Ad Hoc Networks Research Articles

An efficient certificateless anonymous signcryption communication scheme for vehicular adhoc network

Certificateless public key infrastructure (PKI) avoids the key escrow problem associated with identity-based PKI and has recently been widely employed in anonymous communication schemes for vehicular adhoc networks (VANETs). In existing certificateless anonymous signcryption schemes for VANETs, vulnerabilities such as potential attacks involving the substitution of pseudonyms and the forging of pseudonymous public-private key pairs exist due to the lack of “identity-key binding” and “non-linearity processing of public-private key pairs.” To address this issue, we propose an improved certificateless anonymous signcryption scheme based on elliptic curve cryptography. The scheme incorporates bilinear pairing as one of the authentication mechanisms, designs pseudonym generation algorithms and public-private key pair structures, and introduces a pseudonym verification mechanism. The correctness of the scheme is proven under the random oracle model, and its security is extensively demonstrated through detailed discussions on its confidentiality, authentication, unforgeability, anonymity, and traceability. Furthermore, the time and space complexity of the scheme are calculated. By comparing with recently published certificateless signcryption schemes, it is shown that the proposed scheme offers higher security with smaller computational and communication overheads. This certificateless vehicular network signcryption algorithm provides an efficient encryption solution for anonymous communication in vehicular networks, thereby ensuring the rapid development of secure technology for intelligent connected vehicle super terminals.

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.

A Hybrid Blockchain-Based Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

A Hybrid Blockchain-Based Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

JamBIT: RL-based framework for disrupting adversarial information in battlefields

During battlefield operations, military radios (hereafter nodes) exchange information among various units using a mobile ad-hoc network (MANET) due to its infrastructure-less and self-healing capabilities. Adversarial cyberwarfare plays a crucial role in modern combat by disrupting communication between critical nodes (i.e., nodes mainly responsible for propagating important information) to gain dominance over the opposing side. However, determining critical nodes within a complex network is an NP-hard problem. This paper formulates a mathematical model to identify important links and their connected nodes, and presents JamBIT, a reinforcement learning-based framework with an encoder–decoder architecture, for efficiently detecting and jamming critical nodes. The encoder transforms network structures into embedding vectors, while the decoder assigns a score to the embedding vector with the highest reward. Our framework is trained and tested on custom-built MANET topologies using the Named Data Networking (NDN) protocol. JamBIT has been evaluated across various scales and weighting methods for both connected node and network dismantling problems. Our proposed method outperformed existing RL-based baselines, with a 24% performance gain for smaller topologies (50-100 nodes) and 8% for larger ones (400-500 nodes) in connected node problems, and a 7% gain for smaller topologies and 15% for larger ones in network dismantling problems.

Golden jackal optimization-based clustering scheme for energy-aware vehicular ad-hoc networks

Golden jackal optimization-based clustering scheme for energy-aware vehicular ad-hoc networks

Integrated VANETS and FANETS Driven Multimodal Smart Transportation System for Delivery

Implementing an intelligent transportation system that integrates Vehicular Ad-hoc Networks (VANETs) and Flying Ad-hoc Networks (FANETs) can significantly enhance the efficiency and safety of delivery services. There has been lots of research on VANETs and FANETs and how they can improve traffic flow by providing real-time traffic information and enabling efficient communication separately by analyzing various parameters like PDR, Average Throughput, end-to-end delay, etc. In our paper, we attempted to integrate VANETs and FANETs to create an intelligent delivery system. For the integration, we compared protocols AODV and OLSR with performance metrics like PDR, Average Throughput, and end-to-end delay for both VANETs and FANETs, and results show that OLSR has high PDR and less delay with VANETs and AODV performs efficiently than OLSR in FANETs.

Lightweight consortium blockchain-enabled secured Vehicular ad Hoc Network using certificateless conditional privacy-preserving authentication mechanism.

