It is our great pleasure to announce the publication of this special issue in JCST, ``Advances in Computer Science and Technology (Part 1) --- Current Advances in the Research of the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao''. ......

Wireless energy charging using mobile vehicles has been a viable research topic recently in the area of wireless networks and mobile computing. This paper gives a short survey of recent research conducted in our research group in the area of collaborative mobile charging. In collaborative mobile charging, multiple mobile chargers work together to accomplish a given set of objectives. These objectives include charging sensors at different frequencies with a minimum number of mobile chargers and reaching the farthest sensor for a given set of mobile chargers, subject to various constraints, including speed and energy limits of mobile chargers. Through the process of problem formulation, solution construction, and future work extension for problems related to collaborative mobile charging and coverage, we present three principles for good practice in conducting research. These principles can potentially be used for assisting graduate students in selecting a research problem for a term project, which can eventually be expanded to a thesis/dissertation topic.

Recent advances in connected vehicles and autonomous driving are going to change the face of ground transportation as we know it. This paper describes the design and evaluation of several emerging applications for such a Cyber Transportation System (CTS). These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.

Multiple description coding (MDC) generates multiple decodable bitstreams for a source to combat information loss. In this paper, multipath routing problem for two-description coded images is investigated for traditional and coded wireless networks without and with coding capability at intermediate nodes, respectively. Firstly, we formulate an interference-aware MDC multipath routing for traditional networks by employing a time-division link scheduling method to eliminate wireless interference, and ultimately obtain an optimal path selection corresponding to the minimum achievable distortion. Secondly, for coded networks, we evaluate practical wireless network coding (NC) in delivering descriptions of multiple unicast sessions. While NC increases maximum supporting flow rate of MDC descriptions in wireless networks, possible undecodability of NC mixed information is alleviated by MDC. To minimize achievable distortion, a proposed interference-and-coding-aware MDC multipath routing strikes a good balance between minimizing side effect of wireless interference avoidance and maximizing NC opportunity. Simulation results validate the effectiveness of the two proposed schemes.

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, Wi-Fi fingerprint-based approaches are the most popular solutions, and the core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner, however the low participation rate blocks the practical use.
In this work, we propose a passive crowdsourcing CSI-based Indoor Localization scheme, C²IL. Despite a crowdsourcing-based approach, our scheme is totally transparent to client except the only requirement is to connect to our 802.11n APs. C²IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n Channel State Information (CSI). Knowing the walking speed of a client and its surrounding APs, a graph-matching algorithm is employed to extract the RSS fingerprints and establish the fingerprint map. In localization phase, we design a trajectory clustering-based localization algorithm to provide precise realtime indoor localization and tracking. We developed and deployed a practical working system of C²IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2m at 80% time in very complex indoor environment.

A common method of prolonging the lifetime of wireless sensor networks is to use low power duty cycling protocol. Existing protocols consist of two categories: sender-initiated, and receiver-initiated. In this paper, we present SA-MAC, a Self-stabilizing Adaptive MAC Protocol for wireless sensor networks. SA-MAC dynamically adjusts the transmission time-slot, waking up time-slot, and packet detection pattern according to current network working condition, such as packet length and wake-up patterns of neighboring nodes. In the long run, every sensor node will find its own transmission phase so that the network will enter a stable stage when the network load and qualities are static. We conduct extensive experiments to evaluate the energy consumption, packet reception rate of SA-MAC in real sensor networking systems. Our results indicate that SA-MAC outperforms other existing protocols.

Designing eco-friendly system has been at the forefront of computing research. Faced with a growing concern about the server energy expenditure and the climate change, both industry and academia start to show high interests in computing systems powered by renewable energy sources. Existing proposals on this issue mainly focus on optimizing resource utilization or workload performance. The key supporting hardware structures for cross-layer power management and emergency handling mechanisms are often left unexplored. This paper presents GreenPod, a research framework for exploring scalable and dependable renewable power management in datacenters. An important feature of GreenPod is that it enables joint management of server power supplies and virtualized server workloads. Its interactive communication portal between servers and power supplies allows datacenter operators to perform real-time renewable energy driven load migration and power emergency handling. Based on our system prototype, we discuss an important topic: virtual machine (VM) workloads survival when facing extended utility outage and insufficient onsite renewable power budget. We show that whether a VM can survive depends on the operating frequencies and workload characteristics. The proposed framework can greatly encourage and facilitate innovative research in dependable green computing.

