Showing papers by "Celimuge Wu published in 2017"

AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling

Abstract: With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.

A Reinforcement Learning-Based Data Storage Scheme for Vehicular Ad Hoc Networks

Abstract: Vehicular ad hoc networks (VANETs) have been attracting interest for their potential roles in intelligent transport systems (ITS). In order to enable distributed ITS, there is a need to maintain some information in the vehicular networks without the support of any infrastructure such as road side units. In this paper, we propose a protocol that can store the data in VANETs by transferring data to a new carrier (vehicle) before the current data carrier is moving out of a specified region. For the next data carrier node selection, the protocol employs fuzzy logic to evaluate instant reward by taking into account multiple metrics, specifically throughput, vehicle velocity, and bandwidth efficiency. In addition, a reinforcement learning-based algorithm is used to consider the future reward of a decision. For the data collection, the protocol uses a cluster-based forwarding approach to improve the efficiency of wireless resource utilization. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

Mobile edge computing based VM migration for QoS improvement

Abstract: QoS of applications in an edge node of mobile edge computing (MEC) could degrade due to congestion of wireless access or short of mobile edge computing resource. In order to improve QoS such as TCP throughput, we propose a VM migration method, which takes a VM from congested node to another node in a mobile edge. Users can choose a far but less-congested node, instead of a near but congested node. The choice is made based on an expected TCP throughput. We analyze how much the migration can improve TCP throughput.

Cooperative Content Delivery in Vehicular Networks with Integration of Sub-6 GHz and mmWave

Abstract: With the rapid increase of vehicular Internet of Things (IoT) applications, it is urgent to design an approach which is possible to distribute a large amount of contents to vehicles on the road. However, the current cellular technology is not sufficient due to its limited bandwidth in a dense vehicle environment. In this paper, we propose a protocol which integrates licensed Sub-6 GHz band, IEEE 802.11p, and mmWave communications for the content distribution in vehicular networks. The proposed protocol uses a cluster-based approach where a fuzzy logic-based algorithm is employed to select efficient gateway nodes which bridge the licensed Sub-6 GHz communication and mmWave communication in order to maximize the overall network throughput. IEEE 802.11p vehicle-to-vehicle communication is used to share information among vehicles in order for achieving efficient clustering. We conduct extensive simulations to evaluate the performance of the proposed protocol under various network conditions. Simulation results show that the proposed protocol can achieve significant improvement in various scenarios compared to the existing approaches.

HVC: A Hybrid Cloud Computing Framework in Vehicular Environments

Abstract: As increasingly more applications are deployed in vehicles, how to provide the demanded computational capability becomes crucial. This paper proposes the hybrid vehicular cloud (HVC) framework for cloud computing on the road to increase the computational capability of vehicles by using resources from the centralized cloud, RSU, and neighboring vehicles. The proposed scheme can be used as a general framework for all types of applications. It is able to satisfy various types of job requirements, such as real time or host requirements, and it is adaptive to the dynamic vehicular environment. Specifically, this paper defines the procedures to support the autonomous organization of a vehicular cloud. An online scheduling algorithm is proposed to solve the job assignment problem with the objective of processing more jobs and reducing cellular network usage. Extensive simulations are conducted, and the results show the superiority of the proposed scheme over competing schemes in typical urban scenarios.

Connected vehicles and Internet of things

Abstract: Vehicular ad hoc networks (VANETs) provide a distributed approach for connecting vehicles on the move. VANETs have been attracting interest for their potential roles in Internet of things (IoT) applications including intelligent transportation systems, autonomous driving, and so on. In VANETs, a multi-hop communication protocol is required when the transmission range is not enough to cover all intended receivers. Many applications including collision warning systems and other value-added applications require inter-vehicle communications. However, due to the various vehicle densities, vehicle movement, limited bandwidth of wireless communications, it is challenging to provide a high packet reception ratio and low end-to-end delay. There have been many studies about the inter-vehicle communications. These studies cover all communication layers and various types of applications. In this paper, we survey the recent works on the inter-vehicle communications for emerging IoT applications, with emphasis on network layer protocols, and explain the corresponding challenges and research directions.

