Search

Abstract: In a hybrid peer-to-peer (P2P) system, flooding and DHT are both employed for content locating. The decision to use flooding or DHT largely depends on the population of desired data. Previous works either use local information only, or do not consider dynamic factors of P2P systems. In this paper, we propose a Popularity Adaptive Search method for Hybrid (PASH) protocol. By dynamically estimating the content popularity, PASH properly selects search methods so as to efficiently saves query traffic cost and response time. We evaluate PASH through synthetic and trace-driven simulations. The results show that PASH outperforms existing approaches and it also scales well. [Copyright &y& Elsevier]

By combining an unstructured protocol with a DHT-based global index, hybrid peer-to-peer (P2P) improves search efficiency in terms of query recall and response time. The major challenge in hybrid search is how to estimate the number of peers that can answer a given query. Existing approaches assume that such a number can be directly obtained by computing item popularity. In this work, we show that such an assumption is not always valid, and previous designs cannot distinguish whether items related to a query are distributed in many peers or are in a few peers. To address this issue, we propose QRank, a difficulty-aware hybrid search, which ranks queries by weighting keywords based on term frequency. Using rank values, QRank selects proper search strategies for queries. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that QRank significantly improves the search quality as well as reducing system traffic cost compared with existing approaches. [ABSTRACT FROM AUTHOR]

Most trust models in Peer-to-Peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent trade-off between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called Pseudo Trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on Zero-Knowledge Proof is designed so that peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the security and the anonymity in PT and evaluate its performance using trace-driven simulations and a prototype PT-enabled P2P network. The strengths of our design include the following: 1) no need for a centralized trusted party or Certificate Authority (CA); 2) high scalability and security; 3)low traffic and cryptography processing overheads; and 4) man-in-the-middle-attacks resistance. [ABSTRACT FROM AUTHOR]

In order to provide scalable live streaming services, we propose an Interoverlay Optimization scheme, IOO. Instead of selecting better paths in the same overlay, IOO constructs efficient paths using peers in different overlays so as to 1) improve global resource utilization of peer-to-peer (P2P) streaming networks, 2) assign resources based on their locality and delay, 3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays, and 4) balance the load among the group (streaming overlay) members. We compare the performance of IOO with existing approaches through trace-driven simulations. Results show that IOO outperforms previous schemes in terms of resource utilization and the quality of service (QoS) of streaming services. The IOO scheme has been implemented in an Internet-based live streaming system, called AnySee. AnySee was successfully released in the summer of 2004 in the China Educational Research Network (CERNET). More than 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences videos. [ABSTRACT FROM AUTHOR]

Abstract-The involvement of only the necessary users and service providers for service discovery in pervasive computing environments is challenging. Without prudence, users' and service providers' requests or service information, their identities, and their presence information may be sacrificed. We identify that the problem may be as difficult as a chicken-and-egg problem, in which both users and service providers want the other parties to expose sensitive information first. In this paper, we propose a progressive and probabilistic approach to solve the problem. Users and service providers expose partial information in turn and avoid unnecessary exposure if there is any mismatch. Although 1 or 2 bits of information are exchanged in each message, we prove that the process converges and that the false-positive overhead decreases quickly. Experiments and hypothesis tests show that security properties hold. We implemented the approach and the performance measurements show that the approach runs efficiently on PDAs. [ABSTRACT FROM AUTHOR]

Sensor networks have emerged as a promising technology with various applications, where power efficiency is one of the critical requirements. The recent IEEE 802.15.4 standard offers a promising platform for wireless sensor networks. Since each node can act as a coordinator or a device in the IEEE 802.15.4 standard, 802.15.4-based sensor networks have various possible network topologies. To reduce power consumption, in this paper, we try to construct network topologies with a small number of coordinators while still maintaining network connectivity. By reducing the number of coordinators, the average duty cycle is reduced and the battery life is prolonged. Three topology control algorithms are proposed in this paper. Self-pruning (SP) is the simplest one with O(1) running time and provides the shortest path to the sink node. Ordinal pruning (OP) can significantly improve SP in terms of power saving with O(n) running time. Layered pruning (LP) is a trade off between the first two pruning algorithms with O(√n) running time and has a slightly higher power consumption than OP. Furthermore, all three algorithms are independent of the physical radio propagation characteristics. Extensive simulations have been performed to verify the effectiveness of the proposed topology control schemes. [ABSTRACT FROM AUTHOR]

