Vehicular Delay Tolerant Networks Research Articles

New Trust Based Smart Data Forwarding Mechanism in Vehicular Delay Tolerant Network

Vehicular Delay Tolerant Network (VDTN) is the growing field with a considerable possibility to handle future wireless application’s requirements. Using vehicles for the data communication purpose can be contemplated as a substitute for the wired and wireless systems. This paper proposes a trust-based data forwarding mechanism for Vehicular Delay Tolerant Network (VDTN). Data forwarding in VDTN requires every vehicular or stationary node to participate in data forwarding. But some time malicious nodes show non-cooperative behaviour in data forwarding. Therefore, malicious nodes must be identified specifically to accelerate the data forwarding. A threshold based social skeleton membership process along with new trust-based data forwarding mechanism is proposed in this paper. We reveal that the social skeleton members perform better in data forwarding in terms of data delivery ratio, data delay and data overhead. Simulation results demonstrate that the trust-based data forwarding improves the data delivery ratio with trust-based data forwarding approach. After simulation a comparative analysis of two different scenarios is presented with epidemic routing, prophet routing and spray and wait routing.

Reliable community card system for detecting and isolating selfish vehicles in smart and connected communities‐based VDTN's

AbstractSmart and connected communities (SCC) is a new area of the Internet of Things with the potential to improve people's lives. The goal of developing SCC for a community is to encourage people to focus on the present, consider the future, and reflect on the past. In SCC, nodes (vehicles) can use their mobility to collect data from interconnected devices and send it to a variety of delay‐tolerant applications. Vehicular delay tolerant networks are an ideal fit for such services. However, the problem is that the devices and nodes (vehicles), having limited resources, may become unwilling to cooperate due to their selfish behavior. This article presents a reliable community card system (RCCS) with two main parts: a reliable community and community‐based card tracking system (CBCTS). The reliable community contains all trustworthy vehicles with a certain level of community reputation determined by their honesty level. In CBCTS, every vehicle needs a community card to participate in the network and the neighboring vehicles act as monitoring vehicle. The evaluation results show that the proposed RCCS is quite effective, with detection time and packet delivery. Similarly, it causes minimal overhead and energy consumption compared to all other existing techniques.

Learning for Adaptive Multi-Copy Relaying in Vehicular Delay Tolerant Network

Learning for Adaptive Multi-Copy Relaying in Vehicular Delay Tolerant Network

Towards a simple and efficient vehicular delay tolerant networks routing protocol for data collection in smart cities

Towards a simple and efficient vehicular delay tolerant networks routing protocol for data collection in smart cities

Bio-inspired Probabilistic Opportunistic Routing for Vehicular Delay-Tolerant Networks Using A Hybrid Swarm Approach

Vehicular Delay Tolerant Network (VDTN) routing has been improved using probabilistic routing to improve the coverage of intermittent vehicular networks. VDTNs are characterized by large transmission ranges of vehicles, rapid vehicular speeds, and restricted mobility movements, particularly in urban areas. As a result, the probabilistic Sore-Carry-and-Forward (SCF) relay vehicle selection can be inaccurate when considering remote vehicles, leading to high overheads. To address this issue, a new bio-inspired VDTN routing protocol is proposed to better estimate the SCF capabilities of remote vehicles so that the duplicated generated bundles’ copies are reduced without altering the bundles’ delivery ratio and average delivery delay. This solution sequentially employs the Ant Colony Optimizer (ACO) and Glowworm Swarm Optimization (GSO) to adaptively control the replication of bundle copies at each bundle forwarding stage based on predefined probabilistic forwarding parameters. Simulation results from a sparse urban mobility scenario show reduced bundle replication rates compared to several probabilistic VDTN routing protocols.

Vehicular delay tolerant network routing algorithm based on trajectory clustering and dynamic Bayesian network

Vehicular delay tolerant network routing algorithm based on trajectory clustering and dynamic Bayesian network

Simulative Survey of Flooding Attacks in Intermittently Connected Vehicular Delay Tolerant Networks

Simulative Survey of Flooding Attacks in Intermittently Connected Vehicular Delay Tolerant Networks

EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs).

