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By Chiasserini, Carla-Fabiani; Gribaudo, Marco; Manini, Daniele

Wireless networks characterize a cheap and handy option to connect with the web. although, regardless of their purposes throughout numerous applied sciences, one problem nonetheless is still: to appreciate the habit of instant sensor networks and verify their functionality in large-scale scenarios.

When a number of community nodes have to have interaction, constructing appropriate analytical versions is vital to make sure definitely the right insurance and throughput of those networks and to augment consumer mobility. this is often intrinsically tough as a result of dimension and variety of diversified community nodes and users.

This booklet highlights a few examples which express how this challenge could be conquer with using diversified strategies. a radical parameter research exhibits the reader how one can the make the most analytical versions for an efficient improvement and administration of alternative forms of instant networks.

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Finally, two successive transmissions on the wireless channel must be separated by an inter-frame spacing (IFS) time period. 2. Model for single-hop network topologies First, a single-hop network topology is considered where all nodes can communicate with the central controller and are in radio proximity of each other. The model is initially presented for the simpler case of a single message being transferred from a sensor to the central controller, then multiple message transfers are represented.

In particular, by exploiting the properties of WSNs, it is possible to design an efficient solution algorithm that significantly reduces the complexity of the model and its solution time. The solution technique is supported by a tool, which converts HA into an equivalent discrete time Markov chain (DTMC). Although being designed for the study of a WSN, the presented methodology has a general validity and can find application in different contexts. 1. Event detection in WSNs Let us consider a WSN for event detection where every point of the monitored area is covered by k sensors.

E. for any distribution of the duration of ON and OFF periods. 2. 27], whereas Pa = T¯ON /(T¯ON + T¯OFF ) is the probability that a sensor is active, which depends only on the average durations T¯ON and T¯OFF of the ON and OFF phases, respectively. 4. 1. 1 the analytical predictions derived from the fluid model are compared with those obtained using a detailed simulator of the sensor network in the case of homogeneous node deployment. 2, the model is exploited to study the behavior of large-scale sensor networks as the active/sleep dynamics and the sensing/communication range of the nodes vary.

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