Vehicular ad hoc networks (VANETs) can efficiently detect traffic congestion, but detection is not enough, because congestion can be further classified as recurrent and nonrecurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, work zones, special events, and adverse weather. We propose a framework for the real-time distributed classification of congestion into its components on a heterogeneous urban road network using VANET. We present models built on an understanding of the spatial and temporal causality measures and trained on synthetic data extended from a real case study of Cologne. Our performance evaluation shows a predictive accuracy of 87.63% for the deterministic classification tree, 88.83% for the naïve Bayesian classifier, 89.51% for random forest, and 89.17% for the boosting technique. This framework can assist transportation agencies in reducing urban congestion by developing effective congestion mitigation strategies knowing the root causes of congestion.
Problem statement:In mobile ad hoc networks devices do not rely on a fixed infrastructure and thus have to be self-organizing. This gives rise to various challenges to network applications. Existing service discovery protocols fall short of accommodating the complexities of the ad-hoc environment. However, the performance of distributed service discovery architectures that rely on a virtual backbone for locating and registering available services appeared very promising in terms of average delay but in terms of message overhead, are the most heavy-weight. In this research we propose a very light-weight, robust and reliable model for service discovery in wireless and mobile networks by taking into account the limited resources to which are subjected the mobile units. Approach: Three processes are involved in service discovery protocols using virtual dynamic backbone for mobile ad hoc networks: registration, discovery and consistency maintenance. More specifically, the model analytically and realistically differentiates stable from unstable nodes in the network in order to form a subset of nodes constituting a relatively stable virtual Backbone (BB). Results: Overall, results acquired were very satisfactory and meet the performance objectives of effectiveness especially in terms of network load. A notable reduction of almost 80% of message signaling was observed in the network. This criterion distinguishes our proposal and corroborate to its light-weight characteristic. On the other hand, results showed reasonable mean time delay to the requests initiated by the clients. Conclusion: Extensive simulation results obtained confirm the efficiency and the light-weight characteristic of our approach in significantly reducing the cost of message overhead in addition to having the best delay values when compared with strategies well-known in the literature.
We developed a distributed data mining system to elaborate a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between vehicles exchanging messages via Vehicle to Vehicle (V2V) communication. Results are based on real-time simulation generated scenarios extended from the realworld traffic Travel and Activity PAtterns Simulation (TAPAS) Cologne scenario. We evaluate a Voting Procedure (VP) useful for obtaining deeper insights using cooperation between vehicles, Belief Functions (BF) aim at improving representation of information and a Data Association Technique (DAT) aiming at data mining and extracting the association rules from the messages exchanged. Methods are tested and compared using a microscopic urban mobility simulator, SUMO and a network simulator, ns-2, for the simulation of communication between CVs. Compared to the Back-Propagation algorithm (BP) extensively used in the past literature, our performance evaluation shows that the proposed methods enhance the estimation of the cause of congestion by 48% for the proposed VP, 58% for the BF, 71% for the DAT and 70% for β-DAT. The methods also enhance detection time from 7.09% to 10.3%, and β-DAT outperforms BP by approximately 1.25% less false alarms triggered by the network, which can be significant in the context of real-time decision making. We show that a market penetration rate between 63% and 75% is enough to ensure satisfactory performance.
Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues by training a global model using distributed nodes. Despite its advantages, there is an increased potential for cyberattacks on FL-based ML techniques that can undermine the benefits. Model-poisoning attacks on FL target the availability of the model. The adversarial objective is to disrupt the training. We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious worker. A fine-grained assessment of the history of the worker permits the evaluation of its behavior in time and results in innovative detection strategies. We present three lines of defense that aim at assessing if the worker is reliable by observing if the node is really training, advancing towards a goal. Our defense exposes an attacker's malicious behavior and removes unreliable nodes from the aggregation process so that the FL process converge faster. Through extensive evaluations and against various adversarial settings, attestedFL increased the accuracy of the model between 12% to 58% under different scenarios such as attacks performed at different stages of convergence, attackers colluding and continuous attacks.
Problem statement:In mobile ad hoc networks devices do not rely on a fixed infrastructure and thus have to be self-organizing. This gives rise to various challenges to network applications. Existing service discovery protocols fall short of accommodating the complexities of the ad-hoc environment. However, the performance of distributed service discovery architectures that rely on a virtual backbone for locating and registering available services appeared very promising in terms of average delay but in terms of message overhead, are the most heavy-weight. In this research we propose a very light-weight, robust and reliable model for service discovery in wireless and mobile networks by taking into account the limited resources to which are subjected the mobile units. Approach: Three processes are involved in service discovery protocols using virtual dynamic backbone for mobile ad hoc networks: registration, discovery and consistency maintenance. More specifically, the model analytically and realistically differentiates stable from unstable nodes in the network in order to form a subset of nodes constituting a relatively stable virtual Backbone (BB). Results: Overall, results acquired were very satisfactory and meet the performance objectives of effectiveness especially in terms of network load. A notable reduction of almost 80% of message signaling was observed in the network. This criterion distinguishes our proposal and corroborate to its light-weight characteristic. On the other hand, results showed reasonable mean time delay to the requests initiated by the clients. Conclusion: Extensive simulation results obtained confirm the efficiency and the light-weight characteristic of our approach in significantly reducing the cost of message overhead in addition to having the best delay values when compared with strategies well-known in the literature.
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