Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

In recent years monitoring of beehives through technology has become increasingly frequent in research and industry. This is due to a decline in beekeeping, and stagnant honey bee populations across the globe, due to, among other factors, pests and disease. Recent advances in the area of low power wireless sensor network technology can be applied to the beehive for a better understanding of the colony's condition. This combination of engineering and beekeeping has led to the emergence of Precision Beekeeping. One of the key metrics of the strength of a beehive is the weight of the colony. Changes in weight can accurately reflect the productivity of the colony, as well as its health and condition. This paper describes the development of a wireless platform weighing scales, for implementation as part of a smart beehive. A single point impact load cell was selected as the most appropriate load sensor and was integrated into the design of the scales. The final weighing system was interfaced via a high precision analogue to digital converter to an off-the-shelf processing platform enabled with a low power Zigbee radio, to allow for data transfer to the base station. An initial simulation of the scale's ability was carried out, using standard weights to simulate the brood chamber of a beehive and varying weight to mimic the production and consumption of honey. The results showed that the initial platform scale has a linear output characteristic. The analogue to digital converter was evaluated and the system was found to be able to detect changes in weight in the order of tens of grams. A power analysis of the system was also undertaken to confirm that the solution was suitable for remote, battery powered, deployments.

Honey bees have played a major role in the history and development of humankind, in particular for nutrition and agriculture. The most important role of the western honey bee (Apis mellifera) is that of pollination. A large amount of crops consumed throughout the world today are pollinated by the activity of the honey bee. It is estimated that the total value of these crops stands at 155 billion euro annually. The goal of the work outlined in this paper was to use wireless sensor network technology to monitor a colony within the beehive with the aim of collecting image and audio data. These data allows the beekeeper to obtain a much more comprehensive view of the in-hive conditions, an indication of flight direction, as well as monitoring the hive outside of the traditional beekeeping times, i.e. during the night, poor weather, and winter months. This paper outlines the design of a fully autonomous beehive monitoring system which provided image and sound monitoring of the internal chambers of the hive, as well as a warning system for emergency events such as possible piping, dramatically increased hive activity, or physical damage to the hive. The final design included three wireless nodes: a digital infrared camera with processing capabilities for collecting imagery of the hive interior; an external thermal imaging camera node for monitoring the colony status and activity, and an accelerometer and a microphone connected to an off the shelf microcontroller node for processing. The system allows complex analysis and sensor fusion. Some scenarios based on sound processing, image collection, and accelerometers are presented. Power management was implemented which allowed the system to achieve energy neutrality in an outdoor deployment with a 525 × 345 mm solar panel.

Honey bees have held a critical role in agriculture and nutrition from the dawn of human civilisation. The most crucial role of the bee is pollination; the value of pollination dependant crops is estimated at € 155 billion per year with honey bees identified as the most important pollinator insect. It is clear that honey bees are a vitally important part of the environment which cannot be allowed to fall into decline. The project outlined in this paper uses Wireless Sensor Network (WSN) technology to monitor a beehive colony and collect key information about activity/environment within a beehive as well as its surrounding area. This project uses low power WSN technologies, including novel sensing techniques, energy neutral operation, and multi-radio communications; together with cloud computing to monitor the behaviour within a beehive. The insights gained through this activity could reduce long term costs and improve the yield of beekeeping, as well as providing new scientific evidence for a range of honey bee health issues. WSN is an emerging modern technology, key to the novel concept of the Internet of Things (IoT). Comprised of embedded sensing, computing and wireless communication devices, they have found applications in nearly every aspect of daily life. Informed by biologists' hypotheses, this work used existing, commercially available WSN platforms together with custom built systems in an innovative application to monitor honey bee health and activity in order to better understand how to remotly monitor the health and behaviour of the bees. Heterogeneous sensors were deployed, monitoring the honey bees in the hive (temperature, CO2, pollutants etc.). Weather conditions throughout the deployment were recorded and a relationship between the hive conditions and external conditions was observed. A full solution is presented including a smart hive, communication, and data aggregation and visualisation tools. Future work will focus on improving the energy performance of the system, introducing a more specialised set of sensors, implementing a machine learning algorithm to extract meaning from the data without human supervision; and securing additional deployments of the system.

In recent years, various United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator insect for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. The work described in this paper utilised heterogeneous wireless sensor networks technologies to gather data unobtrusively from a beehive, describing the conditions and activity of the honey bee colony. A wide range of sensors were deployed for monitoring the multidimensional conditions within a living beehive (including oxygen, carbon dioxide, pollutant levels, temperature, and humidity). Meteorological and environmental conditions outside the hive were also monitored throughout the deployment. The data were then analysed from a biological perspective to provide insights into honey bee behaviour and health. This led to the development of an algorithm for automatically determining the status of the bee colony. Analysis was also undertaken from a meteorological perspective, which led to the development of an algorithm for predicting short term external weather conditions (rain) based on the conditions observed within the hive. The meteorological conditions were seen to have an impact on the data provided by biological sensors (bees) and physical sensors. This can be exploited to improve the accuracy of local weather prediction. Applications of this algorithm include agricultural and environmental monitoring for accurate short term forecasts for the area local to the beehive.