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Abstract. There are a lot of researches on self-organizing network at present. However, due to the characteristics of topological change of the Internet of Things, node resource constraints and random distribution and so on. Most of the existing self-organizing network are not applicable. This paper is aimed at the characteristics of the Internet of Things, and draws lessons from small world model, energy multi-path routing and hybrid routing algorithm. On the basis of GEAR protocol, a model of high efficient self-organizing network for large scale aware node is proposed: A multi-path hybrid routing model based on small world model, referred as SMH (Small World Multi-Path Hybrid Routing) model. In this paper, the model is implemented and an improved protocol based on GEAR is obtained, which is SMH-GEAR protocol. And SMH-GEAR protocol and GEAR protocol are compared and analyzed with simulation by using NS-2 network simulation platform. The result of the experiment shows SMH-GEAR protocol is better than GEAR protocol in terms of overall performance.

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Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.