Recent demand for Internet of things (IoT) based sensing devices has increased in many significant areas, such as greenhouse monitoring, remote patient monitoring, military applications, wild-life tracking and habitat monitoring, precision agriculture, etc. Moreover, some critical issues are present in the perpetual sensor network like node energy consumption rate, coverage/connectivity, quality of service, network throughput and charging cycle capacity of sensor node batteries. Thereby, various network issues can arise, especially regarding coverage holes, proper connectivity, and network life-time. In this paper, we have studied these problems well and have shown the necessary constraints for the connectivity of the IoT sensor network. Moreover, we have also presented various helpful IoT applications and energy harvested IoT sensor networks. Any network can perform well for a long time because of energy management which is very crucial. This paper presents various feature scopes used in wireless sensor networks (WSNs). It also illustrates distinct approaches like relay node placement, efficient communication, machine-learning systems i.e. reinforcement learning, and heuristic techniques. It also discusses real-world deployments to highlight practical connectivity challenges and failures.
Decentralized applications increasingly require eligibility checks based on Web2-held attributes, yet provider-side signing and centralized attestations limit deployability and can expose sensitive user data. This paper presents Scribe, a distributed eligibility verification service that bridges TLS-derived Web2 evidence to decentralized applications without any modification to Web2 servers. In Scribe, a client engages in a standard TLS session with a Web2 service and, using a DECO/TLSNotary-style provenance workflow, obtains an attestation node signature on a commitment to an extracted value. The client then generates a transparent zero-knowledge proof that verifies the attestation and proves compliance with an application policy without revealing the underlying value. Each proof outputs an application-scoped spend tag (nullifier) to provide replay resistance. An aggregator verifies proofs off-chain, maintains a Merkle-indexed spent-tag set, and publishes batched state roots to an on-chain anchor. Root publications are authorized using a post-quantum signature scheme, providing post-quantum integrity for the on-chain state without requiring on-chain proof verification. We specify the protocol and establish provenance binding, policy soundness, privacy, and replay resistance under stated trust assumptions.
Software-Defined Networking (SDN) provides a programmable and centrally managed network architecture, but its centralized control plane introduces significant vulnerability to Distributed Denial of Service (DDoS) attacks. These attacks can overwhelm controller resources and disrupt network availability, making efficient and reliable detection mechanisms essential. This study proposes an optimization-driven ensemble learning framework for DDoS detection in SDN environments by integrating a Gradient Boosting Classifier (GBC) with Particle Swarm Optimization (PSO). PSO is employed to automatically tune key hyperparameters of the ensemble model, improving classification stability and enhancing detection performance while maintaining lightweight inference suitable for real-time deployment in SDN monitoring systems. The proposed framework is evaluated using an SDN-specific dataset generated in a realistic Mininet-based environment with OpenFlow-enabled switches and a Ryu controller. Experimental results under stratified 5-fold cross-validation show near-perfect detection performance, achieving accuracy and F1-score values 0.9999, with consistently high precision and recall. To further assess generalization capability, the model is validated on the CICDDoS2019 benchmark dataset, where it achieves more than 99
Attribute-based encryption (ABE) is a promising primitive to protect data privacy and achieve flexible data sharing. Considering the increasing number of resource-limited devices in the Internet of Things (IoT) era, outsourcing the expensive decryption operations of ABE to a powerful proxy is helpful and necessary for users. However, the innate distrust between the user and the proxy makes security and fairness challenging. That is, the user would worry about whether the proxy executes the outsourced decryption honestly without breaking data confidentiality. Conversely, the proxy may be concerned that if she can get the deserved reward as promised. In this paper, we propose a blockchain-based scheme to provide secure and fair outsourced decryption service of attribute-based ciphertext. Specifically, we propose an efficient attribute-based encryption scheme with verifiable outsourced decryption (ABE-VOD), which enables the user to outsource most of the decryption operations securely and verify the returned result with little cost. We then integrate blockchain into ABE-VOD to manage the outsourced decryption tasks and deal with the reward and punishment issues as a trusted mediator, which could effectively guarantee fairness. Finally, we implement a proof-of-concept prototype on a local Ethereum testnet to evaluate the feasibility and performance of the proposed scheme.
