Reputation based selfishness prevention techniques for mobile ad-hoc networks

Mobile ad-hoc networks require nodes to cooperate in the relaying of data from source to destination. However, due to their limited resources, selfish nodes may be unwilling to forward packets, which can deteriorate the multi-hop connectivity. Different reputation-based protocols have been proposed to cope with selfishness in mobile ad-hoc networks. These protocols utilize the watchdog detection mechanism to observe the correct relaying of packets, and to compile information about potential selfish nodes. This information is used to prevent the participation of selfish nodes in the establishment of multi-hop routes. Despite its wide use, watchdog tends to overestimate the selfish behavior of nodes due to the effects of radio transmission errors or packet collisions that can be mistaken for intentional packet drops. As a result, the availability of valid multi-hop routes is reduced, and the overall performance deteriorates. This paper proposes and evaluates three detection techniques that improve the ability of selfishness prevention protocols to detect selfish nodes and to increase the number of valid routes.Ingeniería, Industria y Construcció

Routing choices in intelligent transport systems

Road congestion is a phenomenon that can often be avoided; roads become popular, travel times increase, which could be mitigated with better coordination mechanisms. The choice of route, mode of transport, and departure time all play a crucial part in controlling congestion levels. Technology, such as navigation applications, have the ability to influence these decisions and play an essential role in congestion reduction. To predict vehicles' routing behaviours, we model the system as a game with rational players. Players choose a path between origin and destination nodes in a network. Each player seeks to minimise their own journey time, often leading to inefficient equilibria with poor social welfare. Traffic congestion motivates the results in this thesis. However, the results also hold true for many other applications where congestion occurs, e.g. power grid demand. Coordinating route selection to reduce congestion constitutes a social dilemma for vehicles. In sequential social dilemmas, players' strategies need to balance their vulnerability to exploitation from their opponents and to learn to cooperate to achieve maximal payouts. We address this trade-off between mathematical safety and cooperation of strategies in social dilemmas to motivate our proposed algorithm, a safe method of achieving cooperation in social dilemmas, including route choice games. Many vehicles use navigation applications to help plan their journeys, but these provide only partial information about the routes available to them. We find a class of networks for which route information distribution cannot harm the receiver's expected travel times. Additionally, we consider a game where players always follow the route chosen by an application or where vehicle route selection is controlled by a route planner, such as autonomous vehicles. We show that having multiple route planners controlling vehicle routing leads to inefficient equilibria. We calculate the Price of Anarchy (PoA) for polynomial function travel times and show that multiagent reinforcement learning algorithms suffer from the predicted Price of Anarchy when controlling vehicle routing. Finally, we equip congestion games with waiting times at junctions to model the properties of traffic lights at intersections. Here, we show that Braess' paradox can be avoided by implementing traffic light cycles and establish the PoA for realistic waiting times. By employing intelligent traffic lights that use myopic learning, such as multi-agent reinforcement learning, we prove a natural reward function guarantees convergence to equilibrium. Moreover, we highlight the impact of multi-agent reinforcement learning traffic lights on the fairness of journey times to vehicles

On Learning in Collective Self-adaptive Systems: State of Practice and a 3D Framework

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

Forecast based traffic signal coordination using congestion modelling and real-time data

This dissertation focusses on the implementation of a Real-Time Simulation-Based Signal Coordination module for arterial traffic, as proof of concept for the potential of integrating a new generation of advanced heuristic optimisation tools into Real-Time Traffic Management Systems. The endeavour represents an attempt to address a number of shortcomings observed in most currently marketed on-line signal setting solutions and provide better adaptive signal timings. It is unprecedented in its use of a Genetic Algorithm coupled with Continuous Dynamic Traffic Assignment as solution evaluation method, only made possible by the recently presented parallelisation strategies for the underlying algorithms. Within a fully functional traffic modelling and management framework, the optimiser is developed independently, leaving ample space for future adaptations and extensions, while relying on the best available technology to provide it fast and realistic solution evaluation based on reliable real-time supply and demand data. The optimiser can in fact operate on high quality network models that are well calibrated and always up-to-date with real-world road conditions; rely on robust, multi-source network wide traffic data, rather than being attached to single detectors; manage area coordination using an external simulation engine, rather than a na¨ıve flow propagation model that overlooks crucial traffic dynamics; and even incorporate real-time traffic forecast to account for transient phenomena in the near future to act as a feedback controller. Results clearly confirm the efficacy of the proposed method, by which it is possible to obtain relevant and consistent corridor performance improvements with respect to widely known arterial bandwidth maximisation techniques under a range of different traffic conditions. The computational efforts involved are already manageable for realistic real-world applications, and future extensions of the presented approach to more complex problems seem within reach thanks to the load distribution strategies already envisioned and prepared for in the context of this work

Autonomous driving: a bird's eye view

[Abstract:] The introduction of autonomous vehicles (AV) will represent a milestone in the evolution of transportation and personal mobility. AVs are expected to significantly reduce accidents and congestion, while being economically and environmentally beneficial. However, many challenges must be overcome before reaching this ideal scenario. This study, which results from on-site visits to top research centres and a comprehensive literature review, provides an overall state-of-the-practice on the subject and identifies critical issues to succeed. For example, although most of the required technology is already available, ensuring the robustness of AVs under all boundary conditions is still a challenge. Additionally, the implementation of AVs must contribute to the environmental sustainability by promoting the usage of alternative energies and sustainable mobility patterns. Electric vehicles and sharing systems are suitable options, although both require some refinement to incentivise a broader range of customers. Other aspects could be more difficult to resolve and might even postpone the generalisation of automated driving. For instance, there is a need for cooperation and management strategies geared towards traffic efficiency. Also, for transportation and land-use planning to avoid negative territorial and economic impacts. Above all, safe and ethical behaviour rules must be agreed upon before AVs hit the road.Ministerio de Economía y Competitividad; TRA2016-79019-R/COO

Survey on RPL enhancements: a focus on topology, security and mobility

International audienceA few years ago, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) was proposed by IETF as the routing standard designed for classes of networks in which both nodes and their interconnects are constrained. Since then, great attention has been paid by the scientific and industrial communities for the protocol evaluation and improvement. Indeed, depending on applications scenarios, constraints related to the target environments or other requirements, many adaptations and improvements can be made. So, since the initial release of the standard, several implementations were proposed, some targeting specific optimization goals whereas others would optimize several criteria while building the routing topology. They include, but are not limited to, extending the network lifetime, maximizing throughput at the sink node, avoiding the less secured nodes, considering nodes or sink mobility. Sometimes, to consider the Quality of Service (QoS), it is necessary to consider several of those criteria at the same time. This paper reviews recent works on RPL and highlights major contributions to its improvement, especially those related to topology optimization, security and mobility. We aim to provide an insight into relevant efforts around the protocol, draw some lessons and give useful guidelines for future developments