Document Type: Original Manuscript
Computer Science Department, Rafsanjan Branch, Islamic Azad University, Rafsanjan, Iran
According to the conditions and characteristics of Internet of Things (IoT), routing and data exchange in these networks are facing with many challenges such as high delay and overhead, congestion and lack of data. The ants-algorithm as a bio-inspired heuristic technique is an intelligent heuristic algorithm that also provides quality in addition to effective optimization. This algorithm distributes computing among the elements of network and can be easily implemented on the internet of things. In this paper, a new method called ALQARM based on the development of ants-algorithm is proposed to improve the IoT routing problems. ALQARM uses special parameters during its operation to support routing quality, congestion control and overhead optimization. To cover the concepts mentioned above, ALQARM focuses on ant-agent exchanges, pheromone updates and learning-enhancing topics. ALQARM is essentially a three-step approach in such a way that in the first and second steps, parent elections and child belong are determined and the network graph is created. In the final step try is to optimize the overheads in terms of reinforcing learning. To evaluate ALQARM, this method is implemented based on the development of RPL protocol in the Cooja simulator and has been compared with previous researches. The simulation results show the superiority of ALQARM in the metrics of network successful receipt rates, control overheads and interaction delays compared to similar methods.