Data aggregation framework for energy-efficient WirelessHART networks

Feng Li

doi:10.1016/j.sysarc.2016.01.009

Return

Data aggregation framework for energy-efficient WirelessHART networks

2016-02-01

PRE

黎峰 (Feng Li)

Lei Ju *

Zhiping Jia

DOI：10.1016/j.sysarc.2016.01.009

Original

Original request for help

Save

Abstract

En 中文

WirelessHART has become an industrial standard for robust and real-time wireless monitoring and control. While energy-efficiency is one of the key design considerations for networks with battery-operated devices, data aggregation has been widely studied in the wireless sensor network (WSN) environments to reduce the traffic and prolong the lifetime of the network. However, existing data aggregation techniques cannot be applied directly to WirelessHART networks due to the multi-channel Time Synchronized Mesh Protocol (TSMP) and the superframe-based communication slot scheduling in WirelessHART. In this work, we propose a data aggregation framework for energy-efficient and real-time WirelessHART communication. In particular, we consider aggregation as a factor during the link selection procedure of the graph routing to improve the chance of data fusion at intermediate routing nodes and reduce the total number of message transmissions. Furthermore, a greedy-based heuristic is applied during the superframe construction phase to allocate package transmissions whose data can be aggregated at intermediate routing nodes into nearby time slots. During the superframe re-scheduling, we make sure that the predefined end-to-end deadline for each package is satisfied as long as the entire network is schedulable without data aggregation. Experimental results show that compared with existing WirelessHART routing algorithms, our proposed framework has significantly improvement on the energy saving and prolongs the overall lifetime of the network. (c) 2016 Elsevier B.V. All rights reserved.

Keywords:

WirelessHART

Data aggregation

Energy efficiency

Real-time communication

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.