The Recursively Decomposable Interconnection Network (RDIN) is a set of interconnection networks that can be recursively decomposed into smaller substructures whose topologies and properties are similar to the original one. The examples of the RDIN are hypercubes, star graph, mesh, tree, pyramid, pancake, and WK-recursive network. This paper proposed a uniform and simple model to represent the RDIN inside computers at first. Based on the model, a generalized and efficient allocation scheme capable of being applied to all the members of the RDIN is developed. The proposed scheme can fully recognize the substructures (such as subcube, substar, subtree,. . . ) more easily than ever, and it is the first one that can fully recognize all the incomplete substructures. The best-fit allocation is also proposed. The criterion aims at keeping the largest free parts from being destroyed, as is the philosophy of the best-fit allocation. Moreover, the proposed scheme can be performed in an injured RDIN with its processors and/or links faulty. Finally, the mathematical analysis and simulations for two instances, hypercubes and star graphs, of the RDIN are presented. The results show that the generalized scheme outperforms or is comparable to the other proprietary allocation schemes designed for the specific structure.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

In this paper, a novel design of planar dual-band multi-way lossless power dividers (PDs), namely Bagley Polygon PDs, is presented. The proposed PDs use Π-type dual-band transformers as basic elements, whose design formulas are analyzed and simplified to a concise form. The equivalent circuit of the dual-band Bagley Polygon PD is established, based on which design equations are derived mathematically. After that, the design procedure is demonstrated, and special cases are discussed. To verify the validity of the proposed design, 3-way and 5-way examples are simulated and fabricated at two IMT-Advanced bands of 1.8 GHz and 3.5 GHz, then simulation and measurement results are provided. The presented PDs have good performances on the bandwidths and phase shifts. Furthermore, the planar configuration leads to convenient design procedure and easy fabrication.

Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

The star graph has been known as an attractive alternative to the hypercube multiprocessor. Like the hypercube, the star graph possesses the properties of symmetry, partionability and fault tolerance, but with a smaller diameter and degree than those of the hypercube. When tasks arrive at the star graph, the tasks should be assigned appropriate free processors before execution. A new model, called Star graph (Sg)-lattice, is proposed to model the construction and free configuration of the star graph. Based on this model, the Sg-lattice scheme can fully recognize the substars. Finally, mathematical analyses and simulation results show that the Sg-lattice scheme outperforms the previous work in the storage and time complexities and the average allocation time.

Android occupies a very large market share in the field of mobile devices, and quantities of applications are created everyday allowing users to easily use them. However, privacy leaks on Android terminals may result in serious losses to businesses and individuals. Current permission model cannot effectively prevent privacy data leakage. In this paper, we find a way to protect privacy data on Android terminals from the perspective of privacy information propagation by porting the concept of contextual integrity to the realm of privacy protection. We propose a computational model of contextual integrity suiting for Android platform and design a privacy protection system based on the model. The system consists of an online phase and offline phase; the main function of online phase is to computing the value of distribution norm and making privacy decisions, while the main function of offline phase is to create a classification model that can calculate the value of the appropriateness norm. Based on the 6 million permission requests records along with 2.3 million runtime contextual records collected by dynamic analysis, we build the system and verify its feasibility. Experiment shows that the accuracy of offline classifier reaches up to 0.94. The experiment of the overall system feasibility illustrates that 70% location data requests, 84% phone data requests and 46% storage requests etc., violate the contextual integrity.

Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.

The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.

The booming developments in embedded sensor technique, wireless communication technology, and information processing theory contribute to the emergence of Internet of Things (IoT), which aims at perceiving and connecting the physical world. In recent years, a growing number of Internet-connected sensors have published their real-time state about the real-world objects on the Internet, which makes the content-based sensor search a promising service in the Internet of Things (IoT). However, classical search engines focus on searching for static or slowly varying data, rather than object-attached sensors. Besides, the existing sensor search systems fail to support the search mode based on a given measurement range. Furthermore, accessing all available sensors to find sought targets would result in tremendous communication overhead. Thus an accurate matching estimation mechanism is proposed to support the search mode based on a given search range and improve the efficiency and applicability of existing sensor search systems. A time-dependent periodical prediction method is presented to periodically estimate the sensor output, which combines with the during the period feedback prediction method that can fully exploit the verification information for enhancing the prediction precision of sensor reading to efficiently serve the needs of sensor search service. Simulation results demonstrate that our prediction methods can achieve high accuracy and our matching estimation mechanism can dramatically reduce the communication overhead of sensor search system.