Experimental Evaluation of Safety through Automatic Identification of Drunk Driving (DD) and Road Accidents (RA) as a part of Vehicular Ad Hoc Network (VANET)

VANET is a wireless network system that gives driving support and motorway wellbeing. Road accidents assert a staggeringly high number of lives each year. As is needless to say; a majority of accidents, which occur, are due to drunk driving.Accordingly, there is no compelling component to keep this. Here we have a designed framework to monitors the driver's state using various sensors and searches for triggers that can cause accidents. When an alert situation is distinguished, the framework informs the driver and tries to caution him. On the off chance that the driver does not react in a stipulated time, the framework controls the speed of the vehicles. An SMS (Short Message Service) which contains the present GPS (Global Positioning System) area of the vehicle is sent via GSM (Global System for Mobile communication) module to the police control room to alarm the police. Here we also intersection the new thought for recover the general population at the time of crisis to overcome such risky accidents and correspondence issues in VANET (Vehicular Ad Hoc Network); we have utilized sensors hubs as a part of the vehicles. Once the vehicle meets an accident, the sensor will inform about the accidents to the nearest health center with the help of RSUs (road side sensor units) and Base Stations (BS). Furthermore, it gives the new route to vehicles to keep away from traffic blockage with the assistance of sensor cooperation situation. In this way, it gives a feasible and effective solution for the issue of intoxicated driving and Accidents Notification in VANET

Improving Security Performance in Smart Campuses

The idea of a smart campus is to combine devices, apps, and people to achieve enhanced operational and educational efficiency. One of the major aspects of the establishment of smart campuses is the building of a smart security system. This research is an effort to review the security technologies and how to increase the security performance of a smart campus using these technologies. The main objective of this study is to discuss asset security and facility access technologies in a smart campus setting. Universities spend millions of dollars on specialized equipment, yet maintaining track of such assets may be challenging. We discussed how security personnel can monitor the whereabouts of high-value items by installing IoT on them and how Smart locks, intelligent ID, and Geofencing can enable the facilities managers to manage campus access, tracking, and define zones. Finally, we review the optimal mix of other technologies and strategies to produce successful deterrent, preventive, protection, and reaction measures. This study argued that using these technologies smart campuses can alter the education system by improving campus security and by offering students and educators an engaged, creative, and safe environment

Individual accessibility and segregation on activity spaces: an agent-based modelling approach

One of the main challenges of cities is the increasing social inequality imposed by the way population groups, jobs, amenities and services, as well as the transportation infrastructure, are distributed across urban space. In this thesis, the concepts of accessibility and segregation are used to study these inequalities. They can be defined as the interaction of individuals with urban opportunities and with individuals from other population groups, respectively. Interactions are made possible by people’s activities and movement within a city, which characterise accessibility and segregation as inherently dynamic and individual-based concepts. Nevertheless, they are largely studied from a static and place-based perspective. This thesis proposes an analytical and exploratory framework for studying individual-based accessibility and segregation in cities using individuals’ travel trajectories in space and time. An agent-based simulation model was developed to generate individual trajectories dynamically, employing standard datasets such as census and OD matrices and allowing for multiple perspectives of analysis by grouping individuals based on their attributes. The model’s ability to simulate people’s trajectories realistically was validated through systematic sensitivity tests and statistical comparison with real-world trajectories from Rio de Janeiro, Brazil, and travel times from London, UK. The approach was applied to two exploratory studies: São Paulo, Brazil, and London, UK. The first revealed inequalities in accessibility by income, education and gender and also unveiled within-group differences beyond place-based patterns. The latter explored ethnic segregation, unveiling patterns of potential interaction among ethnic groups in the urban space beyond their residential and workplace locations. Those studies demonstrated how inequality in accessibility and segregation can be studied both at large metropolitan scales and at fine level of detail, using standard datasets, with modest computational requirements and ease of operationalisation. The proposed approach opens up avenues for the study of complex dynamics of interaction of urban populations in a variety of urban contexts

Pedestrian real-time location and routing information delivered to mobile digital architectural guides

The work described in this thesis deals with two particular issues relating to the effective delivery of Architectural information that includes textual, 2D graphics and 3D graphic information to small mobile digital devices on location. These issues were investigated, and a solution was suggested in this thesis as part of an ongoing research project, 'City in the Palm of your Hand', that is being applied in the city of Liverpool, UK. The outcomes have broader implications for other applications of the theories and technologies related to pedestrian guides

