Name
Abstract | ||
|---|---|---|
Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3342516 | ACM Transactions on Sensor Networks (TOSN) |
Keywords | Field | DocType |
Internet of Things, heterogeneous radios, optimization, power efficiency, quality of service | Electrical efficiency,Transmission (mechanics),Wireless,Computer science,Internet of Things,Quality of service,Baseline (configuration management),Testbed,Computer network,Artificial intelligence,Robotics | Journal |
Volume | Issue | ISSN |
15 | 4 | 1550-4859 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Mu | 1 | 1 | 0.37 |
| Yunpeng Ge | 2 | 1 | 0.37 |
| Mo Sha | 3 | 251 | 23.52 |
| Steve Paul | 4 | 3 | 0.73 |
| Niranjan Ravichandra | 5 | 1 | 0.37 |
| Souma Chowdhury | 6 | 7 | 7.63 |