To overcome the Internet ossification, network virtualization has been proposed as a promising method because of its advantages (e.g., on demand and efficient resource allocation). Virtual network embedding (VNE) is one of the main challenges for network virtualization. Energy costs of servers in data centers (DCs) are major contribution to power consumption in information and communication technology (ICT). Therefore, VNE should consider both acceptance ratio and power consumption. In this paper, a mixed integer linear programming (MILP) model is proposed with the objective of minimizing the total power consumption in software defined optical data center networks (SD-ODCNs) by reducing the active data centers and power-consuming network components. In addition, the coordinates of nodes and delay of links are considered for more realistic scenario. Comparing with existing node ranking method, proposed global topology resource (GTR) can effectively evaluate the possibility of each DC node to host virtual nodes. Based on GTR method, we propose location aware energy efficient VNE algorithm, namely GTR-VNE. Simulation results show that GTR-VNE can obtain up to 9.3% and 5% improvement of power consumption and acceptance ratio compared with benchmarks. Furthermore, based on GTR and artificial intelligence ant colony optimization (ACO), another energy efficient algorithm ACO-VNE is proposed. ACO-VNE can obtain up to 28.7% improvement on power consumption compared with GTR-VNE. In addition, ACO-VNE has better performance in terms of revenue cost ratio and acceptance ratio.
Secure outsourced aggregation in the Internet of Things (IoT) can solve the problem that sensing devices are limited in energy and bandwidth by outsourcing data aggregation task to a third-party service provider. Location-based secure outsourced aggregation (LBOA), aggregating data whose location satisfies user's location strategy, is very important in some location-critical scenarios (e.g., smart homes, intelligent transportation, and smart city). Recent work studied secure data aggregation to reduce transmission overhead and network bandwidth by optimizing topology of networks or adopting the cryptographic approach. However, as far as we know, scarcely any work considers the location information of the data source and the privacy protection of the data at the same time in the studies of secure outsourced aggregation. In this paper, we first propose an LBOA scheme LBOA Max , which can return the maximum value of sensory data whose location satisfies location strategy by applying one-way chain, order-preserving encryption, and some other cryptographic operation. Then, we proposed scheme LBOA Top−k and scheme LBOA Sum , which can return the largest k values of data and the summation value of data based on location, respectively. The security analysis results show that our schemes can satisfy the defined requirements and the experiment results show that our schemes are feasible and efficient for each entity in practice. INDEX TERMS Location-based, secure aggregation, cloud computation, privacy protection, one-way chain, order-preserving encryption.
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