In Internet of Things (IoT), Wireless Sensor Networks (WSNs) play crucial role on data sensing and collection for the Base Station (BS) communication.In WSN-IoT, the primary challenge is the efficient management of limited energy resources during communication with IoT-integrated devices.Clustering algorithms consume abundant energy to maintain the network node processing over extended periods.Conventional algorithms typically rely on random probability in Cluster Head (CH) selection.Therefore, improving the network lifetime requires the development of enhanced CH selection algorithms.This study proposes an Energy-based Cauchy Mechanism -Pufferfish Optimization Algorithm (ECM-POA) for ideal CH selection and route path selection.Cauchy mechanism is incorporated in traditional POA for enhanced global search capabilities for effective CH and route path selection.Fitness functions like residual energy, intra to inter cluster distance, distribution density parameter and Intracluster Communication Cost (ICC) are taken into consideration for a selection of optimal CHs.The developed ECM-POA obtains the highest network throughput of 99.34% for 100 nodes, 98.87% for 200 nodes, 98.26% for 300 nodes, 97.71% for 400 nodes and 97.15% for 500 nodes.These results prove superiority of the proposed model against existing models.The ECM-POA algorithm offers energy efficacy with enhanced network lifetime in WSN-IoT through optimized CH and route path selection.

Device-to-device (D2D) communication has emerged as a critical technology in modern wireless networks, enabling efficient and cost-effective data exchange.However, its decentralized nature poses significant security and privacy challenges.This research proposes a novel methodology, Blockchain-based Onion Routing (BbOR) with Deep Convolutional Neural Networks (DCNN), to enhance the security, privacy, and robustness of D2D communication systems.Blockchain ensures data integrity and trustworthiness by maintaining an immutable, tamper-resistant ledger of encrypted tokens and messages.Onion routing adds a layer of privacy by anonymizing the communication path through multiple layers of encryption.The integration of Deep CNN for attack prediction enhances the system's ability to proactively detect and mitigate security threats, by analyzing token behavior and Blockchain transaction patterns.The proposed framework was validated using the Wireless Sensor Network Data Set (WSNDS), where data preprocessing, cryptographic token generation, secure routing, and attack detection mechanisms were systematically implemented.Results demonstrate the efficiency of the BbOR-Deep CNN system in providing robust security and privacy for D2D communication, making it suitable for applications in IoT, healthcare, and finance.

Recently, the Internet of Vehicles (IoV) technology has played a pivotal role in enhancing transportation efficiency and safety.In this context, high-density vehicles generate more sensitive heterogeneous data, increasing privacy concerns in secure IoV data sharing.Federated Learning (FL) and Split Learning (SL) are trending paradigms of collaborative learning that facilitate potential solutions to privacy and heterogeneous data concerns.Thus, developing a privacy-preserving collaborative learning strategy is crucial for improving performance.This paper introduces a novel Privacy-Preserving Federated and Adaptive Split Learning (PPFedSL) strategy, enabling secure and efficient data sharing for IoV in smart city environments.This model integrates adaptive SL and FL by establishing a dual-tier privacy-preserved data-sharing strategy.Exploiting lightweight and hybrid cryptographic algorithms across different tiers ensures security and efficiency in data sharing across edge and cloud infrastructures without imposing significant computational overhead.This approach has designed two phases: privacy-preserving SL-enabled vehicle-edge collaboration and privacy-preserving FL-enabled edge-cloud collaboration.The proposed strategy effectively addresses latency constraints by delegating emergency decision-making at the edge level, where data is processed close to the IoV devices.Edges can inspect and respond to pressing data streams in realtime and guarantee timely interventions for latency-sensitive traffic management and collision avoidance scenarios.Finally, the experimental results demonstrate the efficiency of this proposed PPFedSL.The PPFedSL enhances the robustness efficiency by 93.2% and learning accuracy by 98.4% with high privacy preservation and heterogeneous data handling.

Vehicular Ad Hoc Networks (VANETs) have become a vital technology to improve road safety and traffic control.In the context of VANETs, precise time synchronization among vehicles is paramount to enable effective real-time data exchange.This research introduces a novel Dynamic Message Time Synchronization (DMTS) protocol designed to optimize timing message coordination in large-scale VANETs.DMTS employs an intelligent selection mechanism to identify the most suitable time synchronization nodes, significantly reducing the number of transmitted timing messages.To ensure robust synchronization, the protocol periodically readjusts the clock offset of interconnected vehicles, enhancing synchronization accuracy.Leveraging a bidirectional timing message synchronization approach, the Maximum Likelihood (ML) estimation for clock offset design is derived, assuming a Gaussian noise model.Through comprehensive simulation analyses, the effectiveness of the DMTS protocol is validated, considering key performance metrics such as delay, packet delivery ratio, and throughput.The proposed DMTS protocol achieves 19.58% and 28.12% less delay in comparison with ABTS and STETS protocols.In terms of packet delivery ratio, it achieves 12.55% and 33.70% improvement when compared with ABTS and STETS protocols.Finally, there is an increase in throughput performance of proposed DMTS by 26.71% and 48.10% with its counterpart protocols.These results demonstrate the superiority of DMTS over existing protocols, making it a promising solution for real-time data exchange in VANETs, with implications for improved road safety and traffic efficiency.

