Machine-to-machine (M2M) communication's severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.
Unlike most human generated or consumed traffic, M2M as defined in this paper is characterized by a very large number of small transactions, often from battery powered devices. The power and energy optimal uplink design for various access strategies is studied in @cite_22 , while an optimal uncoordinated strategy to maximize the average throughput for a time-slotted RACH is developed in @cite_6 . For the small payload sizes relevant for M2M, a strategy that transmits both identity and data over the RACH is shown to support significantly more devices compared to the conventional approach, where transmissions are scheduled after an initial random-access stage.
@cite_22 @cite_6
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To enable full mechanical automation where each smart device can play multiple roles among sensor, decision maker, and action executor, it is essential to construct scrupulous connections among all devices. Machine-to-machine communications thus emerge to achieve ubiquitous communications among all devices. With the merit of providing higher-layer connections, scenarios of 3GPP have been regarded as the promising solution facilitating M2M communications, which is being standardized as an emphatic application to be supported by LTE-Advanced. However, distinct features in M2M communications create diverse challenges from those in human-to-human communications. To deeply understand M2M communications in 3GPP, in this article, we provide an overview of the network architecture and features of M2M communications in 3GPP, and identify potential issues on the air interface, including physical layer transmissions, the random access procedure, and radio resources allocation supporting the most critical QoS provisioning. An effective solution is further proposed to provide QoS guarantees to facilitate M2M applications with inviolable hard timing constraints. For wireless systems in which randomly arriving devices attempt to transmit a fixed payload to a central receiver, we develop a framework to characterize the system throughput as a function of arrival rate and per-device data rate. The frame- work considers both coordinated transmission (where devices are scheduled) and uncoordinated transmission (where devices com- municate on a random access channel and a provision is made for retransmissions). Our main contribution is a novel character- ization of the optimal throughput for the case of uncoordinated transmission and a strategy for achieving this throughput that relies on overlapping transmissions and joint decoding. Simula- tions for a noise-limited cellular network show that the optimal strategy provides a factor of four improvement in throughput compared with slotted ALOHA. We apply our framework to eval- uate more general system-level designs that account for overhead signaling. We demonstrate that, for small payload sizes relevant for machine-to-machine (M2M) communications (200 bits or less), a one-stage strategy, where identity and data are transmitted opti- mally over the random access channel, can support at least twice the number of devices compared with a conventional strategy, where identity is established over an initial random-access stage and data transmission is scheduled.