The local caching policy of nodes is always one of the hottest issues in caching networks. It can be further divided into two branches, the decision policy and the replacement policy. In this paper, a replacement policy named Least Popularly Used (LPU) is proposed for cache nodes in Information-Centric Networking (ICN) to improve the network performance. Both the request number and the arrival time of a content are taken into account in LPU for judging content popularity. Thus, recently popular contents are cached near consumers, reducing the consumers’ distance to access contents and the traffic load on the original sever. Based on the string, tree and ISPlike topology, extensive simulations are performed, and the results indicate that the performance of LPU is better than that of other replacement policies, including Most Recently Used (MRU), Least Recently Used (LRU), Random, Greedy Dual-Size (GDS), Most Frequently Used (MFU), Least Frequently Used (LFU) and LFU-Dynamic Aging (LFU-DA), in terms of the average hop counts, the server hit ratio and the cache eviction counts.

After using side-channel information leaked by the FPGA chip which is embedded with a block cipher algorithm, there is a scheme called improved combinational side-channel attack. This scheme mainly aimed at logical circuits implemented in S-box in background of power consumption attack and we make a level of models by power consumption in combinational logic circuits which is engendered by glitches different from traditional sequential logical circuits which is engendered by flip in the output of register with the Hamming model. In addition, we take advantage of match algorithm in a linear model and in multivariate Gaussian model from template attack to process an important data twice and the key would be recovered. At last, we make a comparison between Hamming model and our model. And prove that this paper improved the attacking efficiency and accuracy and also provided a clue to the security cipher chip from countermeasures.

With the quick development of body sensor networks (BSNs), BSNs data are increasing exponentially. At present, the data is stored in a disorder way, thereby leading to difficult to utilize. To make them be used expediently, this paper presents a body-oriented cloud storage model. This paper firstly designs some metadata which contain BSNs contexts, time, sensor position, sensor type and health state, to describe the physiological data in BSNs. Then, the physiological data are encapsulated with the metadata in a coordinator of BSNs. At last, all of them are stored in repositories based on the body-oriented cloud storage model. We experimentally compare the performance of the proposed model to that of traditional one. The results show it can reduce the storage space and make BSNs data utilized expediently.

Routing in ad hoc networks is a challenging task. During the route discovery phase of AODV-like routing schemes, flooding of route request (RREQ) packets creates broadcast storm, which increases the possibility of packet collision. On the other hand, location-aided routing schemes may mitigate the problem by limiting the broadcast region. However, such schemes might fail in many cases where conventional routing schemes, such as AODV, can easily find a route. Besides, it is costly for every node to maintain location information of all nodes in the network. To overcome the above mentioned problems, we propose a routing scheme named Farthest Node Only (FARNO), where only the farthest node in each quadrant is designated to forward an RREQ packet. Simulation results reveal that the proposed scheme can prevent the above mentioned problems and perform well in many aspects.

A collaborative wireless sensor network can be particularly vulnerable to cloud security attacks lacking physical boundaries to the environment and also has constrained energy reserves. In this paper, we look at linear network coding protocols and cloud computing optimizations which can have significant reduce the overall energy dissipation of collaborative wireless sensor networks and provide a paradigm to enable convenient and on-demand network access to a shared pool of configurable computing resources. Based on our findings that the conventional encryption protocols of public key algorithm and symmetric key algorithm may not be optimal for collaborative wireless sensor networks, we propose LWLNC (Light-Weight Linear Network Coding), a fundamental arithmetic instruction that utilizes randomized rotation of coefficients to achieve cloud computing optimization. LWLNC incorporates data fusion into the transmission which can reduce the amount of energy dissipation. Simulation results show that LWLNC can achieve as much as a factor of 8 reduction in energy consumption compared with conventional encryption protocols. In addition, LWLNC is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

In recent years, many energy efficient algorithms and routing protocols have been proposed for wireless sensor networks (WSNs) with the object to balance the energy consumption and maximize the network lifetime. It has been proved that utilizing clustering technique and adding sink mobility into sensor networks can bring in new opportunities to improve energy efficiency for WSNs. Glowworm Swarm Optimization (GSO), which belongs to the swarm intelligence field inspired by simulated experiments of the lighting worms' behavior, is also an effective method to improve network performance. In this paper, we proposed a clustering algorithm based on GSO with mobile sink (CAGM) for WSNs, which tries to combine GSO algorithm, clustering technique and sink mobility strategies together to extend network lifetime and improve network performance. We first divide the whole network into several clusters and select cluster heads of each cluster by utilizing GSO algorithm. After then, cluster heads collect data from their member nodes and do data fusion. In order to improve network performance, a mobile sink was used to roam about the network and collect data from cluster heads through really short communication range. Extensive simulation results show that our proposed algorithm can efficiently prolong the network lifetime of WSNs.

Personal Health Record (PHR) system is a patientcentered system that patients, and health care providers could securely access the patient's health information through internet. It should be noted that security is an important issue of PHR systems, especially if it is accessible through the open internet. In this paper, we propose an efficient mediated ciphertext-policy attributebased encryption (m-CP-ABE) which can realize attribute revocation instantaneously and support monotonic access structure by using Linear Secret-Sharing Schemes (LSSS). These security measures make our scheme greatly reduce the risk that an unauthorized person could access to a patient's health information contained within a PHR system. At the same time, most of the computational overhead in decryption is outsourced to the mediator side, and leave only a little computational workload to the user side. With development of mobile internet, many users many use the mobile devices to access the PHR systems. Thus, this measure make our scheme be very suitable for the application scenario of mobile internet.

Wearable sensing technology uses sensors to detect body information, which sends monitoring and control exercise information to a receiver through wireless transmission. This study proposes a low power consumption wearable shoe type sensing module for fall detection. An algorithm for directional identification of footsteps is designed to detect both the motion state and movement direction of footsteps. The information is transmitted to the healthy exercise service application of a mobile phone through wireless transmission, where consumed calories are calculated by the motion state, allowing the user to observe their health status at any time. The most significant cause of sensor energy consumption is massive data communication; therefore, this study effectively applies few parameters, and uses the action segment classified signal, as inspired by continuous speech recognition, to optimize the parameters, in order to improve energy loss by reducing data transfer.

Recently NVidia proposed their first cloud based GPU architecture called Kepler. One of the key features of Kepler GPU is that it allows multiple user processes to access the GPU concurrently. We use this feature to design a cloud computing system that allow multiple users to share the computing power of a Kepler board anytime, anywhere over the internet. Our system improves the utilization of the Kepler GPU and lowers the cost in providing GPU cloud services. We conduct experiments to evaluate the overhead of our system, and preliminary results indicates that our system provides convenient services with very little overhead.

Group-oriented service composition is still challenging for big group and personalized QoS requirements. Therefore, this paper presents a Hybrid QoS-aware Web Service Composition Strategies for Group Pareto Optimal Plan (HOWSCS_GGOP) based on multi-attribute group decision making (MAGDM) theory. It includes five main steps: calculating group preference, clustering weighted cluster weights, constructing cluster weighted normalized decision-making matrix, determining the ideal solution, and evaluating alternatives for group synthetically. In addition, a hybrid Web service QoS model is introduced to describe QoS values with real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers. Experimental results show that the proposed algorithm can well solve the problem of Web service composition for big group and personalized QoS requirements.