Asynchronous FDRL-based low-latency computation offloading for integrated terrestrial and non-terrestrial power IoT

Integrated terrestrial and non-terrestrial power internet of things (IPIoT) has emerged as a paradigm shift to three-dimensional vertical communication networks for power systems in the 6G era. Computation offloading plays key roles in enabling real-time data processing and analysis for electric services. However, computation offloading in IPIoT still faces challenges of coupling between task offloading and computation resource allocation, resource heterogeneity and dynamics, and degraded model training caused by electromagnetic interference (EMI). In this article, we propose an asynchronous federated deep reinforcement learning (AFDRL)-based computation offloading framework for IPIoT, where models are uploaded asynchronously for federated averaging to relieve network congestion and improve global model training. Then, we propose Asynchronous fedeRated deep reinforcemenT learnIng-baSed low-laTency computation offloading algorithm (ARTIST) to realize low-latency computation offloading through joint optimization of task offloading and computation resource allocation. Particularly, ARTIST adopts EMI-aware federated set determination to remove aberrant local models from federated averaging and improve training accuracy. Next, a case study is developed to validate the excellent performance of ARTIST in reducing task offloading and total queuing delays

Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput

Optimizing C-RAN Backhaul Topologies: A Resilience-Oriented Approach Using Graph Invariants

ABSTRACT: At the verge of the launch of the first commercial fifth generation (5G) system, trends in wireless and optical networks are proceeding toward increasingly dense deployments, supporting resilient interconnection for applications that carry higher and higher capacity and tighter latency requirements. These developments put increasing pressure on network backhaul and drive the need for a re-examination of traditional backhaul topologies. Challenges of impending networks cannot be tackled by star and ring approaches due to their lack of intrinsic survivability and resilience properties, respectively. In support of this re-examination, we propose a backhaul topology design method that formulates the topology optimization as a graph optimization problem by capturing both the objective and constraints of optimization in graph invariants. Our graph theoretic approach leverages well studied mathematical techniques to provide a more systematic alternative to traditional approaches to backhaul design. Specifically, herein, we optimize over some known graph invariants, such as maximum node degree, topology diameter, average distance, and edge betweenness, as well as over a new invariant called node Wiener impact, to achieve baseline backhaul topologies that match the needs for resilient future wireless and optical networks

Water safety in healthcare facilities. The Vieste Charter

The Study Group on Hospital Hygiene of the Italian Society of Hygiene, Preventive Medicine and Public Health (GISIO-SItI) and the Local Health Authority of Foggia, Apulia, Italy, after the National Convention "Safe water in healthcare facilities" held in Vieste-Pugnochiuso on 27-28 May 2016, present the "Vieste Charter", drawn up in collaboration with experts from the National Institute of Health and the Ministry of Health. This paper considers the risk factors that may affect the water safety in healthcare facilities and reports the current regulatory frameworks governing the management of installations and the quality of the water. The Authors promote a careful analysis of the risks that characterize the health facilities, for the control of which specific actions are recommended in various areas, including water safety plans; approval of treatments; healthcare facilities responsibility, installation and maintenance of facilities; multidisciplinary approach; education and research; regional and national coordination; communication

Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications

The goal of this work is to propose an energy-efficient algorithm for dynamic computation offloading, in a multi-access edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delay-critical applications, incorporating an upper bound on the probability that the overall time required to send the data and process them exceeds a prescribed value. In a dynamic setting, the above constraint translates into preventing the sum of the communication and computation queues' lengths from exceeding a given value. Ultra-reliable low latency communications (URLLC) are also taken into account using finite blocklengths and reliability constraints. The proposed algorithm, based on stochastic optimization, strikes an optimal balance between the service delay and the energy spent at the mobile device, while guaranteeing a target out-of-service probability. Starting from a long-term average optimization problem, our algorithm is based on the solution of a convex problem in each time slot, which is provided with a very fast iterative strategy. Finally, we extend the approach to mobile devices having energy harvesting capabilities, typical of Internet of Things scenarios, thus devising an energy efficient dynamic offloading strategy that stabilizes the battery level of each device around a prescribed operating level

Escherichia coli strains isolated from retail meat products: Evaluation of biofilm formation ability, antibiotic resistance, and phylogenetic group analysis

