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Guest Editorial: Internet of Things for In-Home Health Monitoring IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2021-01-14 Joel J. P. C. Rodrigues; Honggang Wang; Simon James Fong; Nada Y. Philip; Jia Chen
Under the pressure of the growing millennial population and senior citizens are aging, which is one of the top societal priorities in many countries, the provision of healthcare needs to evolve and improve. A roadmap paved by World Health Organization (WHO) in March 2019 called Global Strategy on Digital Health 2020-2024, specified a grand vision of promoting healthy lives and well-beings for everyone
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Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2021-01-14 Nada Y. Philip; Joel J. P. C. Rodrigues; Honggang Wang; Simon James Fong; Jia Chen
Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations
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Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-12-25 Zhaolong Ning; Peiran Dong; Xiaojie Wang; Xiping Hu; Lei Guo; Bin Hu; Yi Guo; Tie Qiu; Ricky Y. K. Kwok
The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home
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Series Editorial: Inauguration Issue of the Series on Machine Learning in Communications and Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-12-15 Geoffrey Y. Li; Walid Saad; Ayfer Ozgur; Peter Kairouz; Zhijin Qin; Jakob Hoydis; Zhu Han; Deniz Gunduz; Jaafar Elmirghani
In the era of the new generation of communication systems, data traffic is expected to continuously strain the capacity of future communication networks. Along with the remarkable growth in data traffic, new applications, such as wearable devices, autonomous systems, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different requirements. This growth
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Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Wei Chen; Bowen Zhang; Shi Jin; Bo Ai; Zhangdui Zhong
Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key
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High Dimensional Channel Estimation Using Deep Generative Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Eren Balevi; Akash Doshi; Ajil Jalal; Alexandros Dimakis; Jeffrey G. Andrews
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires
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Model Refinement Learning and an Example on Channel Estimation With Universal Noise Model IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-11 Hui-Ming Wang; Jia-Cheng Jiang; Yu-Ning Wang
Model-based method and data-based method are two basic approaches for the design of wireless communication systems. Model-based methods suffer from inaccurate modeling assumptions due to excessively complex environment. Recently, data-based methods have achieved remarkable performances in the communication system design without the knowledge of accurate model but encounter some challenges such as,
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Generative-Adversarial-Network Enabled Signal Detection for Communication Systems With Unknown Channel Models IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Li Sun; Yuwei Wang; A. Lee Swindlehurst; Xiao Tang
The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater
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Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Yuxin Lu; Peng Cheng; Zhuo Chen; Wai Ho Mow; Yonghui Li; Branka Vucetic
Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several
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perm2vec: Attentive Graph Permutation Selection for Decoding of Error Correction Codes IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Avi Caciularu; Nir Raviv; Tomer Raviv; Jacob Goldberger; Yair Be’ery
Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum likelihood rule, which is NP-hard due to the curse of dimensionality . For practical realizations, sub-optimal decoding algorithms are employed; yet limited theoretical insights prevent one from exploiting the full potential
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Wireless Image Retrieval at the Edge IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-16 Mikolaj Jankowski; Deniz Gündüz; Krystian Mikolajczyk
We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize the accuracy of the retrieval task under power and bandwidth constraints over the wireless link. Due
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Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Yifei Shen; Yuanming Shi; Jun Zhang; Khaled B. Letaief
Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target
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Situation-Aware Resource Allocation for Multi-Dimensional Intelligent Multiple Access: A Proactive Deep Learning Framework IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Yanan Liu; Xianbin Wang; Jie Mei; Gary Boudreau; Hatem Abou-Zeid; Akram Bin Sediq
To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional communication resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, a deep learning-based framework for multi-dimensional intelligent multiple access (MD-IMA) scheme is developed for beyond 5G and 6G wireless
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Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-10 Haixia Peng; Xuemin Shen
In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller
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A Lite Distributed Semantic Communication System for Internet of Things IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Huiqiang Xie; Zhijin Qin
The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs
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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Xiaofei Wang; Ruibin Li; Chenyang Wang; Xiuhua Li; Tarik Taleb; Victor C. M. Leung
