Vehicular Delay Tolerant Networks Research Articles

A Routing Protocol for UAV-Assisted Vehicular Delay Tolerant Networks

Vehicular delay tolerant networks (VDTNs) enable information sharing among mobile nodes in scenarios where cellular base stations are unavailable and the connections between the mobile nodes are intermittent. While unmanned aerial vehicles (UAVs) have shown to improve the performance of VDTNs, existing routing protocols, such as PRoPHET, consider the encounter probability between mobile nodes only, which does not fully address the characteristics of UAVs in route selection. In this paper, we propose a routing protocol, which considers both the encounter probability and the persistent connection time between mobile nodes for each encounter, in UAV-assisted VDTNs. By introducing the persistent connection time in route selection, the proposed protocol is able to evaluate the stability of a communication link more accurately in the UAV-assisted VDTN environment. The proposed protocol is evaluated using realistic simulations by comparing it with existing baselines. The simulation results verify that the proposed protocol can improve the reliability of message forwarding while reducing network overhead and end-to-end delay.

Honesty based democratic scheme to improve community cooperation for Internet of Things based vehicular delay tolerant networks

AbstractMany Internet of Things (IoT) applications have been developed and implemented on unreliable wireless networks like the delay tolerant network (DTN); however, efficient data transfer in DTN is still an important issue for the IoT applications. One of the application areas of DTN is vehicular delay tolerant network (VDTN) where the network faces communication disruption due to lack of end‐to‐end relay route. It is challenging as some of the nodes show selfish behavior to preserve their resources like memory and energy and become noncooperative. In this article, an honesty‐based democratic scheme (HBDS) is introduced where vehicles with higher honesty level are elected as heads—during the process. Vehicles involved in the process would maximize their rewards (reputation) through active participation in the network activities, whereas vehicles with noncooperative selfish behavior are punished. The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves (VCG) model. The mathematical model and algorithms developed in the proposed HBDS technique are simulated using the VDTNSim framework to evaluate their efficiency. The performance results show that the proposed scheme dominates current schemes in terms of packet delivery probability, packet delivery delay, number of packets drop, and overhead ratio.

A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach

The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.

An efficient and intelligent routing strategy for vehicular delay tolerant networks

The vehicular delay-tolerant network is the real-life application based area of Delay tolerant network where communication takes place using vehicular nodes and roadside units. The topology used in vehicular networks is highly dynamic by architecture due to the use of moving vehicular nodes. It operates in such a scenario where a direct path between source and destination remains absent on the most piece of the time. In case of non-existence of connected path vehicular delay-tolerant network works opportunistically and uses the same store, carry, and forward paradigm as Delay Tolerant Network. However, the routing protocols designed for vehicular delay-tolerant network faces crucial challenges like inadequate relay node, incomplete data transfer, a large number of packet drop, and uncertain delivery time. In this research paper, we propose a novel routing strategy for the vehicular delay-tolerant network. The proposed routing strategy selects efficient vehicular relay node for complete packet transfer and intelligently reduces the packet drop for timely packet delivery. We implement the proposed routing strategy in the ONE simulator; the ONE simulator provides an opportunistic environment for nodes. We analyze the performance of the proposed strategy under various simulations results using different parameters. The results show that the proposed strategy outperforms standard routing protocols in terms of considered parameters and provide an efficient solution for the problem of disconnection.

Joint mobile vehicle–UAV scheme for secure data collection in a smart city

A vehicular delay-tolerant network (VDTN) allows mobile vehicles (MVs) to collect data from widely deployed delay-tolerant sensors in a smart city through opportunistic routing, which has proven to be an efficient and low-cost data collection method. However, malicious MVs may report false data to obtain rewards, which will compromise applications. In this paper, the Active Trust Verification Data Collection (ATVDC) scheme is proposed for efficient, cheap, and secure data collection. In this scheme, an unmanned aerial vehicle (UAV) is adopted to collect baseline data from sensors to evaluate the trust of MVs, and a high-trust MV priority recruitment (HTMPR) strategy is proposed to recruit credible MVs at a low cost. In addition, a genetic-algorithm-based trajectory planning (GATP) algorithm is proposed to allow the UAV to collect more baseline data at the minimum flight cost. After sufficient experiments, the strategy proposed in this paper is seen to greatly improve performance in terms of the error-free ratio EF, the symbol error ratio ES, and the data coverage ratio ϑ.

