Drone Cell Research Articles

Understanding the differential performance in hygienic behavior against dead brood of drones and workers of Apis mellifera: a chemical approach

Hygienic behavior is a social health mechanism displayed by honey bee workers of Apis mellifera by which dead or diseased brood is removed from the hive. Such behavior is known to be induced by olfactory cues. The efficiency of hygienic behavior toward drone brood cells is significantly lower than that of worker brood cells. A possible explanation for this difference lies in the cell wax cap of drone brood, which may act as a barrier to the diffusion of characteristic odors associated with diseased brood. The aim of this research was to explore the importance of drone cell wax cap as an interfering factor in the diffusion of chemical signals within honey bee colonies. To this end, brood removal percentages were compared among several types of brood: pin-killed worker, pin-killed drone, pin-killed worker with a healthy drone wax cap, healthy worker with a pin-killed drone wax cap, and healthy worker covered with a healthy drone wax cap. Results showed that the removal behavior did not differ between cells with pin-killed worker and pin-killed worker with a healthy drone wax cap, but was significantly higher towards healthy worker pupae covered with dead drone wax cap compared to the negative control (healthy worker pupa covered with a healthy drone cell wax cap). Present evidence suggests that chemical signals associated with the dead drone brood would be less volatile and retained to some extent in the wax cap, and partially explain the differential hygienic behavior between both types of brood.

Feedback-based algorithm for aerial cell's trajectory using deep learning for efficient performance in 6G

Sixth (6th) Generation (6G) wireless communication proposes Non-Terrestrial aerial Network (NTN) architecture to support dynamic scaling of both coverage and capacity. In this paper, we discuss Low Altitude Platform (LAP) based aerial cell for NTN which augments the terrestrial cell through Dual Connectivity (DC) or Carrier Aggregation (CA) on an independent and non-interfering frequency carrier. The determination of an optimal trajectory for a LAP based aerial cell is critical in the deployment for improving its resource utilization within the given flight time. The available literature on selecting trajectory for drone cell have independently considered the data traffic requirements or the mobility of the User Equipment (UE) while improving the throughput or coverage respectively. We propose for the first time; a novel Zone-based Drone Positioning and Trajectory algorithm for selecting UEs to be served by a drone cell as well as for choosing the position and trajectory of the LAP based aerial cell. The proposed algorithm utilizes a new feedback quantity from the UE called Feedback for Aerial Cell Trajectory (FACT), which contains UE's predicted data traffic, traffic preference, and mobility state. The algorithm and FACT are presented mathematically with a combination of closed-form equations with a deep learning (Long Short-Term Memory (LSTM)) model and evaluated through extensive simulations. The simulation shows improvement by 44% in spectrum resource utilization of an aerial cell and a 27% improvement in user throughput over available schemes in the literature.

Uplink and Downlink Variation in Drone Technology for Cloud, Edge, Fog and Smart Dust Integrated IoT Architecture: Demonstrated Over WSNs

IoT concepts are heavily applicable in drone communication integrated with different network architecture for optimization. Distributing the burden allows more IoT devices to execute calculations, rather than everything being done on the cloud. There are numerous IoT designs that have emerged as a result of this. By relocating calculations away from the cloud, these designs make use of the enhanced processing capacity of the devices. Based on our needs, we’ve limited it down to four architectures, each of which we have discussed for optimized flow useful in drone technology. We have also applied the one live dataset for the test drone using raspberry pi processor system powered with for end-to-end drone communication establishment. The analysis of downlink and uplink were studied for time analysis for IoT architecture using drone cell characteristics. New technology makes it possible to implement drone cell (DC) connectivity, which is highly flexible and cost-effective for the gathering of Internet-of-things (IoT) data when terrestrial networks are not yet accessible. DC’s flight path has a substantial impact on data collecting systems.

Drone cell cappings of Asian cavity-nesting honey bees (Apis spp.)

