Rain observations with fine spatio-temporal granularity are significant for professional researches, decision-making, and our daily lives. However, the existing rain gauges can only cover less than 1% of the earth surface, and its amount is still decreasing. Even with the help of several other limited and immature supplementary techniques, rain observations today are still not precise enough. In such context, crowdsourcing paves the avenues toward a fault-tolerant rain observation network with unprecedented resolution and coverage, based on an alternative, nowadays omnipresent source, smartphones, which are integrated with abundant advanced sensors and are becoming more and more ubiquitous around us. In this paper, we propose Chaac, a novel system that exploits opportunistically crowdsourced audio clips from smartphone users to achieve precise detection and intensity measurement of rain. The evaluation results of performing Chaac on 1-s long audio segments demonstrate that it can detect and measure rain with 92.0% and 93.9% true positive rates, respectively.

Hansong Guo;河 黄;Yu E. Sun;Youlin Zhang;式刚 陈;刘生 黄

IEEE Internet of Things Journal

2019-2

Traffic volume measurement is one of the most basic functions of road planning and management. In this paper, we investigate an important problem of privacy preserving 'point-to-point' traffic volume measurement. We formalize 'point-to-point' traffic as an origin-destination (O-D) flow, which represents the set of vehicles traveling from one geographical location (origin) to another location (destination). We take advantage of vehicular cyber-physical systems (VCPS) to exploit the potential for a fundamental shift in the way how O-D data are collected. The challenge is to allow the collection of statistical O-D flow information, yet protect identities of individual vehicles. To address that, we design two novel schemes which utilize both the latest technological advance in VCPS and the nice properties of a family of commutative one-way hash functions. Furthermore, we adopt statistical methodology and use sampling to achieve far better efficiency with graceful degradation in measurement accuracy. We perform simulations to demonstrate the feasibility and scalability of our schemes.

Yian Zhou;式刚 陈;Zhen Mo;Yafeng Yin

2013

Thanks to the Internet, voters can cast their ballots over the electronic voting (E-voting) systems conveniently and efficiently without going to the polling stations. However, existing E- voting protocols suffer from anonymity issues and/or high deployment overhead. In this paper, we design a practical anonymous E-voting protocol (referred to as MVP) based on a novel data collection technique called \emph{dual random matrix masking} (DRMM), which guarantees anonymity with low overhead of computation, and achieves the security goals of receipt-freeness, double voting detection, fairness, ballot secrecy, and integrity. Through extensive analyses on correctness, efficiency, and security properties, we demonstrate our proposed MVP protocol can be applied to E-voting in a variety of situations with accuracy and anonymity.

You Zhou;Yian Zhou;式刚 陈;Samuel S. Wu

2016

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer communications between nearby tags, the emerging networked tags represent a fundamental enhancement to today's RFID systems. They support applications in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper is the first study on identifying state-free networked tags, which is a basic, fundamental function for most tagged systems. To prolong the lifetime of networked tags and make identification protocols scalable to large systems, energy efficiency and time efficiency are most critical. Our investigation reveals that the traditional contention-based protocol design will incur too much energy overhead in multihop tag systems. Surprisingly, a reader-coordinated design that significantly serializes tag transmissions performs much better. In addition, we show that load balancing is important in reducing the worst-case energy cost to the tags, and we present a solution based on serial numbers.

Min Chen;式刚 陈

2016-3-18

This paper ¹ proposes a new DiffServ routing architecture (PAP) that integrates the admission control signaling and the QoS routing. It differs from traditional routing in its ability to route most Expedite Forwarding (EF) traffic along the shortest paths while making use of alternative paths to absorb transient overload. Once an EF data flow is admitted, its performance is assured. The overhead for storing alterative path information is minimal since only one routing entry at a branching point is needed for each alternative path. The route map of Cisco IOS provides a mechanism for implementing PAP.

Shiping Chen;Yibei Ling;式刚 陈;Yong Tang

2003

Measuring point traffic volume and point-to-point traffic volume in a road system has important applications in transportation engineering. The connected vehicle technologies integrate wireless communications and computers into transportation systems, allowing wireless data exchanges between vehicles and road-side equipment, and enabling large-scale, sophisticated traffic measurement. This paper investigates the problems of persistent point traffic measurement and persistent point-to-point traffic measurement, which were not adequately studied in the prior art, particularly in the context of intelligent vehicular networks. We propose two novel estimators for privacypreserving persistent traffic measurement: one for point traffic and the other for point-to-point traffic. The estimators are mathematically derived from the join result of traffic records, which are produced by the electronic roadside units with privacypreserving data structures. We evaluate our estimation methods using simulations based on both real transportation traffic data and synthetic data. The numerical results demonstrate the effectiveness of the proposed methods in producing high measurement accuracy and allowing accuracy-privacy tradeoff through parameter setting.

