For the problem of coverage in 3D Wireless Sensor Network (WSN), this paper introduced a Three-Dimensional Coverage Algorithm based on Adjustable Radius in wireless sensor network (3D-CAAR). Virtual force was used to achieve uniform distribution of nodes in WSN, at the same time, the distance between a sensor node and the target points in the covered area were determined by the radius adjustable coverage mechanism of sensor nodes. An energy consumption threshold was introduced to enable nodes to adjust their radii according to their own situations, thus reducing the overall network energy consumption and improving node utilization rate. Finally, compared with the traditional ECA3D (Exact Covering Algorithm in Three-Dimensional space) and APFA3D (Artificial Potential Field Algorithm in Three-Dimensional space) by experiments, 3D-CAAR can effectively solve the problem of target node coverage in sensor network.

In order to solve the problem that the accuracy of indoor localization algorithm based on Received Signal Strength Indicator (RSSI) for Wireless Sensor Network (WSN) is easy to be influenced by channel interference and environment, a new indoor localization algorithm, namely FC-DNN, was proposed based on Feedback Correction of Dynamic Nearest Neighbors. Firstly, the minimum localization region was determined by partitioning the whole environment based on Voronoi diagram before positioning. Then the parameters of the path loss model for each region were calculated to obtain the precise distance between nodes. Finally, the Spearman rank correlation coefficient was introduced to select neighbor anchor nodes dynamically, and the feedback of neighbor anchor nodes was used to further improve the localization accuracy. The simulation results confirm that the proposed FC-DNN algorithm has low time complexity, small computation and low energy consumption; furthermore, compared with conventional Distance Difference Localization Algorithm (DDLA) based on RSSI and sensor network localization in COnstrained 3-D spaces (CO-3D), the average positioning error is reduced by about 15 percentage points, which can well meet the requirements of indoor localization.

In order to solve the problem of redundancy and transmission energy consumption in the process of data acquisition in wireless sensor networks, a method for designing the measurement matrix of compressive sensing was proposed in this paper. The method is based on the linear representation theory of diagonal matrix orthogonal basis and the process of constructing the matrix is simple with short time, high sparsity and low redundancy, which is very suitable for the nodes with limited hardware resources. The simulation results show the measurement method based on the linear representation theory of diagonal matrix gains higher signal recovery rate compared with Gauss random matrix and part Hadamard matrix under the same signal reconstruction accuracy. This method in the paper greatly reduces the traffic of networks, saves the network energy consumption and prolongs the network life cycle.

To meet the requirement of non-uniform coverage of nodes, a Three-Dimensional Coverage Algorithm based on Virtual Force (3D-CAVF) in sensor network was introduced. In this algorithm the virtual force was applied in wireless sensor network to implement node arrangement. By the means of virtual force and the congestion degree control, the nodes could automatically cover the events, and then the nodes and density of events could present a balanced effect. According to the simulation experiment in Matlab, when the events are in T-shaped non-uniform arrangement and linear non-uniform arrangement, the efficiency of event set covering by the proposed algorithm is 3.6% and 3.1% higher than the APFA3D (Artificial Potential Field Algorithm in Three-Dimensional Space) and ECA3D (Exact Covering Algorithm in Three-Dimensional Space) respectively. The simulation results indicate that the proposed algorithm can arrange the nodes efficiently in three-dimensional wireless sensor networks.

In view of mobile Agent routing problem in Wireless Sensor Networks (WSN), a mobile Agent routing algorithm for WSN based on Q learning hybrid with ant colony optimization was proposed. A new path choosing probability model was introduced and the optimal path was efficiently maintained in the algorithm. The simulation results show that the mobile Agent routing efficiency is highly improved and delay requirements in multiple tasks are fulfilled, the reliability of the optimal path is enhanced, and network energy consumption is reduced.