A feedback-based decentralised coordination model for distributed open real-time systems

Moving towards autonomous operation and management of increasingly complex open distributed real-time systems poses very significant challenges. This is particularly true when reaction to events must be done in a timely and predictable manner while guaranteeing Quality of Service (QoS) constraints imposed by users, the environment, or applications. In these scenarios, the system should be able to maintain a global feasible QoS level while allowing individual nodes to autonomously adapt under different constraints of resource availability and input quality. This paper shows how decentralised coordination of a group of autonomous interdependent nodes can emerge with little communication, based on the robust self-organising principles of feedback. Positive feedback is used to reinforce the selection of the new desired global service solution, while negative feedback discourages nodes to act in a greedy fashion as this adversely impacts on the provided service levels at neighbouring nodes. The proposed protocol is general enough to be used in a wide range of scenarios characterised by a high degree of openness and dynamism where coordination tasks need to be time dependent. As the reported results demonstrate, it requires less messages to be exchanged and it is faster to achieve a globally acceptable near-optimal solution than other available approaches

Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars

The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture is that intention aware adaptation with a constraint on simultaneous decision making has the potential to avoid unwanted behaviour. The online routing game model is expected to be the basis to formally prove this conjecture.Comment: In Proceedings FVAV 2017, arXiv:1709.0212

Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Blockchain, Leadership And Management: Business AS Usual Or Radical Disruption?

The Internet provided the world with interconnection. However, it did not provide it with trust. Trust is lacking everywhere in our society and is the reason for the existence of powerful intermediaries aggregating power. Trust is what prevents the digital world to take over. This has consequences for organisations: they are inefficient because time, energy, money and passion are wasted on verifying everything happens as decided. Managers play the role of intermediaries in such case: they connect experts with each others and instruct them of what to do. As a result, in our expert society, people's engagement is low because no one is there to inspire and empower them. In other words, our society faces an unprecedented lack of leadership. Provided all those shortcomings, the study imagines the potential repercussions, especially in the context of management, of implementing a blockchain infrastructure in any type of organisation. Indeed, the blockchain technology seems to be able to remedy to those issues, for this distributed and immutable ledger provides security, decentralisation and transparency. In the context of a blockchain economy, the findings show that value creation will be rearranged, with experts directly collaborating with each others, and hierarchy being eliminated. This could, in turn, render managers obsolete, as a blockchain infrastructure will automate most of the tasks. As a result, only a strong, action-oriented, leadership would maintain the organisation together. This leadership-in-action would consist in igniting people to take action; coach members of the organisations so that their contribution makes sense in the greater context of life

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Reforming the implementation of European structural funds: A next development step

The authors assess the performance of the Structural Funds’ implementation system in six Member States of the European Union. Considering the strengths and weaknesses, they develop a reform model for the implementation of European structural policy after 1999. The strengths of the existing implementation system lie mainly in innovation effects triggered by the Structural Funds' model of policy implementation. Its main weaknesses, inter alia, are an interwoven structure of the decision-making processes, an insufficient time management and a lack of in-built improvement loops in the implementation process. To overcome these shortcomings, the authors propose a strategic management and decentralisation model. It demands a de-coupling of strategic programming on the one hand, and detailed programming and implementation on the other. Under this model, the Commission and the Member State would negotiate on the strategic issues. In the framework of the agreement, the Member State together with the monitoring committees would be responsible for the implementation of the programmes. Strengthened feedback loops would help to assure the attainment of the strategic objectives. -- Die Autoren untersuchen die Leistungsfähigkeit des Implementationssystems der Strukturfondsförderung in sechs Mitgliedstaaten der Europäischen Union. Vor dem Hintergrund der Stärken und Schwächen entwickeln sie ein Reformmodell zur Implementation der Strukturfonds in der nächsten Förderperiode nach der Reform 1999. Die Stärken des bestehenden Implementationssystems liegen vor allem in den prozeduralen Innovationen, die z.T. auf das Politikmodell der Strukturfonds und seine Kopplung an mitgliedstaatliche Verwaltungsprozesse zurückgeführt werden können. Die wichtigsten Schwächen sind u.a. die verflochtene Struktur der Entscheidungsprozesse, ein ungenügendes Zeitmanagement und fehlende inhärente Verbesserungsmechanismen des Implementationsprozesses. Um diese Schwächen zu überwinden, schlagen die Autoren ein strategisches Management- und Dezentralisierungsmodell vor. Sein Kern besteht in der Trennung von strategischer Programmierung einerseits und Detailprogrammierung und Implementation andererseits. Die Europäische Kommission und der jeweilige Mitgliedstaat handeln demnach die strategischen Teile der Programme aus. Im Rahmen dieser strategischen Vereinbarung ist dann der Mitgliedstaat für die Detailprogrammierung und Umsetzung der Programme verantwortlich, wobei er vom Begleitausschuß unterstützt wird. Verstärkte Feedbackinstrumente tragen dazu bei, die Einhaltung der strategischen Vorgaben zu sichern.

Passive Fault-Tolerance Management in Component-Based Embedded Systems

It is imperative to accept that failures can and will occur even in meticulously designed distributed systems and to design proper measures to counter those failures. Passive replication minimizes resource consumption by only activating redundant replicas in case of failures, as typically, providing and applying state updates is less resource demanding than requesting execution. However, most existing solutions for passive fault tolerance are usually designed and configured at design time, explicitly and statically identifying the most critical components and their number of replicas, lacking the needed flexibility to handle the runtime dynamics of distributed component-based embedded systems. This paper proposes a cost-effective adaptive fault tolerance solution with a significant lower overhead compared to a strict active redundancy-based approach, achieving a high error coverage with a minimum amount of redundancy. The activation of passive replicas is coordinated through a feedback-based coordination model that reduces the complexity of the needed interactions among components until a new collective global service solution is determined, hence improving the overall maintainability and robustness of the system

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains