Aug 2026· International Journal of Communication Systems· Vol 39· 0 citations· 49 references
TL;DR
This work introduced an integrated cross‐layer framework featuring three innovative algorithms: mobility‐aware black hole clustering (M‐BHC), energy‐aware piranha optimization algorithm (EPOA), and cross‐layer multi‐attribute blockchain routing with congestion control (CL‐MABRC).
Abstract
Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate real‐time communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and ever‐changing network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestion‐free communication simultaneously. To address these challenges, this work introduced an integrated cross‐layer framework featuring three innovative algorithms: mobility‐aware black hole clustering (M‐BHC), energy‐aware piranha optimization algorithm (EPOA), and cross‐layer multi‐attribute blockchain routing with congestion control (CL‐MABRC). The M‐BHC algorithm enhances the stability of clusters and counters black‐hole attacks by forming clusters dynamically based on real‐time vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bio‐inspired resource allocation strategy modeled on piranha predation behavior. CL‐MABRC addresses network congestion and security using blockchain‐based verification and cross‐layer routing decisions informed by multi‐attribute metrics. Extensive simulations were conducted with a setting of 100 veh/km2. The proposed framework showed significant performance improvements over benchmark protocols, such as optimal security‐aware cluster‐based hybrid geographical and opportunistic routing (OSC‐GOR), enhanced location‐aided ant colony routing (ELAACR), trust‐based multi‐objective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, end‐to‐end delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the M‐BHC, EPOA, and CL‐MABRC algorithms create a robust, energy‐efficient, and secure communication framework for VANETs, enhancing scalability, reliability, and real‐time performance in transportation systems.
The need for efficient routing arises in Internet of Things (IoT)–based networks that comprise low‐power and lossy nodes. Routing Protocol for Low‐Power and Lossy Networks (RPL) has multiple limitations, including rank manipulation attacks, inefficient energy utilization, poor link quality, and vulnerability to malicious nodes. Moreover, conventional routing metrics typically evaluate only a limited set of parameters and are unable to capture the multiple risks and dynamic behaviors encountered in practical IoT environments. To overcome these limitations, this research proposes a Blockchain‐enabled Risk‐Aware RPL (Bc‐RARPL) framework that enhances routing security, adaptability, and performance. The proposed framework establishes a unified routing architecture by integrating real‐time rank verification, Multi‐Attribute Utility Theory (MAUT)–based risk assessment, trust‐aware consensus, and blockchain‐based route validation into a continuous routing decision process. Each node evaluates candidate parent nodes using a composite risk utility score derived from multiple factors, including residual energy, link quality, latency, and node failure probability, thereby enabling secure and intelligent parent selection. Routing path optimization is performed using a risk‐aware decision‐making strategy supported by the Starfish Optimization Algorithm, which jointly minimizes energy consumption, improves route stability, and mitigates dynamic network risks. In addition, a lightweight Zigbee blockchain securely stores routing histories, node rankings, and risk values, whereas a Trust‐Weighted Byzantine Fault Tolerance (TW‐BFT) consensus protocol restricts validation to trusted nodes, thereby reducing the impact of malicious attacks. Experimental results demonstrate that the proposed framework achieves a packet delivery ratio of up to 96%, reduces end‐to‐end delay and energy consumption, and improves routing reliability while providing enhanced resilience against rank manipulation and insider attacks.
Abhay Deep Seth, Ankur Ratmele, Vikas Kumar Jain· International Journal of Com...· 0 citations
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations
Amid the explosive growth of latency-aware and computation-sensitive services, mobile edge computing (MEC) assisted by aerial networks, such as high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), has emerged as an effective solution for providing computational capabilities to regions with sparse terrestrial infrastructure. Nevertheless, aerial networks are highly sensitive to energy cost and inherently constrained in hosting dense computing resources, while the exposed wireless environment renders them particularly vulnerable to attacks from malicious nodes. Consequently, it is imperative to develop effective task scheduling and computing resources management mechanisms that satisfy users quality-of-service (QoS) requirements while minimizing system cost and ensuring network reliability. In this paper, we develop a multi-cell MEC network composed of multiple UAVs and multiple HAPs, and further propose a dual-layer blockchain-enabled, federated election (FE)-based (DBFE) group relative policy optimization (GRPO) algorithm to jointly reduce the task offloading latency and system energy expenditure. In particular, blockchain techniques enhance system resilience against non-Byzantine failures, whereas the FE mechanism suppresses the influence of Byzantine behaviors. Simulation results demonstrate that, compared with existing approaches, the proposed method reduces the overall system cost by 19% and 31% under scenarios without malicious nodes and with malicious nodes, respectively.
