# Autonomous Wireless Sensor Networks in
## 1 Introduciton  
Pest monitoring and control is becoming an increasingly important issue worldwide, with invasive pest species seizing the spotlight and adapting to new environments at an alarming rate. This is supported by the impact of climate change turning previously unsuitable areas to current and future hotspots for invasive species and emerging infectious disease. The main threat of invasive species is their ability to vector a wide array of diseases. The World Health Organisation (WHO) estimates that vector-borne diseases account for more than 17% of all infectious diseases, with more than 9.8 billion people at risk, 96 million reported cases, and over 700 thousand deaths annually [1]. Efficient local monitoring or surveillance is essential to prevent the spread and establishment of pests, especially in ports, warehouses with imported plants, stockpiles of tires, rest areas on highways andtrain stations. The European Centre for Disease Control (ECDC), the European Environment Agency (EEA), and health/environmental protection agencies in all EU member states are responsible for the implementation of mosquito surveillance programmes. There are still many regions in Europe without any kind of mosquito surveillance programmed being implemented, but this is rapidly changing due to the threat of climate change. The role of precise in situ sensor data, as well as Earth Observation (EO), is manifold. Remote sensed data can provide information for determining eccclimatic zones with different levels of environmental suitability for the pest and precise WSN data can drive intelligent dynamical population models to target peaks in the pest population and inform control action, limiting the harmful effect on the environment from the excessive application of pesticides. Sensor networks are already widely used in many urban and suburban settings. This number will only increase given the rise of \"Internet of Things\" (IoT). According to [PERSON] there are already more connected \"things\" than people in the world. The number of devices in the IoT network is expected to reach 20.4 billion by 2020 [2]. Accordingly, there have already been a number of studies to monitor and control pests using WSN technologies. However, there is still a considerable gap in pest management using WSN with most current applications failing to provide a low-cost, self-sufficient monitoring system that can operate in remote areas. A lot of the existing systems rely on the availability of a power grid and WiFi or a cellular communication network. This is not a problem in urban areas however, pest management requires surveillance in remote areas where a local network and power supply are not present, and the dependency on terrestrial networks is untrustworthy. In addition to the lack of coverage, communication reliability and data acquisition and processing delays are also a significant consideration. Until recently, data acquisition in integrated pest management (IPM) relied on manual collection of pest and meteorological data. This is tedious and cumbersome and incompatible with the models being developed to assist pest monitoring mainly because of the lag created by manual collection and processing. These models rely on NRT (near-real-time), subdaily environmental parameters to create the forcing fields necessary for computation. Satellite communication could provide a solution to the limitations of current systems and an uninterrupted NRT forcing for pest population dynamics forecast models. Although local and cellular networks are widespread and commonly used, the spatial distribution is not homogenized around the world. They are mostly concentrated around major population hotspots. Large gaps in the spatial coverage of cellular networks do exist. This makes it hard to conduct automatic surveying and monitoring in remote locations. Another reason in favor of using multiple communication modules within a single WSN is redundancy in case of weather interference.