Drone pupae of Apis cerana are sealed in their cells under a hard silken cap with a central pore, a feature notably lacking in the closely related species, Apis nigrocincta. In this paper, the hard cap with a pore is confirmed for drone cells of four additional species or putative species of cavity-nesting honey bees of Asia. Inaccuracies in the literature related to cell cappings of Asian bees are clarified. The character state of the drone cell cap for each taxon was added to a recent phylogeny for the genus Apis. Parsimony analysis indicates that the hard cap evolved once in the common ancestor to all Asian cavity-nesting honey bees and was subsequently lost in Apis nigrocincta. Possible functions of the hard drone cell cap are reviewed, but the true functions of the cap and the reasons underlying its loss in A. nigrocincta remain unresolved.

Drone-Cell Trajectory Planning and Resource Allocation for Highly Mobile Networks: A Hierarchical DRL Approach

Drone cell (DC) is envisioned to enable the dynamic service provisioning for radio access networks (RANs), in response to the spatial and temporal unevenness of user traffic. In this article, we propose a hierarchical deep reinforcement learning (DRL)-based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of the environment, we decouple the multi-DC TPRA problem into two hierarchical subproblems, i.e., the higher level global trajectory planning (GTP) subproblem and the lower level local TPRA (LTPRA) subproblem. First, the GTP subproblem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the subproblem, we propose a multiagent DRL-based GTP (MARL-GTP) algorithm in which the nonstationary state space caused by the multi-DC environment is addressed by the multiagent fingerprint technique. Second, based on the GTP results, each DC solves the LTPRA subproblem independently to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient (DEP)-based LTPRA (DEP-LTPRA) algorithm is then proposed to solve the LTPRA subproblem. With the two algorithms addressing both subproblems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the nonlearning-based TPRA scheme.

Underlay Drone Cell for Temporary Events: Impact of Drone Height and Aerial Channel Environments

Providing seamless connection to a large number of devices is one of the biggest challenges for the Internet of Things (IoT) networks. Using a drone as an aerial base station (ABS) to provide coverage to devices or users on ground is envisaged as a promising solution for IoT networks. In this paper, we consider a communication network with an underlay ABS to provide coverage for a temporary event, such as a sporting event or a concert in a stadium. Using stochastic geometry, we propose a general analytical framework to compute the uplink and downlink coverage probabilities for both the aerial and the terrestrial cellular system. Our framework is valid for any aerial channel model for which the probabilistic functions of line-of-sight (LOS) and non-line-of-sight (NLOS) links are specified. The accuracy of the analytical results is verified by Monte Carlo simulations considering two commonly adopted aerial channel models. Our results show the non-trivial impact of the different aerial channel environments (i.e., suburban, urban, dense urban and high-rise urban) on the uplink and downlink coverage probabilities and provide design guidelines for best ABS deployment height.

3D UAV placement and user association in software-defined cellular networks

With the onset of unexpected or temporary problems resulting in degraded user performance, the flexibility and elasticity requirements of future cellular networks may not be fully satisfied by fixed ground base stations. A promising solution for this deficiency is to establish drone cells, which are formed by quickly deploying unmanned aerial vehicles (UAVs) equipped with base stations. Consequently, a UAV placement and user association algorithm for future software-defined cellular networks (SDCN) is proposed in this study. In consideration of the optimal three-dimensional placement of UAVs and the optimal drone cell users’ associations, a utility maximization problem is formulated by utilizing a global view of the SDCN controller. Following mathematical manipulation, the intractable multidimensional problem is transformed into a two-phase algorithm involving the optimal UAV placement altitude-to-radius ratio and the optimal two-dimensional (2D) drone cell horizontal coverage combined with user association. Simulation results indicate the superiority of the proposed algorithm, which increases the average throughput and average utility of all users compared with random, center and 2D UAV placement schemes. By deploying the new design, the maximum average throughput gain can reach up to $$36.4\%$$ .

Adaptive tuning of an extended phenotype: honeybees seasonally shift their honey storage to optimize male production

Organisms face the challenge of optimally allocating limited resources among investments that promote survival, growth or reproduction. In species whose members build complex nests, this resource allocation problem also applies to the building and use of the nest structure, a critical part of an individual's extended phenotype. Honeybee colonies face an acute problem of properly allocating one nest resource in particular, large cells of drone comb built for rearing drones, between reproduction (rearing drones) and survival (storing honey). Here the trade-off is inescapable, because a drone cell cannot be used simultaneously for drone production and honey storage. We predicted that the workers in a honeybee colony would solve this problem by preferentially using drone comb for producing drones when their mating opportunities are good (spring and early summer) and for honey storage when the drones' mating opportunities are poor (late summer and autumn). To test our prediction, we experimentally tested how drone comb and worker comb were used for honey storage from April to September. In spring and early summer, workers preferentially removed honey from drone comb, making it available for producing drones. In late summer and autumn, workers did not preferentially remove honey from drone comb. This study shows that a honeybee colony is able to fine-tune its extended phenotype by adaptively allocating a key nest resource, its drone comb, between survival and reproduction.