Individual accessibility and segregation on activity spaces: an agent-based modelling approach

One of the main challenges of cities is the increasing social inequality imposed by the way population groups, jobs, amenities and services, as well as the transportation infrastructure, are distributed across urban space. In this thesis, the concepts of accessibility and segregation are used to study these inequalities. They can be defined as the interaction of individuals with urban opportunities and with individuals from other population groups, respectively. Interactions are made possible by people’s activities and movement within a city, which characterise accessibility and segregation as inherently dynamic and individual-based concepts. Nevertheless, they are largely studied from a static and place-based perspective. This thesis proposes an analytical and exploratory framework for studying individual-based accessibility and segregation in cities using individuals’ travel trajectories in space and time. An agent-based simulation model was developed to generate individual trajectories dynamically, employing standard datasets such as census and OD matrices and allowing for multiple perspectives of analysis by grouping individuals based on their attributes. The model’s ability to simulate people’s trajectories realistically was validated through systematic sensitivity tests and statistical comparison with real-world trajectories from Rio de Janeiro, Brazil, and travel times from London, UK. The approach was applied to two exploratory studies: São Paulo, Brazil, and London, UK. The first revealed inequalities in accessibility by income, education and gender and also unveiled within-group differences beyond place-based patterns. The latter explored ethnic segregation, unveiling patterns of potential interaction among ethnic groups in the urban space beyond their residential and workplace locations. Those studies demonstrated how inequality in accessibility and segregation can be studied both at large metropolitan scales and at fine level of detail, using standard datasets, with modest computational requirements and ease of operationalisation. The proposed approach opens up avenues for the study of complex dynamics of interaction of urban populations in a variety of urban contexts

On Comparative Algorithmic Pathfinding in Complex Networks for Resource-Constrained Software Agents

Software engineering projects that utilize inappropriate pathfinding algorithms carry a significant risk of poor runtime performance for customers. Using social network theory, this experimental study examined the impact of algorithms, frameworks, and map complexity on elapsed time and computer memory consumption. The 1,800 2D map samples utilized were computer random generated and data were collected and processed using Python language scripts. Memory consumption and elapsed time results for each of the 12 experimental treatment groups were compared using factorial MANOVA to determine the impact of the 3 independent variables on elapsed time and computer memory consumption. The MANOVA indicated a significant factor interaction between algorithms, frameworks, and map complexity upon elapsed time and memory consumption, F(4, 3576) = 94.09, p \u3c .001, h2 = .095. The main effects of algorithms, F(4, 3576) = 885.68, p \u3c .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p .001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p \u3c .001, h2 = .992, were also all significant. This study may contribute to positive social change by providing software engineers writing software for complex networks, such as analyzing terrorist social networks, with empirical pathfinding algorithm results. This is crucial to enabling selection of appropriately fast, memory-efficient algorithms that help analysts identify and apprehend criminal and terrorist suspects in complex networks before the next attack

Eyes-Off Physically Grounded Mobile Interaction

This thesis explores the possibilities, challenges and future scope for eyes-off, physically grounded mobile interaction. We argue that for interactions with digital content in physical spaces, our focus should not be constantly and solely on the device we are using, but fused with an experience of the places themselves, and the people who inhabit them. Through the design, development and evaluation of a series ofnovel prototypes we show the benefits of a more eyes-off mobile interaction style.Consequently, we are able to outline several important design recommendations for future devices in this area.The four key contributing chapters of this thesis each investigate separate elements within this design space. We begin by evaluating the need for screen-primary feedback during content discovery, showing how a more exploratory experience can be supported via a less-visual interaction style. We then demonstrate how tactilefeedback can improve the experience and the accuracy of the approach. In our novel tactile hierarchy design we add a further layer of haptic interaction, and show how people can be supported in finding and filtering content types, eyes-off. We then turn to explore interactions that shape the ways people interact with aphysical space. Our novel group and solo navigation prototypes use haptic feedbackfor a new approach to pedestrian navigation. We demonstrate how variations inthis feedback can support exploration, giving users autonomy in their navigationbehaviour, but with an underlying reassurance that they will reach the goal.Our final contributing chapter turns to consider how these advanced interactionsmight be provided for people who do not have the expensive mobile devices that areusually required. We extend an existing telephone-based information service to support remote back-of-device inputs on low-end mobiles. We conclude by establishingthe current boundaries of these techniques, and suggesting where their usage couldlead in the future

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis