Abstract
Stochastic geometry, in particular Poission point process theory, has been widely used in the last decade to provide models and methods to analyze wireless networks. It is a branch of mathematics which deals with the study of random point processes. There are various models for point processes, typically based on but going beyond the classic homogeneous Poisson point process. Poisson point process cannot be used to model the spatial distribution of the simultaneously active transmitters. A novel framework has been presented for modeling the intensity of simultaneous active transmitters of a random carrier sense multiple access wireless sensor network. This thinning rule uses a second-neighbors distance-dependent method, which controls too many nodes deleted of points close together.
Highlights
WBSNs enable wireless communications between several miniaturized body sensors and a single coordinator worn on the human body
WBSNs for health monitoring systems are required to meet stringent performance demands regarding the tradeoff between reliability, latency, and power efficiency
Since the Poisson Point Process (PPP) is highly tractable, it is frequently used to model a variety of networks, such as celluar networks, mobile ad hoc networks, cognitive radio networks and wireless sensor networks[5,6]
Summary
WBSNs enable wireless communications between several miniaturized body sensors and a single coordinator worn on the human body. WBSNs offer many promising new applications in the area of remote health monitoring systems to measure specified physiological data and provide location-based information. WBSNs feature limited range and bandwidth and they are prone to interference. These modes incur issues regarding the coexistence of multiple WBANs. There is often a group of BSNs together to happen in hospitals for patients and staff or for the elderly at nursing homes [3]. We proposed a PCMP model to mitigate this problem. We propose a new thinning method of CSMA network to mitigate the node intensity underestimation problem of HCPP model.
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