With advancement in device miniaturization and efficacy of network protocols, in a variety of civilian and military applications, wireless sensor networks (WSNs) architectures find room as viable network paradigm. Invariably, in all these WSN architectures, devising suitable algorithms for the efficient network resources utilization has been a challenging task. In certain events driven scenarios, random arrival pattern of queries generation; their geographical distribution (spatial aspect) and generation rate (temporal aspect) are hard to predict precisely. However, these phenomenons could be appropriately modelled using probabilistic framework while yielding adequate accuracy. Usually, in adopted probabilistic models, the associated control parameters are treated as crisp numbers, which fail to encompass uncertainties that are inevitably associated with the modeled parameters. To include impact of such uncertainties, we propose a modified Poisson PMF expressions in that dependency on spatial and temporal aspects is incorporated based on interval concepts. The paper also validates the dynamic fuzzy c-means algorithm as the most efficient clusters formation scheme. Sink node is an important entity/interface between end users and remotely located sensor nodes. To exploit implications of sink nodes attributes, three different case studies are presented. Wherein, we explore the network surveillance by a single stationary/portable sink and four stationary sinks. Obtained simulation results are analyzed for different scenarios which in principle governed by usage of four distinct clustering schemes and sink(s) attribute driven network surveillance.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering