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Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device's energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.

Hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening and generally underdiagnosed clinical entity. This syndrome describes patients with severe systemic hyperinflammation. The diagnosis is made on the basis of clinical, genetic and analytical criteria. Two main types of HLH are described: primary and secondary. Primary HLH is associated with genetic defects in: perforin (PRF), UNC13D, Syntaxin 11, Syntaxin BP2, molecules involved in the mechanism of cellular cytotoxicity. The sHLH are associated with infectious causes such as Epstein Barr virus (EBV), tuberculosis, brucellosis, leishmaniasis and SARS-CoV-2, rheumatologic (in this case it is defined as macrophage activation syndrome) and hemato-oncological causes., La linfohistiocitosis hemofagocítica (HLH) es una entidad clínica poco frecuente, potencialmente mortal. Este síndrome describe a pacientes con hiperinflamación sistémica grave. El diagnóstico se realiza en función de criterios clínicos, genéticos y de laboratorio. Se describen dos tipos principales de HLH: primaria y secundaria. La HLH primaria está asociada a defectos genéticos en: perforina, UNC13D, sintaxina 11, sintaxina BP2, moléculas intervinientes en el mecanismo de citotoxicidad celular. Las HLH secundarias (sLHL) se asocian a causasinfecciosas (virus Epstein Barr -VEB-, tuberculosis -TBC-, brucelosis, leishmaniasis, SARS-CoV-2), reumatológicas (síndrome de activación macrofágica) y hematooncológicas.

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.

© National Academy of Sciences: Transportation Research Board 2020. New data, new technologies, and greater computational power are changing the possibilities for involving stakeholders in transportation planning. This article explores these possibilities by comparing deployments of an interactive web-based public transportation mapping system in four cities of the Global South. Structured workshops focused on different public transportation improvement projects and involved different types of stakeholders. Despite the differences across the workshops, they allow for some comparison of the effects. In relation to the technology itself, participants broadly agreed about its usefulness and usability. Pre-/post-workshop surveys suggest that participants learned about the transportation projects presented but reveal modest evidence in relation to other impacts, such as learning about the concept of accessibility, and gaining an appreciation for the potential broader urban system impacts of public transport projects. Knowledge among the participants tended to converge, and the tool helped promote dialog among participants and generated some empathy for others. The workshop experiences provide some hope for such tools in enhancing public transportation planning processes, globally, but ‘‘scaling up’’ the technology would need to overcome institutional, technical, and procedural challenges.

Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect data from ground sensors in remote and hostile areas. A key challenge is the joint design of the velocities and data collection schedules of the UAVs, as inadequate velocities and schedules would lead to failed transmissions and buffer overflows of sensors and, in turn, significant packet losses. In this paper, we optimize jointly the velocity controls and data collection schedules of multiple UAVs to minimize data losses, adapting to the battery levels, queue lengths and channel conditions of the ground sensors, and the trajectories of the UAVs. In the absence of the up-to-date knowledge of the ground sensors' states, a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MADRL-SA) is proposed to allow the UAVs to asymptotically minimize the data loss of the system under the outdated knowledge of the network states at individual UAVs. Numerical results demonstrate that the proposed MADRL-SA reduces the packet loss by up to 54% and 46% in the considered simulation setting, as compared to an existing DRL solution with single-UAV and non-learning greedy heuristic, respectively.

Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q -network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.

Los microorganismos pertenecientes al género Streptomyces son de los mayores productores de antibióticos, representando más del 50% del total de compuestos antimicrobianos producidos. En este trabajo se evaluó la actividad antibacteriana de 57 Actinomicetos y 19 bacterias bacilares provenientes de diferentes muestras de suelo obtenidas de diferentes regiones de nuestro país, donde se realizó el aislamiento en medio de papa dextrosa adicionado con extractos de malta y levadura, y después en experimentos en placa Petri se confrontaron cada uno de los microorganismos aislados contra 2 bacterias fitopatógenas y 8 bacterias de interés médico. Los resultados obtenidos indicaron que el 53% (41) de todos los aislados presentaron actividad al menos contra una de las bacterias. Las bacterias B. subtilis, B. cereus y S. aureus fueron inhibidas por un mayor número de aislados, mientras que contra Pseudomonas sp., K. pneumoniae, L. monocytogenes, Salmonella sp. y E. coli fueron pocas las especies que presentaron actividad inhibitoria (1-4 aislados). Los mejores resultados de inhibición bacteriana se obtuvieron sobre las cepas Gram (+), contra K. pneumoniae una cepa presentó 100% de inhibición.

Los microorganismos pertenecientes al género Streptomyces son de los mayores productores de antibióticos, representando más del 50% del total de compuestos antimicrobianos producidos. En este trabajo se evaluó la actividad antibacteriana de 57 Actinomicetos y 19 bacterias bacilares provenientes de diferentes muestras de suelo obtenidas de diferentes regiones de nuestro país, donde se realizó el aislamiento en medio de papa dextrosa adicionado con extractos de malta y levadura, y después en experimentos en placa Petri se confrontaron cada uno de los microorganismos aislados contra 2 bacterias fitopatógenas y 8 bacterias de interés médico. Los resultados obtenidos indicaron que el 53% (41) de todos los aislados presentaron actividad al menos contra una de las bacterias. Las bacterias B. subtilis, B. cereus y S. aureus fueron inhibidas por un mayor número de aislados, mientras que contra Pseudomonas sp., K. pneumoniae, L. monocytogenes, Salmonella sp. y E. coli fueron pocas las especies que presentaron actividad inhibitoria (1-4 aislados). Los mejores resultados de inhibición bacteriana se obtuvieron sobre las cepas Gram (+), contra K. pneumoniae una cepa presentó 100% de inhibición.

© 2000-2011 IEEE. In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads' throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV’s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon's driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multihop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests.We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

© 2017 IEEE. In a wireless powered sensor network, a base station transfers power to sensors by using wireless power transfer (WPT). Inadequately scheduling WPT and data transmission causes fast battery drainage and data queue overflow of some sensors who could have potentially gained high data reception. In this paper, scheduling WPT and data transmission is formulated as a Markov decision process (MDP) by jointly considering sensors' energy consumption and data queue. In practical scenarios, the prior knowledge about battery level and data queue length in MDP is not available at the base station. We study reinforcement learning at the sensors to find a transmission scheduling strategy, minimizing data packet loss. An optimal scheduling strategy with full-state information is also investigated, assuming that the complete battery level and data queue information are well known by the base station. This presents the lower bound of the data packet loss in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcement learning scheduling algorithm significantly reduces network packet loss rate by 60%, and increases network goodput by 67%, compared to existing non-MDP greedy approaches. Moreover, comparing the optimal solutions, the performance loss due to the lack of sensors' full-state information is less than 4.6%.

© 1967-2012 IEEE. Unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. Criminals or terrorists can potentially use UAVs for committing crimes and terrorism, thus endangering public safety. In this paper, we consider that a legitimate UAV is employed to track flight of suspicious UAVs for preventing safety and security threats. To obtain flight information of the suspicious UAVs, the legitimate UAV intentionally jams the suspicious receiver so as to force the suspicious UAV to reduce its data rate, and hence increase the eavesdropping success. An energy-efficient jamming strategy is proposed for the legitimate UAV to maximize the amount of eavesdropped packets. Moreover, a tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter's signal. A new simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in MATLAB) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of packet eavesdropping rate and tracking accuracy of the suspicious UAVs' trajectory.

© 1967-2012 IEEE. Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-To-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.