Showing papers by "Celimuge Wu published in 2015"

Toward Practical and Intelligent Routing in Vehicular Ad Hoc Networks

Abstract: Apart from vehicle mobility, data rate (bit rate) and multihop data transmission efficiency (including route length) have a significant impact on the performance of a routing protocol for vehicular ad hoc networks (VANETs). Existing routing protocols do not seriously address all these issues and are not evaluated for a real VANET environment. Therefore, it is difficult for these protocols to attain a high performance and to work properly under various scenarios. In this paper, we first discuss the challenges of routing in VANETs based on the data acquired from real-world experiments and then propose a routing protocol that is able to learn the best transmission parameters by interacting with the environment. The protocol takes into account multiple metrics, specifically data transmission rate, vehicle movement, and route length. We use both real-world experiments and computer simulations to evaluate the proposed protocol.

Joint Fuzzy Relays and Network-Coding-Based Forwarding for Multihop Broadcasting in VANETs

Abstract: In vehicular ad hoc networks (VANETs), due to the limited radio propagation range of wireless devices, many safety applications require a multihop broadcast protocol to disseminate traffic warning information. However, providing an efficient multi-hop forwarding of broadcast messages has been a challenging problem due to vehicle movement, limited wireless resources, and unstable signal strength. In this paper we propose a broadcast protocol that can provide a low message overhead and a high packet dissemination ratio. The proposed scheme uses a fuzzy logic algorithm to choose the next hop relay nodes and uses network coding to improve the packet dissemination ratio without increasing the message overhead. By using the fuzzy logic algorithm, the protocol can choose the best relay node by taking intervehicle distance, vehicle velocity, and link quality into account. Network coding is used to improve the packet reception ratio by utilizing the broadcast nature of wireless channels. We show the effectiveness of the proposed scheme by using both theoretical analysis and computer simulations.

Efficient Broadcasting in VANETs Using Dynamic Backbone and Network Coding

Abstract: Multihop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and many other interesting applications. However, designing an efficient data dissemination protocol for VANETs has been a challenging issue due to vehicle movements, limited wireless resources, and the lossy characteristics of wireless communication. In this paper, we propose a protocol that can provide a lightweight and reliable solution for data dissemination in VANETs. The protocol employs dynamically generated backbone vehicles to disseminate broadcast packets to reduce the MAC-layer contention time at each node while maintaining a high packet dissemination ratio by taking into account vehicle movement dynamics and the link quality between vehicles for the backbone selection. The protocol also uses network coding to reduce the protocol overhead and to improve the packet reception probability as compared with conventional approaches. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

Packet Size-Aware Broadcasting in VANETs With Fuzzy Logic and RL-Based Parameter Adaptation

Abstract: Most existing multi-hop broadcast protocols for vehicular ad hoc networks do not consider the problem of how to adapt transmission parameters according to the network environment. Besides the propagation environment that determines the channel bit error rate, packet payload size has a significant effect on the packet loss rate. In this paper, we first discuss the effect of packet size on the packet reception ratio, and then propose a broadcast protocol that is able to specify the best relay node by taking into account the data payload size. The proposed protocol employs a fuzzy logic-based algorithm to jointly consider multiple metrics (link quality, inter-vehicle distance, and vehicle mobility) and uses a redundancy transmission approach to ensure high reliability. Since the fuzzy membership functions are tuned by using reinforcement learning, the protocol can adapt to various network scenarios. We use both real-world experiments and computer simulations to evaluate the proposed protocol.

An Intelligent Broadcast Protocol for VANETs Based on Transfer Learning

Abstract: Designing an efficient multi-hop broadcast protocol is very important for the realization of collision avoidance systems and other many interesting applications in vehicular ad hoc networks (VANETs). Existing protocols are optimized for a specific scenario, and are not capable of working in various scenarios. Therefore, designing an intelligent protocol which can tune itself in relation to the change of network environment is particularly important. In this paper, we propose a broadcast protocol which is able to make forwarding decision based on a self-learning mechanism. The protocol employs a fuzzy logic-based relay node selection approach to take into account multiple metrics for the forwarding algorithm. The parameters used for the fuzzy logic are tuned online using a reinforcement learning approach. Transfer learning is used to transfer knowledge to new arriving vehicles (agents) in order to shorten the convergence time. The combination of reinforcement learning, transfer learning and fuzzy logic can provide an intelligent solution for broadcasting in VANETs. We conduct computer simulations to evaluate the proposed protocol.

A learning approach for traffic offloading in stochastic heterogeneous cellular networks

Abstract: This paper addresses energy-aware traffic offloading in stochastic heterogeneous cellular networks (HCNs). The objective is to minimize energy consumption of the HCN while maintaining Quality-of-Service experienced by the mobile users. For each cell, the energy consumption depends on its associated system load, which is coupled with system loads in other cells due to the sharing over a common spectrum band. Such a traffic offloading problem is modeled by a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller learns to solve the optimal traffic offloading strategy with no prior knowledge of the DTMDP statistics. To deal with the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized QC-learning algorithm is developed such that the macro-cell base stations (BSs) can independently manage the operations of small-cell BSs by making use of the network information obtained from the network controller. Simulations validate the proposed studies.