Towards the intelligent transportation systems, Location Based Service (LBS) are widely engaged in Vehicular Ad Hoc Networks (VANETs) that are becoming as significant application that change the human driving experience in today's world. LBS systems facilitate the users with intelligent services by collecting an accurate location information. Due to the frequent exchange rate of the location information in an open environment, VANETs are inherently susceptible to privacy and security attacks. In past, many schemes have been proposed to ensure the privacy and security of exchanged location information; but fail to deploy in practical VANETs. At the same time, system efficiency is compromised which is another primary requirement of VANETs. Leveraging the semi-decentralized and lightweight nature of consortium blockchain technology, and Certificateless conditional privacy protection scheme to reduce the node authentication overhead, this paper introduces Consortium Blockchain assisted Certificateless Conditional Privacy Protection scheme to address the aforementioned challenges. Additionally, the proposed scheme has ability to develop anonymous regions for a particular time stamp ensuring the location privacy of vehicles. Rigorous security analysis and experiments show the practicality and resilience to various attack models, and achieve ADP 83% with maximum malicious attacks. Comparing with existing state of the art methods, the proposed scheme exhibits the privacy improvement and low computational complexity.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Implementation of A Delay-Tolerant Routing Protocol in the Network Simulator NS-3

The paper explores the implementation of the Epidemic Routing Protocol using the NS-3 simulator, focusing on its application in intermittently connected networks like Mobile Adhoc Networks (MANETs). The Epidemic Protocol maximizes message delivery by flooding the network with multiple copies, ensuring reliability even in sparse or highly mobile environments. Through NS-3 simulations, the study examines the protocol’s performance, considering factors such as network size, node mobility, and message Time-To-Live (TTL). The simulation, which models an ad-hoc wireless network using IEEE 802.11b, demonstrates that the protocol achieves a 100-percentage packet delivery ratio with minimal latency. Tools like NetAnim provide detailed insights into node interactions and message dissemination, highlighting the protocol’s effectiveness in maintaining communication under challenging conditions. The paper concludes that while the Epidemic Protocol is resource- intensive, it is highly effective for scenarios where traditional routing methods fail, offering a reliable solution for dynamic and partitioned network.

Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era

5G Mobile Adhoc Networks (5G-MANETs) are a popular and agile solution for data transmission in local contexts while maintaining communication with remote entities via 5G. These characteristics have established 5G-MANETs as versatile communication infrastructures for deploying contextual applications, leveraging physical proximity while exploiting the possibilities of the Internet. As a result, there is growing interest in exploring the potential of these networks and their performance in real-world scenarios. However, the management and monitoring of 5G-MANETs are challenging due to their inherent characteristics, such as highly variable topology, unstable connections, energy consumption of individual devices, message routing, and occasional inability to connect to 5G. Considering these challenges, the proposed work aims to address real-time monitoring of 5G-MANETs using a connection-aware Digital Twin (DT). The approach provides two main functions: offering a live virtual representation of the network, even in scenarios where multiple nodes lack 5G connectivity, and estimating the performance of the infrastructure, enabling the specification of customized conditions. To achieve this, a communication architecture is proposed, analyzing its components and defining the involved processes. The DT is implemented and evaluated in a laboratory setting, assessing its accuracy in representing the physical network under varying conditions of topology and Internet availability. The results show 100% accuracy for the DT in fully connected topologies, with ultra-low latency averaging under 80 ms, and suitable performance in partially connected contexts, with latency averages below 3000 ms.

Assessing the Impact of Cryptography on the Security of Wireless Ad Hoc Networks

The use of wireless networks for communication in bistros, airports, hotels, offices, colleges, and other public locations is increasing as they become more widespread. Although remote organisations and its applications are becoming increasingly widely recognised, security concerns related to them have grown to be of great concern. In this investigation, we'll develop a different plan to implement quantum cryptography for key distribution in 802.11 companies. They are helpful because of their adaptability and quick data transfer in residences, workplaces, and businesses. Given the principles of material science, quantum cryptography enables unfettered security for the exchange of cryptographic keys between two distant meetings. This investigation's main goal is to investigate potential vulnerabilities and security concerns in remote organization correspondence and give a structure to unqualified security in remote systems administration utilizing quantum key dispersion (QKD).

