Showing papers by "Gyorgy Dan published in 2019"

Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks

Abstract: Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper, we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs. We provide a game theoretical analysis of the problem, prove the existence of pure strategy Nash equilibria, and provide an efficient decentralized algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices, we show that all improvement paths are finite. Furthermore, we provide an upper bound on the price of anarchy of the game, which serves as an upper bound on the approximation ratio of the proposed decentralized algorithms. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the price of anarchy of the game under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.

Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems

Abstract: Fog computing is identified as a key enabler for using various emerging applications by battery powered and computationally constrained devices. In this paper, we consider devices that aim at improving their performance by choosing to offload their computational tasks to nearby devices or to an edge cloud. We develop a game theoretical model of the problem and use a variational inequality theory to compute an equilibrium task allocation in static mixed strategies. Based on the computed equilibrium strategy, we develop a decentralized algorithm for allocating the computational tasks among nearby devices and the edge cloud. We use the extensive simulations to provide insight into the performance of the proposed algorithm and compare its performance with the performance of a myopic best response algorithm that requires global knowledge of the system state. Despite the fact that the proposed algorithm relies on average system parameters only, our results show that it provides a good system performance close to that of the myopic best response algorithm.

Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing

Abstract: We consider the problem of allocating wireless and computing resources to a set of autonomous wireless devices in an edge computing system. Devices in the system can decide whether or not to use edge computing resources for offloading computing tasks so as to minimize their completion time, while the edge cloud operator can allocate wireless and computing resources to the devices. We model the interaction between devices and the operator as a Stackelberg game, prove the existence of Stackelberg equilibria, and propose an efficient decentralized algorithm for computing equilibria. We provide a bound on the price of anarchy of the game, which also serves as an approximation ratio bound for the proposed algorithm. Our simulation results show that the joint allocation of wireless and computing resources by the operator can halve the completion times compared to a system with static resource allocation. At the same time, the convergence time of the proposed algorithm is approximately linear in the number of devices, and thus it could be effectively implemented for edge computing resource management.

Joint Assignment and Scheduling for Minimizing Age of Correlated Information

Abstract: Age of information has been recently proposed to quantify the freshness of information, e.g., in cyber-physical systems, where it is of critical importance. Motivated by wireless camera networks where multi-view image processing is required, in this paper we propose to extend the concept of age of information to capture packets carrying correlated data. We consider a system consisting of wireless camera nodes with overlapping fields of view and a set of processing nodes, and address the problem of the joint optimization of processing node assignment and camera transmission scheduling, so as to minimize the maximum peak age of information from all sources. We formulate the multi-view age minimization (MVAM) problem, and prove its NP-hardness under the two widely used interference models as well as with given candidate transmitting groups. We provide fundamental results including tractable cases and optimality conditions of the MVAM problem for two baseline scenarios. To solve MVAM efficiently, we develop an optimization algorithm based on a decomposition approach. Numerical results show that by employing our approach the maximum peak age is significantly reduced in comparison to a traditional centralized solution with minimum-time scheduling.

Joint Management of Wireless and Computing Resources for Computation Offloading in Mobile Edge Clouds

Abstract: We consider the computation offloading problem in an edge computing system in which an operator jointly manages wireless and computing resources across devices that make their offloading decisions ...

Next Steps in Security for Time Synchronization: Experiences from implementing IEEE 1588 v2.1

Abstract: The lack of integrated support for security has been a major shortcoming of Precision Time Protocol version 2 (PTPv2) for a long time. The upcoming PTPv2.1 aims at addressing this shortcoming in a variety of ways, including the introduction of lightweight message authentication. In this paper we provide an overview of the planned security features, and report results based on an implementation of the proposed integrated security mechanism based on the open source Linux PTP, including support for hardware timestamping. Our implementation includes an extension of Linux PTP to support transparent clocks. We provide results from an experimental testbed including a transparent clock, which illustrate that the extensions can be implemented in software at a low computational overhead, while supporting hardware timestamping. We also provide a discussion of the remaining vulnerabilities of PTP time synchronization, propose countermeasures, and discuss options for key management, which is not covered by the standard.

On Emptying a Wireless Network with Minimum-Energy Under Age Constraints

Abstract: Timely information delivery and low energy consumption are of critical importance for a variety of wireless applications. In this paper, we address the link scheduling problem of emptying a network with minimum energy, subject to a maximum peak age constraint for each information source. We formulate the minimum-energy scheduling with age constraints (MESA) problem in its general form and prove that it is NP-hard. We derive fundamental results, such as lower and upper bounds of the minimum energy consumption, and the conditions when a TDMA schedule is optimal. We propose the deadline-first-with-revision (DFR) algorithm for constructing a scheduling solution, and evaluate its performance under two rate functions. Numerical results show that DFR achieves a significant energy reduction compared to a minimum age scheduling solution.

