ARARUMA Arthur Rodrigues. Caminho mínimo com restrição probabilística de atraso máximo. 2013. 89 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2013. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-08T19:26:26Z No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-13T13:35:18Z (GMT) No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) / Made available in DSpace on 2016-07-13T13:35:18Z (GMT). No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) Previous issue date: 2013 / In the Probabilistic Delay Constrained Shortest Path problem we aim to consider the time factor in the design of cargo routing paths in road networks at minimum cost, considering the increasing uncertainty in travel times of these routes in real networks, and keeping in mind strategies of quality of service, in order to obtain a compromise between the travel costs and the compliance of the arrival time at the destination. We conducted a study of related problems in the literature of transport networks optimization, in order to better understand the problem to be addressed, about which we are not aware of existing works. We developed a scheme for enumerating partitions of the solution space of this problem, which uses an L decomposition to select these partitions wisely, and is aided by solutions to relaxations of the problem to obtain bounds for the optimal cost. In addition, we developed some branching and pruning strategies for a Branch-and-Bound scheme, with a pre-processing phase, in order to try and solve the problem directly. The computational results show that we are competitive with the commercial tool used for comparison in the smaller instances. For the remaining instances, this tool is more efficient in the time required for solving the problem. / No problema do Caminho Mínimo com Restrição Probabilística de Atraso Máximo visamos considerar o fator tempo no projeto de rotas de transporte de cargas em malhas viárias a custo mínimo, atentando à crescente incerteza nos tempos de percurso dessas rotas em malhas reais, e observá-lo tendo em mente estratégias de qualidade de serviço, de forma a obtermos um compromisso entre o custo de percurso e a conformidade ao prazo de chegada ao destino. Realizamos um estudo de problemas relacionados na literatura da área de otimização em redes de transporte, de forma a tentarmos conhecer melhor o problema a ser estudado, sobre o qual não tomamos conhecimento de trabalhos existentes. Desenvolvemos um esquema para enumeração de partições do espaço de soluções do problema, que utiliza uma decomposição em L para selecionar partições de forma inteligente, e que é auxiliado por soluções de relaxações do problema de forma a obter cotas para o custo ótimo. Além disso, desenvolvemos algumas estratégias de ramificação e de poda para um esquema de Branch-and-Bound, com uma fase de pré-processamento, de forma a tentar resolver o problema diretamente. Os resultados computacionais obtidos demonstram que somos competitivos com a ferramenta comercial utilizada para comparação em instâncias de menor porte para o problema. Para as demais instâncias, essa ferramenta se mostrou mais eficiente quanto ao tempo necessário para a resolução.

ARARUNA, Arthur Rodrigues. Caminho mínimo com restrição probabilística de atraso máximo. 2013. 88 f. : Dissertação (mestrado) - Universidade Federal do Ceará, Centro de Ciências, Departamento de Computação, Fortaleza-CE, 2013. / Submitted by guaracy araujo (guaraa3355@gmail.com) on 2016-06-01T19:53:59Z No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) / Approved for entry into archive by guaracy araujo (guaraa3355@gmail.com) on 2016-06-01T19:54:22Z (GMT) No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) / Made available in DSpace on 2016-06-01T19:54:22Z (GMT). No. of bitstreams: 1 2013_dis_arararuna.pdf: 2167566 bytes, checksum: cd1f84fd0b24a51bd2b955d8e18a7ea1 (MD5) Previous issue date: 2013 / In the Probabilistic Delay Constrained Shortest Path problem we aim to consider the time factor in the design of cargo routing paths in road networks at minimum cost, considering the increasing uncertainty in travel times of these routes in real networks, and keeping in mind strategies of quality of service, in order to obtain a compromise between the travel costs and the compliance of the arrival time at the destination. We conducted a study of related problems in the literature of transport networks optimization, in order to better understand the problem to be addressed, about which we are not aware of existing works. We developed a scheme for enumerating partitions of the solution space of this problem, which uses an L decomposition to select these partitions wisely, and is aided by solutions to relaxations of the problem to obtain bounds for the optimal cost. In addition, we developed some branching and pruning strategies for a Branch-and-Bound scheme, with a pre-processing phase, in order to try and solve the problem directly. The computational results