Name
Abstract | ||
|---|---|---|
Recent developments in processor, memory and radio technologies have made it possible to apply Wireless Sensor Network (WSN), which consists of nodes with limited computing capability and energy, to gather various information, for instance, to gather water consumption data from residential area. However, due to the limited energy of the working nodes, energy efficient data gathering protocol should be designed to prolong the lifetime of the network. Power Efficient Algorithm for Data Gathering (PEADG) is one of such protocols. In order to evaluate the performance of the protocol, this paper proposes an approach to model and analyze PEADG using Probabilistic Automata (PAs) and PRISM model checker. The properties which can be analyzed here include the ``probability of finishing a round of data gathering", ``energy costs on a round". Moreover, in order to analyze relatively larger network, we propose an improved technique based on the concept of product of PAs, and show the advantage of the technique by an example in the paper. Finally, a tool is developed to support the application of our approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICECCS.2012.30 | ICECCS |
Keywords | Field | DocType |
data gathering protocol,prism model checker,limited energy,energy efficient data,energy cost,larger network,power efficient algorithm,water consumption data,limited computing capability,performance analysis,improved technique,data gathering,prism,probabilistic logic,topology,base stations,protocols,wireless sensor network,data handling,automata theory,probabilistic automata,automata,wireless sensor networks | Base station,Data collection,Data analysis,Efficient energy use,Computer science,PRISM model checker,Real-time computing,Probabilistic logic,Wireless sensor network,Group method of data handling,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kang He | 1 | 3 | 2.53 |
| Hongli Yang | 2 | 191 | 14.80 |
| Yachao Feng | 3 | 1 | 0.71 |
| Yuan Liu | 4 | 100 | 11.86 |
| Zongyan Qiu | 5 | 436 | 41.04 |