Name
Abstract | ||
|---|---|---|
For energy conservation, a wireless sensor network is usually designed to work in a low-duty-cycle mode, in which a sensor node keeps active for a small percentage of time during its working period. In applications where there are multiple data delivery tasks with high data rates and time constraints, low-duty-cycle working mode may cause severe transmission congestion and data loss. In order to alleviate congestion and reduce data loss, the tasks need to be carefully scheduled to balance the workloads among the sensor nodes in both spatial and temporal dimensions. This paper studies the load balancing problem, and proves it is NP-Complete in general network graphs. Two efficient scheduling algorithms to achieve load balance are proposed and analyzed. Furthermore, a task scheduling protocol is designed relying on the proposed algorithms. To the best of our knowledge, this paper is the first one to tackle multiple task scheduling for low-duty-cycled sensor networks. The simulation results show that the proposed algorithms greatly improve the network performance in most scenarios. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/INFCOM.2011.5934916 | 2011 PROCEEDINGS IEEE INFOCOM |
Keywords | Field | DocType |
algorithm design and analysis,wireless sensor networks,schedules,load balance,network performance,sensor network,wireless sensor network,scheduling algorithm,resource allocation,scheduling,graph theory,np complete,duty cycle,scheduling algorithms,protocols,energy conservation | Sensor node,Key distribution in wireless sensor networks,Load balancing (computing),Scheduling (computing),Computer science,Computer network,Real-time computing,Schedule,Mobile wireless sensor network,Wireless sensor network,Network performance,Distributed computing | Conference |
Volume | Issue | ISSN |
null | null | 0743-166X |
Citations | PageRank | References |
24 | 0.88 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuguang Xiong | 1 | 99 | 6.46 |
| Jianzhong Li | 2 | 3196 | 304.46 |
| Mo Li | 3 | 2324 | 106.92 |
| Jiliang Wang | 4 | 564 | 43.33 |
| Yunhao Liu | 5 | 8810 | 486.66 |