Routing In Ad Hoc Networks Research Articles

Multiobjective Secure Trust Aware Redundant Array Shifting Encryption and Clustering Based Routing in Mobile Ad Hoc Networks

ABSTRACTMobile ad hoc networks (MANETs) are characterized by their self‐configuring and decentralized nature in wireless networking. In MANETs, energy‐constrained devices face increased packet loss due to malicious nodes, leading to higher delays and energy consumption. Addressing this issue is crucial to improve network performance and efficiency. Therefore, to overcome these issues, a multiobjective secure trust‐aware redundant array shifting encryption adaptive sparrow search optimization (MORS‐ASO) was introduced in this manuscript for secure data transmission in MANETs. This encryption enhances security by distributing data across multiple nodes and dynamically shifting it, making it more resistant to attacks and unauthorized access. The hybrid integrated cat swarm optimization is the combination of integrated optimization algorithm and cat swarm optimization. Adaptive sparrow search optimization is applied to carry out the data transmission securely. The proposed method helps to offer security against various routing disruption attacks, genuine and hurtful attack. The MORS‐ASO achieves a packet delivery ratio of 94.3%, an end‐to‐end delay of 3.362 s, a throughput of 2952.7548 bps, and a jitter of 1.245 ms. Also, it consumes 32 joules of energy, supports a network lifetime of 3500 s, and has a packet loss ratio of 19%.

Hybrid optimization-based secured routing in mobile ad-hoc network

Mobile Ad hoc Networks (MANETs) pose significant routing challenges due to their decentralized and dynamic nature. Node mobility results in frequent changes in network topology, leading to unsFIG connectivity and link quality. Traditional RPs designed for static networks are inadequate for MANETs. To deal with these issues, a secure routing approach is proposed using the Red Panda-Lyrebrid Optimization (RePLO) algorithm, which combines the advantages of the Red Panda Optimization (RPO) and Lyrebird Optimization Algorithm (LOA) algorithms. The proposed approach consists of five steps: (i) configuring the system model, (ii) developing the energy model, (iii) creating the mobility model, (iv) selecting cluster heads using the RePLO algorithm, and (v) routing using the RePLO algorithm. The RePLO algorithm optimizes cluster head selection and routing while considering specific constraints such as delay, distance, energy, & security for Cluster Head (CH) selection, and link quality and enhanced trust for routing optimization. The effectiveness of the proposed approach is evaluated using various metrics to demonstrate its efficiency in MANET routing. By integrating multiple optimization techniques and considering critical constraints, the RePLO algorithm offers a systematic and secure solution for MANET routing. The evaluation results confirm the efficacy of the proposed approach in improving network performance, reliability, and security. Overall, the RePLO algorithm presents a promising approach to tackle the routing issues inherent in MANETs, paving the way for more robust and efficient communication in mobile ad hoc networks.

An Improved Multi-objective Optimization Framework with a Hybrid Model for Vehicular Adhoc Network Routing Services

An Improved Multi-objective Optimization Framework with a Hybrid Model for Vehicular Adhoc Network Routing Services

Kookaburra Node Aware Trustable and Enhanced Clustering and Routing in Mobile Ad Hoc Networks

Kookaburra Node Aware Trustable and Enhanced Clustering and Routing in Mobile Ad Hoc Networks

Evaluating the quality of service of Opportunistic Mobile Ad Hoc Network routing algorithms on real devices: A software-driven approach

Opportunistic Mobile Ad Hoc Networks (MANETs) offer versatile solutions in contexts where the Internet is unavailable. These networks facilitate the transmission between endpoints using a store-carry-forward strategy, thereby allowing information to be stored during periods of disconnection. Consequently, selecting the next hop in the routing process becomes a significant challenge for nodes, particularly because of its impact on Quality of Service (QoS). Therefore, routing strategies are crucial in opportunistic MANETs; however, their deployment and evaluation in real scenarios can be challenging. In response to this context, this paper introduces a monitoring software-driven tool designed to evaluate the QoS of routing algorithms in physical opportunistic MANETs. The implementation and its components are detailed, along with a case study and the outcomes provided by an implementation of the proposed solution. The results demonstrate the effectiveness of the implementation in enabling the analysis of routing protocols in real scenarios, highlighting significant differences with simulation results: mobility patterns in simulations tend to be inaccurate and overly optimistic, leading to a higher delivery probability and lower latency than what is observed in the real testbed.

