Experimental validation of the random waypoint mobility model through a real world mobility trace for large geographical areas

User mobility models are used in simulations of mobile communications systems to study characteristics of network performance. One of the models which is in common use is the Random Waypoint Model (RWP). The RWP is a simple mobility model based on random destinations, speeds and pause times. The RWP is often criticised as not representing how humans actually move. Paradoxically, validation against real mobility data is seen as being difficult due to the impracticalities of obtaining real mobility data.We give details of a real world user movement trace from which we obtained data about one individual's destinations, travel routes, average speed and rest times whilst moving throughout a city-wide area. We present results from this real life data and use it to validate some of the key characteristics of the RWP. In this paper we consider the RWP as a model of user mobility in networks that cater for a large geographical area - such as a city

Towards an activity-based model for pedestrian facilities

This paper develops a framework for understanding pedestrian mobility pattern from WiFi traces and other data sources. It can be used to forecast demand for pedestrian facilities such as railway stations, music festivals, campus, airports, supermarkets or even pedestrian area in city centers. Scenarios regarding the walkable infrastructure and connectors, the scheduling (trains in stations, classes on campus, concerts in festivals) or the proposed services in the facility may then be evaluated. It is inspired by activity-based approach. We assume that pedestrian demand is driven by a willingness to perform activities. Activity scheduling decision is explicitly taken into account. Activity-based approach for urban areas is adapted for pedestrian facilities, with similarities (scheduling behavior) and differences (no ``home'' in pedestrian facilities, thus no tours). This is a first attempt to define a integrated system of choice models in the context of pedestrian facilities

A Self-Organising Distributed Location Server for Ad Hoc Networks

Wireless networks allow communication between multiple devices (nodes) without the use of wires. Range in such networks is often limited restricting the use of networks to small offices and homes; however, it is possible to use nodes to forward packets for others thereby extending the communication range of individual nodes. Networks employing such forwarding are called Multi-Hop Ad Hoc Networks (MANETS) Discovering routes in MANETS is a challenging task given that the topology is flat and node addresses reveal nothing about their place in the network. In addition, nodes may move or leave changing the network topology quickly. Existing approaches to discovering locations involve either broadcast dissemination or broadcast route discovery throughout the entire network. The reliance on the use of techniques that use broadcast schemes restricts the size of network that the techniques are applicable to. Routing in large scale ad hoc networks is therefore achieved by the use of geographical forwarding. Each node is required to know its location and that of its neighbours so that it may use this information for forward packets. The next hop chosen is the neighbour that is closest to the destination and a number of techniques are used to handle scenarios here the network has areas void of nodes. Use of such geographical routing techniques requires knowledge of the destination's location. This is provided by location servers and the literature proposes a number of methods of providing them. Unfortunately many of the schemes are limited by using a proportion of the network that increases with size, thereby immediately limiting the scalability. Only one technique is surveyed that provides high scalability but it has a number of limitations in terms of handling node mobility and failure. Ad hoc networks have limited capacity and so the inspiration for a technique to address these shortcomings comes from observations of nature. Birds and ants are able to organise themselves without direct communication through the observation of their environment and their peers. They provide an emergent intelligence based on individual actions rather than group collaboration. This thesis attempts to discover whether software agents can mimic this by creating a group of agents to store location information in a specific location. Instead of requiring central co-ordination, the agents observe one another and make individual decisions to create an emergent intelligence that causes them to resist mobility and node failures. The new technique is called a Self Organising Location Server (SOLS) and is compared against existing approaches to location servers. Most existing techniques do not scale well whereas SOLS uses a new idea of a home location. The use of this idea and the self organising behaviour of the agents that store the information results in significant benefits in performance. SOLS significantly out performs Terminode home region, the only other scalable approach surveyed. SOLS is able to tolerate much higher node failure rates than expected in likely implementations of large scale ad hoc networks. In addition, SOLS successfully mitigates node mobility which is likely to be encountered in an ad hoc network