show that we are competitive with the commercial tool used for comparison in the smaller instances. For the remaining instances, this tool is more efficient in the time required for solving the problem. / No problema do Caminho Mínimo com Restrição Probabilística de Atraso Máximo visamos considerar o fator tempo no projeto de rotas de transporte de cargas em malhas viárias a custo mínimo, atentando à crescente incerteza nos tempos de percurso dessas rotas em malhas reais, e observá-lo tendo em mente estratégias de qualidade de serviço, de forma a obtermos um compromisso entre o custo de percurso e a conformidade ao prazo de chegada ao destino. Realizamos um estudo de problemas relacionados na literatura da área de otimização em redes de transporte, de forma a tentarmos conhecer melhor o problema a ser estudado, sobre o qual não tomamos conhecimento de trabalhos existentes. Desenvolvemos um esquema para enumeração de partições do espaço de soluções do problema, que utiliza uma decomposição em L para selecionar partições de forma inteligente, e que é auxiliado por soluções de relaxações do problema de forma a obter cotas para o custo ótimo. Além disso, desenvolvemos algumas estratégias de ramificação e de poda para um esquema de Branch-and-Bound, com uma fase de pré-processamento, de forma a tentar resolver o problema diretamente. Os resultados computacionais obtidos demonstram que somos competitivos com a ferramenta comercial utilizada para comparação em instâncias de menor porte para o problema. Para as demais instâncias, essa ferramenta se mostrou mais eficiente quanto ao tempo necessário para a resolução.

abstract: The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived from a pre-defined transportation network or constructed in continuous space. However, continuous space movement adds substantial complexity to identifying the shortest path as the influence of obstacles has to be considered to avoid errors and biases in a derived path. This obstacle-avoiding shortest path in continuous space has been referred to as Euclidean shortest path (ESP), and attracted the attention of many researchers. It has been proven that constructing a graph is an effective approach to limit infinite search options associated with continuous space, reducing the problem to a finite set of potential paths. To date, various methods have been developed for ESP derivation. However, their computational efficiency is limited due to fundamental limitations in graph construction. In this research, a novel algorithm is developed for efficient identification of a graph guaranteed to contain the ESP. This new approach is referred to as the convexpath algorithm, and exploits spatial knowledge and GIS functionality to efficiently construct a graph. The convexpath algorithm utilizes the notion of a convex hull to simultaneously identify relevant obstacles and construct the graph. Additionally, a spatial filtering technique based on intermediate shortest path is enhances intelligent identification of relevant obstacles. Empirical applications show that the convexpath algorithm is able to construct a graph and derive the ESP with significantly improved efficiency compared to visibility and local visibility graph approaches. Furthermore, to boost the performance of convexpath in big data environments, a parallelization approach is proposed and applied to exploit computationally intensive spatial operations of convexpath. Multicore CPU parallelization demonstrates noticeable efficiency gain over the sequential convexpath. Finally, spatial representation and approximation issues associated with raster-based approximation of the ESP are assessed. This dissertation provides a comprehensive treatment of the ESP, and details an important approach for deriving an optimal ESP in real time. / Dissertation/Thesis / Doctoral Dissertation Geography 2015

abstract: Social media has become popular in the past decade. Facebook for example has 1.59 billion active users monthly. With such massive social networks generating lot of data, everyone is constantly looking for ways of leveraging the knowledge from social networks to make their systems more personalized to their end users. And with rapid increase in the usage of mobile phones and wearables, social media data is being tied to spatial networks. This research document proposes an efficient technique that answers socially k-Nearest Neighbors with Spatial Range Filter. The proposed approach performs a joint search on both the social and spatial domains which radically improves the performance compared to straight forward solutions. The research document proposes a novel index that combines social and spatial indexes. In other words, graph data is stored in an organized manner to filter it based on spatial (region of interest) and social constraints (top-k closest vertices) at query time. That leads to pruning necessary paths during the social graph traversal procedure, and only returns the top-K social close venues. The research document then experimentally proves how the proposed approach outperforms existing baseline approaches by at least three times and also compare how each of our algorithms perform under various conditions on a real geo-social dataset extracted from Yelp. / Dissertation/Thesis / Masters Thesis Computer Science 2016