Research on AdHoc network routing algorithm for link stability prediction

Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link stability prediction. This mechanism takes the path with the minimum delay as the main path, uses the cost function as the standard for selecting backup paths, and then uses the ARIMA model to predict links with good link stability as backup paths. We simulate and compare LS-BPRAODV and AODV protocols using the QualNet simulation platform. The simulation results show that the LS-BPRAODV, with the addition of a prediction mechanism, improves network performance, reduces the number of source node routing discoveries, reduces the number of route breaks, significantly reduces the packet loss rate and information reception delay in the network, and improves the average throughput of the network. This study provides new theoretical and empirical support for the routing algorithm for link stability prediction in AdHo networks, which is of great significance for promoting the development of wireless self-organizing networks.

Review of Routing Protocols for Mobile Ad Hoc Networks

Research on Mobile Ad Hoc Networks is of great significance in various fields including military, emergency response, Internet of Things (IoT), vehicular networking, aviation, and scientific research. This research contributes to the advancement and application of mobile communication technologies. Both domestic and international researchers have conducted in-depth studies on routing protocol design, which encompasses various aspects such as protocol design, topology management, link quality estimation, security, and energy efficiency. These areas represent current research hotspots and challenges. With the continuous development of wireless communication technologies and increasing demands for application, research on routing in Mobile Ad Hoc Networks remains highly significant with broad prospects for development. Therefore, research on routing strategies for Mobile Ad Hoc Networks has garnered widespread attention in academic circles worldwide, leading to numerous valuable research outcomes. This paper comprehensively analyzes the requirements related to the design of routing protocols for mobile self-organizing networks and comprehensively investigates the existing routing protocols, and classifies and investigates the routing protocols for mobile self-organizing networks in order to promote the research of next-generation mobile self-organizing network routing in China, which provides a reference.

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

Intelligent Routing in Directional Ad Hoc Networks Through Predictive Directional Heat Map From Spatio-Temporal Deep Learning

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routing paths meet at the same relay node. A path may be unable to provide high throughput since some of its path nodes are helping other paths to forward the heavy traffic. Therefore, those busy nodes should be avoided when a new path is to be established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses spatio-temporal deep learning algorithm to predict the next-time snapshot of directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. Such a DHM will be used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It is able to automatically split a single path into multiple ones that later on get converged again into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than traditional routing mechanisms such as AODV and OLSR.

Cooperative Sensing Assisted Cross layer QoS Assured Routing in Cognitive Radio Adhoc Networks: Ensuring Security and Privacy

Cooperative Sensing Assisted Cross layer QoS Assured Routing in Cognitive Radio Adhoc Networks: Ensuring Security and Privacy

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

A novel TT-SHO QoS aware and secured Vehicular Adhoc Network (VANET) routing protocol for smart intelligent transportation

A novel TT-SHO QoS aware and secured Vehicular Adhoc Network (VANET) routing protocol for smart intelligent transportation

Efficient and Secure Graph-Based Trust-Enabled Routing in Vehicular Ad-Hoc Networks

Efficient and Secure Graph-Based Trust-Enabled Routing in Vehicular Ad-Hoc Networks

SecureZone: Secure and Efficient Multipath Routing for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) represent collaborative networks formed by mobile nodes without a centralized infrastructure, finding application across diverse domains including military and security-sensitive operations. However, securing the routing process within MANETs poses significant challenges due to the absence of a central authority and the dynamic nature of the network. Existing routing protocols suffer from limitations such as bandwidth consumption and route request delays. In response, this paper introduces the Secure and Energy-efficient Zone-based Multipath Routing Protocol (SEZMRPR) for MANETs. SEZMRPR integrates proactive and reactive approaches while incorporating digital signatures and encryption techniques to ensure message integrity, data confidentiality, and end-to-end authentication at the IP layer. The paper provides a comprehensive design of SEZMRPR, assesses its resilience against security attacks, and evaluates its performance through simulations. Results demonstrate SEZMRPR's robustness against security threats, its efficient security performance with acceptable overhead, and its applicability in securing MANETs for military and security-sensitive operations. SEZMRPR, in conjunction with existing security measures, establishes a reliable framework for ensuring secure operations in ad hoc networks. Future enhancements may focus on optimizing performance, scalability, energy efficiency, and addressing quality of service requirements to further refine the protocol.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

<span lang="EN-US">A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.</span>

Swarm Intelligence-Inspired Meta-Heuristics Hybrid Optimization for Multi-Constraint Routing in Vehicular Adhoc Networks

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system’s recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.