In the era of Industry 5.0, digital transformation of smart grid substations is crucial to address growing demands for efficiency and reliability. However, conventional substations lack real-time monitoring. Fault detection takes a long time, and predictive maintenance is substandard. In this research, an entirely new approach is proposed to transform substation work processes by combining Digital Twin (DT) technologies with an algorithm based on Gradient Boosting Machine (GBM) designs. Within the confines of a digital twin technology, a substation can begin to operate, and hence a virtual representation, which can be termed, an operating digital twin of a substation can be created ready for real time, predictive fault diagnosis, and active asset management. The solution proposed reduces total operational downtime by 30
Node localization is a fundamental challenge in Wireless Sensor Networks (WSNs), where accurate position information is crucial for data interpretation while maintaining low power consumption and minimal hardware costs. This paper presents the Upgraded DV-Hop algorithm based on Polynomial Approximation (UDV-PA), a novel three-dimensional localization method that significantly enhances accuracy while preserving the range-free characteristics essential for resource-constrained WSN deployments. Unlike existing 3D DV-Hop variants that primarily focus on hop-size refinement or optimization techniques in isolation, our approach uniquely integrates polynomial distance modeling with the Multi-Verse Optimizer (MVO) featuring dynamically bounded search spaces and adaptive hyperparameter tuning. The algorithm operates in three phases: (i) hop-count acquisition using flooding with minimum hop selection, (ii) distance estimation through polynomial fitting of hop-distance relationships followed by MVO-based refinement with constrained search bounds, and (iii) coordinate computation using optimally selected anchor nodes (ANs) based on proximity metrics. Key innovations include adaptive polynomial degree selection ( 2^nd to 4^th order) based on network density, dynamic MVO parameter adjustment (WEP: 0.2-1.0, TDR: 0.6-1.0) scaled to network size, and energy-efficient anchor selection limiting multilateration to the four nearest ANs. Extensive simulations in 100 × 100 × 100 m^3 volumes with 100-300 nodes demonstrate average localization errors of 8.23
In this work, we present a model for multipath routing in Mobile Ad Hoc Networks (MANETs) that considers both bounded and unbounded buffer sizes at each Mobile Node (MN). Traditional multipath routing approaches primarily focus on traffic distribution and path optimization but often overlook the impact of queuing dynamics in practical network scenarios. Existing methods typically assume either infinite buffer capacity or use simplistic delay models that fail to capture the queuing effects caused by buffer constraints at intermediate nodes. As a result, they may not accurately estimate end-to-end latency, leading to suboptimal routing decisions. To address this gap, we analyze the delay characteristics of multipath routing using M/M/1/R, M/M/m, and M/M/m/R queuing networks, which allow for a more precise evaluation of network performance under varying buffer sizes and service capacities. Unlike previous studies, which predominantly rely on simplified queuing assumptions, our model explicitly incorporates both finite and infinite buffer constraints at each MN to assess their impact on delay. The analysis is based on Burke’s Theorem for traffic distribution and Little’s Theorem for latency estimation, enabling optimal path selection based on real-time queuing behavior. Simulation results validate the effectiveness of our approach, demonstrating significant improvements in selecting the best path based on realistic queuing effects. The model is also benchmarked against AOMDV and demonstrates significant improvements in delay, throughput, routing overhead and node lifetime under realistic traffic conditions. This research bridges the gap between theoretical queuing models and practical routing strategies, contributing to the development of more efficient routing protocols for MANETs.
The Industrial Internet of Things (IIoT) plays a pivotal role in enhancing cost-effective solutions to reduce resource waste in industrial processes. An IIoT environment processes the real-time data across public channels which poses several kinds of security and privacy concerns. In today’s industrial requirement, different domain authenticated access control is crucial; there is a need to design a mechanism that considers production issues induced by unauthorized access. This work develops a robust and efficient cross-domain three-factor user-authenticated access control method that balances both security and efficiency. After that, a comprehensive security analysis and algorithm verification are performed using the AVISPA tool, which also determines whether an adversary with a stronger attack capacity is capable of breaking the protocol. Then performance analyses are calculated using NS3 to show the efficiency and applicability of the proposed protocol, also the results show that the protocol is better than the other existing protocols. In particular, this scheme demonstrates that the computation cost is 75.3% lesser than the cost of the four compared protocols. Finally, the energy evaluation outcomes regarding network latency and energy usage show that the proposed protocol for industry is feasible.