Escherichia coli is a ubiquitous organism capable of forming a biofilm. This is an important virulence factor and is critical in certain diseases and in the development of antibiotic resistance, which is increased by biofilm synthesis. In the present study, the potential health risk associated with handling and consumption of foods of animal origin contaminated with E. coli- producing biofilm was evaluated. We analyzed the ability of 182 E. coli strains isolated from pork, poultry, and beef, purchased in three different supermarkets in the area of the "Italian Food Valley" (Parma, northern Italy), to form biofilms. Positive strains were also tested for the presence of 12 biofilm-associated genes. Moreover, the 182 E. coli were characterized for antibiotic resistance, presence of multidrug resistance, extended-spectrum β-lactamase strains, and phylogenetic diversity through PCR. Twenty-five percent of the isolates produced biofilm. The majority showed weak adherence, five were moderate, and three were strong producers. E. coli with a strong adherence capability (three of three) harbored eight biofilm-associated genes, while weak and moderate producers harbored only five (frequencies ranging from 80 to 100%). Multidrug resistance was observed in 20 biofilm-producing E. coli, and 15 of these belonged to phylogenetic group D. Among nonbiofilm producers, the percentage of strains belonging to phylogenetic groups B2 and D was approximately 40%, highlighting a potential health risk for consumers and people handling contaminated products. The present study underlines the importance of monitoring the prevalence and characteristics of E. coli contaminating retail meat in relation to the potential virulence highlighted here

Energy-efficient hardware architectures for the packet data convergence protocol in LTE-advanced mobile terminals

In this paper, we present and compare efficient low-power hardware architectures for accelerating the Packet Data Convergence Protocol (PDCP) in LTE and LTE-Advanced mobile terminals. Specifically, our work proposes the design of two cores: a crypto engine for the Evolved Packet System Encryption Algorithm (128-EEA2) that is based on the AES cipher and a coprocessor for the Least Significant Bit (LSB) encoding mechanism of the Robust Header Compression (ROHC) algorithm. With respect to the former, first we propose a reference architecture, which reflects a basic implementation of the algorithm, then we identify area and power bottle-necks in the design and finally we introduce and compare several architectures targeting the most power-consuming operations. With respect to the LSB coprocessor, we propose a novel implementation based on a one-hot encoding, thereby reducing hardware’s logic switching rate. Architectural hardware analysis is performed using Faraday’s 90 nm standard-cell library. The obtained results, when compared against the reference architecture, show that these novel architectures achieve significant improvements, namely, 25% in area and 35% in power consumption for the 128-EEA2 crypto-core, and even more important reductions are seen for the LSB coprocessor, that is, 36% in area and 50% in power consumption

Antibiotic Treatment Administered to Pigs and Antibiotic Resistance of Escherichia coli Isolated from Their Feces and Carcasses

Antimicrobial resistance (AMR) in bacteria is a frequent and widespread phenomenon. The European Food Safety Authority (EFSA) reports that multidrug resistant (MDR) Escherichia coli is considered an important hazard to public health. The lack of data on the correlation between the administration of antibiotics to pigs and the diffusion of MDR E. coli necessitates an in-depth study. The aims of our study were first of all to determine the presence of MDR and/or extended spectrum β-lactamase (ESβL) E. coli isolated from feces and carcasses of pigs; and second, to evaluate the correlation between antibiotic resistance and the antibiotic treatment administrated to the animals considered. The examined E. coli was isolated from 100 fecal swabs and 100 carcass sponges taken from farms and slaughterhouses located in Reggio Emilia province in Italy. The MDR isolates were tested following the protocol defined by EUCAST (2015). Subsequently, a real-time PCR and an endpoint-PCR were used for the genomic analysis. Data highlighted 76.5% of MDR E. coli with a marked presence of the ampicillin (AMP)-streptomycin (STRE)-tetracycline (TETRA) pattern. Moreover, 13 isolates were ESβL producers, and the blaCTXM gene was the most frequently observed in genomic analysis. Results confirm the complexity of the AMR phenomenon showing a partial correlation between the administration of antibiotics and the resistance observed. Pigs destined to the production of Protected Designation of Origin items are colonized by bacteria resistant to a wide range of antibiotic classes even if data are encouraging for colistin and third generation cephalosporin. Furthermore, in-depth study focused on food production could be useful in a view of high safety standards for consumers