Since several Internet of Things (IoT) applications have been widely deployed on unstable wireless networks, such as the Delay Tolerant Network (DTN), data communication efficiency in DTN remains a challenge for IoT applications. Vehicular Delay Tolerant Network (VDTN) has become one of DTN's potential applications, in which the network experiences connectivity interruption due to the lack of an end-to-end relay route. VDTNs focus on node cooperation to achieve this goal. As a result, it is essential to ensure that almost all network nodes adopt the protocol to preserve network performance. This is a challenging task since nodes may diverge from the basic protocol to optimize their effectiveness. This article presents an Efficient Monitoring System (EMS) to detect and respond to just selfish nodes to minimize their entire network and data communication efficacy. The scheme is based on a network-wide cooperative sharing of node reputation. It is also necessary to increase overall network efficiency by tracking selfish nodes. The NS-2 simulator is used to run this experimental setup. Simulation results indicate that the proposed scheme performs better in terms of probability of package delivery, package delivery delay, energy consumption, and amount of packet drops. For 80% selfish nodes in the network, the packet delivery of EMS is 37% and 31% better than SOS and IPS. Similarly, the average delivery delay of EMS is 22% and 18% lower than SOS and IPS when 80% selfish nodes are incorporated in the network.

An improved multipath video data communication in a vehicular delay-tolerant network.

A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame for video decoding. The second path carries neighboring frames during video transmission. For the efficient selection of an optimal relay vehicle, a communication cost function is introduced into the existing DTN. This communication cost function is based on three key enhancement parameters: link stability rate, accessible bandwidth estimation, and transmission delay. The improvement in this study, is the integration of store-carry-forward strategy to the existing multipath data forwarding strategy. On the basis of the simulation outcomes, the proposed multipath video data communication in a vehicular DTN (MVDTN) scheme can enhance video data delivery in terms of packet loss ratio, end-to-end delay, structural similarity index measure, and peak signal-to-noise ratio. Considering the aforementioned metrics, our proposed schemes outperform the baseline schemes, namely, road-based multi-metrics forwarder selection evaluation for multipath video streaming and quality of service-aware multipath video streaming for an urban vehicular ad hoc network by using ant colony optimization.

Misbehavior of nodes in IoT based vehicular delay tolerant networks VDTNs

Misbehavior of nodes in IoT based vehicular delay tolerant networks VDTNs

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

The evolution of VDTN routing came to solve the problem of routing in sparse Vehicular Ad-hoc Networks (VANETs). Recently, the implication of Unmanned Aerial Vehicles (UAVs) in VANET communication has gathered quicker information dissemination than Roadside Units (RSUs) in urban VANETs, where ground vehicles’ mobility is restricted by the topology of city roads. Gradually, UAVs are emerging for VDTNs: UAV-aided VDTN (UVDTN) routing field has been introduced for sparse areas in order to reduce the network gaps between Vehicle-to-Vehicle (V2V) connections. Meanwhile, the conventional UVDTN routing protocols expose the short contact opportunity time in the case of Vehicle-to-UAV (V2U) and UAV-to-Vehicle (U2V) connections. This constraint entails more congested bundles’ flooding rates and a higher number of lost bundle copies compared to V2V-based forwarding. In this paper, a bio-inspired UVDTN routing protocol is conceived to improve the bundles’ forwarding between ground vehicles and UAVs in terms of flooding optimization and Quality-of-Service (QoS) performances for urban VANET environments. For this purpose, a swarm-inspired approach called the Social Spider Optimization (SSO) is proposed. SSO seeks to find a better selection of next Store-Carry-and-Forward (SCF) relay nodes specifically for V2U and U2V connections. In order to control the bundle replication rate and enhance bundles’ delivery ratio and delay, the proposed bio-inspired UVDTN protocol adjusts the SCF selection decision. The latter is based on predefined historical knowledge-based forwarding parameters and urban geographic topology information. The proposed bio-inspired routing protocol is simulated using the Opportunistic Network Environment (ONE) simulator and compared to a few VDTN and UVDTN routing protocols.

