Network

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

Current federated identity management systems lack contextual awareness of workflows across independent systems, creating security gaps and workflow integrity challenges. This article details the design and implementation of Alohomora, a distributed workflow-aware authentication system that maintains cross-system workflow context through path-bound tokens. Alohomora complements existing identity providers such as OAuth and SAML by adding workflow orchestration capabilities while leveraging standard authentication protocols for initial user verification. The system introduces workflow graphs as a formal model for representing dependencies between functions across heterogeneous systems and employs a distributed caching architecture with collaboration groups for scalable session management. In a typical deployment scenario, an employee onboarding workflow across human resources services, account provisioning, and benefits systems forms a trust group where Alohomora enforces ordered step execution, validates prerequisite completion at each transition, and generates cryptographic completion assertions upon workflow finalization. Extensive performance evaluation under concurrent user requests demonstrates polynomial performance characteristics with superior scalability compared to centralized OAuth introspection. The results show that Alohomora maintains high throughput under heavy load while providing strong, secure access control through workflow path binding and distributed trust orchestration. The prototype implementation is available as open source.

Heterogeneous wireless sensor networks (HWSNs), comprising super nodes and normal sensors, offer a promising solution for monitoring diverse environments. However, their deployment is constrained by the limited battery life of sensors. To address this issue, clustering and routing techniques have been employed to conserve energy. Nevertheless, existing approaches often struggle with suboptimal energy distribution and weak network coverage. Additionally, they mostly failed to exploit other energy saving techniques such as sleep scheduling. This paper proposes a novel genetic algorithm (GA)-based approach to optimize sleep scheduling, routing, and clustering in HWSNs. The method comprises two phases, namely join sleep scheduling and tree construction, and clustering of normal nodes. Inspired by the concept of unequal clustering, the HWSN is split into some rings in the first phase, and the number of awake super nodes in each ring keeps the same. This approach addresses the challenges of balancing energy consumption and network lifetime. Furthermore, including network coverage and energy-related criteria in the proposed GA yields long-lasting network operation. Through rigorous simulations, we demonstrate that, on average, our algorithm reduces energy consumption and improves network coverage by 23% and 21.9%, respectively, and extends network lifetime by 501 rounds, compared to the state-of-the-art methods.