Showing papers on "Link-state routing protocol published in 2022"

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks

Abstract: With the rapid development of the Internet of vehicles (IoV), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. Due to the features of the dynamic network structure, constraints of road topology and variable states of vehicle nodes, VANET routing protocols face many challenges, including intermittent connectivity, large delay and high communication overhead. Location-based geographic routing is the most suitable method for VANETs, and such routing performs well on paths with an appropriate vehicle density and network load. We propose an intersection-based V2X routing protocol that includes a learning routing strategy based on historical traffic flows via Q-learning and monitoring real-time network status. The hierarchical routing protocol consists of two parts: a multidimensional Q-table, which is established to select the optimal road segments for packet forwarding at intersections; and an improved greedy strategy, which is implemented to select the optimal relays on paths. The monitoring models can detect network load and adjust routing decisions in a timely manner to prevent network congestion. This method minimizes the communication overhead and latency and ensures reliable transmission of packets. We compare our algorithm with three benchmark algorithms in an extensive simulation. The results show that our algorithm outperforms the existing methods in terms of network performance, including packet delivery ratio, end-to-end delay, and communication overhead.

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Abstract: Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.

A Performance Analysis of VANETs Propagation Models and Routing Protocols

Abstract: Vehicular Ad hoc Networks (VANETs) thrive on providing a communication channel between vehicles and infrastructures that facilitate efficient and safe Vehicle-to-Vehicle (V2V) as well as Vehicle-to-Infrastructure (V2I) communications. Reliable and efficient transmission amongst vehicles and Road Side Units (RSUs) is a prime concern of Intelligent Transportation System (ITS). One of the primary challenges involved in vehicular communication is designing an efficient routing mechanism for data dissemination from node to node via a reliable route. The harsh vehicular environment with varied road conditions and obstacles in the signal propagation path induces another challenge. Therefore, this paper presents a comparative analysis of existing routing protocols with propagation models to assist researchers in gaining insight into the existing propagation model and routing protocols. The study also optimizes routing and propagation models for reliable packet dissemination. This work uses a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO. The trace files generated from SUMO are used for further simulation in NS-3. The simulation results are presented and studied in detail. The results show that the Two-Ray Ground and FRIIS propagation model outperforms the compared models, and the routing protocol OLSR outperforms AODV and DSDV.

Stellar and black hole assembly in z < 0.3 infrared-luminous mergers: Intermittent starbursts versus super-Eddington accretion

Abstract: We study stellar and black hole mass assembly in a sample of 42 infrared-luminous galaxy mergers at z < 0.3 by combining results from radiative transfer modelling with archival measures of molecular gas and black hole mass. The ratios of stellar mass, molecular gas mass, and black hole mass to each other are consistent with those of massive gas-rich galaxies at z < 0.3. The advanced mergers may show increased black hole mass to stellar mass ratios, consistent with the transition from AGN to ellipticals and implying substantial black hole mass growth over the course of the merger. Star formation rates are enhanced relative to the local main sequence, by factors of ∼100 in the starburst and ∼1.8 in the host, respectively. The starburst star formation rates appear distinct to star formation in the main sequence at all redshifts up to at least z ∼ 5. Starbursts may prefer late-stage mergers, but are observed at any merger stage. We do not find evidence that the starbursts in these low-redshift systems substantially increase the total stellar mass, with a soft upper limit on the stellar mass increase from starburst activity of about a factor of two. In contrast, 12 objects show evidence for super-Eddington accretion, associated with late-stage mergers, suggesting that many AGN in infrared-luminous mergers go through a super-Eddington phase. The super-Eddington phase may increase black hole mass by up to an order of magnitude at an accretion efficiency of $42\pm 33{{\ \rm per\ cent}}$ over a period of 44 ± 22 Myr. Our results imply that super-Eddington accretion is an important black hole growth channel in infrared-luminous galaxies at all redshifts.

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

Abstract: The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyzes the performance of four topology-based routing protocols, dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), geographic routing protocol (GRP), and optimized link state routing (OLSR), by using practical simulation software OPNET 14.5. Performance evaluation carries out various metrics such as throughput, delay, and data drop rate. Moreover, the performance of the OLSR routing protocol is enhanced and named “E-OLSR” by tuning parameters and reducing holding time. The optimized E-OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper.

