Kaveh Shafiee

Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs

In this paper, we propose the connectivity-aware minimum-delay geographic routing (CMGR) protocol for vehicular ad hoc networks (VANETs), which adapts well to continuously changing network status in such networks. When the network is sparse, CMGR takes the connectivity of routes into consideration in its route selection logic to maximize the chance of packet reception. On the other hand, in situations with dense network nodes, CMGR determines the routes with adequate connectivity and selects among them the route with the minimum delay. The performance limitations of CMGR in special vehicular networking situations are studied and addressed. These situations, which include the case where the target vehicle has moved away from its expected location and the case where traffic in a road junction is so sparse that no next-hop vehicle can be found on the intended out-going road, are also problematic in most routing protocols for VANETs. Finally, the proposed protocol is compared with two plausible geographic connectivity-aware routing protocols for VANETs, A-STAR and VADD. The obtained results show that CMGR outperforms A-STAR and VADD in terms of both packet delivery ratio and ratio of dropped data packets. For example, under the specific conditions considered in the simulations, when the maximum allowable one-way transmission delay is 1min and one gateway is deployed in the network, the packet delivery ratio of CMGR is approximately 25% better than VADD and A-STAR for high vehicle densities and goes up to 900% better for low vehicle densities.

Optimal Distributed Vertical Handoff Strategies in Vehicular Heterogeneous Networks

This paper addresses the problem of optimal vertical handoff (VHO) in a vehicular network setting. The VHO objective can be minimizing the data transfer time or alternatively minimizing the cost of transmitting traffic. As a framework for performance evaluations, we first analyze a heterogeneous network consisting of a wide-area cellular network interworking with wireless local area networks (WLAN) with fixed inter-distance between access points (APs) placed along roadsides. We further analyze a scenario with random inter-distance between WLAN APs. In both aforementioned cases, only Vehicle-to-Infrastructure (V2I) capability is assumed. We show that in order to minimize the cost of transmission or alternatively transmission time, performing VHOs is an appropriate choice at lower speeds, whereas it would be better to avoid VHO and stay in the cellular network at higher speeds. We further generalize our study, to investigate the VHO strategies in a random inter-distance scenario with both V2I and Vehicle-to-Vehicle (V2V) communication capabilities. We demonstrate that the combination of WLAN plus cellular plus ad hoc networking outperforms any other networking strategies considered in this work in terms of transmission times and transmission costs. The presented results provide insightful guidelines for optimal VHO decision making based on the characteristics of the network as well as the user mobility profile.

Innovative Schemes for Resource Allocation in the Cloud for Media Streaming Applications

Media streaming applications have recently attracted a large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand. Media content providers are charged for the amount of resources allocated (reserved) in the cloud. Most of the existing cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, an open problem is to decide on both the right amount of resources reserved in the cloud, and their reservation time such that the financial cost on the media content provider is minimized. We propose a simple-easy to implement-algorithm for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud. Based on the prediction of demand for streaming capacity, our algorithm is carefully designed to reduce the risk of making wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

Position-Based Directional Vehicular Routing

Routing of data packets in vehicular ad hoc networks (VANETs) is challenging due to dynamic changes in the network topologies. As nodes in VANETs can obtain accurate position information from onboard Global Positioning System receivers, position-based routing is considered to be a very promising routing strategy for VANETs. This paper presents a novel Position-based Directional Vehicular Routing (PDVR) method. To make sure the packets can be sent to the destination in an efficient and stable route, PDVR selects the next-hop from vehicles traveling in the same direction as the forwarding vehicle based on their angular directions relative to the destination. We analyze the straight and the curve highway scenarios, and present the realizing algorithm based on position and velocity vectors. The method is evaluated using NS2 and compared with typical ad hoc routing protocol Ad hoc On-Demand Distance Vector (AODV), the position-based routing protocol Distance Routing Effect Algorithm for Mobility (DREAM), and VANETs routing based on the Cartesian space method. Simulation results show that PDVR can find and maintain more stable routes compared with the other routing protocols.

A Reliable Robust Fully Ad Hoc Data Dissemination Mechanism for Vehicular Networks

Many applications in vehicular networks need the data to be disseminated from a source vehicle to a large number of vehicles in the network. Although many solutions to this problem have been previously proposed by the research community, some challenges and failure scenarios have still remained unsolved particularly when the forwarding vehicle is located at the intersections and it wants to disseminate the data to all the intersecting road segments. In this paper, we first evaluate some of the previously proposed directional mode (along the straight roads) data dissemination mechanisms and then integrate the one with the best performance with our novel reliable robust intersection mode data dissemination mechanism in order to come up with a united mechanism for disseminating data both along the straight roads and at intersections. The effectiveness of our proposed mechanism is also verified by performance evaluations.

