Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Internet of Things (IoT) Application in Meliponiculture

This paper presents an investigation on the environmental factors affecting meliponiculture (the cultivation of stingless bees on a commercial scale for honey production or pollination) using an internet of thing (IOT) application. Manual collection of data for chosen factors can be sporadic and produce variations from incorrect measurement taking; this can cause complications in producing any important interpretation. The feasibility of online monitoring of bee hive based on IoT application has not been properly explored. For a particular bee hive, the amount of departing and arriving bee was estimated by using non-intrusive sensors of infrared transmitter and receiver. A basic temperature-humidity sensor to monitor the temperature and humidity was placed inside the bee hive.  All sensors were integrated with a microcontroller and a Wi-Fi module which sent the data wirelessly to IoT cloud platform where the data was continuously saved, monitored, and can be retrieved anytime. Colonies of Trigona Itama were active throughout the whole period at the experimental site but the daily activity period was intense in the warmer days (over 30 °C) whereas the relative humidity had no significant effect on the flight activities. Intensity of daily flight activity was greatest in December, 2016. Temperature was the most important variable affecting flight

Recent developments on precision beekeeping: A systematic literature review

The aim of this systematic review was to point out the current state of precision beekeeping and to draw implications for future studies. Precision beekeeping is defined as an apiary management strategy based on monitoring individual bee colonies to minimize resource consumption and maximize bee productivity. This subject that has met with a growing interest from researchers in recent years because of its environmental implications. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was selected to conduct this review. The literature search was carried out in the Scopus database for articles published between 2015 and 2023, being a very recent issue. After two rounds screening and examination, 201 studies were considered to be analysed. They were classified based on the internal parameters of the hive, in turn divided by weight, internal temperature, relative humidity, flight activity, sounds and vibrations, gases, and external parameters, in turn divided by wind speed, rainfall and ambient temperature. The study also considered possible undesirable effects of the use of sensors on bees, economic aspects and applications of Geographic Information System technologies in beekeeping. Based on the review and analysis, some conclusions and further directions were put forward

Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Precision beekeeping requires data acquisition, data analysis, and applications where the initial phase data on the beehive plays a fundamental role. This method of apiculture could be used to measure different bee colony parameters in real time, leveraging on wireless sensing technologies, which aid monitoring of a bee colony, and enhances the monitoring of infectious diseases like colony collapse disorder–a major loss in the management of honey bee population. In this paper, a low-cost wireless technology-based system, which measures in real-time, the temperature in and around the beehive, and the sound intensity inside the hive is presented. This monitoring system is developed using an Arduino microprocessor, an ESP8266 communication module, and a web-based server. The proposed system provides valuable information concerning the bee colony behavior in terms of temperature variations and sound characteristics. Based on the measured temperature and sound information, colony beekeepers could easily detect events like increased food usage by the bees, breeding start time, pre-swarming action, actual swarming pattern, and the bee colony's death

An IoT-Based Beehive Monitoring System for Real-Time Monitoring of Apis cerana indica Colonies

A study was conducted to monitor the bee activity in the colonies of diferente strengths in real time using an IoT-based device. The in-hive temperature and relative humidity were measured in the colonies of Apis cerana indica Fabricius of different strengths using the sensor-laden IoT device that was correlated with the movement of foragers into and out of the hive. A significantly higher movement of foragers was recorded at an in-hive temperature and relative humidity of 27.84 ºC and 61.47% at 5-6 p.m. with an observed activity of 9,638 bees/hive/hour in the strong colonies. In the weak colonies, the mean forager activity was 1,436.3 bees/hive/hour, which was recorded at an in-hive temperature of 26.52 ºC and 61.42% relative humidity. The mean honey area in the strong and weak colonies were 1,300.80±177.61 cm2 and 508.80±156.84 cm2, respectively. Pollen area in the strong and weak colonies were 447.60±112.08 cm2 and 116.20±66.43 cm2, respectively. In the strong and weak colonies, the area under egg brood was 470±53.06 cm2 and 88.20±36.85 cm2, larvae brood was 583.40±11.04 cm2 and 80.00±24.67 cm2 and sealed brood was 684.20±57.98 cm2 and 102.80±16.59 cm2, respectively. The real-time data on the movement of foragers in the colonies of different strengths enabled us to undertake timely intervention in the maintenance of the bee colonies