Smart city operations increasingly depend on timely and secure data management, yet Fog-IoT ecosystems face persistent issues related to latency, energy consumption, and data integrity. This study proposes a Cloud-Fog-IoT architecture integrated with a Directed Acyclic Graph (DAG)-based blockchain to enhance scalability and security. Experimental evaluation was conducted using a 10-node peer-to-peer topology (8:2 transaction mix of reads to writes, 1 MB block size, 500 ms block timeout) across Raspberry Pi 4B devices (4GB RAM, gigabit Ethernet). Compared to a cloud-IoT baseline (centralized AWS m5.large, mean network latency 70 ms) and a fog-only configuration (Intel NUC edge nodes, direct aggregation), the proposed system demonstrated a mean transaction throughput of 560 tx/s (95
With the steady development of geostationary orbit (GEO) satellites in China and the accelerated construction of low-orbit (LEO) satellite internet, the integration application of high and low orbit heterogeneous constellations has become an important direction for future development. Current research mainly focuses on resource allocation within a single constellation, such as GEO constellations or LEO constellations, while there is insufficient attention to the collaborative allocation of heterogeneous resources in mixed high and low orbit and cross-constellation scenarios. Therefore, this paper takes the China-Sat, Asia-Pacific and LEO satellite internet systems as research objects, deeply analyzes the service transmission modes and resource characteristics of different satellite systems such as transparent forwarding, high throughput and LEO constellations. At the same time, from the current engineering construction status, a heterogeneous resource allocation strategy for cross-high and low orbit mixed satellite networks is proposed. This strategy takes dynamic communication service demands as input and collaboratively allocates beam bandwidth, frequency, time slot, power and inter-satellite links and other heterogeneous resources. The research results can provide support for the simulation modeling and business planning of high and low orbit mixed satellite networks.
This study introduces an integrated system for detecting and managing driver inattention using a combination of artificial intelligence (AI), blockchain technology, and edge computing. The proposed system utilizes You Look Only Once version 8 Nano (YOLOv8n) for real-time object detection and a Raspberry Pi 5 for edge-based analysis, ensuring efficient and accurate detection of inattentive behaviors such as drowsiness, distraction, and other unsafe driving patterns. Blockchain integration with Hyperledger Besu, using the Istanbul Byzantine Fault Tolerance version 2.0 (IBFT 2.0) consensus protocol, provides a secure and tamper-resistant ledger for recording and verifying driver inattention events. The system demonstrated robust performance metrics, including a mean precision of 92.99
The absence of central infrastructure and the mobility of nodes in mobile ad hoc networks (MANETs) pose challenges in maintaining reliable, secure, and energy-efficient communication. To address these, we propose a novel Trust and Localization Adaptive Optimized Routing (TLAOR) algorithmic framework, integrating trust-based security, adaptive node positioning, and intelligent route optimization. TLAOR employs a threefold strategy: First, a trust-based node selection mechanism evaluates nodes based on location consistency, energy levels, and historical behavior, ensuring only reliable nodes participate in routing, mitigating malicious activities. Second, the Whale Optimization Algorithm (WOA) dynamically adjusts node positions to maximize Received Signal Strength Indicator (RSSI) and optimize energy distribution, improving coverage and efficiency. Third, an optimized routing mechanism integrates the Ad-Hoc On-Demand Distance Vector (AODV) routing protocol with the Cuckoo Search Algorithm (CSA) to minimize hop count, delay, and energy consumption, enhancing routing performance. To validate TLAOR, simulations were conducted and compared against existing methods based on packet delivery ratio (PDR), throughput, end-to-end delay, energy consumption, and malicious node detection accuracy. Results show TLAOR achieves PDR above 98 percent, reduces average delay to less than 0.05s, and improves malicious node detection accuracy to over 99 percent, outperforming conventional approaches in tested scenarios. Additionally, TLAOR significantly reduces energy consumption, supporting prolonged network sustainability. This cohesive approach demonstrates potential for application in scenarios such as disaster response, military communications, and smart city networks, where secure, efficient, and adaptive connectivity is critical. By integrating trust-driven node selection, adaptive mobility, and energy-efficient routing, TLAOR improves the reliability, security, and performance of MANETs under simulated conditions.
