A lightweight dynamic optimization methodology and application metrics estimation model for wireless sensor networks

Technological advancements in embedded systems due to Moore's law have led to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g., defense, health care, surveillance systems) with different application requirements (e.g., lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g., processor voltage and frequency) to specialize the operating state. Since a sensor node's performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is an extremely challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average. We also propose an application metrics estimation model, which is leveraged by our dynamic optimization methodology, to estimate high-level application metrics (e.g., lifetime, throughput) from sensor node tunable parameters and hardware specific internals.