Reliable data exchange in vehicular delay tolerant networks using AI‐based hybrid trust management

SummaryVehicular delay‐tolerant networks (VDTNs) play prominent part in smart transportation and smart city paradigm to diminish the drivers' risk to meet accidents and offer many infotainment and entertainment services. All the operations are carried out by exchanging data among nodes. However, due to open communication nature of VDTN, the data broadcasted by mobile nodes are quiet susceptible to security attacks. Recently, a number of protocols are proposed for open communication. However, securing such communications and establishing trust value among vehicles are major addressable issues. This is because the existing fraudulent peers can lead to catastrophic circumstances on roads. Along with securing the data carriers, the authenticity and reliability of data is also equally important. In order to identify reliable nodes and maintaining the authenticity of data being communicated, a new artificial intelligence (AI)‐based hybrid trust management framework is proposed in this article. The study particularly exploits the proximity‐based k‐nearest neighbour (kNN) classification model for selecting controlling nodes. Further, decision stump is employed to accomplish trust estimation rules, and backpropagation is used to self‐train the mobile nodes, whenever anticipated trust value is not attained. The proposed scheme maintains the integrity of vehicular nodes and efficiently handles the specified. The trust framework uses a multifaceted direct and recommended trust estimation approach to calculate the global trust values. For comparison, the performance of the proposed method was compared with other trust management approaches.

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.

A Review on Detection of Malicious Node in Delay Tolerance Network

TODAY, The use of a wireless network enables enterprises to avoid the costly process of introducing cables into buildings or as a connection between different equipment locations. The basis of wireless systems are radio waves, an implementation that takes place at the physical level of network structure. Wireless network is installations a method by which homes, telecommunications networks and enterprise (business) avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H->S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

Malicious Node Detection in Delay Tolerance Network

Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H->S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

A Geographical Routing Approach Based on Probability Delegation Replication (GR-PDR) in VDTN

The routing in vehicular delay tolerant networks (VDTNs) does not presume an end-to-end connectivity from source to destination. One substitute is to use the real-time geographical information of vehicles to relay messages. However, if mobile destination is considered, then real-time information might not be generally available due to the sparse network density in VDTN. In this paper, a geographical routing approach based on the probability delegation replication (GR-PDR) is proposed. The functionality of GR-PDR is categorized into forwarder node selection phase and buffer management phase. The forwarder node selection utilizes the advantages of delegation replication approach to conquer the constraints of routing decisions and local maximum problem. Further, the buffer management is employed to prioritize packets to ensure delivery before the expiration of message lifetime. The proposed GR-PDR ensures that the message will be forwarded to higher quality node, resulting in a high delivery ratio. The approach utilizes the advantages of single-copy and multiple-copy forwarding approaches by avoiding redundant replication and enhancing delivery probability, respectively. The performance results show that the proposed GR-PDR outperforms in terms of packet delivery ratio, average delay and network overhead, when compared with the protocols of the same segment. Statistical analysis validates the effectiveness of the proposed protocol.

Optimizing the performance of vehicular delay tolerant networks using multi-objective PSO and artificial intelligence

Vehicular delay tolerant network (VDTN) technology uses vehicles on the road as moving nodes to deliver data from source to destination using several intermediate nodes. Efficient data dissemination in VDTN is a difficult problem due to existing trade-offs between several metric variables such as delivery ratio, delay, and overhead ratio. Inclusion of important social network parameters (like community, social strength, trust, friendship, and selfishness) in computation of forwarding probability may help to improve the performance of a routing algorithm. However, different tuning of these parameters results in different outcomes for the metric variables. A proper balancing of these parameters may result in an optimized outcome solving the trade-off between the metric variables. Nonetheless, dependency of the variables on a number of social network parameters and their mutual trade-offs makes this a non-trivial optimization problem. In this paper, we propose a novel approach to optimize these trade-offs using multi-objective particle swarm optimization (MOPSO). The proposed approach provides a set of pareto-optimal solutions also known as non-dominating solutions. Further, based on the requirements of a target application scenario, a specific optimal solution out of the pareto-optimal solution set is delivered using artificial intelligence (AI) technique. The proposed methodology is simulated in a VDTN scenario using the opportunistic network environment (ONE) simulator and Matlab. Furthermore, based on the experimental results, an exhaustive analysis is provided about how the metric variables are affected by the involvement of the social-based parameters. The capabilities of the proposed approach are validated using a statistical comparative analysis of the results. In future, the outcome of the study may play a helpful role to decide the priorities of the network parameters while designing new data dissemination algorithms for VDTN.