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

FMORT: The Meta-Heuristic routing method by integrating index parameters to optimize energy consumption and real execution time using FANET

Decreasing energy consumption in Unmanned Aerial Vehicles (UAVs) while simultaneously enhancing their reliability and processing capabilities is considered a fundamental challenge. The routing mechanisms employed in Flying Ad Hoc Networks (FANETs) are more complex compared to those in Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), a challenge addressed by the FMORT method. To tackle these complex routing challenges, clustering techniques that utilize hybrid Meta-heuristic algorithms can be applied. Data analysis within the FMORT framework identified factors influencing service integration, leading to a reduction in redundant request transmissions and overall redundancy in the proposed method. The identification of food sources in the hybrid Meta-heuristic algorithm of the FMORT method is achieved through the integration of the Sparrow and Dragonfly algorithms. These algorithms work simultaneously to increase energy efficiency and increase network lifetime. This strategy optimizes information exchange by selecting an intelligent threshold detector and categorizing inputs, thereby minimizing node mobility. As a result, it improves performance metrics and decreases delivery costs, energy consumption, and delays. In the proposed method, a balanced performance is achieved by comparing existing methods in terms of transmission delay, Packet Delivery Ratio( PDR), throughput, and energy consumption. Simulation results show that the FMORT approach provides effective and stable outcomes in terms of reliability, decreased delays, and improved packet delivery rates. The FMORT framework includes principles for neighbor selection, determining suitable cluster heads, and scoring based on the average Euclidean distance. Additionally, it manages topology access, ensures proper distribution, guarantees data connectivity, and accurately categorizes inputs. By optimizing the sensitivity rate, this method minimizes the average delays and meekly values input data through effective load balancing. Key parameters considered for real time optimization of overall performance include the number of cluster heads during re-clustering, the ratio of request-to-acknowledgment packet transmission, node, and network lifetime, end-to-end delay, and energy consumption. Ultimately, the simulation results show that compared to the MWCRSF algorithm, the average optimization of index parameters,% 0.73 decrease in energy consumption,% 2.23 network lifetime, 1.35 re-cluster construction time and also% 0.11 re-cluster lifetime has increased.

Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in vehicular ad hoc networks (VANETs)

This research tackles the critical challenge of BeiDou signal spoofing in vehicular ad-hoc networks and addresses significant risks to vehicular safety and traffic management stemming from increased reliance on accurate satellite navigation. The study proposes a novel hybrid machine learning framework that integrates Autoencoders and long short-term memory (LSTM) networks with an advanced cryptographic method, attribute-based encryption, to enhance the detection and mitigation of spoofing attacks. Our methodology leverages both real-time and synthetic navigational data in a comprehensive experimental setup that simulates various spoofing scenarios to test the resilience of the proposed system. The findings demonstrate a significant improvement in the accuracy of spoofing detection and the robustness of mitigation strategies by ensuring the integrity and reliability of navigational data. This investigation enhances the existing body of knowledge by demonstrating the effectiveness of integrating machine learning with cryptographic techniques to secure VANETs. Ultimately, it effectively paves the way for future research into adaptive security mechanisms that can dynamically respond to evolving cyber threats.

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Survey on securing wireless networks through a blockchain-based framework

In the contemporary era, blockchain technology has brought about a significant transformation in the realm of digital currency through innovations like Bitcoin. A blockchain serves as a decentralized ledger, ensuring an immutable record of transactions across a network. Recent observations indicate the pivotal role of blockchain technology not only in the financial sector but also in networking. This study considers blockchain as the essential link in establishing a genuinely decentralized, trustless, and secure environment for network nodes. The objective is to provide a systematic and comprehensive overview of futuristic endeavours in this domain. The exploration begins with an examination of the fundamental operational concepts of blockchains and how these systems achieve decentralization, security, and suitability. The focus then shifts towards addressing open research challenges within blockchain technologies, particularly in securing diverse communication networks such as Distributed Computing, Vehicular Ad-hoc Networks, Opportunistic Networks, and Delay Tolerant Networks. Simulation results underscore the superior security performance of blockchain, especially under conditions of attack.

A Decision Framework for Enhancing Adhoc Network Stability and Security

A Decision Framework for Enhancing Network Stability and Security. In the modern digital era, the growing complexity of networks and increasing frequency of cyber threats demand robust strategies for ensuring network stability and security. This paper proposes a decision framework that integrates real-time analytics, proactive monitoring, and adaptive response mechanisms to enhance network resilience against failures, attacks, and performance degradation. The framework leverages advanced machine learning algorithms, network flow analysis, and security protocols to dynamically adjust to network conditions and mitigate risks .The decision framework operates in two key dimensions: Stability: It focuses on ensuring uninterrupted network performance by optimizing resource allocation, traffic management, and fault tolerance mechanisms. By analyzing network traffic patterns and identifying potential bottlenecks or vulnerabilities, the framework makes proactive decisions to reroute traffic, adjust bandwidth, and prioritize critical data flows.Security: The framework enhances security by detecting potential threats, such as Distributed Denial of Service (DDoS) attacks, unauthorized access, or malware propagation. Using a combination of intrusion detection systems (IDS), firewalls, and behavioral anomaly detection, it identifies threats in real-time and implements automatic countermeasures, such as isolating affected network segments, patching vulnerabilities, or blocking malicious traffic.A key innovation of this framework is its use of multi-criteria decision-making (MCDM) techniques to balance trade-offs between network performance and security in real time. The model continuously evaluates factors such as latency, throughput, and risk exposure to make informed, optimal decisions that ensure both stability and protection. Furthermore, the framework adapts to evolving network conditions using reinforcement learning, allowing it to learn from past incidents and improve its decision-making over time.Simulation results demonstrate that the proposed framework significantly reduces network downtime, improves threat detection response times, and mitigates the impact of security breaches. This decision framework presents a scalable solution for modern, dynamic networks, offering enhanced protection while maintaining high performance in the face of complex challenges.

New routing method based on sticky bacteria algorithm and link stability for VANET

With the rapid development of Telematics, the role of edge computing (Mobile Edge Computing, MEC) is becoming more and more significant. Mobile users can obtain massive computing and storage resources in a local way, thus effectively solving the congestion problem of the core network. However, in general urban edge network scenarios of VANET (Vehicular Ad-hoc Network), due to low node density and poor connectivity, there are few chances to encounter suitable forwarding nodes. In order to avoid the above situation and adapt to sparse scenarios, we propose new link-stabilized routing method & protocol based on sticky bacteria algorithm in this paper. The new idea and the significant findings of this proposed algorithm in enhancing the routing protocol is as follows: five factors affecting routing decision are identified: evaluation distance, deflection angle, number of neighboring nodes, rate difference, and the traffic of the road section at which it is located, and then these five factors are comprehensively evaluated by using the our designed Analytic Hierarchy Process (AHP) strategy, so as to determine the score of each node and the candidate routing paths; And the best routing path between two communicating nodes is found through the stronger global exploration capability of the sticky bacteria algorithm. Our experiments (that are based on our developed C++ programs) have proved that the method proposed in this paper has stronger link stability and the highest packet delivery rate. And the experiments and their results has enhanced our new method credibility in the field of ad hoc networks.

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Intensive study, tuning and modification of reactive routing approach to improve flat FANET performance in data collection scenario.

The Flying Ad-hoc Network (FANET) can be defined as the Ad-hoc network that connects unmanned aerial vehicles flying in the space with each other and with a ground base station. However, the 3D movement of these drones with higher speeds results in a network of highly dynamic topology and intermittent connections, making the standard Ad Hoc routing protocols are not suitable for FANET. The approaches followed to address this issue include designing from scratch a routing protocol specific to FANET or modifying the existing protocols. From the view point of reliability, accuracy, and time, it is preferable to base the work on a protocol standard. But before amending the standard, tuning its performance and applying it under suitable conditions may be satisfactory for the new use. Therefore, this work considers flat FANET of fully mission-controlled drones and performs an extensive parametric simulation study to determine the best conditions and parameters' values for applying the popular Ad Hoc On-demand Distance Vector (AODV) to it. After deducing the recommended operating environment (FAODVN-OE), some examples of amendments were suggested to further improve the performance. It was found that the modified FAODVN-OE achieves high performance compared to the default standard in terms of jitter and delay. It helped reduce jitter and delay by an average of 93.2% and 83.8%, respectively, while exhausting less energy; however the network experiences a 24.5% reduction in packet delivery ratio.