Receive diversity based transmission data rate optimization for improved network lifetime and delay efficiency of Wireless Body Area Networks

Abstract

Wireless Body Area Network (WBAN) has become the emerging technology due to its ability to provide intelligent and cost-effective healthcare monitoring solution. The biological sensors used in WBAN are energy-constrained and required to be functional for a longer duration. Also, the sensed data should be communicated in reasonable time. Therefore, network lifetime and delay have become the primary concerns in the design of WBAN. In this paper, Receive Diversity based Transmission Data Rate Optimization (RDTDRO) scheme is proposed to improve the network lifetime and delay efficiency of Multi level-Quadrature Amplitude Modulation (M-QAM) based WBAN. In the proposed RDTDRO scheme, minimum energy consumption is ensured by optimizing the transmission data rate with respect to a given transmission distance and number of receive antennas while satisfying the Bit Error Rate (BER) requirements. The performance of proposed RDTDRO is analyzed in terms of network lifetime and delay difference and is compared with conventional Baseline and Rate optimized schemes. The results show that at a transmission distance of 0.3 m, the proposed RDTDRO scheme with a receive diversity order of 4 achieves 1.30 times and 1.27 times improvement in network lifetime over conventional Baseline and Rate optimized schemes respectively. From the results, it is also evident that at a transmission distance of 0.3 m, the proposed RDTDRO scheme with a receive diversity order of 4 is delay efficient as it achieves delay difference of 0.75 μs and 0.29 μs over conventional Baseline and Rate optimized schemes respectively.

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks

“…Calculating the center O and the radius r (function f(p 1 , p 2 , p 3 )) 1 three points p 1 = (x 1 , y 1 ), p 2 = (x 2 , y 2 ), p 3 = (x 3 , y 3 ); 2 a = 2 × (x 2 − x 1 ); b = 2 × (y 2 − y 1 ); c ¼ x 2 2 þ y 2 2 À x 2 1 À y 2 1 ; d = 2 × (x 3 − x 2 ); e = 2 × (y 3…”

“…LVSNs are widely used in many fields such as visual data surveillance for the earthquake, forest fire and other disasters [2]. Visual data are collected from networked smart distributed visual sensors, processed collaboratively, and transmitted to the sink, which is a control center [3]. An example of visual data gathering network system in PLOS LVSN is illustrated in Fig 1. The visual sensors construct a complicated mesh network topology.…”