You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy. The efficacy of our solutions is verified through both synthetic and trace-driven simulations.

Cited Abstract(s):
[1]: We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco.

[2]: The Follow-Me Cloud (FMC) concept enables service mobility across federated data centers (DCs). Following the mobility of a mobile user, the service located in a given DC is migrated each time an optimal DC is detected. The detailed criterion for optimality is defined by operator policy, but it may be typically derived from geographical proximity or load. Service migration may be an expensive operation given the incurred cost in terms of signaling messages and data transferred between DCs. Decision on service migration defines therefore a tradeoff between cost and user perceived quality. In this paper, we address this tradeoff by modeling the service migration procedure using a Markov Decision Process (MDP). The aim is to formulate a decision policy that determines whether to migrate a service or not when the concerned User Equipment (UE) is at a certain distance from the source DC. We numerically formulate the decision policies and compare the proposed approach against the baseline counterpart.

[3]: Mobile micro-cloud is an emerging technology in distributed computing, which is aimed at providing seamless computing data access to the edge of the network when a centralized service may suffer from poor connectivity and long latency. Different from the traditional cloud, a mobile micro-cloud is smaller and deployed closer to users, typically attached to a cellular base station or wireless network access point. Due to the relatively small coverage area of each base station or access point, when a user moves across areas covered by different base stations or access points which are attached to different micro-clouds, issues of service performance and service migration become important. In this paper, we consider such migration issues. We model the general problem as a Markov decision process (MDP), and show that, in the special case where the mobile user follows a one-dimensional asymmetric random walk mobility model, the optimal policy for service migration is a threshold policy. We obtain the analytical solution for the cost resulting from arbitrary thresholds, and then propose an algorithm for finding the optimal thresholds. The proposed algorithm is more efficient than standard mechanisms for solving MDPs.

[4]:
Another line of related work is service migrations in MECs. Service migrations in MECs are primarily driven by the need to keep a service close to its user, where the decision on whether to migrate a service depends on both the migration cost (if migrating the service to follow the user) and the communication cost (if serving the user from the original location as the user moves away). Modeling the user's mobility as a , several solutions based on have been proposed to minimize the total cost under 1-D [2] [3] or 2-D mobility models [1] [4] . Here we consider the worst case (in terms of location privacy) that the real service follows the user, and focus on protecting the user's location privacy using chaffs. We leave the study of privacy-aware service migration to future work.