Ad Hoc Networks Research Articles

INVESTIGATION OF THE MANET NETWORK PERFORMANCE CONCERNING DIFFERENT ROUTING PROTOCOLS

Mobile Ad-Hoc Network is a decentralized organization that operates without a foundation. Due to the Mobile Ad-Hoc Network's self-configurable and simple organizational component, many applications may be run. Because of this, various applications are available. If helpful guidelines are established, Mobile Ad-Hoc Network will become dependable. We shall research the organization's competent steering convention for hypertext transfer protocol traffic. We must reach this conclusion with accuracy since this will be the main focus of our inquiry. Latency and throughput were employed to achieve show research goals. For this work reenactment inquiry, your expectations must be based on the conventions it chose since they performed better on all four perspectives. After analyzing its needs, an organization may improve its operations by choosing better conventions. This may boost an organization's efficiency. This research examined AODV, DSR, and OLSR routing methods. This study used OPNET Modeler 14.5 to enhance ad-hoc network performance.

Fuzzy‐Markov Routing Policy and Priority‐Based Load Balancing for Cognitive Radio Ad Hoc Networks

ABSTRACTA cognitive radio ad hoc network (CRAHNs) is an infrastructure‐free network containing a cognitive radio, which can use the spectrum resources effectively through an adaptive change of parameter settings and decision‐making. Hence, it enhances the spectrum efficacy for satisfying the growing mobile traffic requests. In multihop CRAHNs, in order to ensure a reliable transmission and energy efficiency, the routing protocol should consider the metrics of channel quality, link stability, and energy consumption. In this paper, a Fuzzy‐Markov routing policy and priority‐based load balancing algorithm (FMRP‐PLB) for CRAHNs is designed. In this protocol, a combined routing metric is derived using delay, energy rate, and link expiration time metrics. For modeling the channel state, a Fuzzy‐Markov decision model is designed, in which the channel states and spectrum availabilities of SU are modeled as state transition matrices. Then, optimum routing decisions are made using the combined routing metric and the predicted transmission success probability. A load balancing metric (LBM) is derived in terms of available link capacity, remaining buffer size of node, and back‐off delay. During data transmission, based on the traffic priority and measured levels of LBM, the optimum paths are selected. Simulation results using NS2 suggest that FMRP‐PLB attains an increased packet delivery ratio with reduced delay and energy consumption.

Fuzzy-based Bi-objective Energy Efficient Routing Protocol for Large Scale Mobile Ad-hoc Network

MANET is an infrastructure-less network, which is comprised of a greater number of nodes that are moving in a random manner. Establishing efficient and reliable routes is the crucial design issue in MANET. In addition, providing energy-efficient routes for performing communication operations in MANET is also a challenging goal. Increasing network lifetime and decreasing overall traffic load are the significant considerations while designing the MANET. An energy optimization routing mechanism with a fuzzy basis that is state-based and influenced by COVID-19 is used to prevent and control epidemics. Based on their lifespan and varying mobility speeds, it is dependent on energy optimization yield under various scenarios. Fuzzy-based bi-objective criteria in energy-efficient routing protocol for the mobile ad-hoc network to select the different constraints like link stability, bandwidth, and battery life. The main goal of this paper is to obtain the optimal solution to choose the best route, limit battery life, and change the movement of mobile nodes for large-scale mobile ad-hoc networks. It is demonstrated that the possible solution relates to the domain of selecting the variables by outlining the connection between the energy function’s variables. Researchers optimize a path's link lifespan, resulting in the choice of the shortest path being particularly efficient in COVID pandemic prevention and treatment.

Protection of Vehicular AdHoc Networks via Efficient Authentication and Message Encryption

Protection of Vehicular AdHoc Networks via Efficient Authentication and Message Encryption