A comparison of the reproductive ability of Varroa destructor (Mesostigmata:Varroidae) in worker and drone brood of Africanized honey bees (Apis mellifera)

Colony infestation by the parasitic mite, Varroa destructor is one of the most serious problems for beekeeping worldwide. In order to reproduce varroa females, enter worker or drone brood shortly before the cell is sealed. To test the hypothesis that, due to the preference of mites to invade drone brood to reproduce, a high proportion of the mite reproduction should occur in drone cells, a comparative study of mite reproductive rate in worker and drone brood of Africanized honey bees (AHB) was done for 370 mites. After determining the number, developmental stage and sex of the offspring in worker cells, the foundress female mite was immediately transferred into an uninfested drone cell. Mite fertility in single infested worker and drone brood cells was 76.5 and 79.3%, respectively. There was no difference between the groups (X(2)= 0.78, P = 0.37). However, one of the most significant differences in mite reproduction was the higher percentage of mites producing viable offspring (cells that contain one live adult male and at least one adult female mite) in drone cells (38.1%) compared to worker cells (13.8%) (X(2)= 55.4, P < 0.01). Furthermore, a high level of immature offspring occurred in worker cells and not in drone cells (X(2)= 69, P < 0.01). Although no differences were found in the percentage of non-reproducing mites, more than 74% (n = 85) of the mites that did not reproduce in worker brood, produced offspring when they were transferred to drone brood.

The reproductive rate of Varroa destructor in drone brood of Africanized honey bees

SummaryThe reproductive rate of Varroa destructor was measured in drone brood of Africanized honey bees (AHB) in Costa Rica. One mature female mite was introduced into a drone cell within 6 h of the cell being capped. Data were obtained from 304 artificially singly infested drone cells from six colonies. Fertility of varroa mites (i.e., those that produced any offspring) was 95.1%. Fifty-three percent of the foundress mites produced viable female offspring. The number of potentially mated female offspring produced per foundress was 2.6 ± 1.0. Eight percent of foundresses produced only immature offspring, 26.5% produced only a female offspring and 7.6% produced only a male offspring. In cells with both female and male offspring, the male or the female was found dead in 8.7% of the cases; male mortality was 6.1%. Although varroa fertility in drone brood was significant in AHB colonies, factors that limited mite reproduction were the absence of a male and the mortality of a male in more than 26.0% of the drone cells.

Influence of cell type and mandibular dentition on the ability of captive honey bee workers and drones (Apis cerana indica Fabr. and Apis mellifera L.) to uncap brood cells: biological implications for parasitic mites (Varroa spp.)

Experiments to study the ability of adults (workers and drones) of the eastern honey bee, Apis cerana indica Fabr., and the western honey bee, Apis mellifera L., to uncap brood cells were undertaken in Bangalore, India. Using halved gelatin capsules, individual adults were confined opposite the intact cap that sealed vacant, isolated brood cells. From inside the cell, workers and drones of both species penetrated the drone-cell cap of A. mellifera with regularity. When placed outside the cell, workers of both species were successful in perforating the same cap. Similarly, A. c. indica workers easily perforated the cap of A. c. indica worker cells from inside. However, significant caste-specific differences in success at perforating the cap of A. c. indica drone cells were observed; whereas drones did so regularly, A. c. indica workers usually perished when so challenged. When held facing the cap while inside or outside the drone cell, none or only 10% of workers, respectively, managed to chew a hole in it. Even when two workers faced the same drone-cell cap, one from each side, only 20% of caps were perforated. These results are consistent with the hypothesis that the tough apex of the drone cocoon of A. cerana is typically impervious to uncapping from outside, and hence has provided an opportunity for the uninterrupted reproduction of invading parasitic mites (Varroa spp.). The mandibles of drones of both species possess a diminutive apical tooth along the distal margins that is absent in workers. The lack of mandibular teeth in workers may explain the disparity in ability to penetrate the tough cocoon of A. c. indica drones. The mandibles of both castes suffer wear.

