Using Service Delay for Facilitating Access Point Selection in VANETs

[[abstract]]With the rapid development of wireless mobile networks, VANET (vehicular ad hoc networks) that adopt transportation tools as mobile platforms have received great attention. Integrating VANETs with wireless infrastructure to provide high-quality transmission services also has become one of the important research topics. Because terminal devices in vehicular environments are highly mobile, a MN (mobile node) will encounter frequent handoffs while accessing wireless network services. However, supposing the chosen handoff AP (access point) presents too long service delay, the quality of the Internet, especially real-time services, like VoIP and multimedia streaming, will be greatly influenced. Therefore, by using the packet scheduling architecture for classified service at APs, this paper proposes a handoff scheme based on service delay prediction. According to the scheduling scheme, we can estimate the load and service delay of different access categories of the regional APs. Our proposed scheme allows the MN requesting real-time services to be allocated to the AP with the lower service delay and the chosen AP thus can reduce the service delay for users.[[sponsorship]]IARIA - International Academy Research and Industry Association[[conferencetype]]國際[[conferencedate]]20130623~20130628[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Rome, Ital

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

PrivHab : A privacy preserving georouting protocol based on a multiagent system for podcast distribution on disconnected areas

Altres ajuts: Universitat Autònoma de Barcelona 472-03-01/2012We present PrivHab, a privacy preserving georouting protocol that improves multiagent decision-making. PrivHab learns the mobility habits of the nodes of the network. Then, it uses this information to dynamically select to route an agent carrying a piece of data to reach its destination. PrivHab makes use of cryptographic techniques from secure multi-party computation to make the decisions while preserving nodes' privacy. PrivHab uses a waypoint-based routing that achieves a high performance and low overhead in rugged terrain areas that are plenty of physical obstacles. The store-carry-and-forward approach used is combined with mobile agents that provide intelligence, and it is designed to operate in areas that lack network infrastructure. We have evaluated PrivHab under the scope of a realistic podcast distribution application in remote rural areas, where these programs have to be recorded into a physical format and distributed to the local radio stations. The usage of PrivHab aims to reduce this spending of resources. The PrivHab protocol is compared with a set of well-known delay-tolerant routing algorithms and shown to outperform them

Quality of Service in Vehicular Ad Hoc Networks: Methodical Evaluation and Enhancements for ITS-G5

After many formative years, the ad hoc wireless communication between vehicles has become a vehicular technology available in mass production cars in 2020. Vehicles form spontaneous Vehicular Ad Hoc Networks (VANETs), which enable communication whenever vehicles are nearby without need for supportive infrastructure. In Europe, this communication is standardised comprehensively as Intelligent Transport Systems in the 5.9 GHz band (ITS-G5). This thesis centres around Quality of Service (QoS) in these VANETs based on ITS-G5 technology. Whilst only a few vehicles communicate, radio resources are plenty, and channel congestion is a minor issue. With progressing deployment, congestion control becomes crucial to preserve QoS by preventing high latencies or foiled information dissemination. The developed VANET simulation model, featuring an elaborated ITS-G5 protocol stack, allows investigation of QoS methodically. It also considers the characteristics of ITS-G5 radios such as the signal attenuation in vehicular environments and the capture effect by receivers. Backed by this simulation model, several enhancements for ITS-G5 are proposed to control congestion reliably and thus ensure QoS for its applications. Modifications at the GeoNetworking (GN) protocol prevent massive packet occurrences in a short time and hence congestion. Glow Forwarding is introduced as GN extension to distribute delay-tolerant information. The revised Decentralized Congestion Control (DCC) cross-layer supports low-latency transmission of event-triggered, periodic and relayed packets. DCC triggers periodic services and manages a shared duty cycle budget dedicated to packet forwarding for this purpose. Evaluation in large-scale networks reveals that this enhanced ITS-G5 system can reliably reduce the information age of periodically sent messages. The forwarding budget virtually eliminates the starvation of multi-hop packets and still avoids congestion caused by excessive forwarding. The presented enhancements thus pave the way to scale up VANETs for wide-spread deployment and future applications

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization