Analysis methodology for flow-level evaluation of a hybrid mobile-sensor network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic

Forecasting Congestion Severity for Smart City Traffic Management

The future of smart city traffic forecasting is two-way communication between residents and the city infrastructure. Today, Intelligent Transportation Systems (ITS) are essential tools for traffic planning, analysis, and forecasting, which use sensor data to forecast traffic. The emergence of crowdsourced traffic reporting using mobile applications is adding new layers of rich data that can be used to improve ITS systems. Resident mobile applications and sensors are reporting traffic congestions, incidents, accidents, and more. However, utilizing this data in city ITS or processes is not common. There are few studies on how to use and integrate this new layer of data to improve ITS systems and increase its capability. More importantly, the study proposed a model that helps the city to forecast traffic congestion in urban roadways and arterials were there are no traffic sensors. This study proposed several models based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to forecast traffic severity without the need of traffic sensor data. The model used six months of Google/Waze traffic reports in the San Francisco Bay Area to forecast traffic congestions. The collected crowdsourced data included Waze incident reports, hourly weather, time, and location features. The model is designed to forecast hourly traffic congestion into five congestion levels. It included temporal and spatial dependencies learned using different types of layers. The proposed model will help city traffic engineers and planners forecast urban roadways and arterials traffic where there are no traffic sensors without the need to install one. This innovation will help reduce costs while improving ITS traffic forecasting. Moreover, future autonomous vehicles may also provide more details that can be used to improve traffic conditions

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

Resilient networking in wireless sensor networks

This report deals with security in wireless sensor networks (WSNs), especially in network layer. Multiple secure routing protocols have been proposed in the literature. However, they often use the cryptography to secure routing functionalities. The cryptography alone is not enough to defend against multiple attacks due to the node compromise. Therefore, we need more algorithmic solutions. In this report, we focus on the behavior of routing protocols to determine which properties make them more resilient to attacks. Our aim is to find some answers to the following questions. Are there any existing protocols, not designed initially for security, but which already contain some inherently resilient properties against attacks under which some portion of the network nodes is compromised? If yes, which specific behaviors are making these protocols more resilient? We propose in this report an overview of security strategies for WSNs in general, including existing attacks and defensive measures. In this report we focus at the network layer in particular, and an analysis of the behavior of four particular routing protocols is provided to determine their inherent resiliency to insider attacks. The protocols considered are: Dynamic Source Routing (DSR), Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing (RWR)

Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model

Although connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-complaint data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities