1,362 research outputs found

    Adaptive neuro-fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs

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    Vehicular Adhoc Networks (VANET) facilitate inter-vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing-based Secure Protocol for VANET Environment (SC-SPVE) method, which aims to tackle security challenges. The SC-SPVE technique integrates an adaptive neuro-fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC-SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2

    Distributed consensus in wireless network

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    Connected autonomous systems, which are powered by the synergistic integration of the Internet of Things (IoT), Artificial Intelligence (AI), and 5G technologies, predominantly rely on a central node for making mission-critical decisions. This reliance poses a significant challenge that the condition and capability of the central node largely determine the reliability and effectiveness of decision-making. Maintaining such a centralized system, especially in large-scale wireless networks, can be prohibitively expensive and encounters scalability challenges. In light of these limitations, there’s a compelling need for innovative methods to address the increasing demands of reliability and latency, especially in mission-critical networks where cooperative decision-making is paramount. One promising avenue lies in the distributed consensus protocol, a mechanism intrinsic to distributed computing systems. These protocols offer enhanced robustness, ensuring continued functionality and responsiveness in decision-making even in the face of potential node or communication failures. This thesis pivots on the idea of leveraging distributed consensus to bolster the reliability of mission-critical decision-making within wireless networks, which delves deep into the performance characteristics of wireless distributed consensus, analyzing and subsequently optimizing its attributes, specifically focusing on reliability and latency. The research begins with a fundamental model of consensus reliability in an crash fault tolerance protocol Raft. A novel metric termed ReliabilityGain is introduced to analyze the performance of distributed consensus in wireless network. This innovative concept elucidates the linear correlation between the reliability inherent to consensus-driven decision-making and the reliability of communication link transmission. An intriguing discovery made in my study is the inherent trade-off between the time latency of achieving consensus and its reliability. These two variables appear to be in contradiction, which brings further performance optimization issues. The performance of the Crash and Byzantine fault tolerance protocol is scrutinized and they are compared with original centralized consensus. This exploration becomes particularly pertinent when communication failures occur in wireless distributed consensus. The analytical results are juxtaposed with performance metrics derived from a centralized consensus mechanism. This comparative analysis illuminates the relative merits and demerits of these consensus strategies, evaluated from the dual perspectives of comprehensive consensus reliability and communication latency. In light of the insights gained from the detailed analysis of the Raft and Hotstuff BFT protocols, my thesis further ventures into the realm of optimization strategies for wireless distributed consensus. A central facet of this exploration is the introduction of a tailored communication resource allocation scheme. This scheme, rooted in maximizing the performance of consensus mechanisms, dynamically assesses the network conditions and allocates communication resources such as transmit power and bandwidth to ensure efficient and timely decision-making, which ensures that even in varied and unpredictable network conditions, consensus can be achieved with minimized latency and maximized reliability. The research introduces an adaptive protocol of distributed consensus in wireless network. This proposed adaptive protocol’s strength lies in its ability to autonomously construct consensus-enabled network even if node failures or communication disruptions occur, which ensures that the network’s decision-making process remains uninterrupted and efficient, irrespective of external challenges. The sharding mechanism, which is regarded as an effective solution to scalability issues in distributed system, does not only aid in managing vast networks more efficiently but also ensure that any disruption in one shard cannot compromise the functionality of the entire network. Therefore, this thesis shows the reliability and security analysis of sharding that implemented in wireless distributed system. In essence, these intertwined strategies, rooted in the intricate dance of communication resource allocation, adaptability, and sharding, together form the bedrock of my contributions to enhancing the performance of wireless distributed consensus

    Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

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    Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively

    Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET

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    Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput

    Power and Energy Optimized Approach towards Sustainable Mobile Ad-hoc Networks and IoT

