The Internet of Things (IoT) is aimed at enabling the interconnection and integration of the physical world and the cyber space. It represents the trend of future networking, and leads the third wave of the IT industry revolution. In this article, we first introduce some background and related technologies of IoT and discuss the concepts and objectives of IoT. Then, we present the challenges and key scientific problems involved in IoT development. Moreover, we introduce the current research project supported by the National Basic Research Program of China (973 Program). Finally, we outline future research directions.

Semantic-based searching in peer-to-peer (P2P) networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval (IR). All these IR-based schemes use one vector to summarize semantic contents of all documents on a single node. For example, GES derives a node vector based on the IR model: VSM (Vector Space Model). A topology adaptation algorithm and a search protocol are then designed according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a new class-based semantic searching scheme (CSS) specifically designed for unstructured P2P networks with heterogeneous single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering (OSKM), to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search cost.

Event detection in wireless sensor networks (WSNs) has attracted much attention due to its importance in many applications. The erroneous abnormal data generated during event detection are prone to lead to false detection results. Therefore, in order to improve the reliability of event detection, we propose a dirty-event cleaning method based on spatio-temporal correlations among sensor data. Unlike traditional fault-tolerant approaches, our method takes into account the inherent uncertainty of sensor measurements and focuses on the type of directional events. A probability- based mapping scheme is introduced, which maps uncertain sensor data into binary data. Moreover, we give formulated definitions of transient dirty-event (TDE) and permanent dirty-event (PDE), which are cleaned by a novel fuzzy method and a collaborative cleaning scheme, respectively. Extensive experimental results show the effectiveness of our dirty-event cleaning method.

With the development of network multimedia technology, more and more real-time multimedia applications need to transmit information using multicast. The basis of multicast data transmission is to construct a multicast tree. The main problem concerning the construction of a shared multicast tree is selection of a root of the shared tree or the core point. In this paper, we propose a heuristic algorithm for core selection in multicast routing. The proposed algorithm selects core point by considering both delay and inter-destination delay variation. The simulation results show that the proposed algorithm performs better than the existing algorithms in terms of delay variation subject to the end-to-end delay bound. The mathematical time complexity and the execution time of the proposed algorithm are comparable to those of the existing algorithms.

Aggregation is an important and commonplace operation in wireless sensor networks. Due to wireless interfe- rences, aggregation in wireless sensor networks often suffers from packet collisions. In order to solve the collision problem, aggregation scheduling is extensively researched in recent years. In many sensor network applications such as real-time monitoring, aggregation time is the most concerned performance. This paper considers the minimum-time aggregation scheduling problem in duty-cycled wireless sensor networks for the first time. We show that this problem is NP-hard and present an approximation algorithm based on connected dominating set. The theoretical analysis shows that the proposed algorithm is a nearly-constant approximation. Simulation shows that the scheduling algorithm has a good performance.

In this paper we present a novel GPU-oriented method of creating an inherently continuous triangular mesh for tile-based rendering of regular height fields. The method is based on tiling data-independent semi-regular meshes of non-uniform structure, a technique that is quite different from other mesh tiling approaches. A complete, memory efficient set of mesh patterns is created by an off-line procedure and stored into the graphics adapter's memory at runtime. At rendering time, for each tile, one of the precomputed mesh patterns is selected for rendering. The selected mesh pattern fits the required level of details of the tile and ensures seamless connection with other adjacent mesh patterns, like in a game of dominoes. The scalability potential of the proposed method is demonstrated through quadtree hierarchical grouping of tiles. The efficiency is verified by experimental results on height fields for terrain representation, where the method achieves high frame rates and sustained triangle throughput on high resolution viewports with sub-pixel error tolerance. Frame rate sensitivity to real-time modifications of the height field is measured, and it is shown that the method is very tolerant and consequently well tailored for applications dealing with rapidly changeable phenomena represented by height fields.

Passwords are used in the vast majority of computer and communication systems for authentication. The greater security and memorability of graphical passwords make them a possible alternative to traditional textual passwords. In this paper we propose a new graphical password scheme called YAGP, which is an extension of the Draw-A-Secret (DAS) scheme. The main difference between YAGP and DAS is soft matching. The concepts of the stroke-box, image-box, trend quadrant, and similarity are used to describe the images characteristics for soft matching. The reduction in strict user input rules in soft matching improves the usability and therefore creates a great advantage. The denser grid granularity enables users to design a longer password, enlarging the practical password space and enhancing security. Meanwhile, YAGP adopts a triple-register process to create multi-templates, increasing the accuracy and memorability of characteristics extraction. Experiments illustrate the effectiveness of YAGP.

