Optimal Time-dependent Sequenced Route Queries in Road Networks

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM SIGSPATIA

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

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

QoS routing in ad-hoc networks using GA and multi-objective optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version

A multiple criteria route recommendation system

The work to be developed in this dissertation is part of a larger project called Sustainable Tourism Crowding (STC), which motivation is based on two negative impacts caused by the tourism overload that happens, particularly, in the historic neighborhoods of Lisbon. The goal of this dissertation is then to mitigate those problems: reduce the tourist burden of points of interest in a city that, in addition to the degradation of the tourist experience, causes sustainability problems in different aspects (environmental, social and local). Within the scope of this dissertation, the implementation of one component of a recommendation system is the proposed solution. It is based on a multi-criteria algorithm for recommending pedestrian routes that minimize the passage through more crowded places and maximizes the visit to sustainable points of interest. These routes will be personalized for each user, as they consider their explicit preferences (e.g. time, budget, physical effort) and several constraints taken from other microservices that are part of the global system architecture mentioned above (e.g. weather conditions, crowding levels, points of interest, sustainability). We conclude it is possible to develop a microservice that recommend personalized routes and communicate with other microservices that are part of the global system architecture mentioned above. The analysis of the experimental data from the recommendation system, allows us to conclude that it is possible to obtain a more balanced distribution of the tourist visit, by increasing the visit to more sustainable places of interest and avoiding crowded paths.O trabalho a desenvolver nesta dissertação insere-se num projeto de maior dimensão denominado Sustainable Tourism Crowding (STC), cuja motivação assenta, essencialmente, em dois impactos negativos provocados pela sobrecarga turística que se verifica, nomeadamente, nos bairros históricos de Lisboa. O objetivo desta dissertação é, então, mitigar esses problemas: reduzir a sobrecarga turística dos pontos de interesse mais visitados numa cidade que, além da degradação da experiência turística, causa problemas de sustentabilidade em diversos aspetos (ambiental, social e local). No âmbito desta dissertação, a implementação de um componente de um sistema de recomendação é a solução proposta. Baseia-se num algoritmo multicritério de recomendação de percursos pedonais que minimiza a passagem por locais mais apinhados e maximizam a visita a pontos de interesse mais sustentáveis. Essas rotas serão personalizadas para cada utilizador, pois consideram as suas preferências (por exemplo, tempo, orçamento, nível de esforço físico) e várias restrições retiradas de outros microsserviços que fazem parte da arquitetura do sistema global mencionado acima (por exemplo, condições meteorológicas, níveis de apinhamento, pontos de interesse, níveis de sustentabilidade). Concluímos que é possível desenvolver um microsserviço que recomenda rotas personalizadas e que comunica com outros microsserviços que fazem parte da arquitetura global do sistema mencionada acima. A análise dos dados experimentais do sistema de recomendação, permite-nos concluir que é possível obter uma distribuição mais equilibrada da visita turística, aumentando a visita a pontos de interesse mais sustentáveis e evitando percursos mais apinhados