As the applications of wireless sensor networks continue to expand, it is important to support fast and simultaneous data aggregation over multiple regions for advanced data analysis. In this paper, we propose a solut...
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As the applications of wireless sensor networks continue to expand, it is important to support fast and simultaneous data aggregation over multiple regions for advanced data analysis. In this paper, we propose a solution by using a novel distributed data structure called distributeddata cube (DDC). A DDC maintains a set of special forms of aggregate values (prefix sum, prefix average, prefix max, and prefix min) in distributed sensor nodes. We will first present fast algorithms to build a DDC within a sharp time bound. Then, we will present efficient distributed query-processing algorithms to handle aggregate queries by using a DDC. For a query region with n sensor nodes, our algorithms can return within O(root n) time. Finally, extensive simulation studies confirm that a DDC can be built very quickly, which is consistent with the theoretical time bound. The network traffic injected while constructing a DDC is acceptable and also scalable as the network size grows. Query processing on a DDC is fast and energy efficient in terms of the time units needed and the number of messages incurred.
We present a novel counting network construction, where the number of input wires in is smaller than or equal to the number of output wires t. The depth of our network is Theta(lg(2) w), which depends only on w. In co...
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We present a novel counting network construction, where the number of input wires in is smaller than or equal to the number of output wires t. The depth of our network is Theta(lg(2) w), which depends only on w. In contrast, the amortized contention of the network depends on the number of concurrent processes n and the parameters w and t. This offers more flexibility than all previously known networks, with the same number in of input and output wires, whose contention depends only on two parameters, w and n. In case n > w lg w, by choosing t > w lg w the contention of our network is O(n lg w/w), which improves by a logarithmic factor of in over all previously known networks with w wires. (C) 2010 Elsevier B.V. All rights reserved.
We examine the design, implementation, and experimental analysis of parallel priority queues for device and network simulation. We consider: 1) distributed splay trees using MPI, 2) concurrent heaps using shared memor...
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We examine the design, implementation, and experimental analysis of parallel priority queues for device and network simulation. We consider: 1) distributed splay trees using MPI, 2) concurrent heaps using shared memory atomic locks, and 3) a new, more general concurrent datastructure based on distributed sorted lists, which is designed to provide dynamically balanced work allocation (with automatic or manual control) and efficient use of shared memory resources. We evaluate performance for all three datastructures on a Cray-T3E900 system at KFA-Julich. Our comparisons are based on simulations of single buffers and a 64 x 64 packet switch which supports multicasting. In all implementations, PEs monitor traffic at their preassigned input/output ports, while priority queue elements are distributed across the Cray-T3E virtual shared memory. Our experiments with up to 60,000 packets and two to 64 PEs indicate that concurrent priority queues perform much better than distributed ones. Both concurrent implementations have comparable performance, while our new datastructure uses less memory and has been further optimized. We also consider parallel simulation for symmetric networks by sorting integer conflict functions and implementing an interesting packet indexing scheme. The optimized message passing network simulator can process similar to 500K packet moves in one second, with an efficiency that exceeds similar to 50 percent for a few thousands packets on the Cray-TBE with 32 PEs. All developed datastructures now form a parallel library. Although our concurrent implementations use the Cray-T3E ShMem library, portability can be derived from Open-MP or MPI-2 standard libraries, which will provide support for one-way communication and shared memory lock mechanisms.
Simple formulas are derived to construct inorder spanning trees in a hypercube network. We identify a routing strategy to ensure the edge-disjointness of the routing paths in executing binary tree algorithms. Large tr...
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Simple formulas are derived to construct inorder spanning trees in a hypercube network. We identify a routing strategy to ensure the edge-disjointness of the routing paths in executing binary tree algorithms. Large trees can be built in a small hypercube by spreading the load congestion uniformly across the nodes of the hypercube. We also discuss the fault tolerance of the embedding method.
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