Mobile wireless ad-hoc network routing protocols comparison for real-time military application

Mobile wireless Ad-hoc has become more popular because it forms quickly, has an easy setup, and has easy extensibility. The mobile ad-hoc wireless networks can be further classified according to their applications as follows: Regular user ad-hoc networks are commercial communication that applies to vehicles to help avoid collisions and accidents and live connections to transfer data from car to car. Another application is disaster rescue ad-hoc networking, usually used when a normal infrastructure network is destroyed by storms, earthquakes, tsunamis, etc. Nowadays, a lot of applications, particularly those related to the military and emergency situations, rely on mobile ad hoc wireless networks, where security needs are more challenging to provide than in regular networks. We present the tactical network needs for the military. This platform attempts to assess the possible advantages of mobile ad hoc networks in tactical military applications. This work proposes route discovery using reactive (on-demand) routing protocols where nodes need to just transfer data. This eliminates the requirement for each node to store and maintain any routing tables. This study presents and contrasts the benefits and drawbacks of two fundamental mobile ad hoc routing systems (AODV and DSR). Both protocols are On-Demand routing techniques, and when data needs to be sent, the discovery phase begins. The results of the simulation, the AODV routing approach outperforms the DSV routing method under identical simulated conditions.

AOR: Adaptive opportunistic routing based on reinforcement learning for planetary surface exploration

Planetary surface exploration mobile ad-hoc networks (PSEMANET) show the characteristics of routing void caused by craters and electromagnetic interference, relatively high dynamics caused by node heterogeneity, and small node capacity caused by load constraints, which is the reason for the increase of communication delay and packet loss. Adaptive opportunistic routing based on reinforcement learning (AOR) is an opportunistic routing protocol that determines the forwarding node based on the current coordinates, queue length, and the number of neighbors. The full forwarding areas are used to avoid routing void, and the dual competition mechanism is designed to avoid the node hidden problem. The Q-value obtained by reinforcement learning is used to design the dynamic delay cost (DDC) so that nodes in the forwarding areas can compete to achieve adaptive path switching to the environment. Compared with representative proactive routing OLSR, reactive routing AODV, geographic routing GPSR and opportunistic routing BLR, AOR and AOR with memory(AOR-M) have high packet delivery ratio (PDR) and low end-to-end delay in planetary surface exploration scenarios implemented by our simulation system. And the AOR-M protocol shows the lowest expected end-to-end delay and highest channel utilization in both high and low speed scenarios. The result shows that the AOR-M provides efficient and robust routing in planetary surface exploration mobile ad-hoc networks with a complex environment, highly dynamic nodes, and performance constraints.

An Intersection-Based QoS Routing for Vehicular Ad Hoc Networks With Reinforcement Learning

Vehicular ad hoc networks (VANETs) have the characteristics of high mobility, frequently changing topology and uneven distribution, which made it a challenge to design an efficient and robust routing protocol with low latency and high packet delivery rate. Currently, intersection-based routing method and full-path based routing method are two popular solutions for the packet routing in VANETs. Although the intersection-based routing method has better real-time performance, it has the problem of local optimization, making the routing results not global optimal. Although the full-path based method can obtain the global optimal solution, it is weak in dealing with the dynamically changing topological network. Aiming at solving the above problems, this paper designs an intersection-based QoS routing (IQRRL) algorithm, which mainly includes two crutial steps: the next intersection selection and the next hop vehicle selection. In the selection of the next intersection, this paper uses an improved intersection-based routing protocol. In addition to considering connectivity and delay, IQRRL also considers the communication quality from the neighbor’s road to the destination node while evaluating the quality of the neighbor’s road, which minimizes the problem of local optimization. When the next intersection is determined, a road is then determined, and then the next hop vehicle within the road should be chosen to relay the packet forward. In the next-hop vehicle selection step, this paper adopts multi-hop evaluation technology based on reinforcement learning. In addition to using “greedy decision-making” to select the next-hop vehicle, it also comprehensively evaluates whether the next-hop vehicle is still optimal in the future, so that the stability and reliability of data forwarding are improved and local optimal problems are avoided. Besides, this article uses a simulation system to compare IQRRL with other routing algorithms. The result reveals that IQRRL outperforms in terms of packet delivery ratio and transmission delay.