GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
A high-throughput path metric for multi-hop wireless routing
Proceedings of the 9th annual international conference on Mobile computing and networking
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Bandwidth- and power-efficient routing in linear wireless networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Efficient geographic routing over lossy links in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Robust topology control for indoor wireless sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
A delay-minimizing routing strategy for wireless multi-hop networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
On hopping strategies for autonomous wireless networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Transmission capacity of wireless ad hoc networks with outage constraints
IEEE Transactions on Information Theory
Routing in ad hoc networks: a case for long hops
IEEE Communications Magazine
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Empirical studies on link blacklisting show that the delivery rate is sensitive to the calibration of the blacklisting threshold. If the calibration is too restrictive (the threshold is too high), all neighbors get blacklisted. On the other hand, if the calibration is too loose (the threshold is too low), unreliable links get selected. This paper investigates blacklisting analytically. We derive a model that accounts for the joint effect of the wireless channel (signal strength variance and coherence time) and the network (node density). The model, validated empirically with mote-class hardware, shows that blacklisting does not help if the wireless channel is stable or if the network is relatively sparse. In fact, blacklisting is most beneficial when the network is relatively dense and the channel is unstable with long coherence times.