Flying ad hoc networks (FANETs) offer flexible, real-time wireless communication solutions for multi-drone systems by utilizing drones as network routers. However, FANETs’ unique characteristics, including high mobility, unstable network topology, and intermittent connectivity, pose significant challenges in designing efficient and reliable routing protocols. Traditional routing protocols for mobile ad hoc networks often fall short in highly dynamic airborne environments due to excessive control overhead, increased latency, and inefficient route maintenance. To address these issues, this paper proposes an enhanced on-demand predictive (EDP) routing protocol that integrates a neighbor coverage-based predictive flooding mechanism and an adaptive link quality-based route maintenance strategy. The flooding mechanism mitigates directional deafness by using a Kalman filter-based probabilistic forwarding model, while the route maintenance method optimizes path selection based on distance, traffic load, and link lifetime. Simulation results show that EDP significantly improves packet delivery rate, reduces network delay, and lowers overhead compared to benchmarks, making it well-suited for applications in FANETs.

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.

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 a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers' data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

With increasing popularity of location-based services (LBSs), there have also been growing concerns for location privacy. To protect location privacy in an LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this paper, we leverage mobile users' social tie structure to motivate them to participate in pseudonym change. Drawing on a social group utility maximization framework, we cast users' decision making of whether to change pseudonyms as a socially aware pseudonym change game (SA-PCG). The SA-PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SA-PCG, we show that there exists a socially aware Nash equilibrium (SNE), and quantify the system efficiency of SNEs with respect to the optimal social welfare. Then, we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. We show that this algorithm efficiently finds an SNE that enjoys desirable properties: 1) it is socially aware coalition-proof, and thus is also Pareto-optimal; 2) it achieves higher social welfare than any SNE for the socially oblivious pseudonym change game. We further quantify the system efficiency of this SNE with respect to the optimal social welfare. We also show that this SNE can be achieved in a distributed manner. Numerical results using real data corroborate that social welfare can be significantly improved by exploiting social ties.

In spite of the pronounced benefit brought by crowdsensing, a user would not participate in sensing without adequate incentive, indicating that effective incentive design plays a critical role in making crowdsensing a reality. In this work, we examine the impact of two conflicting factors on incentives for users' participation: 1) the concern about privacy leakage and 2) the (positive) network effect from many sensing participants. The former factor hinders privacy- aware users from participating, whereas the latter encourages users' participation. Taking into consideration both factors, we devise a privacy-preserving crowdsensing scheme, in which a reverse `privacy' auction is first run by the crowdsensing platform to select users based on their privacy valuations and the network effect. Then the trusted platform carries out differentially private data aggregation over the collected data such that the released sensing result remains useful for the task agent, while all participants' data privacy is guaranteed. A natural objective here is then to maximize the profit of the task agent, i.e., the difference between its utility and the total reward to the participants. To this end, the platform utilizes a random-sampling based mechanism for the 'privacy' auction, followed by a Laplace mechanism for data aggregation. We show that this auction mechanism design is 4-competitive, and further it exhibits desirable properties, including individual rationality, truthfulness, computational efficiency. Simulation results corroborate the theoretical properties of the proposed privacy-preserving crowdsensing scheme.

In this paper, we study a privacy-preserving spectrum sharing system to protect secondary users' location privacy while enhancing spectrum access. The location privacy of secondary users can be compromised by an external adversary via the received signal strength (RSS)-based localization technique. To mitigate such privacy threat, we employ a random power perturbation approach that allows each secondary user to judiciously obfuscate the RSS captured by the adversary. While it can protect users' location privacy, the power perturbation approach would inevitably degrade the system performance and bring challenges to the design of the spectrum allocation algorithm. In this work, we adopt a socially-aware database assisted spectrum access system and cast the spectrum allocation under users' power perturbation as a stochastic channel selection game played among the users. To tackle the challenge brought by the privacy protection, we develop a two time-scale distributed learning algorithm, which is shown to converge almost surely to a socially-aware ε-Nash equilibrium. The numerical results show that the higher the privacy protection level is, the more significant the degradation of the network throughput would be.

With increasing popularity of location-based services (LBSs), there have been growing concerns for location privacy. To protect location privacy in a LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this study, we leverage the social tie structure among mobile users to motivate them to participate in pseudonym change. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to change pseudonyms as a socially-aware pseudonym change game (PCG). The PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SGUM-based PCG, we show that there exists a socially-aware Nash equilibrium (SNE), and quantify the system efficiency of the SNE with respect to the optimal social welfare. Then we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. It turns out that this algorithm can efficiently find a Pareto-optimal SNE with social welfare higher than that for the socially-oblivious PCG, pointing out the impact of exploiting social tie structure. We further show that the Pareto-optimal SNE can be achieved in a distributed manner.

Cooperative networking is a promising technology to meet the rapidly growing demand of mobile data traffic. To stimulate effective and trustworthy user cooperation, we leverage the knowledge of the social tie structure among mobile users and develop a social trust based cooperative D2D relaying framework, which takes into account both physical distances and social distances among users. Based on (finite-horizon) optimal stopping theory, we derive the optimal social aware relay selection strategy, which strikes a balance between performance gain and relay probing cost. We further show that the optimal stopping policy for social aware relay selection exhibits a stage-dependent threshold structure that has a monotonically non-increasing property. Numerical results demonstrate that the proposed mechanism can yield significant throughput gain over the direct transmission scheme.