A Game Theoretic Approach to Uplink Pilot and Data Power Control in Multi-Cell Multi-User MIMO Systems

Abstract: In multi-user multiple-input–multiple-output (MU-MIMO) systems that employ pilot-symbol aided channel estimation, the pilot-to-data power ratio (PDPR) has a large impact on the system performance. In this paper, we consider the problem of setting the PDPR in multi-cell MU-MIMO systems in the presence of channel estimation errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a model of the multi-cell MU-MIMO system and derive a closed-form expression for the mean squared error of the uplink received data symbols. Building on this result, we then propose two decentralized PDPR-setting algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that do not properly set the PDPR, while they maintain different levels of fairness.

Adversarial Attacks on Continuous Authentication Security: A Dynamic Game Approach

Abstract: Identity theft through phishing and session hijacking attacks has become a major attack vector in recent years, and is expected to become more frequent due to the pervasive use of mobile devices. Continuous authentication based on the characterization of user behavior, both in terms of user interaction patterns and usage patterns, is emerging as an effective solution for mitigating identity theft, and could become an important component of defense-in-depth strategies in cyber-physical systems as well. In this paper, the interaction between an attacker and an operator using continuous authentication is modeled as a stochastic game. In the model, the attacker observes and learns the behavioral patterns of an authorized user whom it aims at impersonating, whereas the operator designs the security measures to detect suspicious behavior and to prevent unauthorized access while minimizing the monitoring expenses. It is shown that the optimal attacker strategy exhibits a threshold structure, and consists of observing the user behavior to collect information at the beginning, and then attacking (rather than observing) after gathering enough data. From the operator’s side, the optimal design of the security measures is provided. Numerical results are used to illustrate the intrinsic trade-off between monitoring cost and security risk, and show that continuous authentication can be effective in minimizing security risk.

Dynamic Cheap Talk for Robust Adversarial Learning

Abstract: Robust adversarial learning is considered in the context of closed-loop control with adversarial signaling in this paper. Due to the nature of incomplete information of the control agent about the environment, the belief-dependent signaling game formulation is introduced in the dynamic system and a dynamic cheap talk game is formulated with belief-dependent strategies for both players. We show that the dynamic cheap talk game can further be reformulated as a particular stochastic game, where the states are beliefs of the environment and the actions are the adversarial manipulation strategies and control strategies. Furthermore, the bisimulation metric is proposed and studied for the dynamic cheap talk game, which provides an upper bound on the difference between values of different initial beliefs in the zero-sum equilibrium.

Fog-Assisted Aggregated Synchronization Scheme for Mobile Cloud Storage Applications

Abstract: Cloud storage applications, such as Dropbox and Google Drive, have recently become very popular among mobile users. In these applications, a cloud server is responsible for synchronizing updates to ...

Scheduling Parallel Migration of Virtualized Services Under Time Constraints in Mobile Edge Clouds

Abstract: Migrating virtualized services (VSs) in mobile edge clouds is essential for maintaining service quality under mobility, for optimizing resource utilization, and for responding to incidents. We consider migrating VSs with heterogeneous resource requirements from a source placement to a target placement under a time constraint, while maintaining service continuity as much as possible. We formulate the VS migration problem as an integer programming problem, and propose an efficient algorithm to compute sequences of migration actions. The algorithm is based on a graphical representation of the VS dependencies, and constructs a collection of acyclic directed hypergraphs with bounded length. We evaluate our algorithm in realistic scenarios and compare it to the optimal solution and to a baseline algorithm. Extensive simulations show that our algorithm achieves near-optimal performance, and is computationally efficient and scalable.

Janus: Privacy-Preserving Billing for Dynamic Charging of Electric Vehicles

Abstract: Dynamic charging is an emerging technology that allows an electric vehicle (EV) to charge its battery while moving along the road. Dynamic charging charges the EV’s battery through magnetic induction between the receiving coils attached to the EV’s battery and the wireless charging pads embedded under the roadbed and operated by Pad Owners (POs). A key challenge in dynamic charging is billing, which must consider the fact that the charging service happens while the EV is moving on the road, and should allow for flexible usage plans. A promising candidate could be the subscription-based billing model, in which an EV subscribes to an electric utility that has a business relationship with various POs that operate charging sections. The POs report charging information to the utility of the EV, and at the end of each billing cycle, the EV receives a single bill for all its dynamic charging sessions from the utility. Overshadowing its advantages, a major shortcoming of such a solution is that the utility gets access to the EVs’ mobility information, invading thus the location privacy of the EVs. To enable subscription based billing for dynamic charging, in this paper we propose Janus, a privacy-preserving billing protocol for dynamic EV charging. Janus uses homomorphic commitment and blind signatures with attributes to construct a cryptographic proof on the charging fee of each individual dynamic charging session, and allows the utility to verify the correctness of the EV’s total bill without learning the time, the location, or the charging fee of each individual charging session of the EV. Our Pythonbased implementation shows that the real-time computational overhead of Janus is less than 0.6 seconds, which is well within the delay constraint of the subscription-based billing model, and makes Janus an appealing solution for future dynamic charging