Routing With Traffic Awareness and Link Preference in Internet of Vehicles

Abstract: Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.

Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols

Abstract: The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings’ physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.

Insight-HXMT, NuSTAR, and INTEGRAL Data Show Disk Truncation in the Hard State of the Black Hole X-Ray Binary MAXI J1820+070

Abstract: Abstract We study X-ray and soft gamma-ray spectra from the hard state of the accreting black hole binary MAXI J1820+070. We perform an analysis of joint spectra from HXMT, NuSTAR, and INTEGRAL. We find an overall agreement between the spectra from all three satellites. Satisfactory fits to the data require substantial spectral complexity, with our models including two Comptonization regions and their associated disk reflection, a disk blackbody, and a narrow Fe K α line. Our fits confirm the presence of the truncation of the reflecting optically thick disk at least at >10 gravitational radii. However, we find that the HXMT data alone cannot significantly constrain the disk inner radii.

RLMR: Reinforcement Learning Based Multipath Routing for SDN

Abstract: In recent years, as a new subject in the computer field, artificial intelligence has developed rapidly, especially in reinforcement learning (RL) and deep reinforcement learning. Combined with the characteristics of Software Defined Network (SDN) for centralized control and scheduling, resource scheduling based on artificial intelligence becomes possible. However, the current SDN routing algorithm has the problem of low link utilization and is unable to update and adjust according to the real-time network status. This paper aims to address these problems by proposing a reinforcement learning-based multipath routing for SDN (RLMR) scheme. RLMR uses Markov Decision Process (MDP) and Q-Learning for training. Based on the real-time information of network state and flow characteristics, RLMR performs routing for different flows. When there is no link that meets the bandwidth requirements, the remaining flows are redistributed according to the Quality of Service (QoS) priority to complete the multipath routing. In addition, this paper defines the forward efficiency (FE) to measure the link bandwidth utilization (LBU) under multipath routing. Simulation results show that compared with the current mainstream shortest path algorithm and ECMP algorithm, the routing algorithm in RLMR has advantages in FE, jitter, and packet loss rate. It can effectively improve the efficiency and quality of routing.

Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review

Abstract: In recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks such as very fast motion of nodes, frequent changes of topology, and low density. Routing protocols determine how to provide communications between drones in a wireless ad hoc network. Today, reinforcement learning (RL) provides powerful solutions to solve the existing problems in the routing protocols, and designs autonomous, adaptive, and self-learning routing protocols. The main purpose of these routing protocols is to ensure a stable routing solution with low delay and minimum energy consumption. In this paper, the reinforcement learning-based routing methods in FANET are surveyed and studied. Initially, reinforcement learning, the Markov decision process (MDP), and reinforcement learning algorithms are briefly described. Then, flying ad hoc networks, various types of drones, and their applications, are introduced. Furthermore, the routing process and its challenges are briefly explained in FANET. Then, a classification of reinforcement learning-based routing protocols is suggested for the flying ad hoc networks. This classification categorizes routing protocols based on the learning algorithm, the routing algorithm, and the data dissemination process. Finally, we present the existing opportunities and challenges in this field to provide a detailed and accurate view for researchers to be aware of the future research directions in order to improve the existing reinforcement learning-based routing algorithms.

Performance Optimization of Multi-Hop Routing Protocols With Clustering-Based Hybrid Networking Architecture in Mobile Adhoc Cloud Networks

Abstract: Mobile networks, in particular, are composed of wireless cellular communication nodes (MANET). Communication between these mobile nodes is not under centric systems. MANET is a network of randomly traveling nodes that self-configure and self-organize. Routing is a fundamental topic of MANET, and performance analysis of routing protocols is the focus of this study. AODV, DSR, and WRP are three routing protocols that are compared in this article. Glomosim will be used for simulation. The throughput, average end-to-end latency, and packet delivery ratio of various routing systems are all examined. Two scenarios depending on mobility and node density are considered in this research. As node density rises, PDR and throughput rise with it. Low node density resulted in the shortest delay. AODV has a higher packet delivery ratio and throughput in both scenarios, while WRP has the shortest delay. The authors also analyzed the average energy consumption with a best routing protocol that was decided by the result and conclude the efficiency of the ad-hoc network.

