Abstract
Internet of Things (IoT)-based paradigm connects multitudinous IoT devices that operate in a wireless mode to gather information about various attributes from their surrounding. These IoT devices suffer from the limited energy resources and hence, these must be used in an optimized way to elongate network lifetime and to improve various performance metrics. A great magnitude of work is presented by the researchers to address the energy efficiency issue of the sensor node with use of various evolutionary algorithms, however, there is still scope for the improvement for the routing mechanism by using appropriate optimization method. To address this concern, in this paper, we present Tunicate Swarm Algorithm (TSA)-based Optimized Routing Mechanism (TORM) that addresses the problem of energy-efficiency of sensor nodes for IoT for longer sustainability. The rationale behind using recently proposed TSA optimization method is its faster convergence and high exploration abilities. The fitness function of TSA used for TORM, is computed by considering the various essential fitness parameters responsible for the selection of Cluster Head (CH) node. It is revealed through the simulation analysis that TORM outperform various state-of-the-art algorithms used for optimized selection of CH.






Similar content being viewed by others
References
Yi L, Yang C, Li J, Xie S, Zhang Y (2019) Intelligent edge computing for iot-based energy management in smart cities. IEEE Netw 33(2):111–117
Raza M, Aslam N, Le-Minh H, Hussain S, Cao Y, Khan NM (2018) A critical analysis of research potential, challenges, and future directives in industrial wireless sensor networks. IEEE Commun Surv Tutorials 20(1):39–95
Yan R, Sun H, Qian Y (2013) Energy-aware sensor node design with its application in wireless sensor networks. IEEE Trans Instrum Measur 62(5):1183–1191
Shahraki A, Taherkordi A, Haugen Ø, Eliassen F (2020) Clustering objectives in wireless sensor networks A survey and research direction analysis. Comput Netw:107376
Verma S, Sood N, Sharma AK (2019) A novelistic approach for energy efficient routing using single and multiple data sinks in heterogeneous wireless sensor network. Peer-to-Peer Netw Appl 12(5):1110–1136. Publisher: Springer
Künzel G, Indrusiak LS, Pereira CE (2019) Latency and lifetime enhancements in industrial wireless sensor networks: A q-learning approach for graph routing. IEEE Trans Ind Inf 16(8):5617–5625
Nakas C, Kandris D, Visvardis G (2020) Energy efficient routing in wireless sensor networks A comprehensive survey. Algorithms 13(3):72
Rani Shalli, Maheswar R, Kanagachidambaresan G R, Jayarajan P (2020) Integration of WSN and IoT for Smart Cities. Springer
Rani S, Ahmed SH, Rastogi R (2019) Dynamic clustering approach based on wireless sensor networks genetic algorithm for iot applications. Wirel Netw:1–10
Verma S, Sood N, Sharma AK (2020) Cost-effective cluster-based energy efficient routing for green wireless sensor network. Recent Adv Comput Sci Commun 12:1–00
Verma S, Sood N, Sharma AK (2019) Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network
De D, Mukherjee A, Das Santosh K, Dey N (2020) Nature inspired computing for wireless sensor networks. Springer
Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw:102237
Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541
Sharma R, Vashisht V, Singh U (2020) eetmfo/ga: a secure and energy efficient cluster head selection in wireless sensor networks. Telecommun Syst:1–16
Qureshi KN, Bashir MU, Lloret J, Leon A (2020) Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision. J Sens:2020
Verma A, Kumar S, Gautam PR, Rashid T, Kumar A (2020) Fuzzy logic based effective clustering of homogeneous wireless sensor networks for mobile sink. IEEE Sens J 20(10):5615–5623
Pokhrel SR, Verma S, Garg S, Sharma AK, Choi J (2020) An intelligent clustering framework for massive communication of industrial sensors. IEEE Transactions on Industrial Informatics
Zhuo X, Liu M, Wei Y, Yu G, Qu F, Sun R (2020) Auv-aided energy-efficient data collection in underwater acoustic sensor networks. IEEE Internet of Things Journal
Wu Y, Dai H-N, Wang H (2020) Convergence of blockchain and edge computing for secure and scalable iiot critical infrastructures in industry 4.0. IEEE Internet of Things Journal
Yan Z, Ge J, Wu Y, Li L, Li T (2020) Automatic virtual network embedding: A deep reinforcement learning approach with graph convolutional networks. IEEE J Sel Areas Commun 38(6):1040–1057
Rafique W, Qi L, Yaqoob I, Imran M, Rasool RU, Dou W (2020) Complementing iot services through software defined networking and edge computing: A comprehensive survey. IEEE Commun Surv Tutorials 22(3):1761–1804
Liu Y, Dai H-N, Wang Q, Shukla MK, Imran M (2020) Unmanned aerial vehicle for internet of everything Opportunities and challenges. Comput Commun 155:66–83
Raza M, Awais M, Singh N, Imran M, Hussain S (2020) Intelligent iot framework for indoor healthcare monitoring of parkinson’s disease patient. IEEE Journal on Selected Areas in Communications
Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645
Wang H, Li K, Pedrycz W (2020) An elite hybrid metaheuristic optimization algorithm for maximizing wireless sensor networks lifetime with a sink node. IEEE Sens J 20(10):5634–5649
Verma S, Kaur S, Sharma KA, Kathuria A, Piran MJ (2020) Dual sink-based optimized sensing for intelligent transportation systems. IEEE Sens J:1–1
Pathak A (2020) A proficient bee colony-clustering protocol to prolong lifetime of wireless sensor networks. J Comput Netw Commun:2020
Alazab M, Lakshmanna K, Reddy T, Pham Q-V, Maddikunta PKR (2021) Multi-objective cluster head selection using fitness averaged rider optimization algorithm for iot networks in smart cities. Sustain Energy Technol Assess 43:100973
Bakshi M, Chowdhury C, Maulik U (2021) Energy-efficient cluster head selection algorithm for iot using modified glow-worm swarm optimization. J Supercomput:1–19
Shyjith MB, Maheswaran C P, Reshma VK (2021) Optimized and dynamic selection of cluster head using energy efficient routing protocol in wsn. Wirel Pers Commun 116(1):577–599
Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for wsn using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317
Panchal A, Singh RK (2021) Eadcr: energy aware distance based cluster head selection and routing protocol for wireless sensor networks. J Circ Syst Comput 30(04):2150063
Theodore S et al (1996) Rappaport Wireless communications: principles and practice, vol 2. Prentice Hall PTR, New Jersey
Grant M, Boyd S (2014) Cvx: Matlab software for disciplined convex programming, version 2.1
Acknowledgements
This work was supported by King Saud University, Riyadh, Saudi Arabia, under Researchers Supporting Project number RSP-2021/18.
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
Dogra, R., Rani, S., Verma, S. et al. TORM: Tunicate Swarm Algorithm-based Optimized Routing Mechanism in IoT-based Framework. Mobile Netw Appl 26, 2365–2373 (2021). https://doi.org/10.1007/s11036-021-01833-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-021-01833-2