Optimize routing and reduce latency when sending information among Internet of Things (IoT) nodes

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Faculty of Technology and Engineering, Lorestan, Iran

2 Department of Computer Science, University of Lorestan, Khorramabad, Lorestan, Iran

10.22075/ijnaa.2023.29318.4120

Abstract

The existing nodes of IoT networks are very small in size, deployed for long periods, and have very limited resources, which means that an IoT network must be very energy efficient to survive for a long time. Therefore, finding optimal routing techniques that lead to better data sharing without wasting energy can lead to more energy savings. This is an optimization problem, which means that we need to use optimization algorithms to find the optimal path in an IoT network. Some of the optimization algorithms are called meta-heuristic algorithms, these algorithms are inspired by nature, such as Artificial Neural Networks (ANN), which are Gradient methods to find the most suitable solution for a given problem. Our next algorithm is Particle Swarm Optimization (PSO). If the search combination of both algorithms is used in parallel, the search power will increase and better answers will be found in less time. For this reason, we suggest using a combination of the above algorithms. This idea is a combination of two optimization algorithms, PSO (Particle Swarm Optimization) and ANN (Neural Network) to optimize routing and reduce latency when sending information between IoT nodes in an IoT system. The proposed protocol is focused on optimizing energy consumption and execution time with the help of the GA-PSO algorithm based on routing-based clustering. Finally, to evaluate the proposed protocol, it was simulated using C++  software and compared with the method presented in the reference article based on the enhanced Ant Colony Algorithm, and the results show the efficiency of the proposed method in terms of energy consumption and execution time. The results show that in the presented algorithm, the execution time has been reduced to almost a quarter of the execution time in the algorithm of the reference article. Also, the results showed that our proposed method consumed 20 kJ less energy.

Keywords

[1] Ch. Chao and Y. Lee, Quorum-based energy saving MAC protocol design for wireless sensor networks, IEEE/IPIP Int. Conf. Embedded and Ubiquitous Comput., (Shanghai, China), vol. 1, IEEE, 2008, pp. 316–322.
[2] S. Chauhan and L.K. Awasthi, Cluster-based task scheduling in wireless sensor network, Int. J. Comput. Appl. 33 (2011), no. 4, 38–43.
[3] F. De Pellegrini, K. Gomez, D. Miorandi, and I. Chlamtac, Distributed wake-up scheduling for energy saving in wireless networks, CoRR 1 (2011).
[4] O. Gaddour, A. Koubˆaa, and M. Abid, SeGCom: A secure group communication mechanism in cluster-tree wireless sensor networks, First Int. Conf. Commun. Network. (Hammamet, Tunisia), 2009, pp. 1–7.
[5] B. Jiang, B. Ravindran, and H. Cho, Energy efficient sleep scheduling in sensor networks for multiple target tracking, S.E. Nikoletseas, B.S. Chlebus, D.B. Johnson, B. Krishnamachari, (eds) Distributed Computing in Sensor Systems, DCOSS 2008, Lecture Notes in Computer Science, vol 5067, Springer, Berlin, Heidelberg, 2008, pp. 498–509.
[6] T. Kavitha and D. Sridharan, Security vulnerabilities in wireless sensor networks: A survey, J. Inf. Assur. Secur. 5 (2009), 31–44.
[7] M.N. Khan, H. Ur Rahman, M.Z. Khan, An energy efficient adaptive scheduling scheme (EASS) for mesh grid wireless sensor networks, J. Parall. Distrib. Comput. 146 (2020), 139—157.
[8] S. Kumar and S Chauhan, A survey on scheduling algorithms for wireless sensor networks, Int. J. Comput. Appl. 20 (2011), no. 5, 7–13.
[9] Sh. Liu, K. Fan, and P. Sinha, Dynamic sleep scheduling using online experimentation for wireless sensor networks, Proc. SenMetrics, San Diego, 2005.
[10] S.V. Manisekaran and R. Venkatesan, Power efficient scheduling technique for multiple sinks in wireless sensor networks, Eur. J. Sci. Res. 89 (2012), no. 2, 191–202.
[11] T. Nagamalar and T.R. Rangaswamy, Sleeping cluster based medium access control layer routing protocol for wireless sensor networks, J. Comput. Sci. 8 (2012), no. 8, 1294–1303.
[12] G. Nan, G. Shi, Z. Mao, and M. Li, CDSWS: Coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks, EURASIP J. Wireless Commun. Network. 2012 (2012).
[13] Ns2 Network Simulator, http://www.isi.edu/nsnam/ns.
[14] P. Priyanka and J.P. Kaur, Ant colony optimization based routing in IoT for healthcare services, Second Int. Conf. Intell. Comput. Control Syst. (Madurai, India), 2018, pp. 1155–1159.
[15] M.R. Ramli, J. Lee, and D. Kim, Hybrid MAC protocol for UAV-assisted data gathering in a wireless sensor network, Internet Things 14 (2021), 100088.
[16] V. Sasikala, and C. Chandrasekar, Energy efficient multipath data fusion technique for wireless sensor networks, ACEEE Int. J. Network Secur. 3 (2012), no. 2, 34–41.
[17] A. Utkarsh, A.S. Kantha, J. Praveen, and J.R. Kumar, Hybrid GA-PSO trained functional link artificial neural network based channel equalizer, 2nd Int. Conf. Signal Process. Integrated Networks (SPIN) (Noida, India), 2015, pp. 285–290.
[18] Y. Wu, S. Fahmy, and N.B. Shroff, Optimal sleep/wake scheduling for time-synchronized sensor networks with QoS guarantees, IEEE/ACM Trans. Network. 17 (2006), 1508–1521.
[19] Y. Wu, S. Fahmy and N. B. Shroff, Sleep/wake scheduling for multi-hop sensor networks: Non-convexity and approximation algorithm, Ad Hoc Networks 8 (2010), no. 7, 681–693.
[20] A.S. Zahmati, B. Abolhassani, A. Beheshti Shirazi, and A.Sh. Bakhtiari, An energy-efficient protocol with static clustering for wireless sensor networks, Int. J. Electron. Commun. Engin. 1 (2007), no. 4, 69–72.

Articles in Press, Corrected Proof
Available Online from 28 January 2024
  • Receive Date: 11 November 2022
  • Revise Date: 14 December 2022
  • Accept Date: 23 January 2023