IEEE Transactions on Mobile Computing · 2006 · 820 citations · 31 references
ComputingComputer ScienceComputer SystemsComputer NetworksDistributed SystemsQueuing TheoryNetwork ReliabilityRoutingRouting AlgorithmsRouting ProtocolsSensor NetworksWireless Sensor NetworksDynamic CompensationMulti-path RoutingMultipathProvisioningQuality Of Service RoutingTimeliness MetricsWireless Sensor Network ProtocolsWireless Sensor Network Routing
The authors introduce MMSPEED, a multi‑path, multi‑speed routing protocol that probabilistically guarantees QoS in wireless sensor networks. MMSPEED achieves QoS by providing multiple delivery‑speed options for timeliness and probabilistic multipath forwarding for reliability, using localized geographic forwarding with dynamic compensation to correct local decisions. Simulations demonstrate that MMSPEED locally guarantees end‑to‑end QoS, improving effective network capacity by up to 50 % (from 12 to 18 flows meeting both reliability and timeliness).
In this paper, we present a novel packet delivery mechanism called Multi-Path and Multi-SPEED Routing Protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. These mechanisms for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates for local decision inaccuracies as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements up to 50 percent (12 flows versus 18 flows).
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