Abstract:Internet of things (IoT), combined with various backbone transmission, is very suitable for remote real-time monitoring. However, along with the expansion of monitoring scope and popularization of the application, the cost and impact caused by system interruption is increasing rapidly. Reliability of system topology is very important in guaranteeing the reliability of remote monitoring system. Taking the actual demand of setting up monitoring region for the IoT-based Three-River Source region remote monitoring as background, this paper proposes a scalable uniform clustering based modular sensor deployment method, and gives calculation formula for the relationship between the layer number of topological architecture, the monitored area and the sensor number. To address the unstable information transmission environment, this paper presents an effective mechanism and quantitative analysis method to guarantee the reliability of monitored region and remote backbone topology. This method highlights a dynamic optimization strategy to the topological reliability of those key transmission nodes. Theoretical analysis and simulation results show that this method is placed on traits such as even data fusion, effective energy saving and extensibility and is remarkable in improving the reliability of IoT-based remote monitoring. At the same time, theories from this paper have important theoretical and actual application reference value in guaranteeing topological reliability of IoT-based remote real-time monitoring of large region, such as agriculture, animal husbandry and forestry related industries.

Abstract:Internet of things (IoT) contains not only large number of services with heterogeneous description but also mobile and highly resource-constrained devices. It is key issue for IoT to find suitable services efficiently and fast. This paper proposes a service discovery approach based on probabilistic topic model for IoT. The key features of this approach include: 1) using the English Wikipedia to train a topic model with high quality and semantically enrich service text description (a form of short text) to help the topic model to extract latent topics of service more effectively; 2) employing non-parametric topic model to infer latent topics of service, which reduces the training time of the topic model; 3) making full use of the latent topics of service to automatically classify service and calculate the text similarity between service request and service, which rapidly decreases the number of services for logic signature matchmaking and accelerates similarity calculation of service text description; 4) providing a logic signature matchmaking method which supports both WSDL-based and RESTful Web service. The experimental results show that the proposed method performs much better than existing solutions in terms of precision and normalized discounted cumulative gain (NDCG) measurement value.

Abstract:Internet of things (IoT) is developed to sense and control physical environment. Its control of physical environment is based on the sensed information and the users' requirements. Therefore, sense-execute model (SEM) is the core module in software architecture of IoT. In order to finally achieve the goal of developing IoT software guided by software architecture, this paper is dedicated to refining SEM in a physical-model driven software architecture (PMDA). The refined sense-execute model is called R-SEM. R-SEM divides the component of SEM into subcomponents according to the features of IoT and the procedures of physical application. Each subcomponent illustrates the functions of a port of a component of SEM, and is expressed by the communication sequential process (CSP). Synchronization between ports of component and subcomponent is illustrated by the pipeline operation of CSP. The interaction between subcomponents is illustrated by CSP as well. R-SEM is verified by the process analysis toolkit (PAT). The result of the verification validates that R-SEM keeps the properties of SEM, namely deadlock-free, nonterminating and divergence-free, which is necessary to guarantee valid interconnections among physical applications. Since R-SEM not only refines components in SEM but also keeps the valid properties of SEM, PMDA can be finally used for guiding software development in IoT.

Abstract:This paper proposes a secure and energy-efficient spatial data aggregation algorithm for sensor networks (SESDA for short). SESDA is an itinerary-based algorithm to achieve data aggregation. Owing to the well-designed itinerary for aggregate request propagation and data aggregation, SESDA is not susceptible to network topology and thus suitable for sensor networks with transient network topology, hence improves energy efficiency. In addition, to counter dramatic energy consumption caused by heavy encryption/ decryption operations, SESDA uses secure channel to obtain data privacy. SESDA needs no encryption/decryption operations during data aggregation, which significantly reduces the energy consumption, prolongs the lifetime of sensor networks, and achieves high accuracy of aggregation results due to small delivery delay. Theoretical analysis and experimental results show that SESDA has low traffic and energy consumption, high safety and accuracy.