Activity choice modeling for pedestrian facilities

This thesis develops models of activity and destination choices in pedestrian facilities from WiFi traces. We adapt the activity-based travel demand analysis of urban mobility to pedestrians and to digital footprints. We are interested in understanding the sequence of activities and destinations of a pedestrian using discrete choice models and localization data from communication antennas. Activity and destination choice models are needed by pedestrian facilities for decision aid when building new infrastructure, modifying existing infrastructures, or locating points of interest. Understanding demand for activities is particularly important when facing an increasing number of visitors or when developing new activities, such as shopping or catering. Data from existing sensors, such as WiFi access points, are cheap and cover entire facilities, but are imprecise and lack semantics to describe moving, stopping, destinations or activities carried out at destinations. Thus, understanding pedestrian behavior first requires to observe the actual behavior and detect stops at destinations, and second to model the behavior. Part I of this thesis focuses on activity-episode sequence detection. We develop a Bayesian approach to merge raw localization data with other data sources in order to take into account the imprecision and describe activity-episode sequences. This approach generates several activity-episode sequences for a single individual. Each activity-episode sequence is associated with a probability of being the true sequence. The prior represents the attractivity of the different points of interest surrounding the measurement and allows the use of a priori information from other sources of data (register data, point-of-sale data, counting sensors, etc.). Part II proposes models for activity and destination choices. The joint choice of activity type and activity timing is modeled by seeing a sequence of activity episodes as a path in an activity network. Time is considered as discrete. Unlike traditional models, our model is not tour-based, starting and ending at the home location, since the daily ``home''activity is meaningless in our context. The choice set contains all combinations of activity types and time intervals. The number of different paths is thus very large (increasing with time resolution and disaggregation of types of activities). Inspired by route choice models, we use a Metropolis-Hastings algorithm for the sampling of paths to generate the choice set. An importance sampling correction of the utility allows the estimation of unbiased model parameters without enumerating the full choice set. While the activity path model describes the choice of an activity type in time, the location where the activity is performed is modeled with a destination choice model conditional on the activity type. Our approach accounts for the panel nature of the data and deals with serial correlation between error terms. Using real WiFi data collected on the EPFL campus, we detect pedestrian activity-episode sequences, estimate an activity path choice model and develop a destination choice model for a specific activity type: eating. Knowing that the individual has decided to eat, which restaurant does she choose? This conditional destination choice model includes in its utility the cost of menus, available types of foods and drinks, distance from a previous activity episode, socioeconomic characteristics and habits

Simulation und Optimierung der Standort- und Kapazitätsauswahl in der Planung von Ladeinfrastruktur für batterieelektrische Fahrzeugflotten

Politische, technologische und ökonomische Faktoren haben dazu beigetragen, dass Elektromobilität in den letzten Jahren einen neuen Aufschwung erfahren hat. Neben dem privaten Sektor sind bestimmte gewerbliche Flotten im innerstädtischen Straßenverkehr besonders als Pilotkunden für batterieelektrische Fahrzeuge geeignet. Der wirtschaftliche Vorteil gegenüber konventionellen Antrieben entsteht durch geringe Energiekosten, wenn die Fahrzeuge eine hohe Laufleistung erreichen und trotzdem genügend Zeit für die benötigten Aufladevorgänge bleibt. Sofern also batterieelektrische Fahrzeuge für den Anwendungsbereich geeignet sind, wird eine bedarfsgerechte Ladeinfrastruktur im Betriebsgebiet benötigt. Schwerpunkt der vorliegenden Arbeit ist die Entwicklung neuer Simulations- und Optimierungsmodelle für die Planungsfragestellungen zur Auslegung von Ladeinfrastruktur für batterieelektrische Fahrzeugflotten. Das erarbeitete Simulationsmodell ermöglicht eine detaillierte Analyse und Bewertung der Auswirkungen verschiedener Handlungsalternativen. Es erlaubt die Variation der Flottengröße, der Fahrzeugtypen, der Batteriekapazität, der Infrastruktur-Konfiguration und der Laderegeln im Betriebskonzept. Das entwickelte Optimierungsmodell ermöglicht die Auswahl von Versorgungsstandorten und die Festlegung einer Betriebskapazität zur Sicherstellung einer geforderten Service-Qualität auf Basis verschiedener Kennzahlen. Für mittlere und große Probleminstanzen gelangen exakte Lösungsverfahren an ihre Grenzen, so dass ein eigenes heuristisches Verfahren vorgestellt wird. Die entwickelten Methoden wurden in einem realen Planungsprojekt eingesetzt, um die Praktikabilität zu demonstrieren.Political, technological and economic factors have led to a new upswing in the development of Electric Mobility. In addition to the private sector, certain industrial fleets are especially suited as pilot customers for battery electric vehicles. Economic advantages compared to conventional vehicles are caused by low energy costs if vehicles reach high mileage and still have enough time to recharge the needed energy. To supply energy to all fleet vehicles, a proper charging infrastructure has to be available in the operational area. The focus of this work is the development of new simulation and optimization models for this strategic planning task. The developed simulation model allows a detailed analysis and evaluation of the impact of various business models. It allows the variation of the fleet size, the vehicle types, the battery capacity, the infrastructure configuration and charging rules in the operational concept. The developed optimization model allows the cost-minimal selection of supply locations and their capacity to ensure the required quality of service. For medium and large problem instances exact solution methods reach their limits, so that a separate heuristic method is presented. The developed methods were used in a real project to demonstrate the practicality.Tag der Verteidigung: 16.08.2012Paderborn, Univ., Diss., 201

Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g