Modes of worker reproduction, reproductive dominance and brood cell construction in queenless honeybee (Apis mellifera L.) colonies

Colonies of A. m. capensis, A. m. scutellata and their natural hybrids were dequeened and debrooded. The ratio of worker/drone cell construction and the sex of laying worker offspring were determined for 26 colonies. All A. m. capensis laying workers were thelytokous and all A. m. scutel- lata arrhenotokous. 42.1% of the hybrid colonies produced only female offspring while none produced only male offspring. This shows a significant advantage for thelytokous laying workers to become reproductively dominant in hybrid colonies. A. m. capensis colonies built only worker cells and A. m. scutellata only drone cells. Hybrid colonies produced either both cell types or only worker cells according to the mode of laying worker reproduction. In all colonies where laying workers produced male offspring drone cell building was found. Our data strongly indicates that the mode of worker repro- duction holds important consequences for cell construction and reproductive dominance. Apis mellifera capensis / Apis mellifera scutellata / honeybee / hybrids / laying worker / repro- ductive dominance / brood cell

Sealing up and non-removal of diseased and Varroa jacobsoni infested drone brood cells is part of the hygienic behaviour in Apis cerana

SUMMARYThe occurrence of drone brood cells with a closed pore in the conical cocoon cap of Apis cerana was investigated in the Philippines and in Nepal. Closed pores in A. cerana drone cell caps result from nurse bees that fill in wax material from outside until the pore is plugged, as SEM pictures and a preliminary experiment revealed. All these closed cells contained dead brood that had been killed either by European foulbrood or by Thai sacbrood virus. In Nepal, some dead drone pupae were found infested with Varroa jacobsoni that also had a sealed pore in the cocoon cap. The results of this study demonstrate that sealing up and non-removal of diseased and V. jacobsoni infested drone brood cells can be considered as part of the hygienic behaviour of A. cerana honey bees.

Differences in drone cappings of Apis cerana and Apis nigrocincta

SUMMARYThe nature of the drone cell caps of Apis cerana and A. nigrocincta were examined in South and Central Sulawesi, Indonesia. All drone cells of A. cerana were capped with a hard conical cocoon structure; recently sealed cells also had an external wax capping. The hardened cocoon cap above drone pupae had a central pore. Drone cell caps of A. nigrocincta were sealed with a fragile thin wax capping. The hardened capping with pore typical of A. cerana was always absent from drone cappings of A. nigrocincta, regardless of the age of the drone brood. The significance of this finding is briefly discussed.

Why do Varroa mites invade worker brood cells of the honey bee despite lower reproductive success?

Varroa jacobsoni reproduces both in drone and worker brood cells of honey bees, but in drone cells reproductive success is higher than in worker cells. A simple model using clonal population growth as a fitness measure has been developed to study the circumstances under which specialization on drone brood would be a better strategy than reproduction in both types of cell. For European Apis mellifera, the model suggests that if mites have to wait less than 7 days on average before they can invade a drone cell, specialization on drone brood would be a better strategy. This is close to the estimated waiting time of 6 days. Hence, small differences in reproductive success in drone and worker cells and in the rate of mortality may determine whether specialization on drone brood will be promoted or not. In European A. mellifera colonies, Varroa mites invade both drone and worker cells, but specialization on drone brood cells seems to occur to some extent because drone cells are more frequently invaded than worker cells. In the parasite-host association of V. jacobsoni with African or Africanized A. mellifera or with A. cerana, the mites also invade both drone and worker cells, but the mites specialize on drone brood for reproduction since a large percentage of the mites in worker brood do not reproduce. Only in the parasite-host association of Euvarroa sinhai, a mite closely resembling V. jacobsoni, and A. forea is specialization complete, because these mites only invade drone brood.

Why do Varroa mites invade worker brood cells of the honey bee despite lower reproductive success?

