Association Control for Wireless LANs: Pursuing Throughput Maximization and Energy Efficiency

International audienceBecause the access points (APs) and the stations (STAs) of a community access network are deployed at the users' desired places, the APs and STAs tend to concentrate in certain areas. A concentration of STAs often results in the AP(s) and STAs in that particular area suffering from severe congestion. A concentration of APs, on the other hand, may cause energy wastage. While a number of association control schemes are proposed to alleviate congestion in WLANs, the existing schemes do not necessarily maximize throughput and do not consider energy consumption. In this paper, we analytically formulate the network throughput as the multiplication of the success probability, frame transmission rate, and channel air-time ratio. The second and third components can easily be monitored and controlled based on measurements of local link and channel condition using the off-the-shelf WLAN devices. On the other hand, the first component, success probability is a function of the number of contending nodes that is extremely difficult to monitor in overlapping WLANs. Due to this reason, we extend our theoretical study and show that success probability can be indirectly maximized by controlling air-time ratio. Finally, we propose an association control scheme that aims at maximizing throughput and reducing energy consumption by taking account of the multiplication of frame transmission rate and air-time ratio. The proposed scheme is evaluated by computer simulations and testbed experiments conducted under real-world complex scenarios with UDP and TCP traffic. Both the simulations and actual implementations confirm the correctness of the theoretical work and the effectiveness of the proposed scheme

CCNに基づく車車間通信による狭域道路・交通情報の効率的な収集方式に関する研究

　近年，車両どうしが互いの位置や速度等の情報を頻繁に交換して衝突を防止する安全運転支援システムの実用化や，近い将来の自動運転のために走行中の車両が周辺の道路・交通情報を把握できるようにする車車間通信技術の研究が注目されている．従来の方法では、情報を生成した車両は周辺の車両へ一方的に配信する Push 型配信により情報を拡散させるものが主流となっている。この Push 型の方法は、周辺のどの車両にも共通に興味がある急ブレーキや緊急車両接近等の緊急に伝えるべき情報の配信に、有効である。しかし、この方法では、遠方交差点の混雑状況等の緊急に伝える必要がない情報には、車両によって興味が違うので必要のない情報を送ってしまうという問題がある。　この問題を解決するために、本論文では、要求と応答の問い合わせで配信するPull 型配信を採用する。ここでは，車車間通信により要求・応答型（ Pull 型）で効率よく道路・交通情報を収集可能とするために，中継ノードでキャッシュ機能を用いて効率的な配信を可能とするコンテンツ指向ネットワーク（ CCN ）を車車間通信に適用した方式を提案する．まず，基本方式として，車両の移動環境を考慮した， 1) コンテンツの名前付け（ネーミング）方法と 2) 通信経路の制御（ルーティング）方法を検討する．ついで，キャッシュをより効率的に活用可能とするためにルーティング方法を拡張した拡張方式や，チャネル使用率に着目したフィードバック制御による拡張方式＋を検討した．　計算機シミュレーションによる評価結果，拡張方式＋はキャッシュのない Non Cache 方式から Data （応答）パケットの平均ホップ数を最大 83 ％削減し Interest （要求） パケットの発行に対するコンテンツ取得成功率はキャッシュのない Non Cache 方式の最大 7 .9 倍になった．電気通信大学201

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

A systematic review of methodologies for human behavior modelling and routing optimization in large-scale evacuation planning

Frequent and escalating natural disasters pose an increasing threat to society and the environment. Effective disaster management strategies are crucial to mitigate their impact. This paper reviews recent methodologies for large-scale evacuation planning, a key element in risk reduction. A systematic analysis of 100 articles and conference proceedings in evacuation planning, focusing on human factors/behavior modeling and evacuation routing optimization, reveals that Agent-Based Simulation (ABS) is commonly used to predict human factors/behaviors. Heuristics/metaheuristics and traffic assignment techniques dominate evacuation routing planning, often aiming to identify the shortest evacuation path. While evacuation decisions and route choice are extensively studied, optimization approaches frequently lack integration with human factors/behavior modeling. This review underscores the need for further research to enhance evacuation planning by integrating human factors/behavior and optimization methodologies for increased effectiveness and efficiency