Throughput Modeling of Differentiation Schemes for IEEE 802.11e MAC Protocol

Most recent analyses on IEEE802.11e quality of service (QoS)-aware enhanced distributed coordination function (EDCA) require a large degree of complexity, making it difficult to apply them to a wide range of parameter settings for the evaluation of service differentiation mechanisms supported in EDCA, including the Contention Window (CW) and Arbitration Inter-Frame Space (AIFS) mechanisms. In this paper, we propose an improved analytical model to analyze the throughput of EDCA with AIFS and CW differentiation schemes. The model is simplified by decomposing the problem into two easily solved Markov chains that can jointly be solved by numerical method. We present simulation and analytical results over a broad range of system parameters to demonstrate the accuracy of the proposed model. The model is simple to implement and can be applied to general configuration circumstances for the evaluation of EDCA. The results are valuable to facilitate proper design of parameters in 802.11e enhanced distributed channel access for the QoS support required by specific applications.

Prediction-based resource allocation in clouds for media streaming applications

Media streaming applications have recently attracted large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is difficult to provide streaming distribution with guaranteed QoS relying only on central resources at the content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., VoD provider) can use to obtain resources on-demand. Since a media content provider is charged for amount of resources (bandwidth) rented from the cloud, an open problem is to decide on the right amount of resources allocated in the cloud and their reservation time such that the financial cost on the content provider is minimized. We consider a practical pricing model that is based on a non-linear tariff (i.e., a pricing scheme that depends non-linearly on the resources purchased or time reserved). We formulate the optimization problem based on prediction of future streaming demand. We then propose a simple (easy to implement) algorithm for resource allocation that exploits the non-linearity in the price contract, while ensuring that sufficient resources is reserved in the cloud without incurring wastage. The results of our numerical evaluation and simulations show that the proposed algorithm mimics the optimum solution very well.

A Novel Localized Data Aggregation Algorithm for Advanced Vehicular Traffic Information Systems

In this paper, we introduce an advanced vehicular networking architecture combining infrastructure and ad-hoc communications, which differentiates the global traffic information the vehicles use for determining the general routes to their destinations from the local traffic information used for avoiding congestion. While the vehicles obtain the real-time global information by accessing the infrastructure (high-speed backhaul network), they are provided with the updated local traffic information through direct vehicle-to-vehicle ad-hoc communications. The provisioning of the local traffic information is based on a novel scalable, efficient, reliable traffic information aggregation algorithm that is the main contribution of this paper. The effectiveness of the proposed algorithm is verified and the right values for the parameters in the algorithm are determined by means of simulations.

Request-Adaptive Packet Dissemination for Context-Aware Services in Vehicular Networks

Many applications in vehicular networks are context-aware in that the observations of sensing nodes, potentially vehicles, at a target location should be made available to the requesting node possibly at a different location. In order to provision such applications, two phases of packet routing between the requesting node and the target location and packet dissemination within the target location need to be implemented. In this paper, we focus on the second phase and propose an efficient reliable packet dissemination mechanism, Request-adaptive Packet Dissemination Mechanism (RPDM), in target location. RPDM allows for different applications to generate request packets based on their exclusive observation needs and delay requirements and adapts the dissemination mechanism in target location to specific needs of the request packet received. Besides, RPDM also takes the intrinsic characteristics of vehicular environments into account to make sure roadmap and connectivity constraints are considered and broadcast storm is prevented. Finally, RPDM is compared with a best-known state-of-the-art data dissemination mechanism, COR, and the results show that RPDM outperforms COR in terms of both resolution time and packet overhead traffic.

Action-based scheduling technique for 802.15.4/ZigBee wireless body area networks

Energy-efficient communication protocols in resource-constrained networks and specifically wireless body area networks (WBANs) has been of significant importance since they emerged nearly last decade. In this work, we use the periodic nature of body actions to propose an action-based scheduling technique in which time-slot allocations are adapted to the periodic connectedness of on-body links. In other words, the periodicity of on-body links is employed to predict the future behaviours of links to help develop energy-efficient communications between on-body nodes, thereby elongating the network lifetime. Analysis and measurement with 2.4GHz IEEE 802.15.4/ZigBee compliant micaZ motes in a fitness environment serve as our tool to do action recognition and subsequently scheduling. The proposed technique helps us reach within less than 7% of power consumption lower bound while it does not have complexity of most channel prediction algorithms that can result in excessive process power consumption.