A new approach buffer-efficient disruption tolerant protocol in vehicular delay-tolerant network

As the percentage of Vehicles are increasing on roads, Vehicular Ad-hoc network have reached to a significant interest of the researcher in recent times. V2V network and V2I are prominent as an organized solution for attaining safety for moving vehicles on road and transmitting data for a secure journey while driving and transferring data in the form of message from one vehicle to other. VDTN is a kind of Delay Tolerant Networks (DTNs), to provide reliable and efficient communications. Various routing protocols have been used previously in VDTN to enhance the performance of the network in terms of forwarding information in the form of messages to their preferred locations. In this paper, we introduce a novel protocol called Buffer-Efficient Disruption Tolerant Routing Protocol (BEDTRP) to improve delivery delay, overhead and latency. This protocol significantly improves the communication performance such as delivery ratio and overhead in terms of increasing buffer size and bandwidth. Evaluation results show that the proposed method BEDTRP achieves high bandwidth utilization performance to provide better delivery ratio, lower delivery delay, and low overhead against buffer changes. We used ns-2 network simulator to implement Delay tolerant network protocol.

Selfishness in Vehicular Delay-Tolerant Networks: A Review.

Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future.

Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks

Vehicular Delay-Tolerant Networks (VDTNs) are networks of vehicles that communicate wirelessly, where there are no permanent end-to-end connections. VDTNs have a highly variable topology, with frequent partitions, and possibly low node density. Thus, delay-tolerant routing adopts a store-carry-and-forward message transfer paradigm, where messages have a useful Time-To-Live (TTL) and are stored until a good contact opportunity arises. Multiple message replicas can be generated to improve delivery probability at the cost of increasing network congestion. In this paper, we propose the V-GRADIENT geocast routing protocol that monitors node density, buffer occupancy, and interest in geocast groups, to adapt the forwarding techniques used dynamically, to disseminate messages within the geographic region of interest. Simulation results show that the V-GRADIENT is capable of controlling network congestion and efficiently delivering messages resulting in better delivery ratios, lower latencies, and a small increase in overhead when compared with existing protocols.

A VDTN scheme with enhanced buffer management

Vehicular delay tolerant networks (VDTNs) enable communications in sparse vehicular ad-hoc networks and other challenged environments where traditional networking approaches fail. We propose a VDTN routing scheme that combines the message deliver strategy of PRoPHET protocol, the message copy control strategy of Spray-and-Wait protocol and an enhanced buffer management scheme. In our proposal, the buffer management scheme is designed to improve certain network performance goals, namely, maximizing the average delivery ratio and minimizing the average delivery delay. Furthermore, we use computer simulations to show that the proposed routing scheme achieves better system performance than the existing baseline routing protocols.

Vehicular Delay Tolerant Network Routing Algorithm Based on Bayesian Network

Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead.

Learning for Multiple-Relay Selection in a Vehicular Delay Tolerant Network

In a vehicular delay tolerant network (VDTN), there is no static connection, and the network behavior is highly temporal. This makes determining the routing protocol critical for the performance of a network. However, traditional routing technology rarely considers the influence of a VDTN’s selfish nodes. Under ideal conditions, all nodes will try to store and transfer as many messages as possible. However, selfish nodes may not transfer messages to other nodes due to limited resources. Taking selfish nodes into account is necessary for improving the performance of a VDTN. In this paper, we calculate each node’s credit value by recording each node’s behavior when messages are transferring to avoid selecting selfish nodes as the relay. We present an efficient VDTN specific multi-copy routing algorithm that combines Q-learning and the credit value to determine whether a candidate node is suitable for delivering a message. Because multi-copy routing protocols require a high buffer to store messages, we also optimize the node buffer to reduce network congestion. The proposed algorithm is evaluated by a number of different performance metrics, such as delivery probability, network overhead, and message latency. The proposed algorithm achieves better results in different configurations and provides improved delivery probability, low message latency, and network overhead.

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.