A wireless sensor network (WSN) is made of tiny sensor nodes usually deployed in high density within a targeted area to monitor a phenomenon of interest such as temperature, vibration or humidity. The WSNs can be employed in various applications (e.g., Structural monitoring, agriculture, environment monitoring, machine health monitoring, military, and health). For each application area there are different technical issues and remedies. Various challenges need to be considered while setting up a WSN, including limited computing, memory and energy resources, wireless channel errors and network scalability. One way of addressing these problems is by implementing a routing protocol that efficiently uses these limited resources and hence reduces errors, improves scalability and increases the network lifetime. The topology of any network is important and wireless sensor networks (WSNs) are no exception. In order to effectively model an energy-efficient routing algorithm, the topology of the WSN must be factored in. However, little work has been done on routing for WSNs with regular patterned topologies, except for the shortest path first (SPF) routing algorithms. The issue with the SPF algorithm is that it requires global location information of the nodes from the sensor network, which proves to be a drain on the network resources. In this thesis a novel algorithm namely, BRALB (Biased Random Algorithm for Load Balancing) is proposed to overcome the issues faced in routing data within WSNs with regular topologies such as square-base topology and triangle-based topology. It is based on random walk and probability. The proposed algorithm uses probability theory to build a repository of information containing the estimate of energy resources in each node, in order to route packets based on the energy resources in each node and thus does not require any global information from the network. It is shown in this thesis by statistical analysis and simulations that BRALB uses the same energy as the shortest path first routing as long as the data packets are comparable in size to the inquiry packets used between neighbours. It is also shown to balance the load (i.e. the packets to be sent) efficiently among the nodes in the network. In most of the WSN applications the messages sent to the base station are very small in size. Therefore BRALB is viable and can be used in sensor networks employed in such applications. However, one of the constraints of BRALB is that it is not very scalable; this is a genuine concern as most WSNs deployment is large scale. In order to remedy this problem, C-BRALB (Clustered Biased Random Algorithm for Load Balancing) has been proposed as an extension of BRALB with clustering mechanism. The same clustering technique used in Improved Directed Diffusion (IDD) has been adopted for C-BRALB. The routing mechanism in C-BRALB is based on energy biased random walk. This algorithm also does not require any global information apart from the initial flooding initiated by the sink to create the clusters. It uses probability theory to acquire all the information it needs to route packets based on energy resources in each cluster head node. It is shown in this thesis by using both simulations and statistical analysis that C-BRALB is an efficient routing algorithm in applications where the message to be sent is comparable to the inquiry message among the neighbours. It is also shown to balance the load (i.e. the packets to be sent) among the neighbouring cluster head nodes.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / No problema do Caminho MÃnimo com RestriÃÃo ProbabilÃstica de Atraso MÃximo visamos considerar o fator tempo no projeto de rotas de transporte de cargas em malhas viÃrias a custo mÃnimo, atentando à crescente incerteza nos tempos de percurso dessas rotas em malhas reais, e observÃ-lo tendo em mente estratÃgias de qualidade de serviÃo, de forma a obtermos um compromisso entre o custo de percurso e a conformidade ao prazo de chegada ao destino. Realizamos um estudo de problemas relacionados na literatura da Ãrea de otimizaÃÃo em redes de transporte, de forma a tentarmos conhecer melhor o problema a ser estudado, sobre o qual nÃo tomamos conhecimento de trabalhos existentes. Desenvolvemos um esquema para enumeraÃÃo de partiÃÃes do espaÃo de soluÃÃes do problema, que utiliza uma decomposiÃÃo em L para selecionar partiÃÃes de forma inteligente, e que à auxiliado por soluÃÃes de relaxaÃÃes do problema de forma a obter cotas para o custo Ãtimo. AlÃm disso, desenvolvemos algumas estratÃgias de ramificaÃÃo e de poda para um esquema de Branch-and-Bound, com