Fair Joint Offloading and Consensus Optimization in Blockchain-Enabled Mobile Edge Computing

PoGTR: A Game-Theoretic Blockchain Consensus for SLA Management in Edge-IoT Systems

Ensuring SLA-compliant service delivery in decentralized Edge–IoT environments remains challenging due to the lack of integration between SLA enforcement and consensus mechanisms. Existing approaches rely on external monitoring or rule-based enforcement, limiting effectiveness under dynamic and adversarial conditions. We present Proof of Game-Token Redistribution (PoGTR), a blockchain consensus protocol that embeds SLA-related outcomes into consensus decisions through incentive-driven token redistribution. Instead of explicit misbehavior detection, PoGTR regulates validator behavior based on observed service performance, enabling robustness under noisy and variable conditions. We evaluate PoGTR using a discrete-event blockchain simulator with realistic IoT workloads and network variability, comparing it with Avalanche, BECP, and Paxos with priority preemption. Using SLA-aware metrics, results show that PoGTR achieves the highest overall throughput (up to $3. 7 \mathrm{K}$ tx/s under multilayer hashing) while maintaining competitive SLA throughput under strict latency constraints, and the highest SLA throughput under relaxed objectives. Under adversarial scenarios, PoGTR maintains stable performance and consistent SLA satisfaction. Moreover, the sensitivity analysis of the approval-cluster size parameter $(k)$ further shows stable SLA-aware throughput across typical configurations, with limited variability only under extreme settings. These results demonstrate that integrating SLA evaluation into consensus enables effective decentralized SLA enforcement in Edge-IoT systems. To support reproducibility, the implementation and experimental framework are available by request at: https://github.com/TasneemMuneera/PyChainSiM.git.

Sustainable and trust-aware multi-tier fog-cloud infrastructure for energy-optimal IIoT operations: adaptive resource management and blockchain-assured security.

Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog-cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog-cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5-2 tasks/s, random waypoint mobility 1-5 m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.

An online task offloading method based on improved starfish optimization and blockchain

Secure and Efficient Task‐Offloading for Decentralized Vehicular Communication Networks

Recently, the combination of Internet of Vehicles (IoV) and blockchain has emerged as a promising solution for enhancing the security and efficiency in vehicular communication networks. However, the deployment of blockchain technique in IoV inevitably derives additional computation and communication overheads, which significantly hinders the development of IoV. In addition, efficient task offloading in IoV is essential to support computation‐intensive and delay‐sensitive vehicular services under dynamic network conditions. To address the above challenge, this paper proposes a deep reinforcement learning‐based joint task‐offloading framework for blockchain‐empowered IoV communication networks. Specifically, it formulates the blockchain‐based task‐offloading problem in IoV as a continuous control Markov decision process, aiming at improving long‐term system performances by jointly optimizing latency, computational cost, throughput and security. Then, a twin delayed deep deterministic policy gradient‐based algorithm is customized to learn the optimal offloading policy efficiently in high‐dimensional continuous action space. Furthermore, a trust‐aware mechanism is incorporated into the state representation and reward design to mitigate the impact of malicious vehicles. Finally, simulation results demonstrate that the proposed method outperforms conventional baseline methods with respect to communication latency, computational cost, throughput and security.

Performance Optimization for Multi-Cell Aerial Networks-Assisted MEC via Dual-Layer Blockchain-Enabled Federated Election

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.

Duet: Co-Optimizing P2P Message Propagation and Rotating-Leader Consensus

In blockchain systems, peer-to-peer (P2P) overlay networks play a crucial role in providing reliable, scalable and efficient message-delivery services to upper layers. However, the consensus layer and the underlying P2P network remain mutually opaque in existing blockchains, waiving the opportunity for further improvement. In contrast to other P2P applications, blockchain can naturally be abstracted as a state machine. We therefore leverage this abstraction to record network topologies and latencies in a trusted and coordinated manner. With this support, we propose three improvements to rotating-leader consensus protocols and their underlying P2P networks: (1) accelerating leader rotation; (2) introducing a reliable-broadcast paradigm that employs tree-based dissemination in the normal case and falls back to gossip only when necessary; and (3) constructing latency-aware dissemination trees. We integrate the above ideas into Tendermint and libp2p, and conduct empirical evaluation on Amazon EC2 platform using up to 300 nodes distributed across 10 regions. The results demonstrate that, compared with gossip-based dissemination over the same topology, our prototype improves peak throughput by up to $7.26\times$.