Surveying Position Based Routing Protocols for Wireless Sensor and Ad-hoc Networks

Abstract: A focus of the scientific community is to design network oriented position-based routing protocols and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few position-based algorithms have actually been adopted for commercial purposes. This article is a survey of almost 50 position-based routing protocols and it comes as an aid in the implementation of this type of routing in various applications which may need to consider the advantages and pitfalls of position-based routing. An emphasis is made on geographic routing, whose notion is clarified as a more restrictive and more efficient type of position-based routing. The protocols are therefore divided into geographic and non-geographic routing protocols and each is characterized according to a number of network design issues and presented in a comparative manner from multiple points of view. The main requirements of current general applications are also studied and, depending on these, the survey proposes a number of protocols for use in particular application areas. This aims to help both researchers and potential users assess and choose the protocol best suited to their interest.

Primordial black holes from CDM isocurvature perturbations

Abstract: We show that primordial black holes can be produced from the collapse of large isocurvature perturbations of the cold dark matter. We develop a novel procedure to compute the resulting black hole abundance by studying matched perturbations of matter-only universes, and we use our procedure to translate observational constraints on black hole abundances into model-independent constraints on cold dark matter isocurvature perturbations over a wide range of scales. The constraint on the typical amplitude of the primordial perturbations weakens slightly slower than linearly on small scales.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Abstract: Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

DRSIR: A Deep Reinforcement Learning Approach for Routing in Software-Defined Networking

Abstract: Traditional routing protocols employ limited information to make routing decisions, which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in Software-Defined Networking (SDN). However, RL-based solutions usually suffer an increase in time during the learning process when dealing with large action and state spaces. This paper introduces a different routing approach, called Deep Reinforcement Learning and Software-Defined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on Dijkstra’s algorithm and RSIR in relation to stretch, packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and feasible solution for routing in SDN.

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

Abstract: Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

MobiRPL: Adaptive, robust, and RSSI-based mobile routing in low power and lossy networks

Abstract: This paper tackles the mobile routing issues in low-power and lossy networks (LLNs). The IPv6 standard routing protocol for LLNs, termed IPv6 routing protocol for low-power and lossy networks (RPL), has mostly been investigated in static LLNs and it has no explicit mechanism to support mobility. In addition, there is no mobile routing protocol that works well in mobile LLNs. Considering the importance of mobility support in many LLN applications, this work designs and implements MobiRPL, an adaptive, robust, and received signal strength indicator (RSSI)-based mobile routing scheme based on the RPL standard. To cope with network dynamics, the MobiRPL design focuses more on maintaining reliable routing topology than on minimizing energy consumption. This design choice significantly improves reliability while maintaining the acceptable energy consumption of mobile LLNs. We implement MobiRPL on Contiki OS, and evaluate its effectiveness extensively through Cooja simulation and testbed experiments. Our results from the testbed show that MobiRPL improves mobile nodes' packet delivery ratio by 11.3% compared to RPL and reduces the energy consumption of mobile nodes by 73.3% compared to the baseline scheme, i.e., the lightweight on-demand ad-hoc distance-vector routing protocol — next generation (LOADng).

Evaluation of VANETs Routing Protocols for Data-Based Smart Health Monitoring in Intelligent Transportation Systems

Abstract: Vehicular Ad-hoc Network (VANET) is an essential part of futuristic Intelligent Transportation Systems. VANET can improve the overall traffic control system and reduce road accident deaths by providing remote health monitoring in hazardous conditions to outdoor patients. Nowadays, vehicles have become so intelligent that they can sense patient health data and transmit it to a nearby ambulance or hospital in emergency or road accident situations. Health professionals can provide appropriate treatment without wasting critical time in further testing. Developing an efficient and reliable routing solution is a significant research problem for VANET based health monitoring applications because of time-sensitives. Routing approaches to reduce the transmission delay for critical applications are based on topological, geographical, clustering, and flooding techniques. This article has evaluated and compared widely used topological and geographical routing protocols for data-based VANETs health monitoring applications. A comprehensive analysis is performed on Ad hoc On-Demand Distance Vector (AODV), Destination-Sequenced Distance-Vector (DSDV), Optimized Link State Routing (OLSR), Greedy Perimeter Stateless Routing (GPSR), Greedy Perimeter Stateless Routing-Modified (GPSR-M), and Max duration-Minangle Greedy Perimeter Stateless Routing (MM-GPSR) protocols with different numbers of nodes, CBR connections, communication range and packet size on Network Simulator (NS-3.23) and Simulation of Urban Mobility (SUMO) platforms. Experimental results give useful knowledge in analyzing routing protocols for VANET's data-based smart health monitoring applications.