In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In
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Privacy for Free: Wireless Federated Learning via Uncoded Transmission With Adaptive Power Control IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Dongzhu Liu; Osvaldo Simeone
Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL can potentially reduce the information on the local data sets that is leaked via communications. In order to provide formal privacy guarantees, however, it is generally necessary to put in place additional masking mechanisms
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Optimal Contract Design for Efficient Federated Learning With Multi-Dimensional Private Information IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Ningning Ding; Zhixuan Fang; Jianwei Huang
As an emerging machine learning technique, federated learning has received significant attention recently due to its promising performance in mitigating privacy risks and costs. While most of the existing work of federated learning focused on designing learning algorithm to improve training performance, the incentive issue for encouraging users’ participation is still under-explored. This paper presents
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Fast-Convergent Federated Learning IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Hung T. Nguyen; Vikash Sehwag; Seyyedali Hosseinalipour; Christopher G. Brinton; Mung Chiang; H. Vincent Poor
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training
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Accelerating DNN Training in Wireless Federated Edge Learning Systems IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Jinke Ren; Guanding Yu; Guangyao Ding
Training task in classical machine learning models, such as deep neural networks, is generally implemented at a remote cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since a large amount of data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged
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Coded Computing for Low-Latency Federated Learning Over Wireless Edge Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Saurav Prakash; Sagar Dhakal; Mustafa Riza Akdeniz; Yair Yona; Shilpa Talwar; Salman Avestimehr; Nageen Himayat
Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel
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Seek Common While Shelving Differences: Orchestrating Deep Neural Networks for Edge Service Provisioning IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Lixing Chen; Jie Xu
Edge computing (EC) platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to users, are providing ultra-low latency and location-awareness to a rich portfolio of services. As monetary costs are incurred for renting computing resources on edge servers to enable service provisioning, ASP has to cautiously decide where to deploy the application and how
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Wanna Make Your TCP Scheme Great for Cellular Networks? Let Machines Do It for You! IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-09 Soheil Abbasloo; Chen-Yu Yen; H. Jonathan Chao
Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can enable machines to automatically boost the performance of the existing/future TCP designs in cellular networks? We answer this question by introducing DeepCC. DeepCC leverages advanced deep reinforcement learning (DRL) techniques to let machines automatically learn how to steer throughput-oriented TCP algorithms
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Physics-Based Deep Learning for Fiber-Optic Communication Systems IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-16 Christian Häger; Henry D. Pfister
We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities
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Table of contents IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-19
Presents the table of contents for this issue of the publication.
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IEEE Journal on Selected Areas in Communications IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-19
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Guest Editorial 5G Wireless Communications With High Mobility IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-19 Ruisi He; Fan Bai; Guoqiang Mao; Jérôme Härri; Pekka Kyösti
The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1] – [3] . The 5G systems will provide advanced communication
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IEEE Communications Society Information IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-11-19
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-10-16
Presents the table of contents for this issue of the publication.
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IEEE Journal on Selected Areas in Communications IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-10-16
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Guest Editorial Special Issue on “Wireless Networks Empowered by Reconfigurable Intelligent Surfaces” IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-10-16 Marco Di Renzo; Merouane Debbah; Mohamed-Slim Alouini; Chau Yuen; Thomas Marzetta; Alessio Zappone
Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.
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IEEE Communications Society Information IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-10-16
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Markov Models for Anomaly Detection in Wireless Body Area Networks for Secure Health Monitoring IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-29 Osman Salem; Khalid Alsubhi; Ahmed Mehaoua; Raouf Boutaba
The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health
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Table of contents IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-15
Presents the table of contents for this issue of the publication.
