The Internet of Things (IoT) is a vision of the upcoming society. It can provide pervasive communication between two or more entities using 4G-LTE (Long Term Evolution) communication technology. In 4G-LTE networks, there are two important problems: helping manage the spectrum demands of IoT devices and achieving much more revenue with the limited resource. This paper proposes a pricing framework to investigate the spectrum leasing in mobile heterogeneous networks with single macrocell and multiple femtocells. We modeled the leasing procedure between the macrocell service provider (MSP) and femtocell holders (FHs) as an auction to motivate the MSP to lease its spectrum resource. All FHs act as the bidders, and the monopolist MSP acts as the auctioneer. In the auction, FHs submit their bids to rent the spectrum resource so that they can make a profit by selling it to their subscribers. The MSP determines the spectrum leasing amount and chooses the winning FHs by solving the dynamic programming-based 0–1 knapsack problem. In our proposed framework, we focus on the spectrum pricing strategy and revenue maximization of the MSP. The simulation results show that the proposed scheme provides effective motivation for the MSP to lease the spectrum to FHs, and both the MSP and FHs can benefit from spectrum leasing.

We investigate the network selection problem over heterogeneous networks. We formulate the decision making problem as a Markov decision process (MDP). Two Poisson processes are introduced to model user arrival and departure, and the change of the number of user in a specific network is described by Skellam Distribution. We apply the Total order preference by similarity to the ideal solution (TOPSIS) method to address the issue of state space explosion in MDP model. To acquire the optimal network selection scheme, the value iteration algorithm is employed to solve the MDP model. We take simulations in Matlab to prove the effectiveness of the proposed scheme. From the experiment results, it is found that our proposed scheme can reduce handover probability by 7.5% and 40.4% at the early stage of connection, compared with the Infinite MDP-based (IMDP) strategy and TOPSISonly strategy. It can also reduce blocking probability by 6.1% compared with IMDP, while maintaining desirable throughput for the target user.