IEEE Systems Journal · 2013 · 72 citations · 21 references
EngineeringComputer SystemsElectrical EngineeringDigital CommunicationReal-time SystemsWireless CommunicationsPropagation DelaySensor NetworksSmart Grid ManagementSmart Grid SystemsSmart GridsWireless Sensor NetworksCritical Data PrioritizationData PrioritizationEnd-to-end DelayMedium Access ControlResource-constrained ComputingSmart Grid Communication NetworksTransmission DelayWireless Data Transmission
Monitoring smart-grid assets in a timely manner is highly desired for emerging smart-grid applications such as transformer monitoring, capacitor bank control, plug-in hybrid-electric-vehicle load management, and power quality assessment. Wireless sensor and actor networks (WSANs) are anticipated to be widely utilized in a wide range of smart-grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health. For resource-constrained WSANs, transmitting delay-critical data from smart-grid assets calls for data prioritization and delay responsiveness. In this paper, we introduce two medium-access approaches, namely, delay-responsive cross-layer (DRX) data transmission and fair and delay-aware cross-layer (FDRX) data transmission, which aim to address the delay and service requirements of smart grids. DRX is based on delay-estimation and data-prioritization steps that are performed by the application layer, in addition to the MAC layer parameters responding to the delay requirements of the smart-grid application and the network condition. On the other hand, FDRX incorporates fairness into DRX by preventing a few nodes from dominating the communication channel. We provide a comprehensive performance evaluation of those approaches. We show that DRX reduces the end-to-end delay while FDRX has lower collision rate compared with DRX. We outline the tradeoffs regarding these approaches and draw future research directions for robust communication protocols for smart-grid monitoring applications.
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Cognitive machine-to-machine communications: visions and potentials for the smart grid
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