Abstract
To ensure long network life-time, the duty-cycle of wireless sensor networks is often set to be low. This brings with itself the risk of either missing a sent packet or delaying the message delivery and dissemination depending on the duration of the duty-cycle and number of hops. This risk is increased in wireless sensor applications with hybrid architecture, in which a static ground wireless sensor network interacts with a network of mobile sensor nodes. Dynamicity and mobility of mobile nodes may lead to only a short rendezvous between them and the backbone network to exchange data. Additionally, such dynamicity generates complex and often random data traffic patterns. To support successful data delivery in case of short rendezvous between static and mobile wireless sensor nodes, we propose MobiBone, an energy-efficient and adaptive network protocol that utilizes data packet traffic to characterize the sleep schedule. Our simulation results show that compared with network protocols with fixed duty-cycles, MobiBone offers a good trade-off between energy consumption, latency, and detection rate of mobile nodes (which indicates awakens of the backbone network at crucial times of mobile node presence).
Original language | English |
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Title of host publication | 2017 International Conference on Computing, Networking and Communications, ICNC 2017 |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 1024-1030 |
Number of pages | 7 |
ISBN (Electronic) | 9781509045884 |
ISBN (Print) | 9781509045891 |
DOIs | |
Publication status | Published - 10 Mar 2017 |
Event | International Conference on Computing, Networking and Communications, ICNC 2017 - Silicon Valley, United States Duration: 26 Jan 2017 → 29 Jan 2017 http://www.conf-icnc.org/2017/ |
Conference
Conference | International Conference on Computing, Networking and Communications, ICNC 2017 |
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Abbreviated title | ICNC |
Country/Territory | United States |
City | Silicon Valley |
Period | 26/01/17 → 29/01/17 |
Internet address |
Keywords
- adaptive duty-cycle
- data traffic learning
- Hybrid wireless sensor networks
- Pipeline scheduling