Hive Weight Research Articles

Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760-weight; 322,570-temperature; 155,373-video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.

Prototype Biodiversity Digital Twin: honey bees in agricultural landscapes

Honey bees are vital to human well-being and are under multiple stresses. We need to be able to assess the viability and productivity of honey bee colonies in different landscapes and under different management and climate-change scenarios. We have developed a prototype digital twin, HONEYBEE-pDT, based on the BEEHAVE model, which simulates foraging, population dynamics and Varroa mite infestation of a single honey bee colony. The main input data are land-cover maps and daily weather data. We have developed the pDT for simulating large areas and have tested it for the whole of Germany. We have also developed a web-based GUI that users can use to run the pDT for specific sites. Hive weight data from hundreds of hives will be used for calibration and validation.

Effects of summer treatments against Varroa destructor on viral load and colony performance of Apis mellifera colonies in Eastern Canada.

Despite the use of various integrated pest management strategies to control the honey bee mite, Varroa destructor, varroosis remains the most important threat to honey bee colony health in many countries. In Canada, ineffective varroa control is linked to high winter colony losses and new treatment options, such as a summer treatment, are greatly needed. In this study, a total of 135 colonies located in 6 apiaries were submitted to one of these 3 varroa treatment strategies: (i) an Apivar® fall treatment followed by an oxalic acid (OA) treatment by dripping method; (ii) same as in (i) with a summer treatment consisting of formic acid (Formic Pro™); and (iii) same as in (i) with a summer treatment consisting of slow-release OA/glycerin pads (total of 27g of OA/colony). Treatment efficacy and their effects on colony performance, mortality, varroa population, and the abundance of 6 viruses (acute bee paralysis virus [ABPV], black queen cell virus [BQCV], deformed wing virus variant A [DWV-A], deformed wing virus variant B [DWV-B], Israeli acute paralysis virus [IAPV], and Kashmir bee virus [KBV]) were assessed. We show that a strategy with a Formic Pro summer treatment tended to reduce the varroa infestation rate to below the economic fall threshold of 15 daily varroa drop, which reduced colony mortality significantly but did not reduce the prevalence or viral load of the 6 tested viruses at the colony level. A strategy with glycerin/OA pads reduced hive weight gain and the varroa infestation rate, but not below the fall threshold. A high prevalence of DWV-B was measured in all groups, which could be related to colony mortality.

Protective covers impact honey bee colony performance and access to outside resources

Protective crop covers are used extensively to protect high-value crops from bird and hail damage, however, these structures may present challenges for honey bees (Apis mellifera L.), the main pollinators in these systems. Some studies have indicated that protective covers result in declines in colony size and resource storage, but few replicated field trials exist. To determine the impact of protective cropping structures on honey bee brood production and resource storage, 14 honey bee colonies were placed inside and outside of protective covers on four blueberry (Vaccinium corymbosum and V. virgatum) farms in northern New South Wales, Australia. We measured changes in brood production, pollen and honey storage, hive weight and ambient hive temperature fortnightly over a 12-week period. Brood production, pollen and honey storage, and hive weight all displayed greater reductions within four weeks of placement under net cover, compared to hives outside of net cover. Although brood and honey metrics gradually increased in all hives during the remainder of the 12-week monitoring period, hives located under protective netting had relatively smaller gains in brood production and pollen storage when compared to hives located outside the protective covering. Hives under protective netting that were in the shade and/or whose entrances were facing south were most impacted by the end of the monitoring period. Our findings highlight the need for protected crop management strategies to consider pollinator health as well as crop pollination needs so that both bee health and pollination services are maintained in these systems.

