Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other update their states order to reach an agreement. Under a general average algorithm, information exchanged through wireless or wired communication could lead disclosure of sensitive private information. In this article, we propose privacy-preserving push-sum approach directed that can protect privacy while achieving simultaneously. Each node...

In recent years, there has been a growing interest in distributed optimization, which collaboratively attains an optimum by exchanging information with neighbours. Among the various algorithms available, optimization gradient tracking is particularly notable for its superior convergence results, especially context of directed graphs. However, privacy concerns arise when transmitted directly would induce more leakage. Surprisingly, literature not adequately addressed associated issues....

Recently, many multireceiver identity-based encryption schemes have been proposed in the literature. However, none can protect privacy of message receivers among these schemes. In this paper, we present an anonymous scheme where adopt Lagrange interpolating polynomial mechanisms to cope with above problem. Our makes it impossible for attacker or any other receiver derive identity a such that every be guaranteed. Furthermore, is quite efficient since each merely needs perform twice pairing...

In this article, we study the problem of consensus-based distributed optimization, where a network agents, abstracted as directed graph, aims to minimize sum all agents' cost functions collaboratively. existing optimization approaches (Push-Pull/AB) for graphs, agents exchange their states with neighbors achieve optimal solution constant step size, which may lead disclosure sensitive and private information. For privacy preservation, propose novel state-decomposition-based gradient tracking...

A vehicle platoon is a group of vehicles driving together with harmonized speed and short inter-vehicle gap by using automation vehicle-to-vehicle communication. Platoons have to share road human-driven (HDVs) can only be applied in heterogeneous traffic flow for long period. Driver cut-in behavior (DCB) towards frequently expected such context. In this paper, understand simulate behavior, we propose platoon-oriented (POCB) model fusing lateral longitudinal control into the queuing network...

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and harmonized velocity. For long period, the VPs human-driven (HDVs) will coexist in mixed traffic flow, where cut-in maneuver HDVs towards can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for by considering tradeoff between platoon integrity safety. Particularly, proposed is designed prevent as many cut-ins possible while...

We consider the problem of communication allocation for remote state estimation in a cognitive radio sensor network~(CRSN). A collects measurements physical plant, and transmits data to estimator as secondary user (SU) shared network. The existence primal users (PUs) brings exogenous uncertainties into transmission scheduling process, how design an event-based scheme considering these has not been addressed literature. In this work, we start from formulation discrete-time process CRSN, then...

Longitudinal vehicle platooning provides one of the critical ancillary services in autonomous intelligent transportations for maintaining appropriate longitudinal distance with preceding vehicles. In view vulnerabilities control module to underlying denial-of-service (DoS) attacks, this paper designs a resilient controller realize security enhancement. Firstly, DoS attacks on communication links from sensors controllers are depicted by random Bernoulli variables. Then, robust mean-square...

This article proposes a dynamic platoon management and cooperative driving framework for mixed traffic flow consisting of multiple connected automated vehicles(CAVs) possible human-driven vehicles(HDVs) that can be regarded as the surrounding vehicles(SVs). Specifically, proposed consists three stages. At first stage, cruising information all SVs will collected by leader CAV through Cellular-Vehicle-to-X(C-V2X) infrastructure, while an automatic decision-making assistance system(ADMDSS) is...

We consider the problem of sensor and actuator (SaA) placement to minimize an infinite-horizon linear quadratic Gaussian (LQG) cost for a discrete-time Gauss–Markov system. Due financial, topology, bandwidth limitations, only subset SaAs can be selected placement. Different from existing literature, which successively iterates partial selection reach suboptimal solution, this article focuses on joint encounters fundamental difficulty in sense that SaA introduces term LQG is difficult...

In this paper, we surveyed the existing literature studying different approaches and algorithms for four critical components in general branch bound (B&B) algorithm, namely, branching variable selection, node pruning, cutting-plane selection. However, complexity of B&B algorithm always grows exponentially with respect to increase decision dimensions. order improve speed algorithms, learning techniques have been introduced recently. We further how machine can be used algorithms. general, a...

In this paper, we consider a remote state estimation problem in the presence of an eavesdropper. A smart sensor takes measurement discrete linear time-invariant (LTI) process and sends its local estimate through wireless network to estimator. An eavesdropper can overhear transmissions with certain probability. To enhance system privacy level, propose novel encryption strategy minimize combination expected error covariance at estimator negative eavesdropper, taking into account cost process....

In this paper, we propose a Bi-layer Prediction-based Reduction Branch (BP-RB) framework to speed up the process of finding high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed predict binary variables' values. After that, subset variables fixed predicted value by greedy method conditioned on probabilities. By exploring logical consequences, learning-based problem reduction proposed, significantly reducing variable and...

For a long period, automated vehicles (AVs) or vehicle platoons will coexist with human-driven (HDVs) in heterogeneous traffic flow, where the cut-in maneuver of human drivers can be frequently expected. In this paper, to understand and simulate driver decisions on whether continue when execute lane-change during process, we propose two-layer prediction-based decision model by integrating dynamic prediction module, continuity an execution module. To our best knowledge, is first study...

The information regarding the driving states of all vehicles is crucial for achieving optimal group performance in a vehicle platoon. This article focuses on fully distributed state estimation problem open platoons, which frequently experience arrivals and departures vehicles. To address this problem, we propose observer inspired by leader–follower consensus technique. can reconstruct global platoon, including positions, velocities, accelerations We also derive necessary sufficient...

Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus. In this paper, we develop a communication-efficient average consensus protocol by utilizing over-the-air aggregation, which exploits the superposition property of wireless channels rather than combat it. Noisy non-coherent transmission are taken into account, only half-duplex transceivers required. We prove that system...

This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to global coupling constraint through local interaction with other agents. Conventional methods for networks require all agents transmit their original data neighbors, which poses the risk of disclosing sensitive information. To address this issue, we propose an algorithm called differentially dual gradient...