The growing interconnectivity of industrial systems has intensified the need for secure, intelligent, and scalable data transfer mechanisms within Industrial Internet of Things (IIoT) environments. Despite rapid IIoT adoption, industrial data transfer remains vulnerable to high-volume, dynamic cyber anomalies and consensus-level attacks, while existing security mechanisms struggle to jointly deliver low-latency, scalable, and trustworthy communication under large-scale adversarial deployments. This study introduces a Secure Dual-Consensus Blockchain-Enabled Deep Learning Framework (SD-BDL) that unifies blockchain security and adaptive anomaly detection to ensure trustworthy and efficient IIoT communication. The framework employs a hybrid consensus mechanism, integrating Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve enhanced fault tolerance, reduced latency, and protection against collusion and Sybil attacks. To address the dynamic and high-volume nature of IIoT data streams, a CNN–LSTM model is deployed for real-time anomaly detection, with hyperparameters optimized using the Adaptive Aquila Optimization (AAO) algorithm—identified as the most effective technique for achieving rapid convergence, high detection accuracy, and balanced exploration–exploitation. The proposed SD-BDL framework is evaluated on an IIoT dataset, incorporating preprocessing steps to mitigate class imbalance, missing values, and noise interference. Experimental outcomes demonstrate a significant improvement in performance metrics, achieving an R² score of 0.985, throughput enhancement of 25.2
The increasingly advanced forms of cyber-attacks have highlighted the importance of advanced threat hunting as a necessary skillset. The current research examines the effectiveness of using Elasticsearch, Kibana, and Lucene for an intelligence-driven threat hunting to identify attack infrastructure or a Command Control (C2) server. By aggregating all system traffic logs and security artifacts into a single data lake/warehouse, organizations are able to leverage centralized analysis of information from different sources on a corporate scale. Utilizing Kibana’s ability to perform network and log analysis, using Lucene’s rich syntax to make sophisticated queries will empower individuals to make valuable findings from log and network traffic logs that identify behaviours and patterns typical of C2 activities. A novel intelligence-based threat hunting approach is presented here that utilizes Elasticsearch, with domain-specific language additions to refine search queries and investigate for C2 related activity. A detailed analysis of the research based on real-world datasets is conducted to evaluation the threat hunting framework’s abilities in detecting C2 servers and minimize true/false positives in relation to organizational security concerns.
With the aid of efficient network selection and routing, this research suggests a unique vertical handoff/handover (VHO) mechanism for Heterogeneous wireless networks (HWN). The three stages of this paradigm are handover execution, handover decision-making, and handover triggering. Initially, the handover triggering phase, which calculates the vehicle's Travelling Distance (TD) using Enhanced Extended Kalman Filter (E2KF) and RSS of the currently serviced network in the coverage area using Long Short-Term Memory (LSTM) is initiated. Secondly, the handover decision-making system is performed optimally by selecting the appropriate network for handover using the proposed XGBoost-Deep Neural Network Fusion Model (XGDN-FM). Finally, after the handover decision phase, the handover execution is carried out by selecting optimal Vehicle 2 Vehicle (V2V) routing using the proposed Trend factor Smoothing Brown Bear Optimization Algorithm (TS-BBOA). The outcomes showed that the model attains the lowest PLR of 0.18
The explosive rise of Brain-Computer Interfaces (BCIs) is unleashing the era of Brain-Brain Interfaces (BBIs) and the Internet of Brains (IoB), enabling seamless neural exchanges that could transform healthcare, collaboration, and human potential, yet at the peril of unprecedented neural data breaches, signal hijacks, and cognitive hijacking. Motivated by these looming threats amid Neuralink’s 2025 milestone of 27+ human implants, this paper dissects the cybersecurity chasm in BBIs, unveiling a novel four-layered IoB architecture, from volition to ultra-tech channels, and a pioneering multi-tiered defense framework fusing neuro-encryption, AI anomaly detection, and post-quantum cryptography. Our contributions are 1) a layered blueprint fortifying neural pathways, 2) defenses slashing known threats by 90
