ACObased Distributed Energy Routing Protocol In Smart Grid The bidirectional flow of power and communication, and the integration of distributed renewable energy sources in smart grid (SG) have lead to the emergence of the energy routing problem. A lot of interest has been given to energy routing problem in aim of minimizing the transmission cost and ensuring the reliability and efficiency of the smart grid. Historically, traditional techniques are used to solve this problem, such as graph theory, autonomous systems, game theory, xe2x80xa6 ect. In this paper, we attempt to solve this problem with different method, we formalized the problem of determining the energy efficient path as an optimization problem. As a solution for this problem we propose a BioInspired Energy Routing Algorithm based on Ant Colony Optimization (ACOERP).

Realtime detection of energy consumption of IoT network nodes based on artificial intelligence Abstract   Low power loss networks and wireless sensor networks are important components of the Internet of Things. With the development and popularization of artificial intelligence, the network application of wireless sensors is gradually scaled up and industrialized. Aiming at the problems of high energy consumption of the routing strategy, the algorithm protocol is easy to fall into the local optimum and the uneven energy consumption of network nodes. This paper proposes a regional energy balance routing algorithm based on intelligent chaotic ant colony. Based on the intelligent chaotic ant colony algorithm, combined with the remaining energy factors of wireless sensor network nodes, this paper propose a neighbor selection strategy. In order to enhance the ant search ability and speed up the algorithm convergence, an adaptive perturbation strategy and algorithm termination conditions are proposed respectively to find the global optimal solution and avoid falling into the local optimal solution. Simulation results show that the routing planning method in this paper has obvious advantages in terms of network energy consumption and network delay, node equilibrium energy distribution, network life cycle and other performance. Compared with similar algorithms, the average energy consumption has been saved by 7.3%.