Thermodynamics of nonlinearly charged anti–de Sitter black holes in four-dimensional critical gravity

Abstract: In this work, we provide new examples of Anti-de Sitter black holes with a planar base manifold in four-dimensional Critical Gravity by considering nonlinear electrodynamics as a matter source. We find a general solution characterized by the presence of only one integration constant where, for a suitable election of coupling constants, we can show the existence of one or more horizons. Additionally, we compute its non-null thermodynamical quantities through a variety of techniques, testing the validity of the first law of thermodynamics as well as a Smarr formula. Finally, we analyze the local thermodynamical stability of the solutions. To our knowledge, these charged configurations are the first example with Critical Gravity where their thermodynamical quantities are not zero.

Improvement of DBR routing protocol in underwater wireless sensor networks using fuzzy logic and bloom filter

Abstract: Routing protocols for underwater wireless sensor networks (UWSN) and underwater Internet of Things (IoT_UWSN) networks have expanded significantly. DBR routing protocol is one of the most critical routing protocols in UWSNs. In this routing protocol, the energy consumption of the nodes, the rate of loss of sent packets, and the rate of drop of routing packets due to node shutdown have created significant challenges. For this purpose, in a new scenario called FB-DBR, clustering is performed, and fuzzy logic and bloom filter are used in each cluster’s new routing protocol in underwater wireless sensor networks. Due to the fuzzy nature of the parameters used in DBR, better results are obtained and bloom filters are used in routing tables to compensate for the deceleration. as the average number of accesses to routing table entries, dead nodes, Number of Packets Sent to Base Station (BS), Number of Packets Received at BS, Packet Dropped, and Remaining Energy has improved significantly.

Worldsheet Correlators in Black Hole Microstates

Abstract: Light probes interacting with heavy bound states such as black holes give rise to interesting observables containing valuable dynamical information. Recently, a family of black hole microstates was shown to admit an exact string worldsheet description. We construct the physical vertex operators of these models, and derive their form in the low-energy AdS$_3$ limit. We then compute an extensive set of novel heavy-light correlators in these backgrounds. A subset of these provide the first match between worldsheet correlators in black hole microstates and the holographically dual conformal field theory, in a highly non-trivial way. We further conjecture a closed formula for heavy-light correlators with an arbitrary number of light insertions. As an application, we compute the analogue of the Hawking radiation rate for these microstates.

Performance Comparison between Delay-Tolerant and Non-Delay-Tolerant Position-Based Routing Protocols in VANETs

Abstract: Vehicular ad hoc networks (VANETs) are a new emerging recently developed advanced technology that allows a wide group of applications related to providing more safety on roads, more convenience for passengers, self-driven vehicles, and intelligent transportation systems (ITS). There are various routing protocol categories used in VANETs, like unicast, multicast, and broadcast protocols. In unicast position-based protocols, the routing decisions are based on the geographic position of the vehicles. This does not require establishment or maintenance of routes but needs location services to determine the position of the destination. Non-delay-tolerant network protocols (non-DTN), also identified as minimum delay protocols, are aimed at minimizing the delivery time of the information. Delay-tolerant protocols (DTN) are used in a variety of operating environments, including those that are subject to failures and interruptions and those with high delay, such as VANETs. This paper discusses the comparison between non-DTN and DTN routing protocols belonging to the unicast delay-tolerant position-based category. The comparison was conducted using the NS2 simulator, and the simulations of three non-DTN routing protocols and three DTN routing protocols were recorded. Simulation results show that the DTN routing protocols outperform in delivery ratio compared to the non-DTN routing protocols, but they lead to more average delay due to buffering, the processing algorithm, and priority calculation. In conclusion, non-DTN protocols are more suitable for the city environment since the distance between nodes is relatively smaller and the variations in the network topology are slower than they are on highways. On the other hand, DTN protocols are more suitable for highways due to the buffering of packets until a clear route to destination is available.