Mobile edge computing (MEC) has been proposed in recent years to offload computation tasks from user equipments (UEs) to the network edge to break hardware limitations and resource constraints at UEs. Although there have been some existing works on computation offloading in 5G, most of them fail to take into account of the unique property of 5G in their scheme design. In this work, we consider small-cell network architecture for task offloading. In order to achieve energy efficiency, we model the energy consumption of offloading from both task computation and communication aspects. Besides, transmission scheduling are carried over both the fronthaul and backhual links. We first formulate an energy optimization problem of offloading, which aims to minimize the overall energy consumption at all system entities and takes into account of the constraints from both computation capabilities and service delay requirement. We then develop an artificial fish swarm algorithm (AFSA) based scheme to solve the energy optimization problem. Besides, the global convergence property of the our scheme is formally proved. Finally, various simulation results demonstrate the efficiency of our scheme.

Mobile edge computing is becoming a major trend in providing computation capacities at the edge of mobile networks. Meanwhile, unmanned aerial vehicles (UAVs) have been considered as distinctly important integrated components to extend services coverage. In order to provide users with higher and satisfied quality of services, edge computing resources need to be allocated between edge computing stations (ECSs) and UAVs in mobile networks. However, there are significant security and privacy problems due to the open environments of ECSs and UAVs. In this paper, we propose a resource pricing and trading scheme based on Stackelberg dynamic game to optimally allocate edge computing resources between ECSs and UAVs, and blockchain technology is applied to record the entire resources trading process to protect the security and privacy. The ECSs control the resources price of the allocated edge computing resources, where the UAVs follow the price announced by the ECSs and make optimal decisions on the edge computing resources demands. Blockchain is integrated in the resource trading process to ensure the security and privacy. Numerical simulations are given to show the effectiveness of the proposed scheme.

While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, a s patio- te mporal fine-granular user traffic p rediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user’s massive dataset for traffic prediction. The prototype of STEP has been implemented. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. Moreover, NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers’ privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. A u ser-centric l ocation p rivacy t rading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget $\xi$ . The higher $\xi$ is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including $\xi$ - privacy , $(\alpha, \beta)$ - accuracy , budget feasibility . Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three.