Location-based services (LBSs) provide enhanced functionality and convenience of ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the users’ privacy. The leakage of private location data in the LBS context has drawn significant attention from academics and industry due to its importance, leading to numerous research efforts aiming to confront the related challenges. However, to the best of our knowledge, none of relevant studies have performed a qualitative and quantitative comparison and analysis of the complex topic of designing countermeasures and discussed the viability of their use with different kinds of services and the potential elements that could be deployed to meet new challenges. Accordingly, the purpose of this survey is to examine the privacy-preserving techniques in LBSs. We categorize and provide an inside-out review of the existing techniques. Performing a retrospective analysis of several typical studies in each category, we summarize their basic principles and recent advances. Additionally, we highlight the use of privacy-preserving techniques in LBSs for enabling new research opportunities. Providing an up-to-date and comprehensive overview of existing studies, this survey may further stimulate new research efforts into this promising field.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users.

Mobile Edge Computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of large amount of computations in the emerging vehicular applications. In this paper, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hotzones. Specifically, a deep learning based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a non-cooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end-users’ QoE guaranteed.

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solve the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput.

With the increasing popularity of location-based services (LBS), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region (CR) via a trusted third-party anonymiser. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIA) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymiser, the probability of disclosing one’s location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

In this paper, with the aims of alleviating the pressure from the shortage of spectrum resource and addressing the inefficient spectrum utilization, we investigate the spectrum sharing for moving vehicles in Heterogeneous Vehicular Networks (HVNs) consisting of the macrocells and the Road Side Units (RSUs) with Cognitive Radio (CR) technology. We first propose an incentive mechanism for encouraging macrocells to share spectrum resource with vehicle users, in which the CR-enabled RSUs perform sensing the spectrum availability in the surrounding urban environments. Furthermore, the downlink resource allocation for vehicle users associated with different RSUs is modeled as an $n$ -person game and solved by designing a noncooperative game theoretic approach. By considering transmission power constraint of RSU and inter-RSU interference, the resource allocation and interference mitigation among RSUs are formulated via maximizing the overall utility in the HVNs. We design a game theoretical strategy optimization algorithm based on regret-matching and then derive the correlated equilibrium solution. Moreover, we propose a heuristic power control algorithm for further mitigating the inter-RSU interference in the noncooperative game based resource allocation. Simulation results demonstrate that the proposed approach can achieve the correlated equilibrium with fast convergence and significantly improve the system performance in high mobility HVNs.