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The frequent items problem is to process a stream as a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Aiming at higher false positive rate of the Space-Saving algorithm, an LRU-based (Least Recently Used, LRU) improved algorithm with low frequency item pre-eliminated...
Although the special characteristics of wireless sensor networks (WSNs) help in reducing the cost of sensor nodes manufacturing and deployment, they added new challenges that directly affect the network functionality. This is increases the probability of network functionality deviation from its norm operation and affects its' collected data accuracy. Moreover, these challenges reduce the network lifetime...
It has been well recognized that identifying very large flows (i.e., elephants) in a network traffic stream is important for a variety of network applications ranging from traffic engineering to anomaly detection. However, we found that many of these applications have an increasing need to monitor not only the few largest flows (say top 20), but also all of the medium-sized flows (say top 20,000)...
In many DSP applications (image and voice processing, baseband symbol decoding in high quality communication channels) several dBs of SNR loss can be tolerated without noticeable impact on system level performance. For power optimization in such applications, voltage overscaling can be used to operate the arithmetic circuitry slower than the critical circuit path delay while incurring tolerable SNR...
Data aggregation is an essential paradigm for energy efficient routing in energy constraint wireless sensor networks (WSN). Data aggregation in WSN can be treated as searching for the Minimum Steiner Tree (MST) including source nodes and sink node. In this paper, we propose a Data-aggregation Algorithm based on Adaptive Ant Colony System (AACS) algorithm. In this algorithm, Directed Diffusion (DD)...
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