Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

A Driving Path Based Opportunistic Routing in Vehicular Ad Hoc Network

Vehicular Ad Hoc Networks is a promising technologythat can widely apply to monitor the physical world in urban areas.Efficient data delivery is important in these networks and optimalroute selection is vital to improve this factor. Vehicular mobility isa reflection of human social activity and human trajectories show ahigh degree of temporal and spatial regularity. Therefore, vehiculardriving paths are predictable in a large extent. A new opportunisticrouting protocol (DPOR) is proposed in this study that uses drivingpath predictability and vehicular distribution in its route selectionprocedure. This protocol is composed of two phases: intersectionand next hop selection phases. A utility function is calculated toselect the next intersection and a new mechanism is also proposedfor the next hop selection phase. Simulation results show thatDPOR achieves high delivery ratio and low end-to-end delay in thenetwork

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Popular Content Distribution in Public Transportation Using Artificial Intelligence Techniques

Outdoor wireless networks suffer nowadays from an increasing data traffic demand which comes at the time where almost no vacant frequency spectrum has been left. A vast majority of this demand comes from popular content generated by video streaming and social media sites. In the future, other sources will generate even more demand with emerging applications such as virtual reality, connected cars and environmental sensing. While a significant progress has been made to address this network saturation in indoor environments, current outdoor solutions, based on fixed network deployments, are expensive to build and maintain. They tend to be immobile and therefore are inflexible in coping with the dynamics of outdoor data demand. On the other hand, Vehicular Ad-hoc NETworks (VANETs) are in nature more scalable, dynamic, flexible, and therefore more promising in terms of addressing such demand. This is especially feasible if we take advantage of public transportation vehicles and stops. These vehicles and stops are often owned and operated by the same administrative entity which overcomes the routing selfishness issue. Moreover, the mobility patterns of these vehicles are highly predictable given their regular schedules; their locations are publicly-sharable and their location distribution is uniform throughout space and time. Given these factors, a system that utilizes public transportation vehicles and stops to build a reliable, scalable and dynamic VANET for wireless network offloading in outdoor environments is proposed. This is done by exploiting the predictability demonstrated by such vehicles using an Artificial-Intelligence (AI) based system for wireless network offloading via popular content distribution. The AI techniques used are the Upper Popularity Bound (UPB) collaborative and group-based recommender based on multi-armed bandits for content recommendation and bayesian optimization based on batch-based Random Forest (RF) regression for content routing. They are used after analyzing the mobility data of public transportation vehicles and stops. This analysis includes both preprocessing and processing the data in order to select the optimal set of stops and clustering vehicles and stops based on cumulative contact duration thresholds. The final system has shown the promising networking potential of public transportation. It incorporates a recommender that has shown a versatile performance under different consumer interest and network capacity scenarios. It has also demonstrated a superior performance using a bayesian optimization technique that offloads as high as 95% of the wireless network load in an interference and collision free manner

A content dissemination framework for vehicular networking

Vehicular Networks are a peculiar class of wireless mobile networks in which vehicles are equipped with radio interfaces and are, therefore, able to communicate with fixed infrastructure (if available) or other vehicles. Content dissemination has a potential number of applications in vehicular networking, including advertising, traffic warnings, parking notifications and emergency announcements. This thesis addresses two possible dissemination strategies: i) Push-based that is aiming to proactively deliver information to a group of vehicles based on their interests and the level of matching content, and ii) Pull-based that is allowing vehicles to explicitly request custom information. Our dissemination framework is taking into consideration very specific information only available in vehicular networks: the geographical data produced by the navigation system. With its aid, a vehicle's mobility patterns become predictable. This information is exploited to efficiently deliver the content where it is needed. Furthermore, we use the navigation system to automatically filter information which might be relevant to the vehicles. Our framework has been designed and implemented in .NET C# and Microsoft MapPoint. It was tested using a small number of vehicles in the area of Cambridge, UK. Moreover, to prove the correctness of our protocols, we further evaluated it in a large-scale network simulation over a number of realistic vehicular trace-based scenarios. Finally, we built a test-case application aiming to prove that vehicles can gain from such a framework. In this application every vehicle collects and disseminates road traffic information. Vehicles that receive this information can individually evaluate the traffic conditions and take an alternative route, if needed. To evaluate this approach, we collaborated with UCLA's Network Research Lab (NRL), to build a simulator that combines network and dynamic mobility emulation simultaneously. When our dissemination framework is used, the drivers can considerably reduce their trip-times