Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning

Vehicular ad hoc networks (VANETs) have been attracting interest for their potential uses in driving assistance, traffic monitoring, and entertainment systems. However, due to vehicle movement, limited wireless resources, and the lossy characteristics of a wireless channel, providing a reliable multihop communication in VANETs is particularly challenging. In this paper, we propose PFQ-AODV, which is a portable VANET routing protocol that learns the optimal route by employing a fuzzy constraint Q-learning algorithm based on ad hoc on-demand distance vector (AODV) routing. The protocol uses fuzzy logic to evaluate whether a wireless link is good or not by considering multiple metrics, which are, specifically, the available bandwidth, link quality, and relative vehicle movement. Based on an evaluation of each wireless link, the proposed protocol learns the best route using the route request (RREQ) messages and hello messages. The protocol can infer vehicle movement based on neighbor information when position information is unavailable. PFQ-AODV is also independent of lower layers. Therefore, PFQ-AODV provides a flexible, portable, and practicable solution for routing in VANETs. We show the effectiveness of the proposed protocol by using both computer simulations and real-world experiments.

Flexible, Portable, and Practicable Solution for Routing in VANETs: A Fuzzy Constraint Q-Learning Approach

Multihop wireless broadcast is an important component in vehicular networks. Many applications are built on broadcast communications, so efficient routing methods are critical for their success. Here, we develop the Distribution-Adaptive Distance with Channel Quality (DADCQ) protocol to address this need and show that it performs well compared to several existing multihop broadcast proposals. The DADCQ protocol utilizes the distance method to select forwarding nodes. The performance of this method depends heavily on the value of the decision threshold, but it is difficult to choose a value that results in good performance across all scenarios. Node density, spatial distribution pattern, and wireless channel quality all affect the optimal value. Broadcast protocols tailored to vehicular networking must be adaptive to variation in these factors. In this work, we address this design challenge by creating a decision threshold function that is simultaneously adaptive to the number of neighbors, the node clustering factor, and the Rician fading parameter. To calculate the clustering factor, we propose using the quadrat method of spatial analysis. The resulting DADCQ protocol is then verified with JiST/SWANS and shown to achieve high reachability and low bandwidth consumption in urban and highway scenarios with varying node density and fading intensity.

Spatial Distribution and Channel Quality Adaptive Protocol for Multihop Wireless Broadcast Routing in VANET

Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.

VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism

Vehicular ad hoc network is a collection of vehicles and associated road-side infrastructure, which is able to provide mobile wireless communication services. This highly dynamic topology structure is still open to many routing and message forwarding challenges. This paper addresses the issue of message delivery from vehicle to a fixed destination, by hopping over neighboring vehicles. We propose a reinforcement-learning-based hierarchical protocol called QGrid to improve the message deliver ratio with minimum possible delay and hops. The protocol works at two levels. First, it divides the geographical area into smaller grids and finds the next optimal grid toward the destination. Second, it discovers a vehicle inside or moving toward the next optimal grid for message relaying. There is no need of routing tables as the protocol builds a Q-value table based on the traffic flow in neighbor grids, which is then used for the grid selection. The vehicle selection process can employ different strategies, like, greedy selection of nearest neighbor, or solution based on the two-order Markov chain prediction of neighbor movement. This combination makes QGrid an offline and online solution. QGrid is further improved giving higher priority to vehicles with fixed routes and better communication capabilities, like buses, when making the vehicle selection. We have carried out extensive simulation evaluation by using real-world vehicular traces to measure the performance of our proposed schemes. The simulation comparisons among QGrid with/without bus aid, and existing position-based routing protocols, show the great improvement in the delivery percentage by our proposed routing protocol.

Hierarchical Routing for Vehicular Ad Hoc Networks via Reinforcement Learning

In Vehicular Ad hoc Networks (VANETs), general purpose ad hoc routing protocols such as AODV cannot work efficiently due to the frequent changes in network topology caused by vehicle movement. This paper proposes a VANET routing protocol QLAODV (Q-Learning AODV) which suits unicast applications in high mobility scenarios. QLAODV is a distributed reinforcement learning routing protocol, which uses a Q-Learning algorithm to infer network state information and uses unicast control packets to check the path availability in a real time manner in order to allow Q-Learning to work efficiently in a highly dynamic network environment. QLAODV is favored by its dynamic route change mechanism, which makes it capable of reacting quickly to network topology changes. We present an analysis of the performance of QLAODV by simulation using different mobility models. The simulation results show that QLAODV can efficiently handle unicast applications in VANETs.

Distributed Reinforcement Learning Approach for Vehicular Ad Hoc Networks

A mobile ad hoc network (MANET) is an autonomous collection of mobile nodes that communicate over relatively bandwidth constrained wireless links. Frequent link changes and limited bandwidth make communication in MANET particularly challenging. Based on AODV, we propose a MANET routing protocol considering link stability and bandwidth efficiency. The protocol uses distributed Q-Learning to infer network status information and takes in to consideration link stability and bandwidth efficiency while selecting a route. The protocol can efficiently handle network mobility by a way of preemptively switching to a better route before current route fails. We study the performance of this protocol through simulation and demonstrate its significant improvement compared to AODV.

A MANET protocol considering link stability and bandwidth efficiency

A hierarchical group key management scheme for secure multicast increasing efficiency of key distribution in leave operation

Many safety applications in Vehicular Ad hoc Networks (VANETs) are based on broadcast. Designing a broadcast protocol that satisfies VANET applications' requirements is very crucial. In this paper, we propose a reliable and efficient multi-hop broadcast routing protocol for VANETs. The proposed protocol provides the strict reliability in various traffic conditions. This protocol also performs low overhead by means of reducing rebroadcast redundancy in a high-density network environment. We also propose an enhanced multipoint relay (MPR) selection algorithm that considers vehicles' mobility and then use it for relay node selection. We show the performance analysis of the proposed protocol by simulation with ns-2 in different conditions, and give the simulation results demonstrating effectiveness of the proposed protocol compared with other VANET broadcast schemes.

A Novel Multi-hop Broadcast Protocol for Vehicular Safety Applications

A mobile ad hoc network (MANET) is a self-configuring network of mobile devices connected by wireless links. Frequent topology changes and limited bandwidth make communication in MANETs particularly challenging. We present a MANET routing protocol that can efficiently handle network mobility by a way of preemptively switching to a better route before the current route fails. The protocol uses a distributed Q-learning algorithm to infer network status information and takes in to consideration link stability and bandwidth efficiency while selecting a route. We study the performance of this protocol through simulation and demonstrate its advantages over existing protocols.

Kazuya Kumekawa

Papers

Distributed Reinforcement Learning Approach for Vehicular Ad Hoc Networks

A MANET protocol considering link stability and bandwidth efficiency

A hierarchical group key management scheme for secure multicast increasing efficiency of key distribution in leave operation

A Novel Multi-hop Broadcast Protocol for Vehicular Safety Applications

A dynamic route change mechanism for mobile ad hoc networks