To solve the problem that traditional Wireless Body Area Network (WBAN) prediction model has low prediction accuracy, large computational complexity and high energy consumption, an adaptive cubic exponential smoothing algorithm based on penalty error matrix was proposed. Firstly, a lightweight prediction model was established between the sensing node and the routing node. Secondly, blanket search was used to optimize the parameters of the prediction model. Finally, penalty error matrix was used to further refine the parameters of the prediction model. The experimental results showed that compared with the ZigBee protocol, the proposed method saved about 12% energy in 1000 time slot range; compared with blanket search method, the prediction accuracy was improved by 3.306% by using penalty error matrix. The proposed algorithm can effectively reduce the computational complexity and further reduce the energy consumption of WBAN.

Pedestrian crowd needs to be divided into groups by using clustering algorithms before using the Social Force Model (SFM) to simulate crowd evacuation. Nevertheless, k-medoids and STatistical INformation Grid (STING) are two traditional clustering algorithms, cannot meet the requirements in the aspect of efficiency and accuracy. To solve the above problem, a new method named Binary Clustering Algorithm (BCA) was proposed in this paper. BCA was composed of two kinds of algorithms:center point clustering and grid clustering. Moreover, the dichotomy was used to divide the grid without repeated clustering. First of all, the data was divided into grids, through the use of dichotomy. Next, the core grid would be selected, according to the data density in a grid. Then, the core grid was used as the center, and the neighbors were clustered. Finally, the residual grids were was merged according to the nearest principle. The experimental results show that, in the clustering time, BCA is only 48.3% of the STING algorithm, less than 14% of the k-medoids algorithm; and in the clustering accuracy, k-medoids is only 50% of BCA, STING doesn't reach to 90% of BCA. Therefore, BCA is better than k-medoids and STING algorithm in both efficiency and accuracy.

In virtual network mapping, most studies only consider one mapping object, which can not reflect the interests of many aspects. To solve this problem, a Virtual Network Embedding algorithm based on Multi-objective Particle Swarm Optimization (VNE-MOPSO) was proposed by combining multi-objective algorithm and Particle Swarm Optimization (PSO) algorithm. Firstly, the crossover operator was introduced into the basic PSO algorithm to expand the search space of population optimization. Secondly, the non-dominated sorting and crowding distance sorting were introduced into the multi-objective optimization algorithm, which can speed up the population convergence. Finally, by minimizing both the cost and the node load balance degree as the virtual network mapping objective function, a multi-objective PSO algorithm was proposed to solve the Virtual Network Mapping Problem (VNMP). The experimental results show that the proposed algorithm can solve the VNMP, which has advantages in network request acceptance rate, average cost, average node load balance degree, and infrastructure provider's profit.

Concerning the high energy consumption, low acceptance rate and unbalanced load in virtual network embedding, a comprehensive energy-aware virtual network reconfiguration algorithm based on resource consolidation, namely HEAR algorithm, was proposed, which consists of two stages including node reconfiguration and link reconfiguration. In node reconfiguration stage, the virtual nodes on the physical node with least mapping virtual nodes and their relevant virtual links were moved to other physical nodes except congested nodes to improve acceptance rate and load balance, as well as suspending or closing the physical nodes with empty load to save energy. In link reconfiguration stage, the energy-aware method was adopted to select substrate link candidate set for migration, and Dijkstra algorithm was used to select the shortest available physical path to redeploy the virtual links on it. The simulation results show that, compared with energy-aware relocation heuristic algorithm, HEAR algorithm can reduce energy consumption by about 20%, and increase acceptance rate by about 10%, which means it can save energy consumption, improve the acceptance rate.