Abstract:While the technical development and applications of the Internet of things (IoT) grow rapidly in recent years, the theoretical research of the IoT is still in the developing stage. The IoT is a typical information and communication system. It possesses not only the capabilities of storing and transferring information as with the existing Internet, but also the capabilities of automatic capturing and processing the information of things. The IoT information modeling is an effective method to analyze the characteristic capabilities of the IoT. In this paper, an information model of the IoT is described by unified modeling language according to the definition and characteristics of the IoT specified by International Telecommunication Union (ITU). The capabilities of things association, autonomic operation, and privacy protection are analyzed and validated based on the information model of the IoT. The conclusions of guiding the standardization of the IoT technology from the information model of the IoT are discussed, and the values of the IoT information model in resolving the issues of debated concepts or opinions of the IoT are analyzed.

Abstract:Wireless sensor network (WSN) plays an important role in the field of IOT (Internet of things), which performs the function of information perception. Thousands of devices as well as sensors are spread in specific areas to collect all kinds of physical information to pass onto the Internet. However, the data gethered from sensors' interfaces is real-time, extremely large and unstructured, hence requiring great effort in mapping to the conceptual application layer. To customize and develop IOT systems more efficiently, this paper proposes an approach based on runtime model to managing wireless sensor networks. First, manageability of wireless sensors is abstracted as runtime models which automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models. Second, a composite model of wireless sensors is constructed through merging their runtime models in order to manage different kinds of devices in a unified way. Third, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and the composite model is ensured through model transformation. Thus, all the management tasks can be carried through executing operating programs on the customized model. The feasibility and efficiency of the approach are validated through a real case study of smart community.

Abstract:In order to extract useful information from a large number of redundant and unreliable RFID (radio frequency identification) streaming data and improve the data quality of the RFID system, a filtering and cleaning method based on finite state machine for RFID streaming data is proposed. According to the experiment, suppressing interference tag data, filtering redundant tag data and extracting effective tag data can be realized in this method with reduced risk of system omission and false positives. Finally, using GIS technologies the monitoring and tracking results are displayed on the world map.

Abstract:RPL has received universal acceptance in IPv6 routing of the Internet of things (IoT). However, for large-scale multi-hops networks, the RPL routing model is faced with the problem that some IoT nodes heavily consume routing table storage. Besides, the flattened address architecture in IoT subnet makes this problem more prominent. In this paper, TFAD (tree forwarding model with address automatically distributed), a light-weight and tree-based forwarding model, is proposed to support automatic IPv6 address assignment. TFAD constructs a forwarding-level-tree for all the IoT nodes that make the IPv6 addresses of nodes aggregate highly in each sub-tree. In TFAD, each node only needs to maintain a few forwarding entries, the number of which is equivalent to the number of its direct son-nodes. Moreover, the backup mechanism of parent node in TFAD is designed. This mechanism supports the network topology reconstitution based on the whole sub-tree, achieving fast route-recovery from network failure. The experiments based on real sensor nodes prove that TFAD model possesses not only high performance on routing table storage but also rapidity on routing table learning and routing recovery from failure.

Abstract:The goal of reliably outperforming non-learning planners via learning is still to be achieved. A novel structure-oriented learning-based planning method (SOLP) is presented. SOLP anaylyses the structure knowledge, decomposes the planning problem into initial sub-state and goal sub-state, its solution into plan fragment, when planner finds out a solution successfully. The structure knowledge from previous experiment, or prior knowledge, will be saved in domain. When encountering new problem, SOLP firstly recalls the prior problem structure equivalent or similar to the current problem and the corresponding plan fragment from the domain file, then instantiates the learned prior knowledge as ground knowledge, and finally, encodes the ground knowledge as a satisfiability clause. These clauses, together with the set of clauses from the problem, form the input of the algorithm. SOLP calls the SAT Solver to determine the final solution. An experiment is conducted to test the algorithm in several different domains from IPC to demonstrate the efficiency and effectiveness of the new approach. The results show that, the speed of SOLP has obvious advantage than that of non-learning planner, with up to 80% improvement in extreme case.

