Industrial Engineering, Machine Design And Automation (Iemda 2014) - Proceedings Of The 2014 Congress & Computer Science And Application (Ccsa 2014) - Proceedings Of The 2nd Congress

Proceedings of the 2014 Congress on IEMDA 2014 & Proceedings of the 2nd Congress on CCSA 2014

Shihong Qin, Xiaolong Li

• Publisher: WSPC
• Year: 2015
• ISBN:
  9789814689014
• Topic: Biological Sciences
• Subtopic: Science General
• Index: Biological Sciences

This proceedings put together 68 selected articles from the joint conferences of 2014 Congress on Industrial Engineering, Machine Design and Automation (IEMDA2014) and the 2nd Congress on Computer Science and Application (CCSA2014), held in Sanya, China during December 12 – 14, 2014.

The conference program of IEMDA 2014 focused on areas of Industrial Engineering, Machine Design and Automation, while the CCSA 2014 program provided the platform for Computer Science and Applications.

Collected together the latest research results and applications on industrial engineering, machine design, automation, and computer science and other related Engineering topics. All submitted papers to this proceedings were subjected to strict peer-reviewing by 2–4 expert referees, to ensure that all articles selected are of highest standard and are relevance to the conference.

Contents:

• Communication and Information Technology
• Research and Design of Machines and Mechanisms for Manufacturing
• Data, Signal and Image Processing, Computational Technology
• Mechanical, Automation and Control Engineering

Readership: Researchers and professional
Key Features:

• The proceedings reported on the latest research results and applications on industrial engineering, machine design, automation, and computer science and other related Engineering topics
• Printed copies are available to authors and (IEMDA2014) and (CCSA2014) conference participants alike with special discount with discount code sent out by conference organisers
• Additional copies will be printed for marketing to include in their library package

An Energy Consumption Assessment Method for WIFI Large-Scale Wireless Sensor Network Based on Dynamic Channel Energy Model

Weikai Tan^a†, Xiaoyuan Lu^b, Yunxiang Xu^c, Kejun Zhao^d and Peng Gao^e

National Engineering Research Center for Broadband Networks & Applications,
Shanghai 200336, China

E-mail: ^a†[email protected], ^b[email protected], ^c[email protected],
^d[email protected], ^e[email protected]

Energy efficiency is one of the most serious constraints for the deployment of large-scale wireless sensor network (WSN) significantly. However, an excellent strategy to raise energy efficient depends on a precise energy consumption assessment method. In this paper, an energy consumption assessment method based on dynamic channel energy model is proposed. Energy consumption is divided into two parts: static and dynamic. The former includes receiving, idle and clear channel assessment state, whose energy consumption is only related to a stationary working current and duration. Transmission energy consumption refers to dynamic energy consumption, function of which is described as a cubic function. The energy consumption calculation is adjusted to meet the transmission power dynamically and timely. Simulation results show that the dynamic energy consumption during transmission is summed accurately. Compared with the others, such as some simple energy models without dynamic case. It provides to support for the deployment of WIFI large-scale WSN.

Keywords: Channel Energy Model; Large-Scale Sensor Network; Energy Consumption Assessment.

1.Introduction

Recently, wireless sensor network (WSN), providing emergency monitoring, remote monitoring and environmental awareness, has been significantly interested. With the development of sensor network theory and technology, it has been widely used. Most of the applications are still limited to a small-scale wireless sensor network. However, many applications require large-scale deployment to achieve high coverage, high-precision sensing purposes, such as forest fire monitoring ¹. Large-scale wireless sensor network based on WIFI has received a lot of attention ².

In WSN, two aspects of problems we face to are the limited battery life and efficient usage of energy, which become more serious in large-scale WSN. In fact, compared to small scale applications, large-scale WSN manages a large number of nodes to achieve high coverage, which leads to greater energy consumption. Therefore, strategies, such as routing and QoS control, must be improved to raise energy efficiency³. However, the design of an excellent strategy depends on a precise energy consumption assessment method extremely. Especially, the assessment results of the impact from the varying circumstances where the sensor works. This directly affects the validity of the strategy design. In ⁴, authors suggest a simple energy model. The model only takes into account energy dissipation during the start-up, receive, and transmit modes. For the transmit energy it’s too simple. In ⁵, a radio energy dissipation model is described but it isn’t accurate enough. Various energy-efficient methods are considered in literatures but only use the simple model of energy consumption, which leads to a fuzzy simulation result and a fuzzy effectiveness of their proposed methods ^{6, 7}. Therefore, an accurate energy consumption assessment method for WIFI large-scale wireless sensor network is proposed to provide an exact reference for the deployment of WIFI large-scale WSN.

2.Sensor Model

A sensor usually consists of the following subsystems: communication subsystem, processing subsystem and sensor subsystem. The energy consumed by communication subsystem is much higher than processing subsystem, up to 80 percent. Thus, the communication subsystem is main source of system energy consumption in WIFI large-scale sensor network ⁸.

Fig.1 Sensor model.

The basic structure of a sensor is shown in Fig.1. We divide the energy consumption calculation into two parts: dynamic and static. Dynamic energy consumption includes a short-circuit power that flows directly from the supply to ground during a transition at the output of a CMOS gate. Dynamic part, namely transmission circuit (TX), is composed of digital to analog converter, modulation circuit and generates emission signal. Static energy consumption is associated with maintaining the logic values of internal circuit nodes between the switching events, such as basic circuit. Static part includes: (a) basic circuit (BA) composed of voltage controlled oscillator (VCO) and frequency synthesizer, provides the power and frequency of the basic circuit; (b) transmission amplifier (PA) is signal modulation circuit to FM and launch; (c) receiving circuit (RX) is the low noise amplifier (LNA), mixer, filter, intermediate frequency amplifier and demodulation circuit, AD converter; (d) Sensor is detection of Sensor signals.

3.Proposed Dynamic Channel Energy Model

In communication subsystem, node state can be divided into four types: transmitting state, receiving state, idle state and clear channel assessment (CCA) state. They are represented by S_TX, S_RX, S_CC, S_ID, respectively.

We have to pass through the state of S_ID when switching between any two states of S_TX, S_RX and S_CC. E_ID, E_TX, E_RX and E_CC are used to represent the energy consumption of each state; I_ID, I_TX, I_RX and I_CC are used to represent the current of each state. From dynamic and static parts analyzed in Section Sensor Model, the total system energy consumption can be calculated as

where E_static represents the energy consumption of those circuits whose power consumption is a constant. E_static is calculated as

Since the energy consumption of S_ID only relates to the basic circuit, we get E_ID = E_BA = P_BAt_ID, Where P_BA represents the power consumption of basic circuit is a consta...

Table of contents

• Cover Page
• Title Page
• Copyright
• Contents
• Editorial
• Chapter 1: Communication and Information Technology
• Chapter 2: Research and Design of Machines and Mechanisms for Manufacturing
• Chapter 3: Data, Signal and Image Processing, Computational Technology
• Chapter 4: Mechanical, Automation and Control Engineering
• Author Index