Precision medicine (PM) is an innovative medical approach that considers differences in the individuals’ omics, medical histories, lifestyles, and environmental information in treating diseases. To fully achieve the envisaged gains of PM, various contemporary and future technologies have to be employed, among which are nanotechnology, sensor network, big data, and artificial intelligence. These technologies and other applications require a communication network that will enable them to work in tandem for the benefit of PM. Hence, communication technology serves as the nervous system of PM, without which the entire system collapses. Therefore, it is essential to explore and determine the candidate communication technology requirements that can guarantee the envisioned gains of PM. To the best of our knowledge, no work exploring how communication technology directly impacts the development and deployment of PM solutions exists. This survey paper is designed to stimulate discussions on PM from the communication engineering perspective. We introduce the fundamentals of PM and the demands in terms of quality of service that each of the enabling technologies of PM places on the communication network. We explore the information in the literature to suggest the ideal metric values of the key performance indicators for the implementation of the different components of PM. The comparative analysis of the suitability of the contemporary and future communication technologies for PM implementation is discussed. Finally, some open research challenges for the candidate communication technologies that will enable the full implementation of PM solutions are highlighted.

The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.

Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances.

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.

The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8⁺ T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. 

As a novel network infrastructure that realizes the interconnection of humans, machines, and things, the 5G-based or blockchain-based applications have been widely deployed in the Industrial Internet of Things (IIoT). However, there are still many challenges to be solved, such as poor scalability, low efficiency, and privacy leakages. In the special issue, we present eight advanced solutions in data analysis, secure communication, authentication, access control, and data deduplication.

To be able to leverage big data to achieve enhanced strategic insight, process optimization and make informed decision, we need to be an efficient access control mechanism for ensuring end-to-end security of such information asset. Signcryption is one of several promising techniques to simultaneously achieve big data confidentiality and authenticity. However, signcryption suffers from the limitation of not being able to revoke users from a large-scale system efficiently. We put forward, in this paper, the first identity-based (ID-based) signcryption scheme with efficient revocation as well as the feature to outsource unsigncryption to enable secure big data communications between data collectors and data analytical system(s). Our scheme is designed to achieve end-to-end confidentiality, authentication, non-repudiation, and integrity simultaneously, while providing scalable revocation functionality such that the overhead demanded by the private key generator (PKG) in the key-update phase only increases logarithmically based on the cardiality of users. Although in our scheme the majority of the unsigncryption tasks are outsourced to an untrusted cloud server, this approach does not affect the security of the proposed scheme. We then prove the security of our scheme, as well as demonstrating its utility using simulations

The Internet of Things is envisioned to provide connectivity and communication between various devices all over the world. Based on the devices and applications involved, the nature of the network formed differs. Thus, an intelligent and holistic ecosystem needs to be created wherein clients, data sources, smart objects, and services can all co-exist and interact with each other. We present Adaptive Ubiquitous Middleware for context-aware IoT ecosystems, which considers the situational context of the applications, devices, or people and the contexts of the network formed and accordingly adapts the behavior of the ecosystem. Adaptive Ubiquitous Middleware is a multi-agent, multi-communication protocol-facilitated middleware that acts as an integration point for applications to access relevant context, share it with other applications, and have relevant services made available via a multi-communication protocol bridge. We also present an optimal service allocation model for a single service class that utilizes available computing resources and achieves a minimum average response time. The system implementation has been evaluated with two use cases to demonstrate its applicability, effectiveness, and generality. The evaluation of optimal service allocation demonstrates the service response is much faster in the proposed model.