Search results

This paper is a review of a number routing protocols in the internet of vehicles (IoV). IoV emphasizes information interaction among humans, vehicles and a roadside unit (RSU), within which routing is one of the most important steps in IoV network.

In this paper, the authors have summarized different research data on routing protocols in the IoV. Several routing protocols for IoV have been proposed in the literature. Their classification is made according to some criteria such as topology-based, position-based, transmission strategy and network structure. This paper focuses on the transmission strategy criteria. There exist three types of protocols that are based on this strategy: unicast protocol, broadcast protocols and multicast protocols. This later type is classified into two subclasses: geocast and cluster-based protocols. The taxonomy of the transmission strategy is presented in this study. We discuss the advantages and disadvantages of each type with a general comparison between the five types.

The authors can deduce that many challenges are encountered when designing routing protocols for IoV.

A simple and well-explained presentation of the functioning of the IoV is provided with a comparison among each categories of protocols is well presented along with the advantages and disadvantages of each type. The authors examined the main problems encountered during the design of IoV routing protocol, such as the quick change of topology, the frequent disconnection, the big volume of data to be processed and stored in the IoV, and the problem of network fragmentation. This work explores, compares existing routing protocols in IoV and provides a critical analysis. For that, the authors extract the challenges and propose future perspectives for each categories of protocols.

The purpose of this paper is to design an efficient on-demand routing protocol for mobile ad hoc networks (MANETs) which combines greedy forwarding and backtracking.

It uses a hybrid approach. It uses greedy approach to determine a route from source to the destination. If the greedy approach fails, then it uses a backtracking approach. Combining greedy and backtracking approaches results in a more efficient protocol.

A new routing algorithm for MANETs which performs better than some of the existing algorithms.

A major limitation of this work is that the authors did not implement the protocol on a test bed and evaluated the performance. They neither had the infrastructure nor the resources to actually implement the protocol. Instead, like most of the researchers, they evaluated the performance of their protocol based on extensive simulation. The mobility model, the area of deployment and the density of nodes chosen to simulate the protocol are consistent with what many of the other researchers have done. Intuitively, the authors’ protocol, as the performance evaluation indicates, is likely to perform well in small networks.

The authors did not implement the protocol on a test bed to evaluate the performance of the protocol. The authors used simulation to study the performance of their protocol. Their simulation model is similar to many of the research works published in the literature.

To the authors’ knowledge, no other paper has combined the greedy and backtracking approach to design a routing protocols for MANETs.

The purpose of this paper is to give an insight in to the routing protocols in Vehicular ad hoc Network (VANET). In this direction, for the efficient data dissemination in VANETs, a street-based forwarding protocol (SBFP) has been proposed.

The interferences among different street segments are considered and a unique street-based forwarding concept is introduced to reduce the local maximum problem. In this protocol, the greedy forwarding concept along with the broadcasting mechanism and suppression technique is implemented to minimize the overhead created in the regular beacons forwarding processes. QualNet simulator is used to implement and simulate SBFP. SUMO and MOVE tools are used to create the vehicle’s movement pattern and network topology.

The simulated results demonstrated improved packet delivery ratio (PDR) with a minimum average number of broadcast by each individual vehicle in the proposed SBFP than in its peer protocols.

This paper will be discussing a unique street-based forwarding technique exploring the advantages of global positioning system to obtain the location of vehicles and streets. This technique results in higher PDR and reduced network overhead.

The vehicular ad hoc network (VANET) is an emerging area for smart cities as observed in last few decades. However, some hurdles for VANET exist that need to be resolved before its full implementation in smart cities. Routing is one of the main factors for having effective communication between smart vehicles that urgently needs to be addressed. One factor that affects communication between the vehicles is the intersection points that obstruct the communication. The paper aims to discuss these issues.

The conventional routing schemes fail to address the intersection problems that occur during the two points of communication. Therefore, this paper analyses the performance of existing position-based routing protocol for inter-vehicle ad hoc networks, considering the impact of a number of intersections. This simulation evaluates different position-based routing protocols such as Intersection-based Distance and Traffic-Aware Routing (IDTAR), Greedy Traffic-Aware Routing, Anchor-based Street and Traffic-Aware Routing and Geographic Source Routing, based on road topology and the number of intersections.

As a result, the protocol IDTAR has a lower end-to-end delay and high packet delivery ratio in terms of the number of intersections as a case study of smart cities. This concludes that IDTAR can be adaptive to smart cities communication, although some questions need to be considered in terms of its security, compatibility, reliability and robustness.

The role of VANET has been highlighted in smart cities due to its implications in day-to-day life. The vehicles in VANET are equipped with wireless communication nodes to provide network connectivity. Such types of network operate without the legacy infrastructure, as well as legacy client/servers.

Additionally, the study contributes to smart cities by measuring the performance of position-based routing protocols for VANETs.

A mobile ad hoc network (MANET) enables providers and customers to communicate without a fixed infrastructure. Databases are extended on MANETs to have easy data access and update. As the energy and mobility limitations of both servers and clients affect the availability of data in MANETs, these data are replicated. The purpose of this paper is to provide a literature review of data replication issues and classify the available strategies based on the issues they addressed.

The selected articles are reviewed based on the defined criteria. Also, the differences, the advantages and disadvantages of these techniques are described. The methods in the literature can be categorized into three groups, including cluster-based, location-based and group-based mechanisms.

