Name
Abstract | ||
|---|---|---|
Reprogramming over the network is an essential requirement in large-scale, long term wireless sensor network deployments. In this paper, we present LACONIC, a history-based code dissemination technique, for programmable wireless sensor networks supporting multiple applications. In order to attain network traffic abatement and timely code delivery, LACONIC exploits (1) application calling history and (2) code dissemination history. The application calling history is modeled by a Application Call Graph(ACG) which represents the calling relationships among multiple applications. Second, the code dissemination history as a set of observed previous code forwarding paths indicates previous code forwarding path of the associated application in the context of the application calling history. Therefore, the code request triggered by a requester is reached to the responder by traveling along the path, not being consecutively flooded. Using a flooding-based existing code dissemination work as the baseline, we show the effectiveness of Laconic in terms of network traffic and time delay through both probability model-based analysis and simulation results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/MSN.2008.46 | MSN |
Keywords | Field | DocType |
timely code delivery,history-based code dissemination,observed previous code forwarding,network traffic,programmable wireless sensor networks,code dissemination history,associated application,previous code forwarding path,flooding-based existing code dissemination,code request,multiple application,history-based code dissemination technique,wireless sensor networks,call graph,graph theory,network programming,wireless sensor network | Graph theory,Code dissemination,Over-the-air programming,Computer science,Computer network,Exploit,Call graph,Information Dissemination,Wireless sensor network,Computer network programming,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seungki Hong | 1 | 9 | 1.71 |
| Yeon Jun Choi | 2 | 1 | 0.70 |
| Yang Yu | 3 | 24 | 13.21 |
| Loren J. Rittle | 4 | 110 | 6.15 |