8882975
Locating Sensors in Concave Areas
In sensor network localization, multihop based approaches were proposed to approximate the shortest paths to Euclidean distances between pairwise sensors. A good approximation can be achieved when sensors are densely deployed in a convex area, where the shortest paths are close to straight lines connecting pairwise sensors. However, in a concave network, the shortest paths may deviate far away from straight lines, which leads to erroneous distance estimation and inaccurate localization results. In this paper, we propose an improved multihop algorithm which can recognize and filter out the erroneous distance estimation, and therefore achieve accurate localization results even in a concave network.
1003958
An Effective P2P Search Scheme to Exploit File Sharing Heterogeneity
Although the original intent of the peer-to-peer (P2P) concept is to treat each participant equally, heterogeneity widely exists in deployed P2P networks. Peers are different from each other in many aspects, such as bandwidth, CPU power, and storage capacity. Some approaches have been proposed to take advantage of the query forwarding heterogeneity such that the high bandwidth of powerful nodes can be fully utilized to maximize the system capacity. In this paper, we suggest using the query answering heterogeneity to directly improve the search efficiency of P2P networks. In our proposed differentiated search (DiffSearch) algorithm, the peers with high query answering capabilities will have higher priority to be queried. Because the query answering capabilities are extremely unbalanced among peers, a high query success rate can be achieved by querying only a small portion of a network. The search traffic is significantly reduced due to the shrunken search space. Our trace analysis and simulation show that the DiffSearch algorithm can save up to 60 percent of search traffic
2951727
Adaptively Routing P2P Queries Using Association Analysis
Unstructured peer-to-peer networks have become a very popular method for content distribution in the past few years. By not enforcing strict rules on the network's topology or content location, such networks can be created quickly and easily. Unfortunately, because of the unstructured nature of these networks, in order to find content, query messages are flooded to nodes in the network, which results in a large amount of traffic. This work borrows the technique of association analysis from the data mining community and extends it to intelligently forward queries through the network. Because only a small subset of a node's neighbors are forwarded queries, the number of times those queries are propagated is also reduced, which results in considerably less network traffic. These savings enable the networks to scale to much larger sizes, which allows for more content to be shared and more redundancy to be added to the system, as well as allowing more users to take advantage of such networks
13965365
Distributed caching and adaptive search in multilayer P2P networks
To improve the scalability of Gnutella-like unstructured peer-to-peer (P2P) networks, a uniform index caching (UIC) mechanism was suggested in some earlier work. In UIC, query results are cached in all peers along the inverse query path such that the same query of other peers can be replied from their nearby-cached results. However, our experiments show that the UIC method causes a large amount of duplicated and unnecessary caching of items among neighboring peers. Aiming at improving the search efficiency, we propose a distributed caching mechanism, which distributes the cache results among neighboring peers. Furthermore, based on the distributed caching mechanism, an adaptive search approach is built which selectively forwards the query to the peers with a high probability of providing the desired cache results. All the enhancements above are defined in a protocol called distributed caching and adaptive search (DiCAS). In the DiCAS enhanced Gnutella network, all the peers are logically divided into multiple layers, with the character that all the peers in the same layer have the same group ID. The query flooding is restricted in one layer with the matched group ID. Our simulation study shows that, with the help of the index caching and search space division, the DiCAS protocol can significantly reduce the network search traffic in unstructured P2P systems without degrading query success rate.
16375842
Differentiated search in hierarchical peer-to-peer networks
Although the original intent of the peer-to-peer (P2P) concept is to treat each participant equally, heterogeneity widely exists in deployed P2P networks. In this paper, we suggest to improve the search efficiency of P2P network by utilizing the query answering heterogeneity. Our proposed differentiated search (DiffSearch) algorithm can evolve an unstructured P2P network to a two-tier hierarchical structure, where peers with high query answering capabilities are grouped in the first tier, which has higher priority to be queried than the second tier. Because the query answering capability is extremely unbalanced among peers, a high query success ratio can be achieved by querying only a small portion of the network. The search traffic is dramatically reduced due to the shrunken search space. Our trace analysis and simulation show that the DiffSearch algorithm can save up to 60% of search traffic.
