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 U.K. 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 with 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 with the fixed sensing system.

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.

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.

Given the future context of fully integrated Intelligent Transport Systems, reliable wireless communications is a necessity. Traditionally, a single wireless technology is selected for communication but by using heterogeneous wireless communication, advantage can be taken of the different transmission characteristics. This paper proposes a novel Multiple Interface Scheduling Algorithm (MISA) which schedules uplink packets over multiple technologies for which no coordination is required among access networks. The algorithm incorporates a dynamic scoring approach for each of the available networks based on selected system performance parameters. A key feature of the proposed scheduler is that it is located between the IP layer and the MAC layer, hence a single IP address is used, and it is not necessary to modify the wireless standards. A model has been developed to simulate the proposed algorithm and the system performance for three multi radio transmission diversity schemes. The results show the advantage that can be gained in throughput by cooperatively using multiple technologies.

This paper proposes green energy efficiency metrics for low-power wireless sensors operating at ground level. The metrics are derived from our previous work on energy efficiency analysis for general wireless networks and a radio propagation model for near ground level wireless sensors. A numerical analysis is carried out to investigate the utilization of the green energy efficiency metrics for ground level communication in wireless sensor networks. The proposed metrics have been developed to calculate the optimal sensor deployment, antenna height and energy efficiency level for the near ground wireless sensor. As an application of the proposed metrics, the relationship between the energy efficiency and the spacing between the wireless sensor nodes is studied. The results provide an accurate guidance for energy efficient deployment of near ground level wireless sensors.

IEEE 802.16j is an extension of IEEE 802.16 to support relay mode operation. This paper applies IEEE 802.16j to vehicular networks to provide Internet access for high-way vehicles. It designs a utility function, which considers different types of services and effect of velocity on vehicular networks. It proposes a dynamic bandwidth allocation (DBA) algorithm specifically for base stations (BSs) in vehicular networks to support QoS requirements of different types of services. The objective of the proposed DBA is to allocate bandwidth from the BS to its serving relay stations (RSs) with QoS consideration. The simulation results have shown the effectiveness and efficiency of the proposed DBA algorithm.

IEEE 802.16 has been regarded as a promising broadband wireless access technology for its large coverage, quality of service (QoS) support for different types of application, and easy deployment. IEEE 802.16j is an extension of IEEE 802.16 to support relay mode operation. This paper applies IEEE 802.16j to vehicular network to provide Internet access for high-way vehicles. In particular, it proposes a dynamic bandwidth allocation (DBA) algorithm specifically for relay stations (RS) to support QoS requirements of different types of service. The objective of the proposed DBA is to allocate bandwidth from relay station to its serving subscriber stations (SSs) with QoS consideration. The simulation results have shown the effectiveness and efficiency of the proposed DBA algorithm.

Pervasive services have attracted numerous attentions from both industries and academia. Its very high adaptability demand makes autonomic computing (AC), and in particular, personal autonomic computing (PAC), an ideal enabling technology. Some generic AC framework has been proposed in the literature but without considering the dynamic deployment and re-configuration of pervasive service components in a self-controlled manner. This paper makes some preliminary attempts towards these two issues. In particular, a service component self-deployment algorithm using partitioning techniques is proposed and evaluated. A simple service reconfiguration algorithm is also presented. In addition, an AC environment is proposed and its major building blocks being discussed. This AC environment serves as the container of the proposed service component self-deployment and self-configuration mechanisms. A prototype of the system has been designed and implemented. Experiment results have shown the effectiveness of the proposed mechanisms.

IEEE 802.16 and Ethernet Passive Optical Network (EPON) are two promising broadband access technologies for high-capacity wireless access networks and wired access networks, respectively. They each can be deployed to facilitate connection between the end users and the Internet but each of them suffers from some drawbacks if operating separately. To combine the bandwidth advantage of optical networks with the mobility feature of wireless communications, we propose a convergence of EPON and 802.16 networks in this paper. First, this paper starts with presenting the converged network architecture and especially the concept of virtual ONU-BS (VOB). Then, it identifies some unique research issues in this converged network. Second, the paper investigates a dynamic bandwidth allocation (DBA) scheme and its closely associated research issues. This DBA scheme takes into consideration the specific features of the converged network to enable a smooth data transmission across optical and wireless networks, and an end-to-end differentiated service to user traffics of diverse QoS (Quality of Service) requirements. This QoS-aware DBA scheme supports bandwidth fairness at the VOB level and class-of-service fairness at the 802.16 subscriber station level. The simulation results show that the proposed DBA scheme operates effectively and efficiently in terms of network throughput, average/maximum delay, resource utilization, service differentiation, etc.

IEEE 802.16 and Passive Optical Network (PON) are two promising broadband access technologies for high-capacity wireless and wired access networks, respectively. In order to better understand the co-existence of both network technologies and to determine whether closer cooperation in the bandwidth provisioning process is advantageous, an access network that utilizes a Gigabit PON (GPON) to backhaul 802.16 network traffic is evaluated. Typical to many network deployments, the equipment is from different manufacturers and has different management and control interfaces. This paper proposes the use of a control bridge that overlooks the operations of both the GPON and 802.16 networks in order to: (1) provide dynamic QoS mapping so as to reduce traffic delivery cost; and (2) to improve overall channel utilization through coordinated dynamic bandwidth allocation. The performance of the converged network under the control of the proposed control bridge is evaluated in terms of cost of data delivery, channel utilization, and service differentiation.

