9,614 research outputs found
Distributed Power Allocation for Sink-Centric Clusters in Multiple Sink Wireless Sensor Networks
Due to the battery resource constraints, saving energy is a critical issue in wireless sensor networks, particularly in large sensor networks. One possible solution is to deploy multiple sink nodes simultaneously. Another possible solution is to employ an adaptive clustering hierarchy routing scheme. In this paper, we propose a multiple sink cluster wireless sensor networks scheme which combines the two solutions, and propose an efficient transmission power control scheme for a sink-centric cluster routing protocol in multiple sink wireless sensor networks, denoted as MSCWSNs-PC. It is a distributed, scalable, self-organizing, adaptive system, and the sensor nodes do not require knowledge of the global network and their location. All sinks effectively work out a representative view of a monitored region, after which power control is employed to optimize network topology. The simulations demonstrate the advantages of our new protocol
An energy-efficient hierarchical multiple-choice routing path protocol for wireless sensor networks
[[abstract]]The energy efficiency is a substantial key design issues in such networks. An efficient routing protocol is critical to prolong the life of sensor nodes. This work presents a hierarchical multiple-choice routing path protocol (HMRP) for wireless sensor networks. According to HMRP, the wireless sensor network is initially constructed as a layered network. Based on the layered network, sensor nodes have multipath routes to the sink node through candidate parent nodes. The simulation results indicate that the proposed HMRP can increase the lifetime of sensor networks better than other clustering or tree-based protocols[[conferencetype]]國際[[conferencedate]]20060605~20060607[[booktype]]紙本[[conferencelocation]]Taichung, Taiwa
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
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