Towards the Optimal Solution for the Routing Problem in Vehicular Delay Tolerant Networks: A Deep Learning Approach

Vehicular networks aim to provide a communication framework for moving vehicles, road infrastructure and pedestrians. Such kind of networks is the entrance to a new era of services that will make areas such as security and safety, information, transactions, entertainment and sustainability (green transportation) more efficient than they are today, especially in the upcoming era of autonomous vehicles and self-driving cars. However, the severe nature of vehicular environments makes efficient inter-vehicular communication very difficult to achieve. Vehicular Delay-Tolerant Networks (VDTN), as these networks are called, have very sparse, intermittent connections, and the lack of a fixed topology gives rise to one of the main challenges that they face: packet routing. A range of routing algorithms has been proposed in recent years to optimize communication in vehicular networks, and significant progress has been made in the matter but the quest for the optimal performance is still ongoing. In this paper, we explore a deep learning approach to the routing problem in VDTN, proposing a routing architecture and algorithm based on Deep Neural Networks that helps routers make packet forwarding decisions based on the current conditions of its surroundings. In order to assess the performance of the proposed architecture, simulations were run showing important gains in terms of network overhead and hop count with respect to popular routers, while maintaining acceptable packet delivery rates and average delivery delays.

Pass and Run: A Privacy Preserving Delay Tolerant Network Communication Protocol for CyberVehicles

Editor’s note: In the era of intelligent transportation systems (ITS) due to vehicle-to-infrastructure (V2I) communication, data sent to roadside units (RSUs) can leak sensitive information of the vehicle such as driving patterns and history, and therefore may seriously violate privacy requirements. To address this very important research challenge, this article presents a Pass and Run protocol for the vehicular delay tolerant network (VDTN). The proposed protocol particularly addresses the vehicle location privacy problem. —Mohammad Al Faruque, University of California, Irvine

Vehicular Delay-Tolerant Networks with Image Recognition-Based Adaptive Array Antenna for Winter Road Surveillance in Local Areas

The rapid growth of the ITS (intelligent transport system) development requires us to realize new kinds of applications, such as the winter road surveillance system. However, it is still necessary to consider the network difficulty areas for LTE (long-term evolution) or 3G transmissions when one visits sightseeing spots such as ski resorts or spas in mountain areas. Therefore, this paper proposes a winter road surveillance system in the local area based on vehicular delay-tolerant networks. The adaptive array antenna controlled by image recognition with the Kalman filter algorithm is proposed as well to the system in order to realize higher delivery rates. The implementations of the prototype system are presented in this paper as well, and the effectivity of the radio transmission in the prototype system is realized by vehicular image recognition methods and the asynchronous voltage controls for antenna elements for the rapid directional controls of the radio transmission. The experimental results showed that the radio directional controls by the prototype system for the target vehicle can proceed within one second, and that the simulation with the GIS (geographic information system) map pointed out the delivery rates of the proposed method—which are better than those of the epidemic DTN (delay-tolerant networking) routing by the nondirectional antenna. The experiments of the proposed methods indicate a higher efficiency of the data transmissions—even in the mountain area. Furthermore, future research subjects are discussed in this paper.

Ant Colony Optimization Based Routing Scheme in Vehicular Delay Tolerant Networks

Delay Tolerant Networks finds its application in variety of environments and addresses issues like intermittent connection, long and variable delays and high latency. Vehicular Delay Tolerant Networks was introduced due to its various characteristic matches with DTNs. In particular class of VDTN there are two types of nodes: Stationary and Mobile nodes. Stationary nodes are deployed along the roadside and mobile nodes are constrained to move over the roads with variable speeds. This letter presents a new routing algorithm based on Ant Colony Optimization in Vehicular Delay Tolerant Network. We performed extensive simulation in ONE simulator and compare with Probabilistic Bundle Relaying Scheme to gauge the benefits of Ant Colony Optimization based scheme over Probabilistic Bundle Relaying scheme.