In this paper, we investigate the problem of determining regions in 3D scene visible to some given viewpoints when obstacles are present in the scene. We assume that the obstacles are composed of some opaque objects with closed surfaces. The problem is formulated in an implicit framework where the obstacles are represented by a level set function. The visible and invisible regions of the given viewpoints are determined through an efficient implicit ray tracing technique. As an extension of our approach, we apply the multiview visibility estimation to an image-based modeling technique. The unknown scene geometry and multiview visibility information are incorporated into a variational energy functional. By minimizing the energy functional, the true scene geometry as well as the accurate visibility information of the multiple views can be recovered from a number of scene images. This makes it feasible to handle the visibility problem of multiple views by our approach when the true scene geometry is unknown.

In this paper, we present a new edit tool for the user to conveniently preserve or freely edit the object appearance during seamless image composition. We observe that though Poisson image editing is effective for seamless image composition. Its color bleeding (the color of the target image is propagated into the source image) is not always desired in applications, and it provides no way to allow the user to edit the appearance of the source image. To make it more flexible and practical, we introduce new energy terms to control the appearance change, and integrate them into the Poisson image editing framework. The new energy function could still be realized using efficient sparse linear solvers, and the user can interactively refine the constraints. With the new tool, the user can enjoy not only seamless image composition, but also the flexibility to preserve or manipulate the appearance of the source image at the same time. This provides more potential for creating new images. Experimental results demonstrate the effectiveness of our new edit tool, with similar time cost to the original Poisson image editing.

With quick development of grid techniques and growing complexity of grid applications, it is becoming critical for reasoning temporal properties of grid workflows to probe potential pitfalls and errors, in order to ensure reliability and trustworthiness at the initial design phase. A state Pi calculus is proposed and implemented in this work, which not only enables flexible abstraction and management of historical grid system events, but also facilitates modeling and temporal verification of grid workflows. Furthermore, a relaxed region analysis (RRA) approach is proposed to decompose large scale grid workflows into sequentially composed regions with relaxation of parallel workflow branches, and corresponding verification strategies are also decomposed following modular verification principles. Performance evaluation results show that the RRA approach can dramatically reduce CPU time and memory usage of formal verification

It is important to verify the absence of deadlocks in asynchronous circuits. Much previous work relies on a reachability analysis of the circuits' states, with the use of binary decision diagrams (BDDs) or Petri nets to model the behaviors of circuits. This paper presents an alternative approach focusing on the structural properties of well-formed asynchronous circuits that will never suffer deadlocks. A class of data-driven asynchronous pipelines is targeted in this paper, which can be viewed as a network of basic components connected by handshake channels. The sufficient and necessary conditions for a component network consisting of Steer, Merge, Fork and Join are given. The slack elasticity of the channels is analyzed in order to introduce pipelining. As an application, a deadlock checking method is implemented in a syntax-directed asynchronous design tool —— Teak. The proposed method shows a great runtime advantage when compared against previous Petri net based verification tools.

In this paper, a meta-structure of piano accompaniment figure (meta-structure for short) is proposed to harmonize a melodic piece of music so as to construct a multi-voice music. Here we approach melody harmonization with piano accompaniment as a machine learning task in a probabilistic framework. A series of piano accompaniment figures are collected from the massive existing sample scores and converted into a set of meta-structure. After the procedure of samples training, a model is formulated to generate a proper piano accompaniment figure for a harmonizing unit in the context. This model is flexible in harmonizing a melody with piano accompaniment. The experimental results are evaluated and discussed.

Temporal specifications for Application Programming Interfaces (APIs) serve as an important basis for many defect detection tools. As these specifications are often not well documented, various approaches have been proposed to automatically mine specifications typically from API library source code or from API client programs. However, the library-based approaches take substantial computational resources and produce rather limited useful specifications, while the client-based approaches suffer from high false positive rates. To address the issues of existing approaches, we propose a novel specification mining approach, called MineHEAD, which exploits heterogeneous API data, including information from API client programs as well as API library source code and comments, to produce effective specifications for defect detection with low cost. In particular, MineHEAD first applies client-based specification mining to produce a collection of candidate specifications, and then exploits the related library source code and comments to identify and refine the real specifications from the candidates. Our evaluation results on nine open source projects show that MineHEAD produces effective specifications with average precision of 97.2%.