In the cluster-based routing algorithm of Wireless Sensor Network (WSN), "energy hole" phenomenon was resulted from energy consumption imbalance between sensors. For this problem, a hybrid multi-hop routing algorithm of effective energy-hole avoidance was put forward on the basis of the research of the flat and hierarchical routing protocols. Firstly, the concept of hotspot area was introduced to divide the monitoring area, and then in clustering stage, the amount of data outside the hotspot area was reduced by using uneven clustering algorithm which could integrate data within the clusters. Secondly, energy consumption was cut down in the hotspot area during clustering stage by no clustering. Finally, in inter-cluster communication phase, the Particle Swarm Optimization (PSO) algorithm was addressed to seek optimal transmission path which could simultaneously meet the minimization of the maximum next hop distance between two nodes in the routing path and the minimization of the maximum hop count, so the minimization of whole network energy consumption was realized. Theoretical analysis and experimental results show that, compared with the Reinforcement-Learning-based Lifetime Optimal routing protocol (RLLO) and Multi-Layer routing protocol through Fuzzy logic based Clustering mechanism (MLFC) algorithm, the proposed algorithm shows better performance in energy efficiency and energy consumption uniformity, and the network lifetime is raised by 20.1% and 40.5%, which can avoid the "energy hole" effectively.

In order to lower the computational burden of wireless positioning algorithm with high accuracy, this paper proposed a new 2D beamspace matrix pencil algorithm to jointly estimate Time-Of-Arrival (TOA) and Direction-Of-Arrival (DOA), which can position target accurately with low complexity. This algorithm first transformed the complex data matrix into real and reduced dimensional matrix via Discrete Fourier Transform (DFT) matrix, which significantly reduced the computational burden; then estimated TOA and DOA of Line-of-Sight signal for positioning via singular value decomposition and solving generalized eigenvalues of matrix pencils. Matlab simulation results prove that this positioning method achieves Root Mean Square Error (RMSE) as small as 0.4m with computation cost no more than 1/4 of corresponding algorithm in element space, which makes it a promising positioning method for resource limited environments like battlefield, earthquake-stricken area and rural places.