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

Honey Production with Remote Smart Monitoring System

The innovative technologies of precision agriculture can be applied to beekeeping, a very important sector both from an environmental and production point of view. Bees are responsible, through pollination, for the reproduction of numerous plants guaranteeing biodiversity and providing a final product, honey, highly energetic and with high health properties. Today, sensors applied to the hives can be used to obtain information on the colony phenology in the field, disturbing them as little as possible, allowing the construction of forecast models to control their health state and production increase. The Department of Agricultural, Food and Forest Sciences of the University of Palermo developed a WNS-type system for continuously monitoring and controlling the main environmental factors, both inside and outside the hive, in order to evaluate their influence on daily honey production. The novel system allows to identify any critical points in honey production recording environmental, sound and production data and real time transmitting them to the operators, accessing a specifically created web interface. The results of the study represent the basis for a precision hive management model that can be applied in different environmental conditions to optimize honey production

The Effect of Months of the Year, Recorded by a Smart Bee Device, on the Temperature and Relative Humidity of Beehives and Broods

Threats from different origins are affecting agriculture in general and beekeeping in particular. Climate change, diseases, the use of pesticides, insecticides, thefts and genetic erosion due to random crossing of exotic and native strains. Internet of Things (IoT) devices have found many applications to reduce these threats, including the honeybees sector. They consist of embedded sensing, computing, and communication devices, connected to the Internet through specific lightweight messaging protocols. A SmartBee%2B Device, developed by Beekeeper Tech (www.smartbeekeeper.com) was used and honeybees information have been gathered during three years period 2020-2021, from over 100 in-field beehives. Each beehive was set up at a different location in Tunisia, France and New Zealand. A SmartBee%2B device connects to one beehive and operates in several modes%253A the Monitoring mode, the Transhumance mode, the Tracking mode, and the hibernate mode. Two embedded sensors and two external sensors measured the hives main parameters%253A The inner beehive%252339%253Bs temperature and relative humidity and the Brood%252339%253Bs temperature and its relative Humidity. In addition, the hive%252339%253Bs location is recorded with a GPS module. A total of 51444 and 50671 temperature and relative humidity records from the hives and 8756 records of the temperature and relative humidity at the brood level were used in this study, analyzed and results presented and discussed. Main results showed how honeybees workers mitigate the heat burden at the brood level by increasing their temperature till 7deg%253BC in winter and decreasing the brood temperature by 8 deg%253BC in summer hot months. Breeding values of queens, based on their endothermic mechanism trait, can be predicted to improve their ability to cope with extreme temperatures and select well-adapted strains. These improvements will affect positively the majority of small beehives keepers in the world by reducing the loss of their colonies

