In this paper, we design privacy-preserving and cost-efficient energy management strategies for smart grid users that are equipped with renewable energy sources. The adversary is assumed to employ a factorial hidden Markov model based inference for load disaggregation, and the corresponding joint log-likelihood of the model is utilized as the privacy measure. The studied dynamic pricing model is applicable to a commodity-limited market, where the price of unit amount of energy is determined by the users’ aggregated power request. The users’ energy management strategies are designed under a non-cooperative game framework, where each user aims to optimize a weighted sum objective of both privacy measure and energy cost saving. The users’ non-cooperative game is shown to admit a unique pure strategy Nash equilibrium. As an extension, a computational-efficient distributed Nash equilibrium energy management strategy seeking method is proposed, which also avoids the privacy leakage due to the sharing of payoff functions between users. The performance of practical designs of the energy management strategies in the equilibrium is finally illustrated by numerical experiments.

We design optimal privacy-enhancing and cost-efficient energy management strategies for consumers that are equipped with a rechargeable energy storage. The Kullback-Leibler divergence rate is used as privacy measure and the expected cost-saving rate is used as utility measure. The corresponding energy management strategy is designed by optimizing a weighted sum of both privacy and cost measures over a finite time horizon, which is achieved by formulating our problem into a belief-state Markov decision process problem. A computationally efficient approximated Q-learning method is proposed as a generalization to high-dimensional problems over an infinite time horizon. At last, we explicitly characterize a stationary policy that achieves the steady belief state over an infinite time horizon, which greatly simplifies the design of the privacy-preserving energy management strategy. The performance of the practical design approaches are finally illustrated in numerical experiments.

In a vehicular communication network, how to efficiently utilize the limited network resources to meet the stringent transmission quality of service (QoS) requirements in the dynamically changing environment is a challenging task. This paper studies a typical vehicle-to-vehicle (V2V) communication scenario, in which multiple source nodes desire to deliver different types of messages to their respective destinations. Considering the case that accurate instantaneous global channel state information (CSI) is unavailable, properly selecting access channel, transmission power, and data rate at each source to optimize the links' performance is an extremely involved mixed-integer stochastic optimization problem. We propose applying the distributed multi-agent deep reinforcement learning technique to solve the problem. Specifically, a distributed multi-agent parameterized deep Q-network (DMA-PDQN) algorithm is employed to explore optimal decision in the discrete-continuous hybrid action space. The idea of federated meta learning (FML) is also adopted to tackle the non-stationarity issue and improve the training efficiency of distributed agents. Using energy efficiency (EE) as the target transmission objective, through extensive simulation we show that our method can achieve excellent convergence and performance.

Data delivery in vehicular networks confronts unique challenges due to vehicle movement and stringent quality-of-service (QoS) demands. This paper investigates the efficient transmission design problem in a typical vehicle-to-vehicle (V2V) communication network, where multiple source-destination pairs with different types of message transmission requirements coexist and the network environment dynamically changes. A sequential transmission decision framework based on the multi-objective optimization (MOO) theory is proposed to maximize the performance of each link, while ensuring the QoS of different messages. We integrate the Lyapunov optimization theory with the weighted Tchebycheff method, as well as other mathematical optimization tools, to transform the original problem into a solvable MOO problem at each time slot. Transmission actions can be determined continuously according to the environment change and time-related design goals. The effectiveness and efficiency of the proposed method are demonstrated through extensive simulation experiments.

This letter addresses a proactive resource allocation problem in an unmanned aerial vehicles (UAV)-assisted vehicle-to-everything (V2X) communication network. The problem, which can be formulated from the perspective of dynamic multi-objective optimization (DMO), focuses on the decisions of power control, rate selection, and channel allocation with the aim to balance the decision objectives of heterogeneous V2X links while ensuring the network’s quality of service (QoS). We propose a novel algorithm that enables quick decision-making in a dynamic environment, by predicting the future channel state information (CSI) and searching for the solution set of the problem before the environment changes. The effectiveness and advantages of the proposed method are demonstrated by simulation experiments.

In this paper, the privacy problem of a parallel distributed detection system vulnerable to an eavesdropper is proposed and studied in the Neyman-Pearson formulation. The privacy leakage is evaluated by a metric related to the Neyman-Pearson criterion. We will show that it is sufficient to consider a deterministic likelihood-ratio test for the optimal detection strategy at the eavesdropped sensor. This fundamental insight helps to simplify the problem to find the optimal privacy-constrained distributed detection system design. The trade-off between the detection performance and privacy leakage is illustrated in a numerical example.