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M2M Access With Dynamic Cognitive Virtual Operators: A Data Aggregator’s Perspective

In this paper, an aggregator-assisted model for machine-to-machine (M2M) communications is proposed. The data aggregator plays the role of a third party that transmits aggregated M2M data to a cognitive operator, which does not own the radio spectrum but rather leases it from a spectrum owner. In the studied system, the cognitive operators for machine services are considered to dynamically appear in the network and compete with one another, each of them having uncertain backhual M2M data traffic. The aggregator can then decide to select one operator with whom to set up either a long-term (LT) or a short-term (ST) service relationship, by anticipating the result of the auction in the second period. Under an ST relationship, the aggregator maintains the option of switching to a new operator in the next period, thereby incurring possible packet losing costs. In contrast, with an LT relationship, the aggregator commits to an operator over a long period of time, but may loss the opportunities to choose an operator with a possible lower operation cost. The optimal decision that maximizes the aggregator’s expected utility is derived. The proposed M2M access model allows the aggregator accounting for both the switching cost for M2M communications, and, the benefit of a long-term service contract. Moreover, it is shown that an LT relationship may induce a higher accessed number of machines than an ST relationship when the cost of switching between operators is relatively high. Simulation results show that the spectrum sharing for the aggregator-assisted M2M model can significantly increase the number of accessed machines in a certain time duration, as well as bring extra revenues for the spectrum owner, the operator, and the aggregator.