Wireless sensor networks are often located in areas where access to them is difficult or dangerous. Today, in wireless sensor networks, cluster-based routing protocols by dividing sensor nodes into distinct clusters and selecting local head-clusters to combine and send information of each cluster to the base station and balanced energy consumption by network nodes, get the best performance in terms of increasing longevity and preservation network coverage as compared to other routine methods. Their main purpose is to control the danger area and transfer data to a sink. Therefore, connecting to sensor networks and coverage rate of controlled area is one of the most important concerns to achieve these goals. In this paper, to cluster wireless sensor networks, a method using the Imperialistcompetitive evolutionary algorithm is purposed that divides wireless sensor nodes into balanced clusters, in addition to, several mobile robots has been used for improving power dissipation of network, to assist in the transmission of sensor networks and improve network connectivity and coverage rate of the controlled area. In this research, the sensor network is based on an islets-based topology that has been used robots to detect and enhance connectivity and cover cavities that are largely around the sensors. The simulation results show that our new model can greatly improve the network coverage connectivity criteria. The simulation results show its successful performance in increasing longevity of the wireless sensor network. تفاصيل المقالة

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. تفاصيل المقالة