Location-Based Hybrid Video Streaming Protocol for VANETs

The services and applications provided by video streaming over Vehicular Ad-hoc Networks (VANETs) have recently become very attractive to technology users. In VANETs, the moving vehicles on the roads take great advantage of this radio link by exchanging information with each other related to the safety, traffic jams, accidents, and condition of the roads via video streaming. On the other hand, this transmission domain poses many challenging issues like frequent disconnections due to fast-speed vehicles, longer transmission delays, and inefficient link consumption etc. numbers of routing strategies have been proposed in the literature that aimed to address these challenges of an Intelligent Transportation System (ITS). This article is based on a Location-Based Hybrid Video Streaming Protocol (LBHVSP) that has been proposed with the intent to minimize the challenges related to video transmission as well as the attainment of high network performance. The idea presented is a motivation from the dynamic nature of transceiver and receiver transmission power, accompanied by the range-based localization techniques for inter-space computation of vehicular nodes. LBHVSP is a 2-way protocol that works as a combination of infrastructure-based and infrastructure-less wireless networks that tends to minimize the average packet delay for efficient utilization of bandwidth. It is a two-phase solution, firstly frames are transmitted to the second hop node available in the direct range that minimizes the traveling path in terms of hop count and ultimately yields minimal delay. Secondly, this technique utilizes the Road Side Units (RSUs) having wired connectivity for a faraway destination. Experimental results using different transmission scenarios reveal its efficacy by attaining maximum average packet throughput, minimum average packet drop rate, and minimum average per-packet delay in Signal Transmission Power and Packet Reception Power Threshold values. The proposed scheme has outperformed the state-of-the-art transmission scenarios in terms of static parameters. Varying transmission and reception powers subject to the inter-distance have shown remarkable outcomes. In comparison to pre-existing VANET protocols like DyTE and DSR, proposed scheme has shown an average improvement of 30% in packet drop rate, and 80% rise in throughput, approximately. Besides, our scheme has shown a remarkable decline in transmission delay as well. On comparing the LBHVSP performance against recent VANET communication protocol, it comes out that it attains the maximum throughput and delay improvement. Furthermore, to cater the packet drop, a smart re-transmission strategy should be designed particularly for multimedia streaming and video message exchange.

Reliable Vehicular Ad Hoc Networks for Intelligent Transportation Systems based on the Snake Optimization Algorithm

Vehicular Ad Hoc Networks (VANETs) represent an environment in which mobility exceeds the normal values, topology changes rapidly, and safety constraints are too high. The fundamental problem with VANETs is making transmission, acceptance, and sending out of messages between vehicles as timely, reliable, and secure as possible. The current study aims to address these challenges by applying the Snake Optimization Algorithm (SOA), enhancing network protocol efficiency, performance, and robustness. In this work, a comprehensive examination of the effects of optimal SOA on VANET protocols is provided over networks with different node sizes of 100, 250, and 500. End-to-end delay, path delivery overhead, and average number of hops improved after the utilization of SOA in all the considered networl configurations.

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

Exploring optimal resource allocation methods for improved efficiency in flying ad-hoc network environments: a survey

Exploring optimal resource allocation methods for improved efficiency in flying ad-hoc network environments: a survey

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

Strong non-repudiation certificateless scheme with an aggregate signature in vehicular ad-hoc network

Strong non-repudiation certificateless scheme with an aggregate signature in vehicular ad-hoc network

A Cross-domain Data Sharing Scheme for VANETs Based on Blockchain

With the continuous development of the Vehicular Ad Hoc Network (VANET), cross-domain sharing of vehicle data has become a significant concern. The Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm plays a key role in data sharing as it can achieve “one-to-many” transmission. In this paper, we propose a cross-domain data sharing scheme based on unpaired CP-ABE in VANETs, utilizing the blockchain and InterPlanetary File System (IPFS) system. The blockchain network is composed of trusted authorities (TAs) from different domains. Due to the high-speed movement characteristics of vehicles, we divide vehicle attributes into two categories: static and dynamic. we design a cross-domain data verification contract based on attribute bloom filter (ABF) for decryption testing. Vehicles that pass the test will receive dynamic attribute decryption keys generated by TAs in the data sharing domain to achieve cross-domain access. In addition, we design an outsourced decryption scheme to reduce the computational overhead during vehicle decryption and propose a direct permission revocation mechanism to ensure the flexibility and security of the system. The simulation experiment results show that our scheme optimizes the efficiency of cross-domain data access significantly compared with other approaches.

Certificateless Aggregate Signatures: A Comprehensive Survey and Comparative Analysis

This paper presents a comprehensive survey of the certificateless aggregate signature scheme in terms of their computational performance and security. This signature scheme is of significant interest because of its relevance in various domains such as Internet of Things (IoT), Vehicular Ad-hoc Networks (VANETs), E-Healthcare Medical Systems, and Mobile Healthcare Systems. Further, we point towards potential future directions for research and development in this field and conclude our study. This survey serves as a valuable resource for expanding the horizons of this scheme by envisioning new applications.

