7558736
An Adaptive Schedule Medium Access Control for Wireless Sensor Networks
Many media access control (MAC) protocols for wireless sensor networks (WSNs) such as sensor MAC (S-MAC) and carrier sense multiple access with minimum preamble sampling (CSMA-MPS) exploit listen/sleep cycles to conserve energy. The cycles of nodes influence the network performances such as energy efficiency, packet latency and throughput in WSNs. In this paper, an adaptive schedule medium access control (AS-MAC) for WSNs is presented. AS-MAC introduces the adaptation of the cycles in MAC layer to the data traffic. The effect of the protocol is that it can decrease the latency, improve the throughput and energy efficiency when traffic is dynamic. We also compare AS-MAC with S-MAC and CSMA-MPS through simulations and the results show it has better performances than the former protocols.
7657998
MAC Performance and Improvement in Mobile Wireless Sensor Networks
Former Media Access Control (MAC) protocols for Wireless Sensor Networks (WSNs) are mainly for static networks. In the networks where mobile nodes exist, the performance will degrade greatly. In this paper the performance of MAC in mobile WSNs (mWSNs) is analyzed and improved algorithms are presented. Simulations validate the effects. A new MAC protocol also is presented for the scenario with more mobile nodes.
29576879
An Ontology-Based Approach to Knowledge Management
Combining with the respective advantages of ontology and Web Service, this paper puts forward a knowledge management approach to overcome the problem of semantic heterogeneity in distributed environments. The emphasis is placed on establishing an XML-oriented semantic data model and the mapping between XML data based on a global semantic view, which enhances knowledge process efficiency, accuracy and the semantic interoperability as well.
35619770
A Heuristic Greedy Optimum Algorithm for Target Coverage in Wireless Sensor Networks
14570653
A New Parallel Segmentation Model Based on Dictionary and Mutual Information
It is difficult to compute the word frequency for mutual information segmentation. Statistic of word frequency of parallel mutual information is integrated with dictionary segmentation to improve efficiency in this paper. The parallel model and dispatching policy are presented, the paper also gives the speed up ratio of parallel model at the same time, periods pattern string and non periods pattern string are optimized in parallel model. Experiment show that the algorithm is available. The parallel model also can use for other segmentation algorithms that base on statistic of word frequency.
17495226
Scheduling strategies for mapping application workflows onto the grid
In this work, we describe new strategies for scheduling and executing workflow applications on grid resources using the GrADS [Ken Kennedy et al., 2002] infrastructure. Workflow scheduling is based on heuristic scheduling strategies that use application component performance models. The workflow is executed using a novel strategy to bind and launch the application onto heterogeneous resources. We apply these strategies in the context of executing EMAN, a bio-imaging workflow application, on the grid. The results of our experiments show that our strategy of performance model based, in-advance heuristic workflow scheduling results in 1.5 to 2.2 times better makespan than other existing scheduling strategies. This strategy also achieves optimal load balance across the different grid sites for this application.
14011281
Temperature wireless sensors network based on fiber Bragg grating
In this paper, we demonstrated a FBG (Fiber Bragg Grating) temperature sensor only using an EDFA and a single FBG in the wireless sensors network. By means of detecting the intensity of the light reflected from the sensing FBG, we can obtain the surrounding temperature perturbation. Using EDFA and photoelectric cell in stead of BBS (broad band source) and OSA (optical spectrum analyzer) respectively, which significantly decreases the cost of the sensing system. Finally, the sensing information can be collected, processed, and transmitted to the control center via the wireless network. This kind of FBG temperature wireless sensor network combined both the advantages of fiber grating sensor and wireless communication, which provides potential commercial application in some special fields, where the conditional sensors will not be feasible.
14246442
A Bottom-up Distance-based index Tree for Metric Space
Similarity search is of importance in many new database applications. These operations can generally be referred as similarity search in metric space. In this paper, a new index construction algorithm is proposed for similarity search in metric space. The new data structure, called bu-tree (bottom-up tree), is based on constructing the index tree from bottom-up, rather than the traditional top-down approaches. The construction algorithm of bu-tree and the range search algorithm based on it are given in this paper. And the update to bu-tree is also discussed. The experiments show that bu-tree is better than sa-tree in search efficiency, especially when the objects are not uniform distributed or the query has low selectivity
449953
Learning to Classify Documents with Only a Small Positive Training Set
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set Pis small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in Pand the hidden positive examples in Uwere not drawn from the same distribution.