PoMeS: Profit-Maximizing Sensor Selection for Crowd-Sensed Spectrum Discovery

In a conventional network management setting, the mobile network operator (MNO) has to account for the traffic fluctuations in its service area and over-provision its network considering the peak traffic. However, this inefficient approach results in a very high cost for the MNO. Alternatively, the MNO can expand its capacity with secondary spectrum discovered opportunistically whenever, wherever needed. While outsourcing the spectrum discovery to a crowd of sensing units may be more advantageous compared to deploying sensing infrastructure itself, the MNO has to offer incentives in the form of payments to the units participating in the sensing campaign. A key challenge for this crowdsensing environment is to decide on how many sensing units to employ given a certain budget under some performance constraints. In this paper, we present a profit-maximizing sensor selection scheme for crowd-sensed spectrum discovery (PoMeS) for MNOs who want to take sensing as a service from the crowd of network elements and pay these sensors for their service. Compared to sensor selection considering the strict sensing accuracy required by the regulations, our heuristics show that an MNO can increase its profit by deciding itself the level of sensing accuracy based on its traffic in each cell site as well as the penalty it has to pay for not satisfying the required sensing accuracy.