Modern datacenter servers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The user perceived performance is the result of a complex interaction of complex workloads in a very complex underlying system. Highly dynamic and bursty workloads of Internet services fluctuate over multiple time scales, which have a significant impact on processing and power demands of datacenter servers. High density servers apply virtualization technology for capacity planning and system manageability. Such virtualized computer systems are increasingly large and complex. This paper surveys representative approaches to autonomic performance and power control on virtualized servers, which control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators. It then presents three designed self-adaptive resource management techniques based on machine learning and control for percentile-based response time assurance, non-intrusive energy-efficient performance isolation, and joint performance and power guarantee on virtualized servers. The techniques were implemented and evaluated in a testbed of virtualized servers hosting benchmark applications. Finally, two research trends are identified and discussed for sustainable cloud computing in green datacenters.

While Scan-based DFT improves test efficiency, it also leaves a side channel to the privacy information stored in the chip. This paper investigates the side channel and proposes a simple yet powerful scan-based attack that can reveal the key and/or state stored in the chips that implement the state-of-the-art stream ciphers with less than 85 scan-out vectors.

Today, designers are forced to reduce performance and increase power requirements in order to reserve larger margins that are required due to the greater variability introduced by smaller feature sizes and manufacturing variations of modern IC designs. The better-than-worst-case design can both address the variability problem and achieve higher performance/energy efficiency than the worst-case design. This paper surveys the progress to date, provides a snapshot of the most representative methods in this field, and discusses the future research directions of the better-than-worst-case design.

Silicon Physical Unclonable Function (PUF) is a popular hardware security primitive that exploits the intrinsic variation of IC manufacturing process to generate chip-unique information for various security related applications. For example, the PUF information can be used as a chip identifier, a secret key, the seed for a random number generator, or the response to a given challenge. Due to the unpredictability and irreplicability of IC manufacturing variation, silicon PUF has emerged as a promising hardware security primitive and gained a lot of attention over the past few years. In this article, we first give a survey on the current state-of-the-art of silicon PUFs, then analyze known attacks to PUFs and the countermeasures, after that we discuss PUF-based applications, highlight some recent research advances in ring oscillator PUFs, and conclude with some challenges and opportunities in PUF research and applications.

Chip multiprocessor presents brand new opportunities for holistic on-chip data and coherence management solutions. An intelligent protocol should be adaptive to the fine-grain accessing behavior. And in terms of storage of metadata, the size of conventional directory grows as the square of the number of processors, making it very expensive in large-scale systems. In this paper, we propose a metadata cache framework to achieve three goals (1) reducing the latency of data access and coherence activities, (2) saving the storage of metadata, and (3) providing support for other optimization techniques. The metadata is implemented with compact structures and tracks the dynamically changing access pattern. The pattern information is used to guide the delegation and replication of decoupled data and metadata to allow fast access. We also use our metadata cache as a building block to enhance the stream prefetching. Using detailed execution-driven simulation, we demonstrate that our protocol achieves an average speedup of 1.12X compared to a shared cache protocol with 1/5 of the storage of metadata.

Performance metrics and models are prerequisites for scientific understanding and optimization. This paper introduces a new footprint-based theory and reviews the research in the past four decades leading to the new theory. The review groups the past work into metrics and their models in particular those of the reuse distance, metrics conversion, models of shared cache, performance and optimization, and other related techniques.

In recent years, to maximize the value of software testing and analysis, we have proposed the methodology of cooperative software testing and analysis (in short as cooperative testing and analysis) to enable testing and analysis tools to cooperate with their users (in the form of tool-human cooperation), and enable one tool to cooperate with another tool (in the form of tool-tool cooperation). Such cooperations are motivated by the observation that a tool is typically not powerful enough to address complications in testing or analysis of complex real-world software, and the tool user or another tool may be able to help out some problems faced by the tool. To enable tool-human or tool-tool cooperation, effective mechanisms need to be developed (1) for a tool to communicate problems faced by the tool to the tool user or another tool, (2) for the tool user or another tool to assist the tool to address the problems. Such methodology of cooperative testing and analysis forms a new research frontier on synergistic cooperations between humans and tools along with cooperations between tools and tools. This article presents recent example advances and challenges on cooperative testing and analysis.

Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. In feature models' construction, one basic task is to ensure the consistency of feature models, which often involves detecting and fixing of inconsistencies in feature models. While many approaches have been proposed, most of them focus on detecting inconsistencies rather than fixing inconsistencies. In this paper, we propose a novel dynamic-priority based approach to interactively fixing inconsistencies in feature models, and report an implementation of a system that not only automatically recommends a solution to fixing inconsistencies but also supports domain analysts to gradually reach the desirable solution by dynamically adjusting priorities of constraints. The key technical contribution is, as far as we are aware, the first application of the constraint hierarchy theory to feature modeling, where the degree of domain analysts' confidence on constraints is expressed by using priority and inconsistencies are resolved by deleting one or more lower-priority constraints. Two case studies demonstrate the usability and scalability (efficiency) of our new approach.