26961598
A Distributed Power-Efficient Data Gathering and Aggregation Protocol for Wireless Sensor Networks
In this paper, we propose a distributed power-efficient data gathering and aggregation algorithm (DPEG), in which a node, according to its residual energy and the strength of signal received from its neighboring nodes, independently makes its decision to compete for becoming a cluster head. In addition, assume that the inter-cluster communication data is, in a multi-hop manner, sent to the designated node, which then sends the data gathered by the whole network to the base station. DPEG also proposes a simple approach to solve the cluster coverage problem. With the increase in node density, this approach lets sensor network lifetime be linear in the number of nodes. Our experimental results have proved that DPEG algorithm, in the best case, lets sensor network lifetime be respectively increased by 1800% and 300% as compared with another two data gathering and aggregation protocols— LEACH and PEGASIS.
2568892
Traffic Adaptive MAC Protocol for Wireless Sensor Network
In this paper, we propose TA-MAC, a traffic load adaptive Medium Access Control protocol for wireless sensor network. TA-MAC modified the contention window mechanism of S-MAC. It adjusts the initial contention window according to the current traffic load to reduce the collision probability and employs a fast backoff scheme to reduce the time for idle listening during backoff procedure, which both reduce the energy consumption. Simulation results show that TA-MAC outperforms S-MAC.
13928372
VRules: an effective association-based classifier for videos
Video classification is an important step towards multimedia understanding. Most state-of-the-art approaches which apply HMM to capture the temporal information of videos have the limitation by assuming that the current state of a video depends only on the immediate previous state. Nevertheless, this assumption may not hold for videos of various categories. In this paper, we present an effective video classifier which employs the association rule mining technique to discover the actual dependence relationship between video states. The discriminatory state transition patterns mined from different video categories are then used to perform classification. Besides capturing the association between states in the time space, we also capture the association between low-level features in spatial dimension to further distinguish the semantics of videos. Experimental results show that the performance of our association rule based classifier is quite promising.
6401713
Stability Analysis of Regional and National Voting Schemes by a Continuous Model
The previous discrete-model-based stability analysis of regional and national voting has been extended to a continuous-model-based analysis in the simultaneous presence of white and concentrated components of noise, reconfirming the previous conclusion that regional voting with smaller sized regions always demonstrates an improved stability over those with larger sized regions, including the national voting in its limiting case in particular. The conclusion remains valid as long as the weak distribution assumption is valid.
44509988
A threshold criterion, auto-detection and its use in MST-based clustering
Clustering is to group data points into homogenous clusters so that data points within the same cluster are more similar than data points belonging to different clusters. There are many effective clustering algorithms for discovering arbitrary shaped clusters, but one common problem of many algorithms is the difficulty for users to decide appropriate parameters for these algorithms. To reduce the dependence of clustering performance on parameters, this paper proposes a threshold criterion for the single linkage cluster analysis and incorporates it into the Minimum Spanning Tree (MST) based clustering method. Since the threshold can be automatically decided according to the underlying data distributions, arbitrary shaped clusters can be discovered with little human intervention. The experimental results on spatial data are very encouraging.
25000873
Design, analysis, and implementation of an agent driven pull-based distributed video-on-demand system
The problem of employing multiple servers to serve a pool of clients on a network based multimedia service is addressed. We have designed and practically implemented a prototype system employing multiple servers to render a long duration movie to the customers. We have employed a multiple server retrieval strategy proposed in the literature [39] to realize this system. In the system, server coordination, client behavior and service facilities are completely controlled by an Agent based approach in which we have used the recent Jini technology. Several issues, ranging from data retrieval from individual server, behavior of the underlying network infrastructure, to client management and resource (client buffers) management, are considered in this implementation. We describe in detail our experiences in this complete design process of every module in the software architecture, its purpose, and working style. Further, the system is shown to be robust amidst unpredictable failures, i.e., in the event of server crashes. The load balancing capability is built-in as a safe guard measure to assure a continuous presentation. We present a comprehensive discussion on the software architecture to realize this working system and present our experiences. A system comprising a series of Pentium III PCs on a fast Ethernet network is built as a test-bed. Through this prototype, a wider scope of research challenges ahead are highlighted as possible extensions.
14175949
GATES: a grid-based middleware for processing distributed data streams
Increasingly, a number of applications rely on, or can potentially benefit from, analysis and monitoring of data streams. Moreover, many of these applications involve high volume data streams and require distributed processing of data arising from a distributed set of sources. Thus, we believe that a grid environment is well suited for flexible and adaptive analysis of these streams. This paper reports the design and initial evaluation of a middleware for processing distributed data streams. Our system is referred to as GATES (grid-based adaptive execution on streams). This system is designed to use the existing grid standards and tools to the extent possible. It flexibly achieves the best accuracy that is possible while maintaining the real-time constraint on the analysis. We have developed a self-adaptation algorithm for this purpose. Results from a detailed evaluation of this system demonstrate the benefits of distributed processing, and the effectiveness of our self-adaptation algorithm.