BeeOpen—An Open Data Sharing Ecosystem for Apiculture

The digital transformation of apiculture initially encompasses Internet of Things (IoT) systems, incorporating sensor technologies to capture and transmit bee-centric data. Subsequently, data analysis assumes a vital role by establishing correlations between the collected data and the biological conditions of beehives, often leveraging artificial intelligence (AI) approaches. The field of precision bee monitoring has witnessed a surge in the collection of large volumes of diverse data, ranging from the hive weight and temperature to health status, queen bee presence, pests, and overall hive activity. Further, these datasets’ heterogeneous nature and lack of standardization present challenges in applying machine learning techniques directly to extract valuable insights. To address this issue, the envisioned ecosystem serves as an open and collaborative information platform, facilitating the exchange and utilization of bee monitoring datasets. The data storage architecture can process a large variety of data at high frequency, e.g., images, videos, audio, and time series data. The platform serves as a repository, providing crucial information about the condition of beehives, health assessments, pest attacks, swarming patterns, and other relevant data. Notably, this information portal is managed through a citizen scientist initiative. By consolidating data from various sources, including beekeepers, researchers, and monitoring systems, the platform offers a holistic view of the bee population’s status in any given area.

Expert knowledge-based modelling approach for mapping beekeeping suitability area

It is becoming increasingly accepted that beekeeping is declining due to the damaging effect of global changes such as climate and land-use change that directly and indirectly impact Apis Melliferas. Despite numerous investigations, a comprehensive study that incorporates both global and local knowledge has yet to be conducted. For a long time, researchers have suggested that expert knowledge should be taken into account when creating decision support tools for managing activities related to natural resources, such as beekeeping. Unlike previous studies, this research seeks to tackle these questions while also introducing the concept of ecosystem service in modelling, offering a fresh perspective on sustainable land use. To achieve this goal, we combined several methods, including using literature knowledge, beekeeper knowledge, and multi-source geospatial data. These data are employed in a hierarchical fuzzy inference system in a unified way. The proposed approach was applied in the Québec region and the suggested technique appears to be both reliable and effective. The validation step revealed that the landscape variable, particularly the area used for agriculture or grassland, had the greatest impact on changes in hive weight throughout the season. In addition, we demonstrated that meteorological factors such as rainfall and relative humidity are strongly correlated to beekeeping. We showed that access to data and knowledge can be a critical factor in decision-making in the beekeeping industry, and thus we suggest that wild-bees conservationists, decision-makers, farmers, beekeepers, and other stakeholders adopt a collaborative approach.

Hive Orientation and Colony Strength Affect Honey Bee Colony Activity during Almond Pollination.

The foraging activity of honey bees used to pollinate almonds was examined in relation to their hive entrance orientation and colony strength. Twenty-four colonies of honey bees, twelve in each group, were situated with their entrances facing east and west cardinal points. Bee out counts were recorded continuously and hive weight data at ∼10 min intervals from 17 February to 15 March 2023. Colony strength was assessed using the frames of adult bees (FOB) metric. East-facing hives started flight 44.2 min earlier than west-facing hives. The hive direction did not affect the timing of the cessation of foraging activity. The hive strength played a significant role: hives assessed as weak (≤3.0 FOB) commenced foraging activity 45 min later than strong hives (>3.0 FOB) and ceased foraging activity 38.3 min earlier. Hive weight data did not detect effects of either the hive direction or colony strength on the commencement and cessation of foraging activity, as determined using piecewise regression on 24 h datasets. However, the hive weight loss due to foraging activity at the start of foraging activity was significantly affected by both direction (East > West) and colony strength (Strong > Weak). Our study showed that, during almond pollination, both hive entrance exposure and hive strength have quantifiable effects on colony foraging behaviour and that these effects combine to regulate the overall foraging activity of the pollinating colonies.

