Wireless Power Transfer with Information Asymmetry: A Public Goods Perspective

Zhang, Meng; Huang, Jianwei; Zhang, Rui

doi:10.1109/tmc.2019.2941205

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…Mechanism design for Network Resource Allocation: There have been many excellent works that proposed mechanisms for divisible network resources (e.g. [28][29][30][31][32][33][34]). These studies focused on achieving the efficient allocation outcomes with limited communication overheads in the presence of private utility information, however, without considering the interdependent valuations.…”

Section: Related Workmentioning

confidence: 99%

Truthful mobile crowd sensing with interdependent valuations

Zhang¹,

Swenson

Huang

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Mobile crowd sensing (MCS) has been used to enable a wide range of resource-discovery applications by exploiting the "wisdom" of many mobile users. However, in many applications, a user's valuation depends on other users' sensory data, which introduces the problem of interdependent valuations. This feature can encourage sensory data misreport, hence makes economic mechanisms challenging. While some work has been done to address this problem, the issues of private utility information and communication overheads remain unsolved. In this study, we formulate the first interdependent-valuation model for the resource-discovery MCS systems, aiming to elicit truthful sensory reports and utility information and to maximize expected social welfare. We design a Truthful Sense-And-Bid (T-SAB) Mechanism based on surrogate functions, which can reveal marginal utility information by only requiring each user to submit one-dimensional signaling per resource. We show that the surrogate function and a reward function can limit users' willingness to misreport, when users have small informational sizes, a reasonable condition in large-scale MCS systems. Consequently, our T-SAB Mechanism yields a Perfect Bayesian Equilibrium (PBE) with the efficient allocation outcome, approximate truthfulness, individual rationality, and approximate budget balance. To illustrate the effectiveness of the T-SAB Mechanism, we perform a case study of a cognitive radio network. We demonstrate that the social welfare gain of the T-SAB Mechanism can achieve up to 20% social welfare gain comparing with a benchmark.

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Section: Related Workmentioning

confidence: 99%

Truthful mobile crowd sensing with interdependent valuations

Zhang¹,

Swenson

Huang

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some works have been done to mitigate energy and information security in EH. For energy security, some admirable methods have been proposed, such as a machine learning-based preservation in [5] and the low-complexity taxation mechanism in [6]. For information privacy, source location-preserving mechanisms and energy consumption protection are studied [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

Pan

Bashir

et al. 2022

IEEE Trans. Ind. Inf.

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Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energyinformation cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the above challenge, this paper proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Secondly, a differential privacy-empowered information preservation scheme is devised, where sensitive information is protected by the customized demand-based noise. Thirdly, a non-cooperative gameenabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieves a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

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“…For example, replacing the batteries of IoTDs located in remote areas, such as deserts and forests, requires high costs. Recently, wireless power transfer has been considered as a promising solution to continuously supply energy for IoTDs, which enables IoTDs to harvest energy from radio frequency (RF) signals radiated by radio frequency energy transmitters into the air [8], [9]. As a result, wireless powered MEC (WP-MEC) that combines MEC with wireless power transfer can provide both computing resources and energy to IoTDs [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Divide-and-Conquer Bilevel Optimization Algorithm for Jointly Pricing Computing Resources and Energy in Wireless Powered MEC

Huang

Wang

2022

IEEE Trans. Cybern.

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This paper investigates a wireless powered mobile edge computing (MEC) system, where the service provider (SP) provides the device owner (DO) with both computing resources and energy to execute tasks from Internet of Things devices. In this system, SP first sets the prices of computing resources and energy whereas DO then makes the optimal response according to the given prices. In order to jointly optimize the prices of computing resources and energy, we formulate a bilevel optimization problem (BOP), in which the upper level generates the prices of computing resources and energy for SP and then under the given prices, the lower level optimizes the mode selection, broadcast power, and computing resource allocation for DO. This BOP is difficult to address due to the mixed variables at the lower level. To this end, we first derive the relationships between the optimal broadcast power and the mode selection and between the optimal computing resource allocation and the mode selection. After that, it is only necessary to consider the discrete variables (i.e., mode selection) at the lower level. Note, however, that the transformed BOP is still difficult to solve because of the extremely large search space. To solve the transformed BOP, we propose a divide-and-conquer bilevel optimization algorithm (called DACBO). Based on device status, task information, and available resources, DACBO first groups tasks into three independent small-size sets. Afterward, analytical methods are devised for the first two sets. As for the last one, we develop a nested bilevel optimization algorithm that uses differential evolution and variable neighborhood search (VNS) at the upper and lower levels, respectively. In addition, a greedy method is developed to quickly construct a good initial solution for VNS. The effectiveness of DACBO is verified on a set of instances by comparing with other algorithms.

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Wireless Power Transfer with Information Asymmetry: A Public Goods Perspective

Cited by 6 publications

References 46 publications

Truthful mobile crowd sensing with interdependent valuations

Truthful mobile crowd sensing with interdependent valuations

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

A Divide-and-Conquer Bilevel Optimization Algorithm for Jointly Pricing Computing Resources and Energy in Wireless Powered MEC

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