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    Investigating how real-time applications in sectors like healthcare, agriculture, construction, and manufacturing can enhance their effectiveness and sustainability through the use of autonomous sensor technologies, green computing, and big data analytics is part of the work with sustainable approaches for optimising performance of networks. This authoritative guide exposes the drawbacks of conventional technology and provides techniques and tactics for addressing Quality of Service (QOS) issues and enhancing network performance. It brings together a broad team of subject-matter specialists. Several in-depth chapters cover topics like blockchain-assisted secure data sharing, intelligent management of ad hoc networks, smart 5G Internet of Things scenarios, and the use of artificial intelligence (AI), machine learning (ML), and learning techniques (DL) techniques in smart healthcare, smart factory, and smart agriculture

    Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

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    Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

    Evaluation of the effectiveness of small aperture network telescopes as IBR data sources

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    The use of network telescopes to collect unsolicited network traffic by monitoring unallocated address space has been in existence for over two decades. Past research has shown that there is a lot of activity happening in this unallocated space that needs monitoring as it carries threat intelligence data that has proven to be very useful in the security field. Prior to the emergence of the Internet of Things (IoT), commercialisation of IP addresses and widespread of mobile devices, there was a large pool of IPv4 addresses and thus reserving IPv4 addresses to be used for monitoring unsolicited activities going in the unallocated space was not a problem. Now, preservation of such IPv4 addresses just for monitoring is increasingly difficult as there is not enough free addresses in the IPv4 address space to be used for just monitoring. This is the case because such monitoring is seen as a ’non-productive’ use of the IP addresses. This research addresses the problem brought forth by this IPv4 address space exhaustion in relation to Internet Background Radiation (IBR) monitoring. In order to address the research questions, this research developed four mathematical models: Absolute Mean Accuracy Percentage Score (AMAPS), Symmetric Absolute Mean Accuracy Percentage Score (SAMAPS), Standardised Mean Absolute Error (SMAE), and Standardised Mean Absolute Scaled Error (SMASE). These models are used to evaluate the research objectives and quantify the variations that exist between different samples. The sample sizes represent different lens sizes of the telescopes. The study has brought to light a time series plot that shows the expected proportion of unique source IP addresses collected over time. The study also imputed data using the smaller /24 IPv4 net-block subnets to regenerate the missing data points using bootstrapping to create confidence intervals (CI). The findings from the simulated data supports the findings computed from the models. The CI offers a boost to decision making. Through a series of experiments with monthly and quarterly datasets, the study proposed a 95% - 99% confidence level to be used. It was known that large network telescopes collect more threat intelligence data than small-sized network telescopes, however, no study, to the best of our knowledge, has ever quantified such a knowledge gap. With the findings from the study, small-sized network telescope users can now use their network telescopes with full knowledge of gap that exists in the data collected between different network telescopes.Thesis (PhD) -- Faculty of Science, Computer Science, 202

    ENHANCED ON-DEMAND DISTANCE VECTOR ROUTING PROTOCOL TO PREVENT BLACKHOLE ATTACK IN MANET

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    Wireless networks are becoming increasingly popular. Mobile ad hoc networks are one category among the different types of wireless networks that transmit packets from the sender node to the receiver node without the use of a base station or infrastructure, as the nodes serve as both hosts and routers. These networks are referred to as mobile because they are movable. MANET is an ad-hoc network that can change positions at any time, and nodes can join or leave at any moment, making it vulnerable to attacks such as Blackhole. Existing solutions, in some ways, led to more memory space consumption, while others led to an overhead. This research proposes an Enhanced On-demand Distance Vector (AODV) routing protocol to prevent Blackhole attacks on MANETs using Diffie Hellman and Message Digest 5 (DHMD), implemented using Network Simulator 2 (NS2). The performance of the proposed protocol was evaluated using the following parameters: Packet Delivery Ratio, throughput, End to End (E2E) Delay, and routing overhead. It was concluded that DHMD has reduced network overhead as it resulted to 23% while AODV resulted at 38% and memory consumption for DHMD gave 0.52ms compared to AODV that gave 0.81ms due to Blackhole prevention. This research will help to mitigate the effect of blackhole attacks in a network and increase network performance by reducing overhead and memory consumption
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