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%.

Optimal Energy Consumption Tasks Scheduling Strategy for MultiRadio WSNs Multiradio technology is regarded as a promising way to improve the performance of Wireless Sensor Networks (WSNs) and has attracted much attention of researchers. It is very important to reduce energy consumption and to prolong the lifetime of MultiRadio WSNs (MRWSNs), since the node is generally batteryoperated in MRWSN environments. In this paper, two typical types of energy consumption process, the transmitting energy consumption and idle listening energy consumption, are analyzed firstly. Based on the above analysis, the energy consumption model of multiradio nodes is built, and then it is considered as the optimization objective for the minimization energy consumption of multiradio nodes. Furthermore, the heuristic optimal energy consumption task scheduling strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and then the detailed steps of the proposed strategy are presented. Finally, the effectiveness and performance of the proposed strategy are evaluated through practical experiments and simulations. Evaluation results show that the proposed strategy outperforms some other algorithms, in terms of energy consumption, network lifetime, and tasks extensibility.