In ubiquitous networks, sensing and compute resources are distributed across numerous heterogeneous nodes. Consequently, fixed Intrusion Detection Systems (IDS) deployments either lead to resource wastage or leave security gaps. LAVA-Control addresses this challenge by treating intelligence density, which is the number and identity of nodes executing detection tasks-as a first-class control variable. Operating atop a trained machine learning detector, it employs policy-driven bounds, particle swarm optimization (PSO)-based subset selection, and cooldown timers to adaptively select node subsets, while maintaining constraints on latency, energy consumption, and residual risk. To ensure system robustness, LAVA-Control introduces explicit stability and oscillation metrics that quantify the smoothness of system responses under varying traffic and attack scenarios. Empirical evaluations on vehicular and Distributed Denial-of-Service (DDoS) datasets demonstrate that LAVA-Control achieves near-baseline detection accuracy at a reduced operational cost compared to heuristic baselines, and exhibits substantially greater stability in its adaptations than reinforcement-learning-based control mechanisms.
Kalibbala Jonathan Mukisa, Jae Min Lee, Dong-Seong Kim· International Conference on...· 0 citations
AI-driven Internet of Medical Things (IoMT) services increasingly rely on federated learning (FL) for privacy-preserving remote patient monitoring; however, existing frameworks often neglect the longitudinal battery sustainability required for persistent clinical care. This paper presents BattFL, an energy-sustainable federated AI framework that reframes distributed healthcare intelligence as a battery-budgeted service. By integrating explicit per-client battery evolution modeling with computation-communication-security energy decomposition, BattFL supports role-heterogeneous participation (attack-only, health-only, and hybrid) via masked multi-task learning. Furthermore, we incorporate risk-driven adaptive sensing to regulate workload intensity based on predicted clinical risk. Experimental results reveal a pronounced sustainability asymmetry: high-workload attack clients experience up to 41.2% battery depletion within five FL rounds, while health-only clients remain near initial capacity. Across extended horizons $(R=5-60)$, we identify diminishing energy-accuracy returns and participation decay, motivating the need for energy-aware stopping and orchestration mechanisms. By explicitly coupling battery dynamics, security overhead, and federated optimization, BattFL provides a systems-level foundation for secure, resilient, and long-lived AI-driven healthcare services in next-generation ubiquitous IoMT networks.
C. A. Nnadiekwe, S. Ajakwe, J. Isong et al.· International Conference on...· 0 citations