Offloading Content with Self-organizing Mobile Fogs

Mobile users in an urban environment access content on the internet from different locations. It is challenging for the current service providers to cope with the increasing content demand from a large number of collocated mobile users. In-network caching to offload content at nodes closer to users alleviate the issue, though efficient cache management is required to find out who should cache what, when and where in an urban environment, given nodes limited computing, communication and caching resources. To address this, we first define a novel relation between content popularity and availability in the network and investigate a node's eligibility to cache content based on its urban reachability. We then allow nodes to self-organize into mobile fogs to increase the distributed cache and maximize content availability in a cost-effective manner. However, to cater rational nodes, we propose a coalition game for the nodes to offer a maximum "virtual cache" assuming a monetary reward is paid to them by the service/content provider. Nodes are allowed to merge into different spatio-temporal coalitions in order to increase the distributed cache size at the network edge. Results obtained through simulations using realistic urban mobility trace validate the performance of our caching system showing a ratio of 60-85% of cache hits compared to the 30-40% obtained by the existing schemes and 10% in case of no coalition

Adaptive traffic lights based on traffic flow prediction using machine learning models

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators

A simulation framework for traffic information dissemination in ubiquitous vehicular ad hoc networks

The ongoing efforts to apply advanced technologies to help solve transportation problems advanced the growing trend of integrating mobile wireless communications into transportation systems. In particular, vehicular ad hoc networks (VANETs) allow vehicles to constitute a decentralized traffic information system on roadways and to share their own information. This research focused on the development of an integrated transportation and communication simulation framework to build a more realistic environment with which to study VANETs, as compared to previous studies. This research implemented a VANET-based information model into an integrated transportation and communication simulation framework in which these independent simulation tools were tightly coupled and finely synchronized. A traffic information system as a VANET application was built and demonstrated based on the simulation framework developed in this research. In this system, vehicles record their own travel time data, share these data via an ad hoc network, and reroute at split sections based on stored travel time data. Disseminated speeds of traffic information via broadcast on a real roadway network were obtained. In this research, Traffic information speeds were approximately between the road speed limit in a low traffic density - in which case they were mostly delivered by vehicles traveling on the opposite directions - and half of the transmission range (250/2 meter) per second in a high traffic density, which means they were delivered by vehicles traveling in the same direction. Successful dynamic routing based on stored travel time data was demonstrated with and without an incident in this framework. At the both cases, the benefits from dynamic routing were shown even in the low market penetration. It is believed that a wide range of VANET applications can be designed and assessed using methodologies influenced by and contributed to by the simulation framework and other methods developed in this dissertation

Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems

Lv, Z.; Song, H.; Lloret, J.; Kim, D.; De Souza, J. (2019). Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems. IEEE Access. 7:18070-18075. https://doi.org/10.1109/ACCESS.2019.2895441S1807018075