Enhancing Stability and Efficiency in Mobile Ad Hoc Networks (MANETs): A Multicriteria Algorithm for Optimal Multipoint Relay Selection

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

A novel blockchain-based anonymous roaming authentication scheme for VANET

With the widespread application of vehicular ad-hoc networks, ensuring secure and seamless cross-regional roaming for mobile users and obtaining corresponding services has become a focal point. However, designing an efficient and secure roaming authentication protocol is challenging due to the confidentiality and privacy issues that data transmission during the roaming authentication process may cause and the limited computational capabilities of mobile devices. Researchers have proposed many security-oriented schemes to address this thorny challenge. However, many state-of-the-art schemes need help meeting various security requirements and facing privacy leakage and single points of failure. Recently, Xue et al. proposed a distributed authentication scheme for roaming services in mobile vehicular networks based on smart contracts. Regrettably, it is noted that their scheme is vulnerable to ephemeral key leakage attacks. Further, we present a blockchain-based anonymous roaming authentication scheme called BARA, which changes how session keys are generated and significantly reduces on-chain storage costs using probabilistic data structure techniques. We utilize Scyther and Burrows–Abadi–Needham (BAN) logic to prove the security of BARA and compare it with similar protocols in terms of computation, communication, and revocation check. The analysis results demonstrate that BARA achieves a good balance between security performance and execution efficiency.

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%.

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

Design and implementation of a hydraulic balancing system based on low-power ZigBee Ad-Hoc network electric household control valves

Accelerating global urbanization highlights the need for sustainable smart cities. Centralized heating systems, vital to intelligent heating networks, enhances energy efficiency and reduces carbon emissions. Hydraulic imbalance, however, compromises efficiency and comfort, with traditional remedies challenging in older districts. This study introduces an electric household control valve and a hydraulic balance control system based on the ZigBee protocol The system utilizes ZigBee's Ad-Hoc network features to monitor residential heating return water temperatures, achieving wireless monitoring of the heating system. The results demonstrate that ZigBee's mesh topology and media access control significantly improve system robustness Utilizing the Time Protocol Synchronization Node time synchronization mechanism, the time synchronization error rate is only 0.76 ‰, and the node current is reduced from 37.33 mA to 35.2uA, significantly lowering the system's power consumption and ensuring long-term stable operation. By adjusting the return water temperature, the maximum temperature differential was reduced from 5.9 °C to 1.6 °C, a reduction of about 73 %. The temperature difference between households was significantly reduced, effectively improving the hydraulic imbalance issue in old residential areas, ensuring an even temperature distribution. This study provides a cost-effective low-carbon solution to address the challenges of urban heating, promote the development of intelligent heating networks.

Cooperative approach for data-centric and node-centric misbehavior detection in VANET

Vehicular Ad Hoc Network (VANET) has risen as a paramount technology for efficiently providing traffic management, safety and infotainment services to road users. Vehicles are allowed to use pseudo identities during vehicular network access to preserve their privacy. This property makes VANET vulnerable to Sybil attack, performed by exploiting the set of pseudo identities to send messages. Detecting a Sybil attack solely by verifying the accuracy of messages received is challenging, as the messages sent through Sybil identities can appear plausible. Current data-centric and certain machine learning-based approaches only identify Sybil attacks within a local context. It is necessary to find the connection between the Sybil nodes both locally and at the Road Side Unit (RSU) level to effectively mitigate this attack. Hence, we introduce a novel cooperative and hybrid misbehavior detection framework for Sybil attack detection in VANET. It does not only detect Sybil identities but also establishes connections between them by analyzing their speed time series with the Dynamic Time Warping (DTW) technique. Furthermore, it confirms the association between Sybil nodes through node-centric detection using Dempster Shafer Theory (DST) at RSU. This advanced detection can help the Linkage Authority (LA) to find and revoke the actual node responsible for carrying out Sybil attack globally. This is the first framework in its category which can provide accurate detection at both local and RSU level in different scenarios. We acquired a higher detection rate by assessing performance with an existing dataset and a generated real-time Sybil attack dataset.

Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.