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Vol. 6, Special Issue, August 2009, pp.69-91

 

 

Bullet

 

Computational Aspects of Sensor Network Protocols (Distributed Sensor Network Simulator)

 

1Vasanth Iyer, 2S. S. Iyengar, 1G. Rama Murthy, 1M. B. Srinivas

1International Institute of Information Technology

Hyderabad, India - 500 032

2Chairman, Department of Computer Science, Louisiana State University,

 LA 70803, USA

E-mail: vasanth@research.iiit.ac.in, iyengar@csc.lsu.edu, rammurthy@iiit.ac.in, srinivas@bitshyderabad.ac.in

 

 

Received: 10 July 2009   /Accepted: 31 July  2009   /Published: 10 August 2009

 

Abstract: In this work, we model the sensor networks as an unsupervised learning and clustering process. We classify nodes according to its static distribution to form known class densities (CCPD). These densities are chosen from specific cross-layer features which maximizes lifetime of power-aware routing algorithms. To circumvent computational complexities of a power-ware communication STACK we introduce path-loss models at the nodes only for high density deployments. We study the cluster heads and formulate the data handling capacity for an expected deployment and use localized probability models to fuse the data with its side information before transmission. So each cluster head has a unique Pmax but not all cluster heads have the same measured value.

 

In a lossless mode if there are no faults in the sensor network then we can show that the highest probability given by Pmax is ambiguous if its frequency is  ≤  n/2 otherwise it can be determined by a local function. We further show that the event detection at the cluster heads can be modelled with a pattern  2m and m, the number of bits can be a correlated pattern of 2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy < 1.

 

These local algorithms are further studied to optimize on power, fault detection and to maximize on the distributed routing algorithm used at the higher layers. From these bounds in large network, it is observed that the power dissipation is network size invariant. The performance of the routing algorithms solely based on success of finding healthy nodes in a large distribution. It is also observed that if the network size is kept constant and the density of the nodes is kept closer then the local pathloss model effects the performance of the routing algorithms. We also obtain the maximum intensity of transmitting nodes for a given category of routing algorithms for an outage constraint, i.e., the lifetime of sensor network.

 

Keywords: Power-aware routing, Sensor network lifetime, MAC layer, Distributed algorithms, Bayesian classifier, Slepian & Wolf theorem, Real-time sensing and simulation, Huffman coding, Entropy

 

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