensor networks,” Frontiers of Electrical and Electronic Engineering in China, vol. 3, pp. 25-33, January 2008.
T. White, B. Pagurek, and F. Oppacher, “Connection management using adaptive mobile agents,” Proc. International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 98), April 2006, pp. 802-809x.
S. Xia and S. Wu, “Ant colony-based energy-aware multipath routing algorithm for wireless sensor networks,” Proc. Second International Symposium on Knowledge Acquisition and Modeling (KAM 09), IEEE Press, vol. 3, December 2009, pp. 198-201.
S. Peng, S. X. Yang, S. Gregori, and F. Tian, “An adaptive QoS and energy-aware routing algorithm for wireless sensor networks,” Proc. International Conference on Information and Automation (ICIA 08), IEEE Press, June 2008, pp. 578-583.
S. K. Dhurandher, S. Misra, M. S. Obaidat, and N. Gupta, “QDV: a quality-of-security-based distance vector routing protocol for wireless sensor networks using ant colony optimization,” Proc. IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WIMOB 08), IEEE Press, October 2008, pp. 598-602.
L. Cobo, A. Quintero, and S. Pierre, “Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics,” Computer Networks, vol. 54, no. 17, pp. 2991-3010, December 2010.
A. El Ghazi and B. Ahiod, “Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks,” Applied Intelligence, vol. 48, pp. 2755-2769, September 2018.
M. Talha et al., “Energy optimization in home energy management system using artificial fish swarm algorithm and genetic algorithm,” Proc. International Conference on Intelligent Networking and Collaborative Systems (INCoS 17), Springer, Cham, August 2017, pp. 203-213.
How to Cite
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.