High flexibility and data consistency are the features of cluster-based mechanisms. The location-based mechanisms are also appropriate for replica allocation, and they mostly have low network traffic and delay. Also, the group-based mechanism has high data accessibility compared to other mechanisms. Data accessibility and time have got more attention to data replication techniques. Scalability as an important parameter must be considered more in the future. The reduction of storage cost in MANETs is the main goal of data replication. Researchers have to consider the cost parameter when another parameter will be influenced.

Data replication in MANETs has been covered in different available sources such as Web pages, technical reports, academic publications and editorial notes. The articles published in national journals and conferences are ignored in this study. This study includes articles from academic main international journals to get the best capability.

The paper reviews the past and the state-of-the-art mechanisms in data replication in MANET. Exclusively, data replication’s main goal, existing challenges, research terminologies and mechanisms in MANET are summarized using the answers to the research questions. This method will help researchers in the future to develop more effective data replication method in MANET.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

The purpose of this paper is to propose, a new multi-path routing protocol that distributes packets over the available paths between a sender and a receiver in a multi-hop ad hoc network. We call this protocol Geometric Sequence Based Multipath Routing Protocol (GMRP).

GMRP distributes packets according to the geometric sequence. GMRP is evaluated using GloMoSim simulator. The authors use packet delivery ratio and end-to-end delay as the comparison performance metrics. They also vary many network configuration parameters such as number of nodes, transmission rate, mobility speed and network area.

The simulation results show that GMRP reduces the average end-to-end delay by up to 49 per cent and increases the delivery ratio by up to 8 per cent.

This study is the first to propose to use of geometric sequence in the multipath routing approach.

Flying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system.

This paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses.

The proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time.

This study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency.

This work focused on a basic building block of an allocation unit that carries out the critical job of deciding between the conflicting requests, i.e. an arbiter unit. The purpose of this work is to implement an improved hybrid arbiter while harnessing the basic advantages of a matrix arbiter.

The basic approach of the design methodology involves the extraction of traffic information from buffer signals of each port. As the traffic arrives in the buffer of respective ports, information from these buffers acts as a source of differentiation between the ports receiving low traffic rates and ports receiving high traffic rates. A logic circuit is devised that enables an arbiter to dynamically assign priorities to different ports based on the information from buffers. For implementation and verification of the proposed design, a two-stage approach was used. Stage I comprises comparing the proposed arbiter with other arbiters in the literature using Vivado integrated design environment platform. Stage II demonstrates the implementation of the proposed design in Cadence design environment for application-specific integrated chip level implementation. By using such a strategy, this study aims to have a special focus on the feasibility of the design for very large-scale integration implementation.

According to the simulation results, the proposed hybrid arbiter maintains the advantage of a basic matrix arbiter and also possesses the additional feature of fault-tolerant traffic awareness. These features for a hybrid arbiter are achieved with a 19% increase in throughput, a 1.5% decrease in delay and a 19% area increase in comparison to a conventional matrix arbiter.

This paper proposes a traffic-aware mechanism that increases the throughput of an arbiter unit with some area trade-off. The key feature of this hybrid arbiter is that it can assign priorities to the requesting ports based upon the real-time traffic requirements of each port. As a result of this, the arbiter is dynamically able to make arbitration decisions. Now because buffer information is valuable in winning the priority, the presence of a fault-tolerant policy ensures that none of the priority is assigned falsely to a requesting port. By this, wastage of arbitration cycles is avoided and an increase in throughput is also achieved.

With the increasing demand for surveillance and smart transportation, drone technology has become the center of attraction for robotics researchers. This study aims to introduce a new path planning approach to drone navigation based on topology in an uncertain environment. The main objective of this study is to use the Ricci flow evolution equation of metric and curvature tensor over angular Riemannian metric, and manifold for achieving navigational goals such as path length optimization at the minimum required time, collision-free obstacle avoidance in static and dynamic environments and reaching to the static and dynamic goals. The proposed navigational controller performs linearly and nonlinearly both with reduced error-based objective function by Riemannian metric and scalar curvature, respectively.

Topology and manifolds application-based methodology establishes the resultant drone. The trajectory planning and its optimization are controlled by the system of evolution equation over Ricci flow entropy. The navigation follows the Riemannian metric-based optimal path with an angular trajectory in the range from 0° to 360°. The obstacle avoidance in static and dynamic environments is controlled by the metric tensor and curvature tensor, respectively. The in-house drone is developed and coded using C++. For comparison of the real-time results and simulation results in static and dynamic environments, the simulation study has been conducted using MATLAB software. The proposed controller follows the topological programming constituted with manifold-based objective function and Riemannian metric, and scalar curvature-based constraints for linear and nonlinear navigation, respectively.

This proposed study demonstrates the possibility to develop the new topology-based efficient path planning approach for navigation of drone and provides a unique way to develop an innovative system having characteristics of static and dynamic obstacle avoidance and moving goal chasing in an uncertain environment. From the results obtained in the simulation and real-time environments, satisfactory agreements have been seen in terms of navigational parameters with the minimum error that justifies the significant working of the proposed controller. Additionally, the comparison of the proposed navigational controller with the other artificial intelligent controllers reveals performance improvement.

In this study, a new topological controller has been proposed for drone navigation. The topological drone navigation comprises the effective speed control and collision-free decisions corresponding to the Ricci flow equation and Ricci curvature over the Riemannian metric, respectively.