Abstract:In decision-theoretic rough set models, since decision regions (positive region or non-negative region) are defined by allowing some extent of misclassification, the monotonicity of decision regions with respect to attribute sets does not hold. The definition of attribute reduction based on the whole decision regions may change decision regions. In order not to change decision regions, the positive region and non-negative distribution preservation reduction are introduced into decision-theoretic rough set models. Moreover, due to the non-monotonicity of decision regions, attribute reduction algorithms must search all possible subsets of an attribute set. The positive region and non-negative region distribution condition information contents are presented to facilitate the design of heuristic algorithms for decision region distribution preservation reduction. In a bid to then solve the minimum attribute reduction problem, heuristic genetic algorithm is applied to decision region distribution preservation reduction. A new modify operator is constructed by using two kinds of decision region distribution condition information contents so that genetic algorithm can find decision region distribution preservation reduction. Experimental results verify the effectiveness of decision region distribution preservation reduction and show the efficiency of the genetic algorithm to solve the minimum attribute reduction problem.

Abstract:With the increasing amount of data being collected, developing fast indexing methods with high accuracy becomes important for information retrieval tasks. To address this issue, this paper proposes an indexing method based on hashing mechanism with subspace learning. Firstly, the subspace is learned on a set of labeled data. To guarantee the locality preserving characteristics in the original space for the samples with similar semantic labels, the distances between the nearest neighbors are computed to measure the intra-class scatter. Besides, the distances between the centers of samples with dissimilar semantic labels are also computed to measure the inter-class scatter in order to enhance the discriminative power of the codes. The projections of the hash functions are then learned by relaxing the constraint of the formula. The biases are further learned based on the projections. Finally, the proposed method is evaluated on the datasets MNIST and CIFAR-10 to compare with the state-of-the-art methods. Experimental results show that the proposed method achieves significant performance and high effectiveness in searching semantically similar neighbors.

Abstract:The two most important properties of a logic are its expressive power and the complexity of reasoning, which are also an opposing relation in the logic. Bisimulations between interpretations are effective way to characterize the expressive power, and the van Benthem characterization theorem is a classical result which gives an exact condition for when a first-order formula with one free variable is equivalent to a modal logic formula. This paper provides a simulation for εLU (including atomic concept, top concept, conjunction concept, disjunction concept, and existential quantification). Based on the simulation, the characterization theorems of expressive power for concept descriptions and TBoxes are established to give the sufficient and necessary conditions for when a first-order formula is equivalent to a concept description or a TBox are set up. The above results provide effective supports for the tradeoff between the expressive power and the complexity of reasoning problems.

Abstract:With the rapid development of smart mobile devices and wireless location techniques, more and more users tend to attempt location-based service. Specifically, mobile users usually request continuous queries based on moving trajectories instead of traditional snap-shot queries for fixed locations. As obstacles can be found everywhere in the real-world or virtual space, more and more attentions has been paid on query processing techniques in the obstructed space. Notably, continuous reverse k-nearest neighbor queries in obstructed space are widely used. This paper presents an in-depth study on the problem of moving reverse k-nearest neighbor queries in obstructed spatial databases. By defining control points and split points, the processing framework for this problem is constructed. Furthermore, several pruning and verification algorithms, including data points reduction, obstacles retrieving, control points calculating and results set updating, are proposed to improve the query efficiency. Extensive experimental evaluation is conducted based on various datasets. Compared with the basic method which computes the k-nearest neighbors for each data point, the proposed methods can significantly improve CPU and I/O efficiency.

