Modeling and Analysis of Reservation Frame Slotted-ALOHA in Wireless Machine-to-Machine Area Networks for Data Collection
AbstractReservation frame slotted-ALOHA (RFSA) was proposed in the past to manage the access to the wireless channel when devices generate long messages fragmented into small packets. In this paper, we consider an M2M area network composed of end-devices that periodically respond to the requests from a gateway with the transmission of fragmented messages. The idle network is suddenly set into saturation, having all end-devices attempting to get access to the channel simultaneously. This has been referred to as delta traffic. While previous works analyze the throughput of RFSA in steady-state conditions, assuming that traffic is generated following random distributions, the performance of RFSA under delta traffic has never received attention. In this paper, we propose a theoretical model to calculate the average delay and energy consumption required to resolve the contention under delta traffic using RFSA.We have carried out computer-based simulations to validate the accuracy of the theoretical model and to compare the performance for RFSA and FSA. Results show that there is an optimal frame length that minimizes delay and energy consumption and which depends on the number of end-devices. In addition, it is shown that RFSA reduces the energy consumed per end-device by more than 50% with respect to FSA under delta traffic. View Full-Text
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Vázquez-Gallego, F.; Alonso, L.; Alonso-Zarate, J. Modeling and Analysis of Reservation Frame Slotted-ALOHA in Wireless Machine-to-Machine Area Networks for Data Collection. Sensors 2015, 15, 3911-3931.
Vázquez-Gallego F, Alonso L, Alonso-Zarate J. Modeling and Analysis of Reservation Frame Slotted-ALOHA in Wireless Machine-to-Machine Area Networks for Data Collection. Sensors. 2015; 15(2):3911-3931.Chicago/Turabian Style
Vázquez-Gallego, Francisco; Alonso, Luis; Alonso-Zarate, Jesus. 2015. "Modeling and Analysis of Reservation Frame Slotted-ALOHA in Wireless Machine-to-Machine Area Networks for Data Collection." Sensors 15, no. 2: 3911-3931.