Maximizing precision for energy-efficient data aggregation in wireless sensor networks with lossy links

摘要: Two main factors that impact the performance of data aggregation in wireless sensor networks (WSNs) are quality and energy efficiency. This paper exploits tradeoff between consumption to maximize precision under heterogeneous per-node constraints. Unlike previous work, we explicitly account for link loss optimization framework. To tackle unreliability, need appropriately allocate limited across incoming outgoing links each individual node. We present a centralized algorithm based on Immune-Genetic heuristic find near-optimal allocation strategy such aggregated received by sink is maximized. The algorithmic complexity implementation issues also discussed. Furthermore, develop localized alternative Gibbs sampler, which more scalable can adapt large-scale distributed WSNs. Finally, conduct numerical simulations demonstrate convergence as well proposed algorithms.