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

Document Type : Research Paper


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

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



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.


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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