Many research activities on Wireless Sensor Networks (WSNs) need detailed performance statistics about protocols, systems, and applications; however, current simulation tools and testbeds lack mechanisms to report these statistics realistically and conveniently. To address this need, we have developed a WSN emulator, VMNet. VMNet emulates networked sensor nodes at the level of CPU clock cycles and executes the binary code of real applications directly. It emulates the radio channel with loss and noise as well as emulates the peripherals in sufficient detail. Moreover, VMNet takes parameter values from the real world and logs detailed runtime information of emulated nodes. Consequently, the application performance, both in response time and in power consumption, is reported realistically in VMNet, as demonstrated by our comparison studies with real sensor networks. [ABSTRACT FROM AUTHOR]

Proposes a prototype of prioritized overlay multicast for manets in which participating nodes can carry out several different functions and thus be associated with more than one overlay tree. Description of the ad hoc multicast routing protocol; Outline of the proposed technique to improve the location-guided tree scheme; Improvements in location sensing techniques.

Presents a practical approach for providing quality of service in the Internet industry. Technical and economic aspects of the approach; Use of an efficient computer network design; Issues within a single network service provider.

The article discusses various reports published within the issue, including one by K. Oikonomou and I. Stavrakakis on the problem with medium access control (MAC) and another by O. Simeone and U. Spagnolini on an entirely novel time synchronization approach based on pulse-coupled oscillators.

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity. [ABSTRACT FROM AUTHOR]

Peer-to-Peer multikeyword searching requires distributed intersection/union operations across wide area networks, raising a large amount of traffic cost. Existing schemes commonly utilize Bloom Filters (BFs) encoding to effectively reduce the traffic cost during the intersection/union operations. In this paper, we address the problem of optimizing the settings of a BF. We show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the statistical information of the involved inverted lists, not the minimized false positive rate as claimed by previous studies. Through numerical analysis, we demonstrate how to obtain optimal settings. To better evaluate the performance of this design, we conduct comprehensive simulations on TREC WT10G test collection and query logs of a major commercial web search engine. Results show that our design significantly reduces the search traffic and latency of the existing approaches. [ABSTRACT FROM AUTHOR]

RFID tag identification is a crucial problem in UHF RFID systems. Traditional tag identification algorithms can be classified into two categories, ALOHA-based and tree-based. Both of them are inefficient due to the incidental high coordination cost. In this paper, we bring CSMA into UHF RFID systems to enhance tag read rate by reducing coordination cost. However, it is not straightforward due to the simple hardware design of passive RFID tags, which is unable to sense the transmissions or collisions of other tags. To tackle this challenge, we propose receiver-based CSMA (RCSMA) in this paper. In RCSMA, the reader notifies the tags channel condition. According to different sensing results of reader's notifications, the tags take corresponding actions, e.g., random back off. RCSMA does not require special RFID tag hardware design. An absorbing Markov chain model is presented to analyze the performance of RCSMA and shown to be consistent with the simulation results. Compared with optimized ALOHA-based algorithms and optimized tree-based algorithms, RCSMA can enhance the tag read rate by 30-70 percent under different reader and tag data rates. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two--phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds. [ABSTRACT FROM AUTHOR]

Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(\sqrtN), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent. [ABSTRACT FROM PUBLISHER]

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40 percent tag read rate gain compared with traditional RFID protocol. [ABSTRACT FROM PUBLISHER]

Counting the number of RFID tags (cardinality) is a fundamental problem for large-scale RFID systems. Not only does it satisfy some real application requirements, it also acts as an important aid for RFID identification. Due to the extremely long processing time, slotted ALOHA-based or tree-based arbitration protocols are often impractical for many applications, because tags are usually attached to moving objects and they may have left the readers interrogation region before being counted. Recently, estimation schemes have been proposed to count the approximate number of tags. Most of them, however, suffer from two scalability problems: time inefficiency and multiple-reading. Without resolving these problems, large-scale RFID systems cannot easily apply the estimation scheme as well as the corresponding identification. In this paper, we present the Lottery Frame (LoF) estimation scheme, which can achieve high accuracy, low latency, and scalability. LoF estimates the tag numbers by utilizing the collision information. We show the significant advantages, e.g., high accuracy, short processing time, and low overhead, of the proposed LoF scheme through analysis and simulations. [ABSTRACT FROM AUTHOR]

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis. [ABSTRACT FROM AUTHOR]