The effect of meteorological and environmental variables on food collection by honey bees (Apis mellifera)

Bees are strongly dependent on environmental and climatic conditions. Anthropogenic and climate changes are mainly responsible for restricted access of bees to food resources. Therefore, this study aimed to evaluate the effect of the most important meteorological and environmental variables affecting food collection by honey bees. Using electronic beehive scales, remote monitoring of daily hive weight changes of 60 colonies from 60 apiaries was performed during three beekeeping seasons. The hive weight changes mainly depended on the meteorological variables, particularly sunshine duration and air temperature. The topographic (e.g. landscape structure, forest habitat type, the share of the area with plant species attractive to honey bees) and spatial (e.g. proportion of forest, arable land, green area) variables contributed to the hive weight changes to a much lesser extent. Monitoring of the hive weight revealed that the meteorological conditions and the share of the area with most attractive plant species affected food collection by honey bees the most strongly. In conclusion, the long-term remote monitoring of honey bee colony hive weight changes on a large scale can be a source of substantial data about the impact of environmental and climate changes on bees and their food resources in order to restore and conserve the environment that will meet the nutritional needs of different species of bees.

Monitoring honeybee (Apis mellifera) flight activity and within-day hive weight changes during rapeseed (Brassica napus) flowering

Honeybee (Apis mellifera) flight activity and beehive changing weight during rapeseed (Brassica napus) flowering was evaluated. The research was carried out during the 2021 agricultural period in the northeastern region of Bulgaria, specifically in Yuper village. The experimental site is situated at 43°54'28.59" N, 26°23'49.02"E coordinates, with an elevation of 107 meters. Within the experiment, there were 130 bee colonies kept in Dadant-Blatt hives. The bees belong to the species known as (Apis mellifera macedonica). Throughout the research duration, the study focuses on tracking the quantity of bees leaving the hive (Nbee) and the changes in hive weight (Y) caused by the secretion of nectar during different time intervals throughout the day. The monitoring included external factors such as the temperature of the outside air (Tair), 0C, the humidity of the air (Hair) %,, and the surface temperature (Tsur) 0C, of the rapeseed inflorescences. The findings indicate a noteworthy and favorable relationship between Nbee and Y. The stronger correlation between Nbee, Y and Tsur was found. The other weather indicators as Tair and Hair are not correlated with Nbee and Y. The monitoring of the honeybee flight activity and beehive weight during rapeseed flowering will help the beekeepers to determine the suitable moment to expand the bee colonies in order to achieve optimal productivity.

Cold storage as part of a Varroa management strategy: effects on honey bee colony performance, mite levels and stress biomarkers

Placing honey bee colonies in cold storage has been proposed as a way to induce a pause in brood production as part of a Varroa mite treatment plan. Here, we exposed colonies to combinations of with or without an October cold storage period and with or without a subsequent miticide application. We then measured the effects of those treatments on colony-level variables (i.e. colony size, Varroa infestation level, survivorship and hive weight and temperature) and pooled individual-level variables that are associated with nutritional and stress responses. Colonies were assessed before and after cold storage, and again post winter, for a total duration of about 5 months, and the experiment was repeated. Brood levels were significantly lower after cold storage, and hive temperatures indicated that most or all brood had emerged after about two weeks in cold storage. However, Varroa levels at the end of the experiments in February were not significantly different among treatment groups. Colonies kept outside (not subjected to cold storage) and treated with a miticide had higher survivorship on average than any other treatment group, but no other group comparisons were significant, and long-term impact of cold storage on adult bee populations and on colony thermoregulation was low. The bee forage environment was also very different between the 2 years of the study, as rainfall and bee forage availability were much higher the second year. Colonies were over 2.5 times larger on average the second year compared to the first, both in terms of adult bee mass and brood area, and expression levels of nutrition and stress response genes were also significantly higher the second year. The results indicate that limited cold storage would likely have little long-term impact on most colony and individual measures of health, but for such a strategy to succeed levels of stressors, such as Varroa, may also need to be low.

Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation

This work presents Apis-Prime,22This study extends the work in Anwar et al. (2022).a hybrid deep learning model for soft sensing and time series forecasting, to estimate the daily weight variations of honeybee hives. Apis-Prime improves the state-of-the-art of earlier proposed WE-Bee (Anwar et al., 2022), and also helps optimize the beehive monitoring systems for the task of daily weight variation estimation. Weight variations of a honeybee hive are the most important indicator of hive productivity, and the health and strength of a bee colony. Currently, precise measurement of the weight of a hive requires an expensive weighing scale under each hive. On the other hand, sensors deployed inside the hive are cheaper than a weighing scale, and are shielded from the extreme weather variations outside the hive. In this work, honeybee activity is monitored using data from sensors inside the hive, along with monitoring the information related to the seasons, time of the day, weather, and the size of the hive. Apis-Prime’s deep learning algorithm is based on two self-attention encoders, which collectively transform the sensor data into daily weight variations of the hive. Two parallel encoders simultaneously pay attention to time-based relationships and feature-based relationships within the daily sensor data and generate daily hive weight estimates with better accuracy. The comparison shows an average error of 19.7 grams/frame for Apis-Prime, compared to 21.05 grams/frame for the earlier proposed model WE-Bee. For system optimization, this work uses the attention weights of trained encoders of Apis-Prime to evaluate the sensor features collected by the monitoring system. This evaluation is used to identify and remove the unnecessary sensors/features from the dataset, reducing the number of features from 36 to 23, hence providing a significant optimization of cost, power, and data bandwidth. We provide a performance analysis of beehive weight estimations by Apis-Prime using the complete, as well as the optimized dataset on 2,170 days of beehive sensor recordings. Equally good results of daily weight estimations using the optimized feature set demonstrate the efficacy of the proposed model for the optimization of the beehive monitoring system for the task of hive weight estimation.

Monitoring of hive weight changes in various landscapes

Bee development is determined by the quantity and quality of food resources available in the environment that can be used by bees only to a certain extent. Therefore, this study aimed to evaluate, by means of changes in hive weight, the amount of food collected by honey bees in various landscapes, in relation to meteorological conditions, landscape, and forest structure. The monitored colonies (n = 104) were placed on electronic beehive scales, which provided data about hive weight, temperature, and humidity over two beekeeping seasons. The landscape valuation within a 2 km radius from each beehive was prepared based on digital maps. The seasonal distribution of food resources collected by honey bees was determined based on hive weight changes analysed in 5-day-periods (i.e. pentads). The hive weight changes mainly depended on the observation period and the air temperature. The hive weight increased from April to the last 10 days of June and when the air temperature was over 24 °C. The proportions of agricultural lands and forests, as well as the air humidity, had a small effect on hive weight changes. Continuous monitoring of the weight of honey bee colonies on a large scale showed that honey bees collected food stores in their nests mainly in the spring. Then, they collected a small amount of food, probably due to limited access to food resources from the last 10 days of June until the end of the growing season.

Manipulating nest architecture reveals three-dimensional building strategies and colony resilience in honeybees.

Form follows function throughout the development of an organism. This principle should apply beyond the organism to the nests they build, but empirical studies are lacking. Honeybees provide a uniquely suited system to study nest form and function throughout development because we can image the three-dimensional structure repeatedly and non-destructively. Here, we tracked nest-wide comb growth in six colonies over 45 days (control colonies) and found that colonies have a stereotypical process of development that maintains a spheroid nest shape. To experimentally test if nest structure is important for colony function, we shuffled the nests of an additional six colonies, weekly rearranging the comb positions and orientations (shuffled colonies). Surprisingly, we found no differences between control and shuffled colonies in multiple colony performance metrics-worker population, comb area, hive weight and nest temperature. However, using predictive modelling to examine how workers allocate comb to expand their nests, we show that shuffled colonies compensate for these disruptions by accounting for the three-dimensional structure to reconnect their nest. This suggests that nest architecture is more flexible than previously thought, and that superorganisms have mechanisms to compensate for drastic architectural perturbations and maintain colony function.

