Multiple interface scheduling system for heterogeneous wireless vehicular networks: Description and evaluation

Reliable wireless communications between vehicles (V2V) and between vehicles and infrastructure (V2I) will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously. This paper proposes an uplink Multi Interface Scheduling System (MISS) located at an intermediate shim layer on the user side, to achieve efficient bandwidth aggregation, or lower end-to-end packet delay. MISS aims to find all the available networks that can meet multiple criteria based on user preference and required performance. Simulation results show that safety critical traffic can be prioritized where the resources are insufficient for all the services. Video delivery quality is also improved by prioritizing the most important frames. This algorithm is ideally suited to vehicular networks, where delivery of safety traffic and/or video is an essential requirement

Evaluating the Benefit of a Smart Scheduler in a Non-Cooperative, Multi-User Heterogeneous Wireless ITS Environment

Heterogeneous wireless networks will play a significant role in providing multiservice connectivity in ITS, and in particular vehicular networks. This paper describes a smart scheduling approach that allows end user nodes to direct packets over the best available wireless access technologies and set priorities for selected services. The performance of this smart scheduler has been simulated in a non-cooperative multi user environment and the results show that, for the prioritised services, the scheduler can provide a lower average packet delay and a higher average packet delivery ratio for all users than a wireless system that selects on signal strength alone

Buried RF Sensors for Smart Road Infrastructure: Empirical Communication Range Testing, Propagation by Line of Sight, Diffraction and Reflection Model and Technology Comparison for 868 MHz–2.4 GHz

Updating the road infrastructure requires the potential mass adoption of the road studs currently used in car detection, speed monitoring, and path marking. Road studs commonly include RF transceivers connecting the buried sensors to an offsite base station for centralized data management. Since traffic monitoring experiments through buried sensors are resource expensive and difficult, the literature detailing it is insufficient and inaccessible due to various strategic reasons. Moreover, as the main RF frequencies adopted for stud communication are either 868/915 MHz or 2.4 GHz, the radio coverage differs, and it is not readily predictable due to the low-power communication in the near proximity of the ground. This work delivers a reference study on low-power RF communication ranging for the two above frequencies up to 60 m. The experimental setup employs successive measurements and repositioning of a base station at three different heights of 0.5, 1 and 1.5 m, and is accompanied by an extensive theoretical analysis of propagation, including line of sight, diffraction, and wall reflection. Enhancing the tutorial value of this work, a correlation analysis using Pearson’s coefficient and root mean square error is performed between the field test and simulation results

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm

Maximizing the Profit of Cloud Broker with Priority Aware Pricing

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm

Detecting on-street parking spaces in smart cities: Performance evaluation of fixed and mobile sensing systems

As the number of vehicles continues to grow, parking spaces are at a premium in city streets. In addition, due to the lack of knowledge about street parking spaces, heuristic circling in the streets not only costs drivers’ time and fuel, but also increases city congestion. In the wake of the recent trend to build convenient, green and energy-efficient smart cities, common techniques adopted by high-profile smart parking systems are reviewed, and the performance of the various approaches are compared. A mobile sensing unit has been developed as an alternative to the fixed sensing approach. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. By extracting parked vehicles’ features from the collected trace, a supervised learning algorithm has been developed to estimate roadside parking occupancy. Multiple road tests were conducted around Wheatley (Oxfordshire) and Guildford (Surrey) in the UK. In the case of accurate GPS readings, enhanced by a map matching technique, the accuracy of the system is above 90%. A quantity estimation model is derived to calculate the density of sensing units required to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the mobile sensing approach can perform at the same level as fixed sensing solutions when accurate location information is available but substantially fewer sensors are needed compared to the fixed sensing system

An offloading system for pervasive services in mobile wireless environments

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multiple Interface Scheduling System for HeterogeneousWireless Vehicular Networks: Description and Evaluation

Reliable wireless communications between vehicles (V2V) and between vehicles and infrastructure (V2I) will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously. This paper proposes an uplink Multi Interface Scheduling System (MISS) located at an intermediate shim layer on the user side, to achieve eÿcient bandwidth aggregation, or lower end-to-end packet delay. MISS aims to find all the available networks that can meet multiple criteria based on user preference and required performance. Simulation results show that safety critical traffic can be prioritized where the resources are insufficient for all the services. Video delivery quality is also improved by prioritizing the most important frames. This algorithm is ideally suited to vehicular networks, where delivery of safety traffic and/or video is an essential requirement