uma fase de prÃ-processamento, de forma a tentar resolver o problema diretamente. Os resultados computacionais obtidos demonstram que somos competitivos com a ferramenta comercial utilizada para comparaÃÃo em instÃncias de menor porte para o problema. Para as demais instÃncias, essa ferramenta se mostrou mais eficiente quanto ao tempo necessÃrio para a resoluÃÃo. / In the Probabilistic Delay Constrained Shortest Path problem we aim to consider the time factor in the design of cargo routing paths in road networks at minimum cost, considering the increasing uncertainty in travel times of these routes in real networks, and keeping in mind strategies of quality of service, in order to obtain a compromise between the travel costs and the compliance of the arrival time at the destination. We conducted a study of related problems in the literature of transport networks optimization, in order to better understand the problem to be addressed, about which we are not aware of existing works. We developed a scheme for enumerating partitions of the solution space of this problem, which uses an L decomposition to select these partitions wisely, and is aided by solutions to relaxations of the problem to obtain bounds for the optimal cost. In addition, we developed some branching and pruning strategies for a Branch-and-Bound scheme, with a pre-processing phase, in order to try and solve the problem directly. The computational results show that we are competitive with the commercial tool used for comparison in the smaller instances. For the remaining instances, this tool is more efficient in the time required for solving the problem.

O problema de caminhos mínimos (SP shortest path problem) é frequentemente colo- cado em prática em uma grande variedade de aplicações em diversas áreas. Nessas aplicações geralmente se deseja realizar algum tipo de deslocamento ou transporte entre dois ou mais pontos específicos em uma rede. Tal ação deve ser executada de forma ótima em relação a algum critério, por exemplo o menor custo possível, ou o menor gasto de tempo ou o máximo de confiabilidade/segurança. Na prática, muitas vezes não desejamos apenas o menor custo ou o menor tempo, mas desejamos otimizar uma combinação de diferentes critérios, por exemplo, um caminho que seja rápido e barato. Como não é possível otimizar sobre todos os critérios de uma só vez, nós escolhemos um dos critérios para representar a função custo, que será minimizada, e para os demais critérios representamos como recursos e definimos os limites que julgamos aceitáveis para o consumo de cada um desses recursos. Esta variação é cha- mada de problema de caminhos mínimos com restrições por recursos, ou como preferimos chamar, problema de caminhos mínimos com recursos limitados (RCSP resource constrained shortest path problem), o qual será o objeto de estudo neste trabalho. A adição de restrições por recursos no SP, infelizmente torna o problema NP-difícil, mesmo em grafos acíclicos, com restrições sobre um único recurso, e com todos os consu- mos de recursos positivos. Temos reduções dos famosos problemas N P-difíceis Mochila e Partição para o nosso problema. Em contextos diversos são encontrados problemas de cunho teórico e prático que po- dem ser formulados como problemas de caminhos mínimos com recursos limitados, o que nos motivou a estudá-lo a fim de desenvolver um trabalho que resumisse informações sufi- cientes para auxiliar pesquisadores ou desenvolvedores que tenham interesse no problema. Nós apresentamos aqui, uma detalhada revisão bibliográfica do RCSP, tendo como foco o desenvolvimento de algoritmos exatos para o caso onde possuímos um único recurso e a im- plementação e comparação dos principais algoritmos conhecidos, observando-os em situações práticas. / The problem of choosing a route to a trip, where we want minimize the distance of the path is a major problem in computing. In this basic form, this is the shortest path problem. But sometimes, besides the length we need to consider more parameters for selecting a good path. A common parameters to consider is the consumption of resources in a limited budget. A shortest path with these additional constraints is called resource constrained shortest path - RCSP. This paper has two main objectives: to present a literature review of the problem RCSP, focusing on exact algorithms for the case where we have a single resource, and implement and compare some algorithms, observing them in practical situations. The Shortest Path (SP) problem is among the fundamental problems of computer sci- ence. Its been deeply studied and subject of many publications. Also, many efficient solutions (polynomial time algorithms) are known for this problem. The SP is widely applied in many fields of science, not only computer science. These situations usually need to transport a load between two or more specific spots of a network. This action must be taken optimally regarding to some criterion, for instance the least