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

<p>A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.</p>

Modelling daily weight variation in honey bee hives.

A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. Here we argue that the combination of these two approaches is essential to obtain interpretable information on the state of bee colonies and show how this can be achieved in the case of time series of intra-day weight variation. We model how the foraging and food processing activities of bees affect global hive weight through a set of ordinary differential equations and show how to estimate the parameters of this model from measurements on a single day. Our analysis of 10 hives at different times shows that the estimation of crucial indicators of the health of honey bee colonies are statistically reliable and fall in ranges compatible with previously reported results. The crucial indicators, which include the amount of food collected (foraging success) and the number of active foragers, may be used to develop early warning indicators of colony failure.

Trans-generational immune priming against American Foulbrood does not affect the performance of honeybee colonies.

Honeybees are major pollinators for our food crops, but at the same time they face many stressors all over the world. One of the major threats to honeybee health are bacterial diseases, the most severe of which is the American Foulbrood (AFB). Recently a trans-generational vaccination approach against AFB has been proposed, showing strong potential in protecting the colonies from AFB outbreaks. Yet, what remains unstudied is whether the priming of the colony has any undesired side-effects. It is widely accepted that immune function is often a trade-off against other life-history traits, hence immune priming could have an effect on the colony performance. In this experiment we set up 48 hives, half of them with primed queens and half of them as controls. The hives were placed in six apiaries, located as pair of apiaries in three regions. Through a 2-year study we monitored the hives and measured their health and performance. We measured hive weight and frame contents such as brood amount, worker numbers, and honey yield. We studied the prevalence of the most common honeybee pathogens in the hives and expression of relevant immune genes in the offspring at larval stage. No effect of trans-generational immune priming on any of the hive parameters was found. Instead, we did find other factors contributing on various hive performance parameters. Interestingly not only time but also the region, although only 10 km apart from each other, had an effect on the performance and health of the colonies, suggesting that the local environment plays an important role in hive performance. Our results suggest that exploiting the trans-generational priming could serve as a safe tool in fighting the AFB in apiaries.

Effects of hive entrance orientation on honey bee colony activity

In an effort to determine the effects of the orientation of hive entrance on honey bee colony activity and temperature, hives were placed facing different cardinal directions (3–5 hives per direction). Hive weight was recorded every 5 minutes and temperature every 30 minutes from April 2019 to June 2020. Daily weight data were analyzed using piecewise regression. In southern Arizona from December to March, hives facing east started daily flight activity 50 minutes earlier than hives facing west and ended flight activity 57 minutes earlier than hives facing south. During that period east-facing hives had the lowest daily hive weight loss, 62 g per d compared to 100 g per d for north-facing hives. East-facing hives were also about 7 °C cooler on average than west-facing hives, suggesting that lower hive weight loss was due to lower food consumption which was directly related to lower colony cluster temperatures. From December to March hives facing east also had significantly lower morning weight loss due to departing foragers than hives facing north (indicating more foragers) but higher weight loss from April to June 2020. Most effects were observed from December to March, probably due to restricted daylight hours and lower ambient temperatures. No significant effects were observed with respect to visually-estimated adult bee numbers (frames of bees) or the surface areas of the sealed brood. We recommend hive orientation be taken into account in the design of field experiments that involve monitoring colony activity.

Trigona PENGARUH POSISI PINTU MASUK TERHADAP PERKEMBANGAN SARANG KOLONI LEBAH Trigona sp.

