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

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

A Data Fusion Approach to Automated Decision Making in Intelligent Vehicles

The goal of an intelligent transportation system is to increase safety, convenience and efficiency in driving. Besides these obvious advantages, the integration of intelligent features and autonomous functionalities on vehicles will lead to major economic benefits from reduced fuel consumption to efficient exploitation of the road network. While giving this information to the driver can be useful, there is also the possibility of overloading the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision making modules which receive the raw data from all available sources, process this data and inform the driver about the existing or impending situations and suggest, or even take actions. Intelligent vehicles can take advantage of using different sources of data to provide more reliable and more accurate information about driving situations and build a safer driving environment. I have identified five general sources of data which is available for intelligent vehicles: the vehicle itself, cameras on the vehicle, communication between the vehicle and other vehicles, communications between vehicles and roadside units and the driver information. But facing this huge amount of data requires a decision making module to collect this data and provide the best reaction based on the situation. In this thesis, I present a data fusion approach for decision making in vehicles in which a decision making module collects data from the available sources of information and analyses this data and provides the driver with helpful information such as traffic congestion, emergency messages, etc. The proposed approach uses agents to collect the data and the agents cooperate using a black board method to provide the necessary data for the decision making system. The Decision making system benefits from this data and provides the intelligent vehicle applications with the best action(s) to be taken. Overall, the results show that using this data fusion approach for making decision in vehicles shows great potential for improving performance of vehicular systems by reducing travel time and wait time and providing more accurate information about the surrounding environment for vehicles. In addition, the safety of vehicles will increase since the vehicles will be informed about the hazard situations