cost, or the least time or maximum relia- bility. While new solutions for SP were presented, new demands were issued too, with new variations for the problem. One of these variations comes from the fact that, in a real scenario, a combination of many criteria must be optimized, for instance a path with least cost and least time. This problem is known as Multiobjective Shortest Path. Since its not possible to optimize all criteria at once, one of them is chosen to represent the cost function to be minimized and the others to represent resources with defined boundary. This variation, known as Resource Constrained Shortest Path (RCSP), was the object of the present study. By adding resource constraints, the SPbecomes N P-hard, even in acyclic graphs with only one resource constrained and all resource consumption being positive. There are reduc- tions from the famous NP-hard problems Knapsack and Partition to our problem. In many fields, are found theoretical and practical problems that may be expressed as a Resource Constrained Shortest Path Problem, which motivated us to study this problem in order to summarize enough information to researchers and developers involved with this problem. This paper presents a detailed bibliographic revision to RCSP, focusing on the development of exact algorithms for the case when there is only only one resource and on the implementation and comparison of the main known algorithms in practical situations.

This thesis presents a comparison between a basic shortest path routing policy of the Destination-Sequence Distance Vector (DSDV) protocol and two power-aware policy variations of it. In the two modified versions, the routes are selected based on the energy available on the nodes and not only the shortest path distance to the destination. Simulations are conducted for a given situation of nodes and the energy efficiency of the three aforementioned policies are evaluated for example scenarios. First, a brief overview of the theory behind the study is presented. It consists of an description of ad hoc networking, DSDV, and our energy-aware modifications to DSDV. After the fundamental theory, the method is presented. It consists of a description of how the simulated scenarios relates to a real-world scenario and the simplifications made in the model. We present an overview of the model used for simulation and the operation of the program. This section ends with an explanation of the three simulated policies: shortest path, simple weighted and doubled weighted. When the theory behind the thesis are completed, the simulations are conducted. The results are examined and a summary of their meaning is discussed. It is explained how the assumptions effect the reliability of the study and an estimation of the accuracy of the results are presented. We find that the power-aware policy variations (simple weighted and double weighted) both achieve better network lifetime than the basic shortest path policy, at the cost of slightly longer per-packet paths. These results are encouraging and show that very simple modifications to DSDV can achieve significant gains in the network lifetime, helping users get the most out of their networks. Future investigation could try to optimize these gains.

Threat modeling for information technology infrastructure can be done using shortest path algorithms in stochastic graphs. By modeling the infrastructure as a graph, potential vulnerabilities may be presented by computing what paths an attacker might take. This thesis project presents and compares two memory efficient algorithms that can be used to solve this problem approximately, the online k-means and sampled k-shortest path algorithm. By computing paths for several different graph types, the two algorithms were compared against a naive algorithm. The online k-means algorithm uses approximately 77 times less memory, executes in the same amount of time, and produces similar path lengths. In our experiments, the sampled k-shortest path algorithm uses approximately 154 times less memory, execution time is seen to be lowered by a factor of 5 to 20 depending on the graph type, but path distances computed are longer.

This paper deals with the analysis of the collected GPS data and the possibility of the prediction of most advantageous route on its basis, by the application written in the PHP scripting language. The suitability of the route is considered by the distance, driving time, or elevation. The thesis also describes a GPS system, the format of the source data and their storing in an appropriate database. There is also a description of the search of the shortest path in the graph and some famous algorithms for finding it. The paper includes information about implementation of new data integration and path finding within this data in PHP scripting language. In conclusion it is evaluated what are the benefits of this application and design saying how this application can be improved in the future.