Multinode topology location model of smart city based on Internet of Things Abstract   Smart City is a new model and new form of urban development based on the Internet of Things, the advanced information technology, intelligent technology and multinetwork integration. In most cases, wireless sensor networks need to be monitored in complex environments or in areas that humans cannot reach. Most of the nodes will adopt aircraft throwing and other methods, resulting in uncontrollable node positions. Such research cannot be carried out without knowing the specific source of the information. Therefore, the problem of node location in wireless sensor networks has become one of the key issues that must be solved. In this paper, a wireless sensor network topology based on correlation neighbor graph is constructed. The DVHop (Distance VectorHop) localization algorithm with hop number correction and average hop weighting is proposed. The signal strength value received by the node is converted into the distance between the nodes by the shadowing model; and the ratio of the distance value to the communication radius between the nodes is used to correct the hop value. In order to further improve the positioning performance of the algorithm, the MDVHop (Modified Distance Vectorxe2x80x93Hop) algorithm is proposed. The MDVHop algorithm mainly improves the error caused by the number of hops between anchor nodes, the average hop distance and the network topology in the DVHop algorithm. Aiming at the problems of the maximum likelihood coordinate calculation method, the localization problem of nodes is transformed into the minimum problem of solving nonlinear equations, and the improved Bat algorithm is used to replace the maximum likelihood algorithm to obtain the coordinates of unknown nodes. The simulation shows that the unknown node has achieved better positioning results in the IoT positioning model based on wireless sensor network topology and has higher positioning accuracy.

Location of threedimensional movement for a human using a wearable multinode instrument implemented by wireless body area networks Abstract   A wireless human body local area network is a wireless network composed of lightweight, wearable or implantable sensor nodes that sense various physiological parameters of the human body. This paper introduces the key technologies and characteristics of wireless sensor networks and the research status of node localization, analyzes the wireless sensor network localization algorithms and their performance evaluation indicators, and conducts indepth research on wearable 3D node localization algorithms. The 3D positioning algorithm uses static beacon nodes to achieve the 3D positioning of public nodes. The positioning accuracy and positioning rate depend on the number of beacon nodes, and the number of beacon nodes increases, which increases network costs and energy consumption. Aiming at the spatiotemporal correlation between motion data collected by multiple sensors in a wireless human local area network, an RSSIbased multinode 3D motion monitoring auxiliary algorithm is used, including the planning of wearable device motion trajectories. Throughout the positioning process, the workflow belongs to the public node and involves the format of the relevant information packet. The 3D wearable multinode positioning algorithm based on RSSI is a distancedependent distributed positioning algorithm. The public node uses the RSSI ranging method to obtain the distance during the twostage positioning process, and then uses the threedimensional multilateral positioning method to perform its own calculation.