Focused on the problem that when traditional Non-negative Matrix Factorization (NMF) is applied to clustering, robustness and sparsity are not considered at the same time, which leads to low clustering performance, a sparse Non-negative Matrix Factorization algorithm based on Kernel technique and HyperGraph regularization (KHGNMF) was proposed. Firstly, on the basis of inheriting good performance of kernel technique, L _2,1 norm was used to improve F-norm of standard NMF, and hyper-graph regularization terms were added to preserve inherent geometric structure information among the original data as much as possible. Secondly, L _2,1/2 pseudo norm and L _1/2 regularization terms were merged into NMF model as sparse constraints. Finally, a new algorithm was proposed and applied to image clustering. The experimental results on six standard datasets show that KHGNMF can improve clustering performance (accuracy and normalized mutual information) by 39% to 54% compared with nonlinear orthogonal graph regularized non-negative matrix factorization, and the sparsity and robustness of the proposed algorithm are increased and the clustering effect is improved.

In Mobile Ad Hoc Network (MANET), the movements of nodes are liable to cause link failures, while the local repair in the classic Ad Hoc On-demand Distance Vector (AODV) routing algorithm is performed only after the link breaks, which has some limitations and may result in the cached data packet loss when the repair process fails or goes on too slowly. In order to solve this problem, an optimized AODV routing algorithm named ARB-AODV was proposed, which can avoid route breaks. In ARB-AODV algorithm, the link which seemed to break was predicted and the stability degrees of the nodes' neighbors were calculated. Then the node with the highest stability was added to the weak link to eliminate the edge effect of nodes and avoid route breaks. Experiments were conducted on NS-2 platform using Random Waypoint Mobility Model (RWM) and Constant Bit Rate (CBR) data. When the nodes moved at a speed higher than 10m/s, the packet delivery ratio of ARB-AODV algorithm maintained at 80% or even higher, the average end-to-end delay declined up to 40% and the overhead of normalized routing declined up to 15% compared with AODV. The simulation results show that ARB-AODV outperforms AODV, and it can effectively improve network performance.

The macro handover has caused high packet loss and long handover latency in Hierarchical Mobile IPv6 (HMIPv6) protocol. To solve these problems, this paper proposed a protocol named Tunnel-based Fast Macro-Handover (TBFMH), which introduced the mechanism of tunnel, acquired care-of addresses on the grounds of handover information, conducted duplication address detection in advance and completed local binding update while building the tunnels. The simulation results show that TBFMH can decrease the handover latency by 50% at least and reduce the packet loss rate compared to HMIPv6, which effectively improves the performance in the macro handover.