Cyber-physical IoT nodes located in environments which are resource-constrained and physically hard to access, like the Arctic tundra, must achieve long operational lifetimes from a single battery and report data over data networks. The nodes sleep most of the time, and only wake up to perform mission tasks, including reporting data. However, networks can become unavailable, or have low bandwidth and require many re-transmissions for multiple reasons, including a sparse network infrastructure and adverse weather. The state of the network can be quantified by the Received Signal Strength (RSS). If nodes wake up to report data when the signal strength is low they waste energy, because the reporting of data will require more energy or take more accumulated time. RSS decreases with increasing temperature and precipitation. Therefore, nodes should wake up when the temperature and precipitation are low. We explore four algorithms for picking a single time to wake up per 24-hr day over one year. For each wake-up-time, we compute the change in RSS as a function of the change in temperature and precipitation. We use historic weather forecasts and measurements from MET Norway. The data covers 37 locations in Northern Norway over one year. The weather-forecast-based algorithm is able to frequently select a timeslot near the highest expected RSS. It also avoids the large decrease in RSS caused by precipitation more often than the other algorithms presented.