A data dissemination mechanism based on evaluating behavior for vehicular delay-tolerant networks

Vehicular delay-tolerant networks are widely used in intelligent transport application. Vehicle nodes exchange and share various information in vehicular delay-tolerant networks. However, current delay-tolerant network routing algorithms do not take into account the dynamic characteristic of traffic flow, and they do not effectively resist cyber attacks, such as black hole attack. To address this issue, we propose a data dissemination mechanism for vehicular delay-tolerant networks. In this mechanism, we develop a combined model to estimate the real-time traffic density. Simultaneously, we propose the metrics which include node interaction dispersion, node interaction freshness, node interaction participation, and node interaction contribution to evaluate behavior of nodes. Based on these metrics, a routing method is constructed. In this routing method, a relay node is selected by evaluating communication interaction behaviors among vehicle nodes. Considering the factors of traffic flow density and communication behaviors of vehicle nodes, a message forwarding strategy scheme is built for different traffic density scenarios. Extensive simulations show that the proposed mechanism exhibits superior performance over existing methods in forwarding traffic information and alleviates negative effects from black hole attacks.

Energy based incentive scheme for secure opportunistic routing in vehicular delay tolerant networks

Opportunistic forwarding mechanism in delay tolerant networks (DTN) makes the network exposed to vulnerabilities. Providing security to existing routing protocols available for delay tolerant network becomes very difficult due to unavailability of sufficient contacts. It is necessary to design a secure routing protocol to overcome these issues. Energy is one of the crucial parameters in data forwarding and incentives should be offered to vehicular DTN nodes on the basis of energy spent for a particular message. This paper presents the secure routing mechanism which is based on the trust value of the vehicular DTN nodes. Incentive scheme is based on energy spent by the vehicular DTN node for forwarding data successfully. Routing is based on the trust value of the vehicular DTN nodes and incentive will be offered on successful delivery of the message. A source node offers the payment for successful delivery based on the time in which the message is delivered. The trust authority evaluates the credit and trust value for the participating vehicular DTN nodes based on energy and efforts put in successful delivery of the message.

A New Variant of Game Theory Based Decision Making (GTDM) Algorithm Routing Protocols to Improve Energy Efficiency on Vehicular Delay Tolerant Network (VDTN)

These days, the application of Delay Tolerant Networks (DTN) have been expanded into various scenarios of communications field. Vehicular Ad hoc Networks (VANETs) as a communication scenario which treat its subject to disruption and disconnection with frequent partitioning and high latency. Therefore, Vehicular Delay Tolerant Network (VDTN) is introduced as a new research paradigm due to several characteristics match according to specific prerequisites. DTNs is proposed in Vehicular Network because its mechanisms which is using store-carry-forward, can be implemented to deliver the packets, without end-to-end connection, to the destination. One of challenging research of DTN in routing protocol is to meet prerequisites of many applications, especially in vehicular network (VDTN). This paper presents a new variant of Game Theory based on Decision Making (GTDM) that can deliver packet to static node due to improve the energy efficiency of DTNs in city environments. Hence, its destination node (Receiver Node) needs to go to the static node to take their packet under Working Day Movement (WDM), because relay node will be passing by the static node with continuously move to its track to deliver packet. In this paper author will analyze the new variant of GTDM (NVGTDM) which can be more useful than original GTDM for application in city environment with using transportation movement. We conclude that modification of GTDM routing algorithm (NVGTDM) improves energy efficiency as much as 10.38% than the original GTDM. Hence, it can be ensured to compare either to Epidemic or PRoPHET routing algorithm with 55.44% and 68.75% in rates of energy efficiency respectively.

Optimal ThrowBoxes assignment for big data multicast in VDTNs

Nowadays, vehicles have been equipped with various kinds of sensors which produce so called big data constantly. Among them, multimedia contents form the major part of the total generated data. They are usually shared among vehicles in a region of interest with less concerns on delay criteria. Therefore, to reduce cost, these data will be delivered using a carry-and-forward method in a Vehicular Delay Tolerant Network. Since the volume of these data is not negligible, not only delivery utility but also memory space usage must be considered while designing forwarding strategy. ThrowBoxes have been utilized to increase encountering opportunities. In this paper, we develop an optimal data packet assignment algorithms to achieve global maximum delivery utilities among ThrowBoxes. We evaluate the proposed scheme with the real world data trace. The results show that our scheme achieves better performance in terms of delay, delivery ratio, cost and other metrics.