Reinforcement Technique for Deploying Intelligent Delay Tolerant Network

The erratic environment of Vehicular ad-hoc network (VANET), specifically in challenged areas like military or battle fields, may lose the data while forwarding certain important multimedia files. As we know Vehicular Delay Tolerant Network (VDTNs) lack permanent grid power connections so to optimize the use of the power of nodes, reinforcement learning techniques to can be applied to the base stations/roadside units (RSUs). The RSUs learn to pick a vehicle based on information gathered about traffic characteristics, budget for infrastructure resources, and the total duration of a contact time. Based on the cumulative knowledge a compensation metric is evaluated. This acts as a performance metric to determine which vehicular node to select for message forwarding. To make decisions based on observations made by RSUs analyses by applying deep reinforcement learning augments. This saves the power of the nodes. This novel energy efficient technique by deploying secure, intelligent mechanisms in the vehicular nodes is proposed, which significantly improves the processing of files in the challenges networks. For simulation purpose, a real-time data set is taken from the crawdad website. Experimental results show that after applying reinforcement technique, there is an increase in 28% of message delivery, decrease in latency of 16% and reduction of 21% in energy consumption.

DEFENSE STRATEGY AGAINST SELFISH NODES TAKING INTO ACCOUNT TRAFFIC DIFFERENTIATION IN VEHICULAR DELAY-TOLERANT NETWORKS

Vehicular Delay-Tolerant Networks (VDTNs) are vehicle networks where there is no end-to-end connectivity between source and destination. As a result, VDTNs rely on cooperation between the different nodes to improve its performance. However, the presence of selfish nodes that refuse to participate in the routing protocol causes a deterioration of the overall performance of these networks. In order to reduce the impact of these selfish nodes, proposed strategies, on the one hand, use the nodes transmission rate that does not take into account the message priority class of service, and on the other hand, are based on traditional buffer management systems (FIFO, Random). As a result, quality of service is not guaranteed in this type of network where different applications are derived from messages with different priorities. In this paper, we propose a strategy for detecting selfish nodes and taking action against them in relation to priority classes in order to reduce their impacts. The operation of this strategy is based, on a partitioned memory management system taking into account the priority and the lifetime of messages, on the calculation of the transmission rate of the node with respect to the priority class of the node with the highest delivery predictability, on a mechanism for calculating the nodes degree of selfishness with respect to the priority class, and on the monitoring mechanism. . The simulations carried out show that the proposed model can detect selfish nodes and improve network performance in terms of increasing the delivery rate of high-priority messages, reducing the delivery delay of high-priority messages, and reducing network overload.

ASSESSMENT OF THE PERFORMANCE OF A BUFFER MANAGEMENT SYSTEM TO IMPROVE HIGH PRIORITY MESSAGE DELIVERY TIME LIMIT

In vehicular delay-tolerant networks, buffer management systems are developed to improve overall performance. However, these buffer memory management systems cannot simultaneously reduce network overload, reduce high priority message delivery time limit, and improve all priority class message delivery rates. As a result, quality of service is not guaranteed. In this paper, we propose a drop policy based on the constitution of two queues according to message weight, the position of the node in relation to the destination and the comparison of the oldness between the high-priority message and the messages in the low-priority queue. The results of the simulations show that compared to the existing buffer management policy based on time-to-live and priority, our strategy simultaneously reduces network overload, reduces the delivery time limit of high-priority messages and allows for an increase in the delivery rate of messages regardless of their priority.