ABSTRACT
 The development of the Trigona sp. beehive affects the productivity of the bee, especially in hive building. The position of beehive’s trigona entrance has an influence on trigona bee ability to build a hive. Increasing the access of bees in building hive will increasing the component of of the beehive. This study was used bee boxes with various of beehive entrance position as observed factors namely top vertical, bottom vertical, and horizontal. The study was carried out from November 2021 to March 2022. The method used 9 units of stup samples with 3 replications for each treatment. The parameters were the weight and dimensions measurement of the hive based on a scale approach on the stup wall. Analysis used one-way Anova (Analysis variance) and BNJ test. The result showed that the largest average of hive weight was 2.679,5 grams in the bottom vertical entrance position treatment. On the other hand, there was significant affect (P<1%) of the entrance position to development of stup dimensions. BNJ test (P<1%) of stup dimensions showed that bottom vertical entrance treatment had different affect to top vertical and horizontal entrance treatment, where bottom vertical entrance treatment had the largest increase of stup dimensions. 

Field and Cage Studies Show No Effects of Exposure to Flonicamid on Honey Bees at Field-Relevant Concentrations.

Simple SummaryThe effects of pesticides on honey bees are of great concern to beekeepers and the general public. In particular, a class of pesticides called neonicotinoids has been found in some studies to affect bee learning and colony function. A pesticide classified as a neonicotinoid, flonicamid, has been detected in the residue analyses of honey and pollen samples for a published study, and that compound was tested at three concentrations (0, 50, and 250 parts per billion (ppb)) in sugar syrup in a field study with bee colonies and in two cage studies. Colony population levels, hive weight changes, thermoregulation, and CO2 concentrations were measured in the field study, and syrup consumption, thermoregulation, and survivorship by worker bees were measured in the cage studies. No significant treatment effects of the pesticide exposure were observed in either the field or the cage studies. These results support the idea that flonicamid is safe for honey bees at these concentrations. Publishing such results when they occur is important, so people use safe pesticides when they need them.The extent to which insecticides harm non-target beneficial insects is controversial. The effects of long-term exposure on honey bees to sublethal concentrations of flonicamid, a pyridinecarboxamide compound used as a systemic insecticide against sucking insects, were examined in a field study and two cage studies. The field study involved the continuous weight, temperature, and CO2 monitoring of 18 honey bee colonies, 6 of which were exposed over six weeks to 50 ppb flonicamid in sugar syrup, 6 exposed to 250 ppb flonicamid in syrup, and 6 exposed to unadulterated syrup (control). Treatments were derived from concentrations observed in honey samples in a published study. No effects were observed on foraging activity, hive weight gain, thermoregulation, or average CO2 concentrations. However, Varroa mite infestations may have also contributed to experimental variability. The two cage studies, in which cages (200 newly-emerged bees in each) were exposed to the same flonicamid concentrations as the field study and kept in a variable-temperature incubator, likewise did not show any experiment-wide effects on survivorship, thermoregulation, or syrup consumption. These results suggest that field applications of flonicamid that result in concentrations as high as 250 ppb in honey may be largely safe for honey bees.

Design and development of low-power, long-range data acquisition system for beehives - BeeDAS

Decision making capability of a system is highly dependent upon the quality and quantity of training data. Majority of beehive monitoring systems developed for research purposes are designed to collect data through a small set of sensors, and from locations with little geographic diversity. This hinders the development of a dataset that can be used to effectively train machine learning models. In this work, we explain the design and development of a multi-sensory, remote data acquisition system for beehives (BeeDAS), with focus on low-power consumption and long-range communication. We address design challenges associated with such systems and highlight the critical issues that need consideration. The proposed system enables collection of data from beehives at remote locations and harsh environment. Results of field deployments elucidate the effectiveness of various sensors which measure temperature, humidity, atmospheric pressure, CO2, acoustics, vibrations and the weight of a hive in hostile environment. This work also uses random forest regression to evaluate the feature importance of different sensors, environmental variables such as temperature, humidity, rain, wind speed as well as the information related to seasons, towards estimating the daily hive weight change, on a dataset comprised of 1,250 days of sensor recordings. We also evaluate the protocol designed for communication using Narrow Band Internet of Things (NB-IoT). The issues related to power optimization, sleep intervals and data storage in remote monitoring are also discussed.