Haoyu Wan, Meng Li, Qi Li et al.· IEEE Transactions on Cogniti...· 0 citations
To overcome the inherent compromises between proactive and reactive data transmission in Vehicular Ad-hoc Networks (VANETs), this research introduces a novel framework tailored for highly unstable vehicular topologies. The developed system, termed the Dynamic Hybrid Routing Protocol (DHRP), merges the Optimised Link State Routing (OLSR) and Ad-hoc On-Demand Distance Vector (AODV) algorithms. A core feature of this architecture is its cross-layer power management module, which dynamically recalibrates transmission strength and routing paths by analysing real-time vehicle clustering and speed metrics. Comprehensive evaluations conducted via NS-3 and SUMO indicate that the proposed DHRP significantly surpasses both contemporary benchmarks and standard baselines. Notably, the architecture achieves a Packet Delivery Ratio (PDR) exceeding 90%, limits communication latency to well below the critical 40 ms safety boundary, and slashes energy expenditure by up to 90%. By effectively solving the traditional routing dichotomy, DHRP offers a highly scalable and sustainable communication backbone vital for the reliable operation of future Intelligent Transportation Systems (ITS).
PBFT provides deterministic finality under Byzantine faults but assumes stable committee membership, an assumption that breaks in vehicular networks where mobility‐driven churn can render committee members unreachable mid‐protocol. Existing IoV adaptations filter participants by trust or reputation, or modify the protocol's internal structure, yet none assess whether a selected committee will survive the full consensus execution window. We propose MAPS‐PBFT, a mobility‐aware orchestration layer around unmodified PBFT. The framework explicitly partitions the fault budget into a Byzantine slice be$$ {b}_e $$ and a mobility‐loss slice he$$ {h}_e $$ , treating mobility‐induced unreachability as crash‐equivalent failure. Committee selection is formulated as a survivability‐constrained problem over a logical multi‐hop committee graph: candidates are scored by beacon freshness, multi‐hop communication delay, predicted displacement, and inter‐vehicle proximity, then assembled via greedy incremental construction that rewards intra‐committee reachability. A model‐based launch score, derived from a joint formulation of mobility and Byzantine‐membership risk, gates each consensus instance through a user‐configurable threshold. To make the scope of our claims explicit, we distinguish components that are theoretically developed (the joint Monte Carlo launch score, the mobility‐loss budget, and the view‐change–inclusive execution horizon), fully implemented (mobility‐aware committee selection and epoch orchestration around unmodified PBFT in a full IEEE 802.11p co‐simulation), and approximated in the present experiments (a deterministic proxy for the candidate score, with the launch gate evaluated post hoc and view changes disabled). A 756‐run co‐simulation using OMNeT++, INET, Veins, and SUMO across three topologies and four vehicle densities shows that MAPS outperforms six baselines, improving average success rate by +30.7% over random, +8.8% over nearest, and +10.2% over connectivity‐only selection, with advantages that widen as committee size grows. Post hoc threshold analysis confirms that the launch score discriminates between viable and non‐viable committees, raising conditional success from 76.0% to 92.8% at a moderate gate threshold.
Hilda Jemutai Bitok, Mingzhong Wang, Dennis Desmond et al.· Concurrency and Computation· 0 citations
The effective management of physical processes in cyber physical systems such as in‐vehicle platforms and industrial automation necessitates a deterministic real‐time communication. Although traditional time‐sensitive networks provide low‐delay data transmissions, there is a need for the real‐time variants of IEEE 802.3 Ethernet to satisfy the low‐cost and high‐bandwidth requirements. IEEE 802.1 Time‐Sensitive Networking (TSN) task group is the leading organization that aims to standardize Ethernet‐based deterministic communication technologies. In this paper, route planning for Audio–Video Bridging (AVB) streams is modeled as a multi‐criteria decision making problem for TSN with mixed criticality support and a K‐shortest paths based heuristic approach is proposed to solve this problem. Our heuristic is integrated into metaheuristic approaches relying on Greedy Randomized Adaptive Search Procedure to construct initial solutions, as generating good‐quality initial solutions can improve the performance of a metaheuristic by increasing the convergence speed and accuracy of the solution. Experimental results relying on realistic automotive and Industry 4.0 use cases show that our approach significantly improves the schedulability of AVB streams in presence of time‐triggered traffic and features a better computational efficiency compared with the other approaches in the literature.
S. Cevher, Ömer Kağan Demir· International Journal of Com...· 0 citations