# Autonomous Wireless Sensor Networks in
## 1 Introduciton
### Impact on Integrated Pest Management  
During the last decade, there was a significant shift in pest control towards integrated pest management (IPM) approaches and practices. The implementation of many mechanistic pest population dynamics models, as well as disease model which directly build on the previous, enables us to apply intelligent, eco-friendly control measures before the pest achieves a significant density. Wireless networks deployed to sense the micrometeorological variables of the environment of the pest coupled with earth observation products such as MODIS [5] and Sentinel [6] form the basis of the input fields driving these population and disease models. Very high-resolution satellite images have been used to a limited extent as a means to determine habitat of insects in general [7] and disease vectors more specifically. [PERSON] et al. [8], for example, used EO to determine the habitat of the vector of liver fluke, F. hepatica in Flanders, with a spatial resolution of 0.5 m. [PERSON] et al. [9] assessed at multiple scales the required grain for pest habitat detection. [PERSON] et al. [10] demonstrate the application of a real-time system for the detection of the Downy Mildew pest. [PERSON] et al. [11] developed a WSN alarm system based on the noise level generated by the pest; similarly, [PERSON] [12] and [PERSON] et al. [13] look at acoustic levels for larvae, and pests inside infected date palm trees, respectively. An overview of is given in [PERSON] et al. [14])  
There are many IPM applications available online in the form of internet-based information delivery systems [15]. Although most applications are in the form of static data repositories, there are also dynamical IPM tools that use weather and climate data as input to GDD (Growing degree-day) models [16; 17], sampling cost calculator [18], as well as more complex phenology and pest population dynamics models [19]. Some authors suggest that SDSS systems could quickly become an absolute requirement for local, regional as well as international implementations of IPM surveillance and management programs [15; 20]. Ambient temperature has the most significant impact on mortality and development rates for the majority of pest species; however, other abiotic factors such as precipitation, relative humidity, soil moisture, wind speed and level of urbanization, as well as biotic parameters such as density-dependent mortality can have an influence on the population dynamics [20; 21; 22]. The potential for very high-resolution EO features to be used for measuring and monitoring habitat quality and biodiversity is confirmed, yet important scaling issues exist and must be taken into consideration. The computational costs involved in the choice of different computational scales for textural high-resolution EO features are substantial as computational time increases exponentially with increasing window size. This is especially relevant for insects where very high resolution is required. WSNs can provide in situ, high resolution, NRT monitoring of the processes essential to the pest life cycle in the zones outlined by EO analysis. The data collection and network design should be driven by the following requirements: (a) Measurement fidelity; (b) Sampling frequency: ideally measurements should be taken every 3 min and optimally every min; (c) Accuracy, precision and range: preferably, the sensor accuracy should not be less than: \(<\pm\)0.5 \({}^{\circ}\)C for air temperature; \(<\pm\)5% for relative humidity and \(<\pm\)4% for precipitation.