Offloading (also known cyber-foraging) is an approach to leverage the severity of resource constrained nature of mobile devices (such as PDAs) by migrating part of the computation of applications to some nearby resource-rich surrogates (e.g., desktop PCs). It is an essential mechanism for the execution of pervasive applications. However, the mobile nature of mobile devices and the unstable connectivity of wireless links all render a less predictability of the performance of a pervasive application running under the control of offloading systems. This paper proposes an analytical model to express the performance of fault- tolerant offloading systems in mobile wireless environments. We model the failure recovery time and total execution time of pervasive applications that run under the control of the fault- tolerant offloading systems. The model is analyzed numerically and primarily evaluated based on a real application.

With the advantage of mobile devices and wireless communication technologies, service offloading, as an important branch of pervasive computing, attracts much research. In service offloading, by offloading part of computation in the mobile device to a powerful surrogate, the total service execution time can be reduced and mobile devices' battery lifetime can be greatly prolonged. In this paper, we focus on mobile ad hoc environments. Given a service's remote execution components, our goal is to find a surrogate and the path to the surrogate (i.e., the surrogate-path pair) so as to achieve a minimum surrogate execution time. Since the topology in mobile ad hoc environments can change frequently due to node mobility, we study the problem of combined routing and surrogate selection. The task is formulated as a combinatorial optimization problem and a combined routing and surrogate selection algorithm (CRoSS) is proposed. Based on cross-layer design principles, CRoSS enables the process of surrogate discovery and selection to be coalesced into the process of route discovery and maintenance of the underlying mobile ad hoc networks. It is analytically proofed that by utilizing the surrogate-path pair found by CRoSS the surrogate execution time is minimized. Simulation evaluations also show the constant efficiency of CRoSS.

Offloading is an approach to leverage the severity of resource constrained nature of mobile devices (such as PDAs, mobile phones) by migrating part of the computation of applications to some nearby resource-rich surrogates (e.g., desktop PCs, mobility support stations). It is an essential mechanism for the execution of pervasive services. However, the mobile nature of mobile devices and the unstable connectivity of wireless links all render a less predictability of the performance of a pervasive service running under the control of offloading systems. This paper proposes an analytical model to express the performance of offloading systems in mobile wireless environments. We investigate the surrogate unreachability when mobile devices move following random waypoint (RWP) mobility scheme. We model the failure recovery time and total execution time of pervasive applications that run under the control of offloading systems. Detailed evaluation and analysis results are reported and the results of this paper can be used as design guidance for pervasive service offloading systems.

Control phase plays a critical role in the performance of time-division multiple access (TDMA)-based networks. Within cluster-based wireless sensor networks, a nimble and adaptive control phase algorithm called NACPA to control the control phase of TDMA-based medium access control (MAC) in cluster-based sensor networks is proposed. This algorithm takes advantage of the wireless sensor hardware feature and presents a more accurate although simpler means to calculate the number of contention nodes in one round. On the basis of the analysis of the features of contention probability against the number of contention nodes, this algorithm can significantly reduce its computation complexity, rendering it practically feasible for resource-constrained sensor networks. Detailed analytical evaluation against two typical MAC algorithms (polling and carrier sense multiple access) is presented both in terms of packet transmission delay and average channel utilisation, the results of which, while also matching the simulation observation, have shown its effectiveness and efficiency.

The practical success of the future generation mobile systems such as 4G relies largely on its flexibility in providing adaptive and cost-effective services. Service discovery is an essential mechanism to achieve this goal. Based on the investigation of the existing service discovery protocols, this paper proposes a new service discovery methodology for future generation mobile systems: model-based service discovery or MBSD. MBSD takes advantage of the OMG (Object Management Group) MDA (Model-Driven Architecture) technique. The system architecture of MBSD and its operation are presented and implemented. The proposed methodology is validated via a mobile service scenario.

Offloading is a kind of mechanism utilized in pervasive systems to leverage the severity of resource constraints of mobile devices by migrating part of the classes of a pervasive service/application to some resource-rich nearby surrogates. A pervasive service application needs to be partitioned prior to offloading. Such partitioning algorithms play a critical role in a high-performance offloading system. This paper proposes an adaptive (k+1) partitioning algorithm that partitions a given application into 1 unoffloadable partition and k offloadable partitions. Furthermore, these partitions satisfy the multiple constraints imposed by either application users or mobile device resources. Underpinning the partitioning algorithm is a dynamic multi-cost graph that models the costs of an application in terms of its component classes (including CPU cost, memory cost and communication cost), and a Heavy-Edge and Light-Vertex Matching (HELVM) algorithm to coarsen the multi-cost graph. An offloading toolkit implementing the above algorithms has been developed, upon which the evaluations are carried out. The outcomes of the evaluation have indicated a higher level of performance of our algorithm in terms of its efficiency and cost-effectiveness

With the advances in mobile terminals and wireless communications, the demand for mobile devices to run heavier applications (e.g., those running on their desktop PC) is on increase. Offloading is a novel approach to leverage the severity of resource constrained nature of mobile devices by migrating part of the computation of pervasive services to some nearby resource-rich surrogates (e.g., base stations, desktop PCs, servers). This paper proposes a runtime offloading system for pervasive services that considers multiple types of system resources and carries out service partitioning and partition offloading in a more adaptive and efficient manner. The system's service partitioning algorithm and partition offloading mechanism are presented in detail. The evaluation outcomes have indicated a higher level of efficiency of this service offloading system

This paper presents a proof-of-the-concept of a novel means to develop in an easy way, to execute in a more pervasive way and to maintain in a more sustainable way context-aware services. The essence of this approach is the integration of the policy-based management (PBM) technique and the MDA (model-driven architecture) technique. The presence of policies grants context-aware services the high flexibility and adaptability as their nature, whereas the introduction of MDA for context-aware service information model fundamentally solves the information model puzzle of current PBM. MDA's middleware-neutral feature also benefits the smooth evolution of context-aware services as a kind of software. The preliminary case study has proved the positive feasibility of this approach.