Resource optimized federated learning-enabled cognitive internet of things for smart industries

Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this article, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Harnessing Maritime Geospatial Information for InsurTech Advancements

peer reviewedThe maritime industry plays a key role in global trade and transportation, but it also poses unique risks and challenges for insurers. In recent years, the availability of maritime geospatial information and advancements in satellite technology and remote sensing capabilities have opened up new opportunities for the insurance sector. This paper explores the potential of harnessing maritime geospatial information for InsurTech advancements. We examine the data sources, including satellite imagery and Automatic Identification System (AIS) that provide valuable insights into vessel movements, traffic patterns, risk factors, and environmental conditions. Furthermore, we explore the challenges and opportunities associated with integrating geospatial information into InsurTech workflows, including issues related to data quality, scalability, and data privacy. The application of data analytics and learning techniques in harnessing maritime geospatial information is also discussed. By leveraging the power of maritime geospatial information and InsurTech advancements, insurers can gain a comprehensive understanding of the maritime domain, enhance risk management strategies, optimize insurance products and pricing, and provide tailored insurance solutions to the maritime industry.9. Industry, innovation and infrastructur

Ruin Theory for User Association and Energy Optimization in Multi-access Edge Computing

In this letter, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: \emph{1) user association phase} and \emph{2) task offloading phase}. In the first phase, a ruin theory-based approach is developed to obtain the users association considering the users' transmission reliability. Meanwhile, in the second phase, an optimization-based algorithm is used to optimize the data offloading process. In particular, ruin theory is used to manage the user association phase, and a ruin probability-based preference profile is considered to control the priority of proposing users. Here, ruin probability is derived by the surplus buffer space of each edge node at each time slot. Giving the association results, an optimization problem is formulated to optimize the amount of offloaded data aiming at minimizing the energy consumption of users. Simulation results show that the developed solutions guarantee system reliability under a tolerable value of surplus buffer size and minimize the total energy consumption of all users.Comment: This paper has been submitted to IEEE Wireless Communications Letter

Latency-sensitive Service Delivery with UAV-Assisted 5G Networks

In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource blocks (RBs) and power allocation, and optimal UAV deployment strategy are studied for UAV-assisted 5G networks to jointly maximize the average sum-rate and minimize the transmit power of UAV while satisfying the URLLC requirements. To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed which derives optimal URLLC scheduling and transmit power strategy. The formulated problem is revealed as a mixed-integer nonlinear programming (MINLP), which is solved following the introduced successive minimization algorithm. Finally, simulation results are provided to show our proposed solution approach's efficiency.Comment: Accepted in IEEE Wireless Communications Letter

A Hopfield Neural Networks Based Mechanism for Coexistence of LTE-U and WiFi Networks in Unlicensed Spectrum

Long-Term Evolution in the unlicensed spectrum (LTE-U) is considered as an indispensable technique to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the base station (BS); however, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raises the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this work, we propose a novel neural networks (NNs) based mechanism for the coexistence of an LTE-U base station (BS) in the unlicensed spectrum alongside with a WiFi access point (WAP). Specifically, we model the coexistence problem as a Hopfield Neural Network (HNN) based optimization problem that aims a fair coexistence considering both the LTE-U data rate and the QoS requirements of the WiFi network. Using the energy function of HNN, precise investigation of its minimization property can directly provide the solution of the optimization problem. Numerical results show that the proposed mechanism allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi network

An Efficient Resource Sharing Model for Multi-UAV-Assisted Wireless Networks

The network capacity is fastened by utilizing unmanned aerial vehicles (UAVs) and mobile users can get feasible services independent of the infrastructure coverage. Furthermore, with the help of network virtualization technology, mobile network operators (MNOs) can lease their cellular network infrastructures and wireless network resources to the service providers (SPs) who are providing specific services to their mobile users. Wireless resource leasing among SPs, on the other hand, is problematic because each aims to maximize its own profit whilst assuring the QoS requirement of their users. Thus, in this paper, we propose a wireless resource sharing problem in the UAVs-assisted virtualized wireless networks with the goal of maximizing the total profit of SPs whilst guaranteeing the QoS requirement of each mobile user and satisfying the resource constraint of the UAVs. Then, we deploy the Lagrangian relaxation-based solution approach in order to address our proposed problem. Finally, we provide detailed numerical results to show the effectiveness of our proposed algorithm.</p