Publication | Closed Access
X-MAC
1.7K
Citations
13
References
2006
Year
Unknown Venue
EngineeringWireless Sensor SystemEdge ComputingComputer EngineeringDefault Mac ProtocolLow Power ListeningInternet Of ThingsMobile ComputingStandard MacSensor ConnectivityMedium Access ControlLightweight ProtocolEnergy-efficient Networking
Standard MAC protocols for duty‑cycled WSNs, such as BMAC, use extended preambles for low‑power listening, but this approach incurs high latency, suboptimal energy use, and excess consumption at non‑target receivers. This paper introduces X‑MAC, a low‑power MAC protocol that addresses these issues by employing a shortened preamble while preserving low‑power listening benefits. X‑MAC achieves this by decoupling transmitter and receiver sleep schedules through a brief preamble, enabling efficient asynchronous communication. Implementation and testbed evaluation show that X‑MAC markedly reduces energy consumption at both ends, lowers per‑hop latency, and adapts flexibly to bursty and periodic sensor traffic.
In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this "low power listening" approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC's shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.
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