Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Decoupling data dissemination from the mobile sink's trajectory in wireless sensor networks: Current Research and Open Issues (1007.4068v2)

Published 23 Jul 2010 in cs.NI

Abstract: In this report, firstly, we presents state of the art survey on Data Management and Data Dissemination techniques with Mobile Sink. Moreover we classify these techniques into two ample sub-categories. Under this classification, we identify, review, compare, and highlight these techniques and their pros and cons. We do a SWOT (Strength, Weaknesses, Opportunities, Threats) analysis of each scheme. We also discuss where each scheme is appropriate. Secondly, we presents a new distributed data management scheme which is based upon Random Walk Based Membership Service to facilitate Data Dissemination in Mobile Sink based Wireless Sensor Networks. Our proposed scheme efficiently deals with the aforementioned problems and we also compare the characteristics of our proposed scheme with the state-of-the-art data-dissemination schemes. We propose using Random Walks (RWs) with uniformly distributed views to disseminate data through the WSN with a controlled overhead. This is performed by the use of a Random Walk Based Membership Service - the RaWMS. Our proposal solves then the problems generated when (a) all nodes are storage motes, being no aggregation performed (b) one center node plays the role of storage mote and aggregates data from all the other nodes (c) replication is performed on all nodes in the network. To the best of our knowledge, we are the first to propose an efficient data dissemination approach (in terms of overhead, adaptiveness and representativeness) to allow a mobile sink to gather a representative view of the monitored region covered by n sensor nodes by only visiting any m nodes, where hopefully m << n.

Citations (3)

Summary

We haven't generated a summary for this paper yet.