Abstract:The sparsity of user-item ratings is a common problem in collaborative filtering recommender systems. In traditional collaborative filtering recommender systems, similarity of users is often calculated with cosine and Pearson methods based on common ratings. When user-item ratings are sparse, the ratio of common rated items is less, and the accuracy of recommendations will be influenced because users with similar preferences can't be found accurately. To change calculation method of user similarity based on the same rated items, this paper applies EMD (earth mover's distance) to implement cross-item similarity calculation of mobile user and proposes a collaborative filtering recommendation method combining item features and trust relationship of mobile users. The experimental results show that, comparing with cosine and Pearson, user similarity calculation method combining item features can relieve influence of the sparsity of user-item ratings on collaborative filtering recommender systems. And the proposed recommender method can improve accuracy of mobile recommendations.

Abstract:With the rapid development of cloud storage, more and more people prefer to store their data in online storage systems. However, recent researches indicate that security problems still remain in current online storage systems, and some recent data leakage accidents of online storage systems also prove the existence of these vulnerabilities. Such security problems seriously hinder the development of online storage system. To address the issue, this study designs and implements a secure online storage system called CorsBox. CorsBox proposes a data synchronous protocol based-on directory trees for fast synchronization between data plaintext and ciphertext, designs a three-level key management scheme to enhance the security of user's data, and presents an effective method to maintain system eventual consistency. The paper finally conducts a set of intensive experiments on modern servers and the result shows that the security mechanisms only incur a little extra performance expenses, indicating that CorsBox can provide enhanced security for user's data while maintaining good performance.

Abstract:Distributed simulation is an effective method to improve simulation speed for computer architecture. In this paper, a general performance model for distributed simulation is established and then some typical distributed simulation systems are analyzed based on the model. The analysis results in some important conclusions about parallel speedup and parallel efficiency for distributed simulation. Next, a scalable and evenly distributed simulation (SEDSim) approach is presented. SEDSim adopts a cost model guided even partition and allocation (CoMEPA) policy for benchmark program instructions to enhance load-balance among parallel simulation nodes. An allocation policy based on minimum equivalent cost (MinEC) is also designed to efficiently integrate arbitrary number of discrete sampling intervals in SEDSim. The study implementes SEDSim based on sim-outorder and evaluates its speed and accuracy using Benchmark programs from SPEC CPU2000. Both theoretical analysis and testing results validate some advantages of SEDSim approach. Compared with existing methods, CoMEPA and MinEC can achieve a speedup of about 1.6 and 1.4 respectively.

Abstract:In cloud environment, all kinds of idle resources can be pooled to establish a resource pool, and different kinds of resources can be combined as a service to the users through virtualization. Therefore, an effective scheme is necessary for managing and allocating the resources. In this paper, economic and intelligent methods are employed to form an intelligent resource allocation scheme based on double combinatorial auction with respect to the characteristics of resources in cloud environment. In the proposed scheme, a reputation system on the basis of quality of experience (QoE) is devised, and the reputation attenuation coefficient and the user credit degree are introduced to decrease the negative effects of malicious behaviors on resource auctions, providing QoE support to resource dealing. In order to determine bidding price rationally, a bidding price decision mechanism based on back propagation (BP) neural network is presented to comprehensively consider various influence factors to make price adapt to the fluctuating market. Finally, due to the fact that the problem of winner determination in combinatorial auction is NP-complete, a group search optimization algorithm is adopted to find the specific resource allocation solution with market surplus and total reputation optimized. Simulation studies are conducted to demonstrate the feasibility and effectiveness of the proposed scheme.

Abstract:Data availability is a desirable feature for all cloud providers. However, adoption of disaster recovery will bring incremental cost due to the continued investment of hardware. This paper illustrates the existing assurance mechanisms for data reliability and points out that the data availability and disaster recovery cost are always two incompatible goals for cloud providers. A rich cloud based disaster recovery (DR) model is designed for cloud providers. In this model, multiple rich cloud proxies are employed in order to reduce the response time and improve the quality of service. With help of this system, a cloud provider is able to replicate data with lower cost by utilizing the virtual resource from other clouds. In order to minimize the DR cost while guaranteeing the data availability, a cost aware high data reliability provision algorithm (CAHRPA) is proposed to tackle such an optimization problem. In the end, the feasibility and efficiency of this CAHRPA is verified by the comparison with some other replication strategies.