Name
Abstract | ||
|---|---|---|
Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path delays. The probabilistic nature of link/path delays makes the basic problem of computing the probabilistic distribution of path delays NP-hard, yet quantifying probabilistic path delays is a basic element of real-time routing and may well have to be executed by resource-constrained devices in a distributed manner; the highly varying nature of link/path delays makes it necessary to adapt to in-situ delay conditions in real-time routing, but it has been observed that delay-based routing can lead to instability, estimation error, and low data delivery performance in general. To address these challenges, we propose the Multi-Timescale Estimation (MTE) method; by accurately estimating the mean and variance of per-packet transmission time and by adapting to fast-varying queueing in an accurate, agile manner, MTE enables accurate, agile, and efficient estimation of probabilistic path delay bounds in a distributed manner. Based on MTE, we propose the Multi-Timescale Adaptation (MTA) routing protocol; MTA integrates the stability of an ETX-based directed-acyclic-graph (DAG) with the agility of spatiotemporal data flow control within the DAG to ensure real-time data delivery in the presence of dynamics and uncertainties. We also address the challenges of implementing MTE and MTA in resource-constrained devices such as TelosB motes. We evaluate the performance of MTA using the NetEye and Indriya sensor network testbeds. We find that MTA significantly outperforms existing protocols, e.g., improving deadline success ratio by 89% and reducing transmission cost by a factor of 9.7 in the NetEye testbed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TSG.2012.2209209 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
dynamic link,delay-based routing,uncertain link delay,statistical distributions,radio links,probabilistic path delay,indriya sensor network,in-situ delay condition,real-time routing protocol,multitimescale adaptation,taming uncertainty,spatiotemporal data flow control,np-hard problem,delay quantile estimation,directed acyclic graph,etx-based dag,wireless sensor network,probabilistic path delay bounds estimation,queueing theory,multi-timescale estimation,wireless sensing and control networks,estimation theory,neteye sensor network,probabilistic nature,spatiotemporal phenomena,real-time routing,estimation error,routing protocols,multi-timescale adaptation,resource constrained device,probabilistic distribution,multitimescale estimation,closed loop control,real-time data delivery,mta,directed graphs,path delay,mte,wireless sensor networks,variance estimation,probabilistic path delay bound,stability,resource-constrained device,basic element,closed loop systems,delay estimation,mean estimation,data flow,real time data,information technology,real time,control network,routing protocol,sensor network,wireless network | Journal | 4 |
Issue | ISSN | Citations |
1 | 1949-3053 | 7 |
PageRank | References | Authors |
0.49 | 36 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohui Liu | 1 | 44 | 3.34 |
| Hongwei Zhang | 2 | 935 | 67.71 |
| Qiao Xiang | 3 | 74 | 9.11 |
| Xin Che | 4 | 46 | 3.33 |
| Xi Ju | 5 | 66 | 4.28 |