Abstract The conventional cloud‐centric Internet of Things (IoT) application fails to meet the latency requirement of time‐critical applications. The idea of edge and fog computing arrived to distribute workloads across the fog devices located in the local area. However, achieving seamless interoperability, platform independence, and automatic deployment of services becomes the major challenge over heterogeneous fog devices. This paper proposes an integrated and standards‐based fog computing federation framework, FogDEFT, that adapts OASIS–Topology and Orchestration Specification for Cloud Applications (TOSCA) for service deployment in fog. The framework standardizes the distributed application design with TOSCA Service Template to deploy Docker Containers in Swarm mode and manages interoperability over heterogeneous fog devices. The framework uses a lightweight TOSCA compliant orchestrator to dynamically deploy various fog applications (user‐developed services) on the fly.

Modern micro/nanorobots can perform multiple tasks for biomedical and environmental applications. Particularly, magnetic microrobots can be completely controlled by a rotating magnetic field and their motion powered and controlled without the use of toxic fuels, which makes them most promising for biomedical application. Moreover, they are able to form swarms, allowing them to perform specific tasks at a larger scale than a single microrobot. In this work, they developed magnetic microrobots composed of halloysite nanotubes as backbone and iron oxide (Fe3 O4 ) nanoparticles as magnetic material allowing magnetic propulsion and covered these with polyethylenimine to load ampicillin and prevent the microrobots from disassembling. These microrobots exhibit multimodal motion as single robots as well as in swarms. In addition, they can transform from tumbling to spinning motion and vice-versa, and when in swarm mode they can change their motion from vortex to ribbon and back again. Finally, the vortex motion mode is used to penetrate and disrupt the extracellular matrix of Staphylococcus aureus biofilm colonized on titanium mesh used for bone restoration, which improves the effect of the antibiotic's activity. Such magnetic microrobots for biofilm removal from medical implants could reduce implant rejection and improve patients' well-being.

WoTemu: An emulation framework for edge computing architectures based on the Web of Things

This paper proposes an optimization strategy for searching moving targets’ locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV’s scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity.

In order to share the global information and resources efficiently over a huge network topology, the Space-Air-Ground Integrated Network (SAGIN) system integrates a number of networks for optimal utilization. Nonetheless, the related communication units between the mobile platforms and air-to-ground links are constrained to a low-bit rate radio-based technology. In addition, the required services to be supported demand a high capacity link. This will ensure effective management of multiple information in parallel and in real-time. One of attractive systems with inherent features to support the network demands is the free-space optical (FSO) communication system. Nevertheless, drift support in the SAGIN could be challenging for the FSO system. This is due to the required line-of-sight link alignment between the receiver and transmitter modules. Besides, FSO system is susceptible to the atmospheric turbulence-induced fading. This can be addressed by operating unmanned aerial vehicles in the SAGIN system in swarm mode. Conversely, this can bring about channel correlation eventually resulting into system performance impairments. This paper considers the effect of correlated FSO channel on the SAGIN system outage performance. To accomplish this, we consider exponential model for modeling the correlations between the apertures. Moreover, to account for the spatial correlation for different diversity orders in the air-to-ground as well as air-to-air communications, we employ a multivariate Gamma–Gamma (\(\varGamma \varGamma\)) distribution. The results of the analysis appropriately quantify the effects of the atmospheric turbulence-induced fading as well as correlation on the system outage performance.

We aim to obtain a modal decomposition of the internal geomagnetic field. In order to do so, we perform a principal component analysis of two virtual observatory datasets, with 4-month sampling time, from the CHAMP (2001–2009) and Swarm (2014–2017) satellite records. The spatial patterns of well-resolved modes calculated from the three field components all have internal origin as expected for these datasets, except for one Swarm mode. For both datasets, we find that the modes with the shortest timescales have also the smallest length scales as expected from a physical standpoint. Also, the energy ordering of the modes is from the least to the most variable, in agreement with independent results on the main field data spectrum. This is not achieved in regularised inversions of geomagnetic field data into time-varying spherical harmonic decomposition, where the highest degree terms have also the poorest time resolution. The improved accuracy of Swarm data is reflected in the lower level of the noise variance.

The CERN OpenStack cloud has been delivering a wide variety of services to the whole laboratory since it entered in production in 2013. Initially, standard resources such as Virtual Machines and Block Storage were offered. Today, the cloud offering includes advanced features such as Container Orchestration (for Kubernetes, Docker Swarm mode, Mesos/DCOS clusters), File Shares and Bare Metal, and the Cloud team is preparing the addition of Networking and Workflow-as-a-Service components. In this paper, we will describe these advanced features, the OpenStack projects that provide them, as well as some of the main usecases that benefit from them. We will show the ongoing work on those services that will increase functionality, such as container orchestration upgrades and networking features such as private networks and floating IPs.

Data of rice plant is important in Indonesia because rice is the staple food for Indonesian people. Rice plant data can be formatted into web service, so anyone can access the information from anywhere by using internet. But, high numbers of request are the problem for web server apps. One of the solution is by distributing request into some server. In this paper, we will compare failed request and time per request in conventional server and clustered server with docker swarm. Server apps in clustered server shows lower value of failed request than conventional server in our experiment. With two containers, number of failed request obtain 0.78% lower than conventional server in 25.000 requests, and 0.69% lower than conventional server in 50.000 requests.

The space-air-ground integrated network (SAGIN) system interconnect several networks in order to achieve a large network topology that is capable of efficient sharing of global information and resources. Nevertheless, the associated communication facilities between the mobile platforms and air-to-ground links are limited to a low-bit rate radio-based technology. Besides, the huge services to be supported require a high capacity link in order to handle multiple information in parallel and in real-time. The free-space optical (FSO) communication system has inherent features to support the network demands. However, support for drifting in the SAGIN system could be challenging for the FSO line-of-sight links because of the requirement for alignment between the receiver and transmitter modules. Also, the FSO system performance is hindered by the atmospheric turbulence-induced fading. In addition, the unmanned aerial vehicles in the SAGIN system can operate in swarm mode to achieve system diversity in order to alleviate turbulence-induced fading. However, this can lead to channel correlation that can impair the system performance. In this paper, we consider the effect of arbitrarily correlated FSO channel on the system performance. To achieve this, we employ exponential model for modeling the correlations between the apertures. Furthermore, to account for the spatial correlation in the air-to-ground as well as air-to-air communications in the SAGIN system, we consider a multivariate Gamma–Gamma ( $$\varGamma \varGamma$$ ) distribution. The results of the study sufficiently quantify the effects of the atmospheric turbulence-induced fading as well as correlation on the system.

This paper proposes a Switching PSO (SW-PSO) based on entropy of swarm and switch mode. Further, a hybrid method based on SW-PSO and Back Propagation (BP) neural network algorithm has been presented for diesel engine optimization. BP was used to construct prediction model for chemical combustion in engine cylinders, and SW-PSO was employed to optimize engine parameters that achieve higher fuel efficiency and fewer exhausts. SW-PSO proved to be superior in convergence performance, and the hybrid method also showed advantages in dealing with practical engine optimization problem.