V2R Communication Protocol Based on Game Theory Inspired Clustering

Abstract: We propose a vehicle-to-roadside communication protocol based on a distributed clustering algorithm 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 taking into account multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles A reinforcement learning algorithm with game theory-based reward discount is employed to guide each vehicle to select the route which can maximize the whole network performance We conduct extensive computer simulations to show the performance advantage of the protocol over other approaches

Multihop Data Delivery Virtualization for Green Decentralized IoT

Abstract: Decentralized communication technologies (i.e., ad hoc networks) provide more opportunities for emerging wireless Internet of Things (IoT) due to the flexibility and expandability of distributed architecture. However, the performance degradation of wireless communications with the increase of the number of hops becomes the main obstacle in the development of decentralized wireless IoT systems. The main challenges come from the difficulty in designing a resource and energy efficient multihop communication protocol. Transmission control protocol (TCP), the most frequently used transport layer protocol for achieving reliable end-to-end communications, cannot achieve a satisfactory result in multihop wireless scenarios as it uses end-to-end acknowledgment which could not work well in a lossy scenario. In this paper, we propose a multihop data delivery virtualization approach which uses multiple one-hop reliable transmissions to perform multihop data transmissions. Since the proposed protocol utilizes hop-by-hop acknowledgment instead of end-to-end feedback, the congestion window size at each TCP sender node is not affected by the number of hops between the source node and the destination node. The proposed protocol can provide a significantly higher throughput and shorter transmission time as compared to the end-to-end approach. We conduct real-world experiments as well as computer simulations to show the performance gain from our proposed protocol.

An Oblivious Game-Theoretic Approach for Wireless Scheduling in V2V Communications

Abstract: This paper addresses the problem of wireless resource scheduling in a vehicle-to-vehicle (V2V) communication network. The technical challenges lie in the fast changing network dynamics, namely, the channel quality and the data traffic variations. For a road segment covered by a road side unit (RSU), especially in a dense urban area, the vehicle density tends to be stable. The incoming service requests from the vehicle user equipment (VUE)-pairs compete with each other for the limited frequency resource in order to deliver data packets. Such competitions are regulated by the RSU via a sealed second-price auction at the beginning of scheduling slots. Each incumbent service request aims at maximizing the expected long-term payoff from bidding the frequency resource for packet transmissions. Markov perfect equilibrium (MPE) can be utilized to characterize the optimal competitive behaviors of the service requests. When the number of incumbent VUE-pairs becomes large, solving the MPE becomes infeasible. We adopt an oblivious equilibrium to approximate the MPE, which is theoretically proven to be error-bounded. The decision making process at each service request is hence transformed into a single-agent Markov decision process, for which we propose an on-line auction based learning scheme. Through simulation experiments, we show the potential performance gains from our proposed scheme, in terms of per-service request average utility.

DTN-based vehicular cloud for post-disaster information sharing

Abstract: We first propose a framework which utilizes vehicular delay tolerant network (DTN) to form a vehicular cloud in order to provide information exchange without communication infrastructure. The framework does not rely on cellular network and therefore provides an approach which is suitable for post-disaster communication where cellular network is unavailable or severely congested. The paper also proposes a protocol which is able to provide vehicle-to-cloud communication in frequently changing vehicular environment. The protocol takes into account the link throughput, additional signal coverage, connection time, and the probability to encounter a RSU for the forwarder selection by using a fuzzy logic-based approach. The protocol also employs a network coding approach to reduce the overhead while maintaining a high data delivery ratio. We use computer simulations to evaluate the proposed framework.

A learning-based probabilistic routing protocol for vehicular delay tolerant networks

Abstract: Existing routing protocols for vehicular delay tolerant networks (VDTNs) do not adequately address the multi-hop delivery probability in the forwarder node selection. We propose a probabilistic routing protocol for VDTNs. The protocol takes into account the vehicle velocity, node centrality, and node buffer size using a fuzzy logic-based approach. Multi-hop forwarding probability is also considered for the next hop node selection by employing a Q-learning algorithm which discounts the encounter probability with the increase of hops. We conduct extensive simulations to evaluate the proposed protocol in various scenarios and show the advantage of the protocol over existing well-known approaches.