Varroa jacobsoni reproduces both in drone and worker brood cells of honey bees, but in drone cells reproductive success is higher than in worker cells. A simple model using clonal population growth as a fitness measure has been developed to study the circumstances under which specialization on drone brood would be a better strategy than reproduction in both types of cell. For European Apis mellifera, the model suggests that if mites have to wait less than 7 days on average before they can invade a drone cell, specialization on drone brood would be a better strategy. This is close to the estimated waiting time of 6 days. Hence, small differences in reproductive success in drone and worker cells and in the rate of mortality may determine whether specialization on drone brood will be promoted or not. In European A. mellifera colonies, Varroa mites invade both drone and worker cells, but specialization on drone brood cells seems to occur to some extent because drone cells are more frequently invaded than worker cells. In the parasite-host association of V. jacobsoni with African or Africanized A. mellifera or with A. cerana, the mites also invade both drone and worker cells, but the mites specialize on drone brood for reproduction since a large percentage of the mites in worker brood do not reproduce. Only in the parasite-host association of Euvarroa sinhai, a mite closely resembling V. jacobsoni, and A. forea is specialization complete, because these mites only invade drone brood.

Reproduction ofVarroa jacobsoni incellsof Apis melliferacontaining one or more mother mites and the distribution of these cells

SUMMARYThis study was carried out in Devon, UK, using Apis mellifera colonies that were naturally infested with Varroa jacobsoni. Study frames with uniform areas of similarly aged sealed brood were used in order to reduce the possibility of fluctuations in infestation rate occurring. Cells were examined at regular intervals and their contents (mother mites and offspring at different stages of development, alive or dead) were determined. A total of 908 worker cells containing 1334 mother mites and 2671 drone cells containing 3455 mother cells were found in the 3228 worker cells and 16 252 drone cells examined. The number of cells containing different numbers of mother mites did not differ significantly from a random (Poisson) distribution, irrespective of brood type (drone or worker), at any constant infestation level. The maximum number of viable adult offspring (male and female) observed in a single drone or worker cell was 16 and eight respectively. The mean number of eggs laid per mite in both drone and worker cells showed a small but steady decline as the number of mites per cell increased. Mortality of the offspring in drone cells containing up to four mother mites remained steady for the first three offspring then increased as the number of offspring in the cell increased. There was little difference in the number of viable female offspring produced in cells containing one or two mites. Competition or effects between the mother mites were less important than between their offspring and competition at the feeding site may be the major factor in offspring mortality.

Invasion of Varroa jacobsoni into drone brood cells of the honey bee, Apis mellifera

Invasion of Varroa mites into drone brood cells of honey bees was studied in colonies without worker brood. The probability for a mite to invade was dependent on the brood/bees ratio, which is defined as the number of drone brood cells capped per kg of bees. When compared with invasion in colonies with exclusively worker cells, Varroa mites invaded drone cells 11.6 times more frequently. This suggests that the biased distribution of mites over drone and worker cells in colonies with both types of brood cells results predominantly from the higher rate of invasion into a drone cell per se, when compared to that into a worker cell per se. Since the rate of invasion is high in drone cells, a trapping method using drone combs may be very effective in controlling the Varroa mite. When no other brood is present, 462 drone cells are estimated to be sufficient to trap 95% of the mites in a colony of 1 kg of bees

Ontogenesis of the MiteVarroa Jacobsoniin Worker and Drone Honeybee Brood Cells

SummaryOntogenesis of the mite Varroa jacobsoni Oudemans (1904) was observed at 4 h intervals in brood cells of the honeybee Apis mellifera cecropia. Between July and November 1981, in the region of Thessaloniki, 241 worker and 107 drone cells infested with fertile mites were examined in the post-capping period. A macroscopic distinction between the sexes was recorded photographically as early as the first mobile phase of mite ontogenesis. In cells containing either worker or drone brood, egg-laying by the mite began about 60 h after the cell was capped and one egg was laid every 30 min. Normally the offspring included only one male, which developed from an egg laid about 96 h after capping. Ontogenesis lasted 7·5 days in the female Varroa and 5·5 days in the male. The mite can lay at most 7 eggs in a drone cell and 6 in a worker cell.