河 黄;Yu E. Sun;式刚 陈;Hongli Xu;Yian Zhou

2017-7-13

Deadlock detection scheduling is an important, yet often overlooked problem that can significantly affect the overall performance of deadlock handling. Excessive initiation of deadlock detection increases overall message usage, resulting in degraded system performance in the absence of deadlocks, while insufficient initiation of deadlock detection increases the deadlock persistence time, resulting in an increased deadlock resolution cost in the presence of deadlocks. The investigation of this performance trade-off, however, is missing in the literature. This paper studies the impact of deadlock detection scheduling on the overall performance of deadlock handling. In particular, we show that there exists an optimal deadlock detection frequency that yields the minimum long-run mean average cost, which is determined by the message complexities of the deadlock detection and resolution algorithms being used, as well as the rate of deadlock formation, denoted as λ. For the best known deadlock detection and resolution algorithms, we show that the asymptotically optimal frequency of deadlock detection scheduling that minimizes the overall message overhead is O((λn)_1/3) when the total number n of processes is sufficiently large. Furthermore, we show that, in general, fully distributed (uncoordinated) deadlock detection scheduling cannot be performed as efficiently as centralized (coordinated) deadlock detection scheduling.

Yibei Ling;式刚 陈;Cho Yu Jason Chiang

IEEE Transactions on Computers

2006-9

Many important network functions (e.g., QoS provision, admission control, traffic engineering, resource management) rely on the availability and the accuracy of the network state information. However, it is impractical to maintain the complete state information of a large internetwork at a single location. Instead, a large network is often hierarchically structured, with each domain advertising its aggregated state. To achieve scalability, the amount of information after aggregation should be minimized. To improve accuracy, the aggregation method must be carefully selected. This paper gives a unified account of state aggregation based on the concept of service curves. The aggregation of network state is modeled as a recursive process of service curve transformation. New approximation methods based on polynomial curves, cubic splines and polylines are proposed, and their scalability/accuracy tradeoffs are studied. Our simulations show that these new methods approximate the network state far more accurate than the existing methods. In particular, the polylines achieve the best scalability/accuracy tradeoff.

Yong Tang;式刚 陈

Conference Record - International Conference on Communications

2004

Distributed denial of service (DDoS) is a major threat to the availability of Internet services. The anonymity allowed by IP networking, together with the distributed, large scale nature of the Internet, makes DDoS attacks stealthy and difficult to counter. To make the problem worse, attack traffic is often indistinguishable from normal traffic. As various attack tools become widely available and require minimum knowledge to operate, automated anti-DDoS systems become increasingly important. Many current solutions are either excessively expensive or require universal deployment across many administrative domains. This paper proposes two perimeter-based defense mechanisms for Internet service providers (ISPs) to provide the anti-DDoS service to their customers. These mechanisms rely completely on the edge routers to cooperatively identify the flooding sources and establish rate-limit filters to block the attack traffic. The system does not require any support from routers outside or inside of the ISP, which not only makes it locally deployable, but also avoids the stress on the ISP core routers. We also study a new problem of perimeter-based IP traceback and provide three solutions. We demonstrate analytically and by simulations that the proposed defense mechanisms react quickly in blocking attack traffic while achieving high survival ratio for legitimate traffic. Even when 40 percent of all customer networks attack, the survival ratio for traffic from the other customer networks is still close to 100 percent.

式刚 陈;Qingguo Song

IEEE Transactions on Parallel and Distributed Systems

2005-6

Most existing research adopts a 'flat' view of radio frequency identification (RFID) systems to perform various functions of collecting tag IDs, estimating the number of tags, detecting the missing tags, etc. However, in practice, tags are often attached to objects of different groups, which may represent different product types in a warehouse, different book categories in a library, etc. As we move from a flat view to an organized group view, there arise many interesting problems. One of them, called multigroup threshold-based classification, is the focus of this paper. It is to determine whether the number of objects in each group is above or below a prescribed threshold value. Solving this problem is important for inventory tracking applications. If the number of groups is very large, it will be inefficient to measure the groups one at a time. The best existing solution for multigroup threshold-based classification is based on generic group testing, whose design is however geared toward detecting a small number of populous groups. Its performance degrades quickly when the number of groups above the threshold becomes large. In this paper, we propose a new classification protocol based on tag sampling and logical bitmaps. It achieves high efficiency by measuring all groups in a mixed fashion. In the meantime, we show that the new method is able to perform threshold-based classification with an accuracy that can be preset to any desirable level, allowing tradeoff between time efficiency and accuracy.

Wen Luo;Yan Qiao;式刚 陈;Min Chen

IEEE/ACM Transactions on Networking

2016-2