Secure localization has become very important in wireless sensor networks. However, the conventional secure localization algorithms used in wireless sensor networks cannot deal with internal attacks and cannot identify malicious nodes. In this paper, a localization based on trust valuation, which can overcome a various attack types, such as spoofing attacks and Sybil attacks, is presented. The trust valuation is obtained via selection of the property set, which includes estimated distance, localization performance, position information of beacon nodes, and transmission time, and discussion of the threshold in the property set. In addition, the robustness of the proposed model is verified by analysis of attack intensity, localization error, and trust relationship for three typical scenes. The experimental results have shown that the proposed model is superior to the traditional secure localization models in terms of malicious nodes identification and performance improvement.
An energy consumption optimization algorithm based on ant colony algorithm is proposed for wireless sensor network. The proposed algorithm allows each node in wireless sensor network to save the distance and residual energy of neighbor nodes. Furthermore, in terms of probability selection of the nodes and the pheromone update, this algorithm focuses on the next hop node through the comparison of distance between the nodes and the residual energy, which ensures less possibility of nodes with low energy selected as the next hop. Therefore, the proposed algorithm improves energy load balancing, stability of wireless sensor network and, eventually, extends the life span of the wireless sensor network. The simulation results show that the improved ant colony algorithm avoids too much energy consumption of a certain local node resulting in more uniform energy consumption for each node.
Load imbalance is a problem faced by the distributed cloud computing platform. It often requires the information collaboration by each server in the cluster to carry out the container migration. Most of the algorithms which aim to reduce the downtime do not consider migration cost of the containers and perform some unnecessary migration. In this paper, with the aim to reduce the unnecessary migration of containers, an optimal minimum migration algorithm (OMNM) is proposed. By fitting the growth rate of Docker containers in the source server, the model can estimate the growth trend of each Docker container and determine which container needs to be migrated. While ensuring the load balancing of the cluster, the number of the migration is reduced, and the utilization ratio of the resource is improved. Experimental results show that the algorithm is effective to reduce the total number of live migration of Docker containers and reduce the workload of migration. Finally, it achieves the load balancing of cloud resources.
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