The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.

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In this paper a software implementation of a reconfigurable Amplitude Modulated (AM) receiver for weak AM signals detection with reduced processing latency is presented. The Stochastic Resonance (SR) algorithm, which is a technique for weak signal detection was developed for software defined, AM receiver. The performance of the SR based AM receiver was evaluated in terms of its output Signal to Noise (SNR) Ratio, and processing latency. From the results from our simulations, this approach provides better performance and lesser processing latency requirement than conventional signal processing methods for detecting AM signals.

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