arXiv subjects

Norio Shiratori

Publications and source records attributed to Norio Shiratori.

8 recordsLinked to original sources

arXiv2023

Marriage Matching-based Instant Parking Spot Sharing in Internet of Vehicles

The rapid development and integration of automotive manufacturing, sensor, and communication technologies have facilitated the emergence of the Internet of Vehicles (IoV). However, the explosive growing demand for parking spots has become a challenging issue to be addressed in IoV. In this paper, we propose a novel Smart Parking System (SPS) for IoV by applying the matching game approach. Specifically, the proposed SPS consists of three types of entities: drivers, parking spot owners (PSOs), and a certificate authority (CA). Drivers and PSOs send parking requests and parking spot information to the CA, respectively. The CA is responsible for identifying legitimate system users, matching drivers with PSOs, and recording their transaction information. We first design rational utility functions for drivers and PSOs, and then establish preference lists for them based on real-time conditions. With these information, we further develop an improved marriage matching (MM) algorithm to achieve stable matching results for drivers' requests and parking spot allocation. Simulation results demonstrate that the proposed MM-based SPS not only ensures stable matching results with the objective of distance minimization but also achieves an overall utility close to that of the optimal algorithm.

cs.SI↗

arXiv2017

Multi-Agent Q-Learning Aided Backpressure Routing Algorithm for Delay Reduction

In queueing networks, it is well known that the throughput-optimal backpressure routing algorithm results in poor delay performance for light and moderate traffic loads. To improve delay performance, state-of-the-art backpressure routing algorithm (called BPmin [1]) exploits queue length information to direct packets to less congested routes to their destinations. However, BPmin algorithm estimates route congestion based on unrealistic assumption that every node in the network knows real-time global queue length information of all other nodes. In this paper, we propose multi-agent Q-learning aided backpressure routing algorithm, where each node estimates route congestion using only local information of neighboring nodes. Our algorithm not only outperforms state-of-the-art BPmin algorithm in delay performance but also retains the following appealing features: distributed implementation, low computation complexity and throughput-optimality. Simulation results show our algorithm reduces average packet delay by 95% for light traffic loads and by 41% for moderate traffic loads when compared to state-of-the-art BPmin algorithm.

cs.NI↗

arXiv2017

Physical Layer Security-Aware Routing and Performance Tradeoffs in Ad Hoc Networks

The application of physical layer security in ad hoc networks has attracted considerable academic attention recently. However, the available studies mainly focus on the single-hop and two-hop network scenarios, and the price in terms of degradation of communication quality of service (QoS) caused by improving security is largely uninvestigated. As a step to address these issues, this paper explores the physical layer security-aware routing and performance tradeoffs in a multi-hop ad hoc network. Specifically, for any given end-to-end path we first derive its connection outage probability (COP) and secrecy outage probability (SOP) in closed-form, which serve as the performance metrics of communication QoS and transmission security, respectively. Based on the closed-form expressions, we then study the security-QoS tradeoffs to minimize COP (resp. SOP) conditioned on that SOP (resp. COP) is guaranteed. With the help of analysis of a given path, we further propose the routing algorithms which can achieve the optimal performance tradeoffs for any pair of source and destination nodes in a distributed manner. Finally, simulation and numerical results are presented to validate the efficiency of our theoretical analysis, as well as to illustrate the security-QoS tradeoffs and the routing performance.

cs.IT↗

arXiv2017

Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead to suboptimal traffic signal controls. In this paper, we propose a deep reinforcement learning algorithm that automatically extracts all useful features (machine-crafted features) from raw real-time traffic data and learns the optimal policy for adaptive traffic signal control. To improve algorithm stability, we adopt experience replay and target network mechanisms. Simulation results show that our algorithm reduces vehicle delay by up to 47% and 86% when compared to another two popular traffic signal control algorithms, longest queue first algorithm and fixed time control algorithm, respectively.

cs.NI↗

arXiv2016

Cost-Efficient Data Backup for Data Center Networks against ε-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup.

cs.NI↗

arXiv2016

Optimal Scheduling for Incentive WiFi Offloading under Energy Constraint

WiFi offloading, where mobile device users (e.g., smart phone users) transmit packets through WiFi networks rather than cellular networks, is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion. However, since WiFi networks are intermittently available, a mobile device user in WiFi offloading usually needs to wait for WiFi connection and thus experiences longer delay of packet transmission. To motivate users to participate in WiFi offloading, cellular network operators give incentives (rewards like coupons, e-coins) to users who wait for WiFi connection and transmit packets through WiFi networks. In this paper, we aim at maximizing users' rewards while meeting constraints on queue stability and energy consumption. However, we face scheduling challenges from random packet arrivals, intermittent WiFi connection and time varying wireless link states. To address these challenges, we first formulate the problem as a stochastic optimization problem. We then propose an optimal scheduling policy, named Optimal scheduling Policy under Energy Constraint (OPEC), which makes online decisions as to when to delay packet transmission to wait for WiFi connection and which wireless link (WiFi link or cellular link) to transmit packets on. OPEC automatically adapts to random packet arrivals and time varying wireless link states, not requiring a priori knowledge of packet arrival and wireless link probabilities. As verified by simulations, OPEC scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint.

cs.NI↗

arXiv2013

End-to-End Delay Modeling for Mobile Ad Hoc Networks: A Quasi-Birth-and-Death Approach

Understanding the fundamental end-to-end delay performance in mobile ad hoc networks (MANETs) is of great importance for supporting Quality of Service (QoS) guaranteed applications in such networks. While upper bounds and approximations for end-to-end delay in MANETs have been developed in literature, which usually introduce significant errors in delay analysis, the modeling of exact end-to-end delay in MANETs remains a technical challenge. This is partially due to the highly dynamical behaviors of MANETs, but also due to the lack of an efficient theoretical framework to capture such dynamics. This paper demonstrates the potential application of the powerful Quasi-Birth-and-Death (QBD) theory in tackling the challenging issue of exact end-to-end delay modeling in MANETs. We first apply the QBD theory to develop an efficient theoretical framework for capturing the complex dynamics in MANETs. We then show that with the help of this framework, closed form models can be derived for the analysis of exact end-to-end delay and also per node throughput capacity in MANETs. Simulation and numerical results are further provided to illustrate the efficiency of these QBD theory-based models as well as our theoretical findings.

cs.NI↗

arXiv2013

Secrecy Transmission Capacity in Noisy Wireless Ad Hoc Networks

This paper considers the transmission of confidential messages over noisy wireless ad hoc networks, where both background noise and interference from concurrent transmitters affect the received signals. For the random networks where the legitimate nodes and the eavesdroppers are distributed as Poisson point processes, we study the secrecy transmission capacity (STC), as well as the connection outage probability and secrecy outage probability, based on the physical layer security. We first consider the basic fixed transmission distance model, and establish a theoretical model of the STC. We then extend the above results to a more realistic random distance transmission model, namely nearest receiver transmission. Finally, extensive simulation and numerical results are provided to validate the efficiency of our theoretical results and illustrate how the STC is affected by noise, connection and secrecy outage probabilities, transmitter and eavesdropper densities, and other system parameters. Remarkably, our results reveal that a proper amount of noise is helpful to the secrecy transmission capacity.

cs.IT↗