Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Quantifying restoration costs in the aftermath of an extreme event using system dynamics and dynamic mathematical modeling approaches

Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The first research contribution is a system dynamics inoperability model that determines inputs, outputs, and flows for roadway networks. This model can be used to identify the connectivity of road segments and better understand how inoperability contributes to economic consequences. The second contribution is an algorithm that integrates critical infrastructure data derived from bottom-up cost estimation technique as part of an object-oriented software tool that can be used to determine the impact of system disruptions. The third contribution is a dynamic mathematical model that establishes a framework to estimate post-disaster restoration costs from a whole system perspective. Engineering managers, city planners, and policy makers can use the methodologies developed in this research to develop effective disaster planning schemas and to prioritize post-disaster restoration operations --Abstract, page iv

A Review of Trip Planning Systems.

This report reviews current information provision in all modes of transport and assesses the needs for and benefits of trip planning systems. The feasibility of trip planning systems is discussed given the current state of technology and information availability and supply. The review was stimulated by technological developments in telecommunications and information technology which are providing the possibility of a greatly enhanced quality of information to aid trip planning decisions. Amongst the conclusions reached were the following: Current information provision is considered deficient in many respects. Travellers are often unaware of alternative routes or services and many are unable to acquire adequate information from one source especially for multi-modal journeys. In addition, there is a lack of providing real time information where it is required (bus stops and train stations) and of effective interaction of static and real time information. Most of the projects, which integrate static and dynamic data, are single mode systems. Therefore there is a need for an integrated trip planning system which can inform and guide on all aspects of transport. Trip planning systems can provide assistance in trip planning (before and during the journey) using one or a number of modes of travel, taking into account travellers preferences and constraints, and effectively integrating static and dynamic data. Trip planning systems could adversely affect traffic demand as people who become aware of new opportunities might be encouraged to make more journeys. It could also affect travellers choice as a result of over-saturation of information, over-reaction to predictive information, and concentration on the same 'best' routes. However, it can be argued, based on existing evidence, that such a system can benefit travellers, and transport operators as well as the public sector responsible for executing transport policies. Travellers can benefit by obtaining adequate information to help them in making optimal decisions and reducing uncertainty and stress associated with travel. Public transport operators can benefit by making their services known to customers, leading to increased patronage. Public transport authorities can use the supply of information to execute their transport policies and exercise more control over traffic management

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A fast heuristic for routing in post-disaster humanitarian relief logistics

In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

a fast heuristic for routing in post disaster humanitarian relief logistics

Abstract In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

Modelling blue-light ambulance mobility in the London metropolitan area

Actions taken immediately following a life-threatening incident are critical for the survival of the patient. In particular, the timely arrival of ambulance crew often makes the difference between life and death. As a consequence, ambulance services are under persistent pressure to achieve rapid emergency response. Meeting stringent performance requirements poses special challenges in metropolitan areas where the higher population density results in high rates of life-threatening incident occurrence, compounded by lower response speeds due to traffic congestion. A key ingredient of data-driven approaches to address these challenges is the effective modelling of ambulance movement thus enabling the accurate prediction of the expected arrival time of a crew at the site of an incident. Ambulance mobility patterns however are distinct and in particular differ from civilian traffic: crews travelling with ashing blue lights and sirens are by law exempt from certain traffic regulations; and moreover, ambulance journeys are triggered by emergency incidents that are generated following distinct spatial and temporal patterns. We use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on a road routing network modified to reflect the differences between emergency and regular vehicle traffic, we develop a methodology for matching ambulances Global Positioning System (GPS) coordinates to road segments, allowing the reconstruction of ambulance routes with precise speed data. We demonstrate how a road speed model that exploits this information achieves best predictive performance by implicitly capturing route-specific patterns in changing traffic conditions. We then present a hybrid model that achieves a high route similarity score while minimising journey duration error. This hybrid model outperforms alternative mobility models. To the best of our knowledge, this study represents the first attempt to apply data-driven methodologies to route selection and estimation of arrival times of ambulances travelling with blue lights and sirens