Wireless sensor networks, actuation, and signal processing for apiculture

Recent 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 for a wide range of crops. Protecting the honey bee population worldwide, and enabling them to maximise productivity, are important concerns. This research proposes a framework for addressing these issues by considering an inter-disciplinary approach, combining recent developments in engineering and honey bee science. The primary motivation of the work outlined in this thesis was to use embedded systems technology to improve honey bee health by developing state of the art in-hive monitoring systems to classify the colony status and mechanisms to influence hive conditions. Specific objectives were identified as steps to achieve this goal: to use Wireless Sensor networks (WSN) technology to monitor a honey bee colony in the hive and collect key information; to use collected data and resulting insights to propose mechanisms to influence hive conditions; to use the collected data to inform the design of signal processing and machine learning techniques to characterise and classify the colony status; and to investigate the use of high volume data sensors in understanding specific conditions of the hive, and methods for integration of these sensors into the low-power and low-data rate WSN framework. It was found that automated, unobtrusive measurement of the in-hive conditions could provide valuable insight into the activities and conditions of honey bee colonies. A heterogeneous sensor network was deployed that monitored the conditions within hives. Data were collected periodically, showing changes in colony behaviour over time. The key parameters measured were: CO2, O2, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were collected to provide an additional analysis dimension. Extensive energy improvements reduced the node’s current draw to 150 µA. Combined with an external solar panel, self-sustainable operation was achieved. 3,435 unique data sets were collected from five test-bed hives over 513 days during all four seasons. Temperature was identified as a vital parameter influencing the productivity and health of the colony. It was proposed to develop a method of maintaining the hive temperature in the ideal range through effective ventilation and airflow control which allow the bees involved in the activities above to engage in other tasks. An actuator was designed as part of the hive monitoring WSN to control the airflow within the hive. Using this mechanism, an effective Wireless Sensor and Actuator Network (WSAN) with Proportional Integral Derivative (PID) based temperature control was implemented. This system reached an effective set point temperature within 7 minutes of initialisation, and with steady state being reached by minute 18. There was negligible steady state error (0.0047%) and overshoot of <0.25 °C. It was proposed to develop and evaluate machine learning solutions to use the collected data to classify and describe the hive. The results of these classifications would be far more meaningful to the end user (beekeeper). Using a data set from a field deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. A correlation between meteorological conditions and beehive data was also observed. This led to the development of an algorithm for predicting short term rain (within 6 hours) based on the parameters within the hive (95.4% accuracy). A Random Forest based classifier was also developed using the entire collected in-hive dataset. This algorithm did not need access to data from outside the network, memory of previous measured data, and used only four inputs, while achieving an accuracy of 93.5%. Sound, weight, and visual inspection were identified as key methods of identifying the health and condition of the colony. Applications of advanced sensor methods in these areas for beekeeping were investigated. A low energy acoustic wake up sensor node for detecting the signs of an imminent swarming event was designed. Over 60 GB of sound data were collected from the test-bed hives, and analysed to provide a sound profile for development of a more advanced acoustic wake up and classification circuit. A weight measuring node was designed using a high precision (24-bit) analogue to digital converter with high sensitivity load cells to measure the weight of a hive to an accuracy of 10g over a 50 kg range. A preliminary investigation of applications for thermal and infrared imaging sensors in beekeeping was also undertaken

IndusBee 4.0 – Integrated Intelligent Sensory Systems for Advanced Bee Hive Instrumentation and Hive Keepers’ Assistance Systems

The importance of insects, and honey bees in particular, for our ecosystem is undisputed. Currently, environmental problems from pesticides to parasites endanger the well-being or even the existence of honey bee colonies and insects in general. This imposes an increasing load on skills and activities of hive keepers. Sensors, instrumentation, and machine learning offer solutions on the one hand to effectively instrument bee hives and on the other hand to provide efficient assistance systems for hive keepers. By advanced hive instrumentation and intelligent evaluation of the acquired information hives can be monitored more easily and with less intrusion. Like in other industrial disciplines, e.g., Industry 4.0, operation can move from scheduled to event driven activity. The development in Micro-Electrical-Mechanical- Systems and Internet-of-Things field in general allows to achieve affordable integrated monitoring solutions. However, not in all tasks a dedicated instrumentation of each hive is required, and mobile assistance systems and devices to be employed in a single instance for the whole apiary will complement the instrumentation activity and the overall approach of our IndusBee 4.0 research project. Examples of this category are, e.g., honey quality assessment tool as an extension of established hygrometers or a system for improved automation of the tedious and time consuming screening for the varroa infestation of hives. This paper provides a review of activities in the field and presents the current status of contributions to both lines of research in our IndusBee 4.0 research project. With regard to hive instrumentation, in addition to standard temperature, moisture, and weight monitoring, an approach of acoustical in-hive monitoring with automated decision making and notification implemented in-hive in a SmartComb has been pursued. Further, integrated gas sensors are currently added to the SmartComb to explore the in-hive detection of infestation and illness, e.g., (American) foulbrood. Visual flight hole inspection is successively explored by a separate system in or at the hive. With regard to hive keepers’ assistance systems, an approach automating the screening for the varroa infestation of hives was tackled first. Here, a cost-effective two step procedure, a first attention step for detecting candidate regions and a final classification step of these candidate regions, is applied. It is aspired to extend the approach to continuous in-hive varroa infestation monitoring. The integration of all information from hive instrumentation and assistance systems with data fusion and data analysis activities in apiary intelligence unit is aspired in the next step