The integration of the Internet of Vehicles (IoV) into the Intelligent Transportation System (ITS) has significantly improved its operations, leading to a reduction in road traffic accidents, efficient traffic control, and a decrease in carbon emissions for a more sustainable environment aligned with the Sustainable Development Goals (SDGs).However, the adoption of IoV networks introduces privacy and security challenges.Although cryptographic techniques such as public-key infrastructure (PKI) proposed by standardization bodies like IEEE and ETSI provide protection against outsider attackers, they fail to address the threat posed by insider attackers.To overcome this limitation, researchers have proposed data-centric machine learning-based misbehavior detection frameworks that focus on identifying and mitigating insider attacks.However, existing approaches primarily rely on the Basic Safety Message (BSM) data received from a single vehicle, which allows attackers to manipulate the BSM data without being detected.In this paper, we present a novel data-centric misbehavior detection framework specifically designed to detect false data injection attacks in IoV networks.Our approach leverages neighboring public transportation vehicles (NPTVs) to enhance the detection capabilities.By incorporating the BSM data from NPTVs, we demonstrate the effectiveness of our proposed framework in different scenarios using deep learning, decision tree, and random forest algorithms.Through extensive evaluation, we achieved precision, recall, F1-Score, and accuracy rates of up to 99%, showcasing the superior performance of our approach.

In this paper two widely used algorithms: Maximum Flow and Open Shortest Path First (OSPF) are applied in a Wireless Sensor Network (WSN) to send information from a member node to a cluster Head (CH).Two parameters of wireless link: Shannon information rate and energy consumption per node for the above two algorithms are compared under three types of fading channels: Nakagami-m, Rician, and Rayleigh.To observe the impact of small-scale fading on the performance of the network, the steady state phase of WSN is considered instead of cluster formation.The final outcome of the paper is that the Nakagami-m channel is found as the best case and Rayleigh as the worst case, where Rician provides an intermediate result in the context of throughput.Again, the maximum flow algorithm provides better throughput compared to OSPF keeping the channel condition fixed.In the context of energy per node OSPF is found better than the case of maximum flow algorithm.The entire comparisons are shown both graphically and in tabular form.The combination of the maximum flow algorithm and Nakagami-m fading is found as the best to achieve maximum throughput whereas energy consumption per node totally depends on the topology of the network but the same combination shows the best result for most of the cases.

The widespread adoption of Internet of Things (IoT) technology and the rise of fintech applications have raised concerns regarding the secure and efficient routing of data in IoT-based ad-hoc networks (IoT-AN).Challenges in this context include vulnerability to security breaches, potential malicious node presence, routing instability, and energy inefficiency.This article proposes the Relentless Firefly Optimization-based Routing Protocol (RFORP) to overcome these issues.Inspired by fireflies' natural behaviour, RFORP incorporates relentless firefly optimization techniques to enhance packet delivery, malicious node detection, routing stability, and overall network resilience.Simulation results demonstrate RFORP's superiority over existing protocols, achieving higher packet delivery ratios, accurate malicious node detection, improved routing stability, and significant energy efficiency.The proposed RFORP offers a promising solution for securing fintech data in IoT-AN, providing enhanced performance, reliability, and security while effectively addressing the identified challenges.This research contributes to advancing secure routing protocols in fintech applications and guides network security and protocol selection in IoT environments.

The Internet of Things (IoT) has transformed how humans engage with technology, allowing pervasive connection and data sharing.In the Wireless Sensor Networks (WSNs) framework, IoT-based applications have been created for several areas, including agriculture, where greenhouse automation has been deployed for enhanced agricultural yields.However, WSNs face significant challenges, such as limited resources, unpredictable communication, and energy consumption.These issues become more pronounced when applied to greenhouse agriculture due to interference, congestion, and quality of service (QoS) requirements.Therefore, efficient routing protocols are crucial to address these challenges.The proposed study addresses the routing issues in IoT-based WSNs (IWSN) for greenhouse agriculture.Specifically, the Enhanced Intelligent Water Drop Algorithm Optimized Routing (EIWDR) is proposed as a novel routing protocol to enhance the QoS in IoTbased WSNs.The EIWDR protocol utilizes the intelligent water drop algorithm to optimize the routing path selection.The algorithm prioritizes energy-efficient routing, selects the most reliable path with minimum delay and data loss, and balances network load to prevent congestion.The proposed protocol also uses a modified weight function to improve the routing performance when applied in IWSN.To test the efficacy of the EIWDR, simulation tests were conducted in the NS-3 simulator.The EIWDR protocol fares better regarding network lifetime, packet delivery ratio, energy consumption, and packet delay than other routing protocols.Improved greenhouse agricultural quality of service using IWSN is possible with the help of the proposed EIWDR protocol.With the help of intelligent routing algorithms, network resources are used effectively, data is sent reliably, and overall performance is enhanced.