28624321
Improving Event-to-Sink Throughput in Wireless Sensor Networks
Disaster relief is an important application of sensor networks, in which bursting data needs to be collected in a short period to the sink through a multi-hop wireless network. In general, packets containing the reported data have few correlations among each other, such that meaningful information can be inferred from partially received packets. For better understanding of monitored events, it is more important to capture the total number of unique reports rather than to reliably deliver each individual packet. Therefore, in the case of monitoring disaster filed with bursting data, communication channel throughput has higher priority than the channel reliability. Under this circumstance, we revisit the sensor network transport protocols, which use hop-by-hop recovery to provide reliable data transmission over unreliable links. We found that the complex recovery mechanism, while assuring high reliable individual packet delivery, reduces channel throughput when measured data is reported at high rate. To provide optimal data transport in a sensor network with bursting data generation, we propose a light weight sink centric transport protocol, which maximizes the channel throughput by minimizing the interference of packet recovery process. We implement the sink centric transport protocol in TinyOS and evaluate its performance in the TOSSIM simulator. The comparison shows that our proposed approach outperforms the hop-by-hop recovery approach in terms of event reporting throughput and transmission costs.
325991
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas
The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.
31866645
Optimal Processor Mapping for Linear-Complement Communication on Hypercubes
In this paper, we address the problem of minimizing channel contention of linear-complement communication on wormhole-routed hypercubes. Our research reveals that, for traditional routing algorithms, the degree of channel contention of a linear-complement communication can be quite large. To solve this problem, we propose an alternative approach, which applies processor reordering mapping at compile time. In this compiler approach, processors are logically reordered according to the given communication(s) so that the new communication(s) can be efficiently realized on the hypercube network. It is proved that, for any linear-complement communication, there exists a reordering mapping such that the new communication has minimum channel contention. An O(n/sup 3/) algorithm is proposed to find such a mapping for an n-dimensional hypercube. An algorithm based on dynamic programming is also proposed to find an optimal reordering mapping for a set of linear-complement communications. Several computer simulations have been conducted and the results clearly show the advantage of the proposed approach.
33560656
Clique covering the edges of a locally cobipartite graph
Abstract We prove that a locally cobipartite graph on n vertices contains a family of at most n cliques that cover its edges. This is related to Opsut's conjecture that states the competition number of a locally cobipartite graph is at most two.
586755
Mixed symbolic representations for model checking software programs
We present an efficient symbolic search algorithm for software model checking. The algorithm combines multiple symbolic representations to efficiently represent the transition relation and reachable states and uses a combination of decision procedures for Boolean and integer representations. Our main contributions include: (1) mixed symbolic representations to model C programs with rich data types and complex expressions; and (2) new symbolic search strategies and optimization techniques specific to sequential programs that can significantly improve the scalability of model checking algorithms. Our controlled experiments on real-world software programs show that the new symbolic search algorithm can achieve several orders-of-magnitude improvements over existing methods. The proposed techniques are extremely competitive in handling sequential models of non-trivial sizes, and also compare favorably to popular Boolean-level model checking algorithms based on BDDs and SAT
36188477
An Indexing Scheme for Case-Based Manufacturing Vision Development
This paper focuses on one critical element, indexing – retaining and representing knowledge in an applied case-based reasoning (CBR) model for supporting strategic manufacturing vision development (CBRM). Manufacturing vision (MV) is a kind of knowledge management concept and process concerned with the competence improvement of an enterprise’s manufacturing system. There are two types of cases within the CBRM – an event case (EC) and a general supportive case (GSC). We designed one set of indexing vocabulary for the two types of cases, but a different indexing representation structure for each of them. In this paper, after the background introduction of the MV, the CBRM and the indexing challenges of the MV cases, we present the structure and content of the index vocabulary and the two indexing representation structures, then illustrate briefly the indexing of cases with two examples. We also summarize the methods, primary conclusions of test runs with the indexing scheme. Further research work to refine the index vocabulary is discussed as well.