Driven by the vision of 5G communication, the demand for mobile communication services has increased explosively. Ultra-dense networks (UDN) is a key technology in 5G. The combination of mobile edge computing (MEC) and UDN can not only cope with access from mass communication devices, but also provide powerful computing capacity for users at the edge of wireless networks. The UDN based on MEC can effectively process computation-intensive and data-intensive tasks. However, when a large number of users offload tasks to the edge server, both the network load and transmission interference would increase. In this paper, the problem of task offloading and channel resource allocation based on MEC in 5G UDN is studied. Specifically, we formulate task offloading as an integer nonlinear programming problem. Due to the coupling of decision variables, we propose an efficient task offloading and channel resource allocation scheme based on differential evolution algorithm. Simulation results show that the proposed scheme can obviously reduce energy consumption and has good convergence.

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 resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

To minimize the sensing cost in MCS while preserving the participants’ privacy, in this paper we propose a Data Sensing mechanism with User Privacy Preserved (DS-UPP). We introduce edge computing into MCS to support task allocation and user privacy protection. In DS-UPP, based on compressive sensing theory we minimize the amount of data needed to be submitted. We also design an algorithm based on local differential privacy theory. Selected participants only need to submit their real data along with the reconstructed data generated by the algorithm. It is proved that DS-UPP satisfies $\varepsilon $ -differential privacy. We give the mathematical lower bound and upper bound of the number of participants needed for task accomplishment with the constraints that privacy budget is $\varepsilon $ and recovery error of task data is 0, as well as the average amount of data that should be submitted by a participant. We also evaluate the performance of DS-UPP through simulations. Compared with the existing method PrivKV, DS-UPP can reduce the needed data amount by about 90% on the average while guarantee users’ privacy preserved.