  As sensor networks for health monitoring become more prevalent, so will the
need to control their usage and consumption of energy. This paper presents a
method which leverages the algorithm's performance and energy consumption. By
utilising Reinforcement Learning (RL) techniques, we provide an adaptive
framework, which continuously performs weak training in an energy-aware system.
We motivate this using a realistic example of residential localisation based on
Received Signal Strength (RSS). The method is cheap in terms of work-hours,
calibration and energy usage. It achieves this by utilising other sensors
available in the environment. These other sensors provide weak labels, which
are then used to employ the State-Action-Reward-State-Action (SARSA) algorithm
and train the model over time. Our approach is evaluated on a simulated
localisation environment and validated on a widely available pervasive health
dataset which facilitates realistic residential localisation using RSS. We show
that our method is cheaper to implement and requires less effort, whilst at the
same time providing a performance enhancement and energy savings over time.