Can DTN improve the performance of vehicle-to-roadside communication?

Abstract: We propose a routing protocol for drive-thru Internet access in delay tolerant vehicular networks. The contribution of this paper is two-folds. First, we propose a new approach which utilizes the concept of delay tolerant network (DTN) to supplement the conventional communication approach. Second, we propose an algorithm to schedule DTN transmissions in order to maximize the system throughput. The proposed protocol is able to attain higher TCP throughput than the conventional approach by providing more efficient wireless resource utilization. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

Reducing Congestion in the Tor Network with Circuit Switching

Abstract: The Tor network is a distributed circuit-switching overlay network, which provides anonymous communication by using voluntarily running onion routers around the world. Tor is vulnerable to network congestion and performance problems because circuit traffics with different rates are competing to transfer their data through a single TCP connection. A large fraction of available network capacity is consumed by the bulk users’ traffic, resulting in increasing delays for the light interactive users. The unfair distribution between the circuit traffics of bulk and light users are contributing to bottleneck in the Tor routers. This problem increases the end-to-end latency and reduces the quality of communication in Tor, which discourages many users from using and joining the network. As a result, the degradation of Tor performance does not only affect the users’ experience, but also degrade the anonymity of Tor. In this work, we discovered that the current Tor design encountered problems from several performance and deployment issues relating to lower network capacity. To improve the problems in Tor, we applied the circuit switching method and addressed the short-comings of limited network capacity, by connecting the congested OR to higher bandwidth ORs. The proposed method is evaluated on our setup testbed environment and partly in the live Tor network. The experimental results showed that TCP socket buffers and Tor network capacity are better utilized and the overall end-to-end latency is reduced.

A routing protocol for VANETs with adaptive frame aggregation and packet size awareness

Abstract: Existing multi-hop routing protocols for vehicular ad hoc networks (VANETs) do not consider the packet payload size and some important MAC layer issues for the route selection. In this paper, we first solve the performance anomaly problem by providing the same transmission time for different nodes which have different channel qualities using an adaptive frame aggregation mechanism. Next, we propose a packet size-aware routing protocol where a communication route is determined by taking into account the payload size of data packets. We consider multiple metrics for the route selection specifically vehicle mobility, frame aggregation efficiency, and link quality. The proposed protocol is evaluated using real-world experiments as well as computer simulations.

Evaluation of dynamic circuit switching to reduce congestion in Tor

Abstract: The Tor network is a widespread overlay anonymous network that is used by millions of users today. Tor focuses on improving the privacy of its users by routing their traffics through a series of onion routers that are located around the world. Since Tor operations mainly depend on volunteers who donated onion routers in the network, there are variances of capacities amongst different onion routers, which degrade the performances of Tor. In an effort to reduce the congestion problems in Tor, first we implemented control metrics to detect congestion on the entry OR. Second, we performed dynamic estimation of circuit congestion to select the best path in Tor. Third, we addressed the unfair distribution of bandwidth between the bulk and light traffics in Tor. We distributed the light and bulk traffics in Tor by having almost the same throughputs compared to default Tor multiplexing circuit approach. We use RTTs, circuit congestion and OR capacities as the main metrics to select the path. From our experimental analysis, we showed that our circuit switching method is effective to reduce the congestion problems in Tor.

Sequential Combination of Two Classifier Algorithms for Binary Classification to Improve the Accuracy

Abstract: Binary classification is a process of classifying the elements of a data set into two groups on the basis of a classification rule. It is useful and widely applied in many fields: Information Technology, Business, Medical Diagnosis, Finance, and so on. The problems of the previous works do not specify clearly which classifier utilizes to minimize which type of false, False Positive (FP) or False Negative (FN), because they are tradeoffs. In this study, we propose a hybrid method for data classification with two different classifier algorithms. The first classifier responds to reduce FN, and the second classifier is in charge of reducing FP. Our experiments utilize the data set of breast cancer, Wisconsin Breast Cancer (WBC) which is popular data set among the researchers for breast cancer diagnosis. The results show that the proposed method improves accuracy.

Performance evaluation of time slot based QoS aware ad hoc network scheme for CBR and TCP flows

Abstract: We propose a QoS supporting ad hoc network scheme which employs a hybrid approach utilizing both TDMA and 80211 DCF and present performance evaluation results of the scheme In the scheme, TDMA period provides contention free transmissions for QoS flows, and DCF period is used to provide contention-based access for best effort or low priority flows We evaluate the proposed scheme for various numbers of TCP flows and different CBR data rates with QualNet simulator Simulation results show that the protocol is able to provide an efficient solution for QoS control in ad hoc networks by combing TDMA and 80211 DCF