Smart cities incorporate smart vehicles that employ wireless communications for distinct types of applications; non-safety and safety services are subcategories of automotive applications. Whereas non-safety applications can tolerate some latency, safety applications demand very low latency. However, existing research has not adequately integrated emerging technologies into VANETs, despite the fact that the future of VANETs is closely tied to them. This study introduces a hybrid architecture that integrates 5G connectivity, edge computing, and lightweight clustering to enhance Emergency Message (EM) dissemination in VANETs. The proposed method offloads cluster formation to a Multi-access Edge Computing (MEC) server, enabling faster, globally-informed decisions and reducing the computational burden on individual vehicles. Vehicles operate in a dual-connectivity model: cluster heads use 5G Ultra-Reliable Low Latency Communication (URLLC) for communication with the edge and IEEE 802.11p for intra-cluster messaging, while standalone vehicles rely on 5G Enhanced Mobile Broadband (eMBB). Emergency messages are disseminated through pre-established paths, with MAC-layer prioritization to reduce transmission delays. A performance comparison between the proposed method and existing approaches revealed a reduction in End-to-End (E2E) delay by 4.33
With the advantages of zero emissions, low noise and efficient energy utilization, electric vehicles (EVs) have become an important part of the modern transportation system. Nevertheless, the widespread adoption of EVs remains constrained by limited battery range and insufficient charging infrastructure, both of which can be effectively mitigated by battery swapping technology. However, the open communication channels among EVs, roadside units and battery swapping stations pose risks to data security and user privacy. To address these issues, we propose a lightweight authentication protocol based on Chebyshev chaotic maps and blockchain. The scheme ensures user privacy while leveraging blockchain technology to guarantee the security and reliability of transaction processing and data storage during battery swapping. Moreover, the Chebyshev polynomial is introduced to reduce the computational cost for EVs during authentication with roadside units. Formal security analysis using the real-or-random (ROR) oracle model and Scyther tool confirms that the proposed scheme is secure. Furthermore, informal security analysis indicates that our scheme can effectively withstand known attacks. Finally, the performance evaluation results show that the proposed scheme is superior in terms of communication and computational overheads compared to existing related schemes.
As the core charging equipment of electric vehicles, the reliable operation of charging piles is directly related to user experience and industrial promotion. Accurate prediction of charging pile failures is crucial to ensure charging safety and build a city-level safety protection system. In this paper, a fault prediction model based on hierarchical modeling of temporal features and hierarchical attention mechanism is proposed to construct a multi-level fault prediction system. Firstly, a time series dataset with hierarchical constraints is generated according to the topological relationship of charging piles, and then a differentiated time series model is designed to realize the basic prediction according to the sparsity characteristics of different levels of time series, and then the cross-level features are fused through the hierarchical attention mechanism to constrain the basic prediction results to meet the hierarchical consistency. Experimental results demonstrate that the proposed model achieves the best overall performance in multi-level prediction, with significantly lower prediction error for outliers compared to the baseline models. At the global level, compared with the Transformer, Bi-LSTM, PROFHiT, and HAILS models, the proposed model reduces the Mean Squared Error (MSE) by 21.53
Modern automobiles rely on CAN buses to connect Electronic Control Units (ECUs), but these connections introduce significant security vulnerabilities due to the lack of inherent security mechanisms. Intrusion Detection Systems (IDS) have become essential tools for securing CAN buses, but developing an effective IDS poses major challenges, such as achieving high detection accuracy across various attack types, ensuring real-time performance, and maintaining efficiency in resource-constrained automotive environments. To address these challenges, we propose a novel IDS based on eXtreme Gradient Boosting (XGBoost), specifically optimized for analyzing CAN bus data. Our approach incorporates tailored feature engineering techniques, including message timing analysis, arbitration priority, and payload evaluation, to effectively detect anomalies in CAN messages. The system is evaluated extensively on multiple datasets encompassing attack types such as Denial of Service (DoS), Fuzzy, Gear, and RPM manipulation. Experimental results demonstrate that our IDS achieves outstanding detection performance, with accuracy reaching up to 99