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IEEE Journal on Selected Areas in Communications IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-15
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-15 Prosper Chemouil; Pan Hui; Wolfgang Kellerer; Noura Limam; Rolf Stadler; Yonggang Wen
Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy
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IEEE Communications Society Information IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-15
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Smart Mutual Authentication Protocol for Cloud Based Medical Healthcare Systems Using Internet of Medical Things IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-07 B. D. Deebak; Fadi Al-Turjman
Technological development expands the computation process of smart devices that adopt the telecare medical information system (TMIS) to fulfill the demands of the healthcare organization. It provides better medical identification to claim the features namely trustworthy, efficient, and resourceful. Moreover, the telecare services automate the remote healthcare monitoring process to ease professional
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Exploiting Transfer Learning for Emotion Recognition Under Cloud-Edge-Client Collaborations IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-07 Dapeng Wu; Xiaojuan Han; Zhigang Yang; Ruyan Wang
Emerging virtual reality/augmented reality games and self-driving cars necessitate accurate/responsive/private emotion recognition. Usually, traditional emotion recognition models are deployed at central servers, which results in the lack of abilities in generalization and covering the individual variation of clients. This paper proposes a responsive, localized, and private transfer learning based
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Reservation Service: Trusted Relay Selection for Edge Computing Services in Vehicular Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-07 Yilong Hui; Zhou Su; Tom H. Luan; Changle Li
Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction
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xTSeH: A Trusted Platform Module Sharing Scheme Towards Smart IoT-eHealth Devices IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-04 Di Lu; Ruidong Han; Yulong Shen; Xuewen Dong; Jianfeng Ma; Xiaojiang Du; Mohsen Guizani
IoT based eHealth system brings a revolution to healthcare industry, with which the old healthcare systems can be updated into smarter and more personalized ones. The practitioners can continue monitoring the physical status of the patients at anytime and anywhere, and develop more precise treatment plans by analyzing the collected data, such as heart rate, blood pressure, blood glucose. Actually,
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Learning-Based URLLC-Aware Task Offloading for Internet of Health Things IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-04 Zhenyu Zhou; Zhao Wang; Haijun Yu; Haijun Liao; Shahid Mumtaz; Luís Oliveira; Valerio Frascolla
In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency
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Cross-Modal Stream Scheduling for eHealth IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Liang Zhou; Dan Wu; Xin Wei; Jianxin Chen
Cross-modal applications that elaborately integrate audio, video, and haptic streams will become the mainstream of the eHealth systems. However, existing stream schedulers usually fail to simultaneously meet the cross-modal transmission requests in terms of low latency, high reliability, high throughput, and low complexity. To circumvent this dilemma, this article proposes a general cross-modal stream
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CURATE: On-Demand Orchestration of Services for Health Emergencies Prediction and Mitigation IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Luis Sanabria-Russo; Jordi Serra; David Pubill; Christos Verikoukis
Telemedicine, or the ability granted to doctors to remotely assist patients has been greatly benefited by advances in IoT, network communications, Machine Learning and Edge/Cloud computing. With the impeding arrival of 5G, virtualized infrastructures and cloud-native approaches enable the execution of unprecedented procedures during such patient/doctor interactions, allowing medical professionals to
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Simulation-Driven Platform for Edge-Based AAL Systems IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Gianluca Aloi; Giancarlo Fortino; Raffaele Gravina; Pasquale Pace; Claudio Savaglio
The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which
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A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Healthcare Monitoring IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Gagangeet Singh Aujla; Anish Jindal
The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the
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Learning From Mislabeled Training Data Through Ambiguous Learning for In-Home Health Monitoring IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Weiwei Yuan; Guangjie Han; Donghai Guan
Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training
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Leveraging IoT Wearable Technology Towards Early Diagnosis of Neurological Diseases IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Andrea Sciarrone; Igor Bisio; Chiara Garibotto; Fabio Lavagetto; Gerhard H. Staude; Andreas Knopp
The leading trends in the framework of the Internet of Things are driving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. One of the major opportunities offered by IoT lies in the ubiquitous connectivity, thus enabling smart services such as remote patient monitoring, in-home therapy/rehabilitation, and assisted living platforms
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Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson’s Disease Patient IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-03 Mohsin Raza; Muhammad Awais; Nishant Singh; Muhammad Imran; Sajjad Hussain
Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring
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Accurate Respiration Monitoring for Mobile Users With Commercial RFID Devices IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-02 Shigeng Zhang; Xuan Liu; Yangyang Liu; Bo Ding; Song Guo; Jianxin Wang
Vital signs (e.g., respiration rate or heartbeat rate) sensing is of great importance to implement pervasive in-home healthcare. Traditional vital signs monitoring approaches usually require users to wear some dedicated sensors. These approaches are intrusive and inconvenient to use, especially for elderly people. Some non-intrusive vital signs monitoring approaches based on wireless sensing have been
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Table of contents IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-08-28
Presents the table of contents for this issue of the publication.
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IEEE Journal on Selected Areas in Communications IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-08-28
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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how can you get your idea to market first IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-08-28
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Information for Authors IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-08-28
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Communications Society Information IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-08-28
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Design of an MISO-SWIPT-Aided Code-Index Modulated Multi-Carrier M-DCSK System for e-Health IoT IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-01 Guofa Cai; Yi Fang; Pingping Chen; Guojun Han; Guoen Cai; Yang Song
Code index modulated multi-carrier M -ary differential chaos shift keying (CIM-MC- M -DCSK) system not only inherits low-power and low-complexity advantages of the conventional DCSK system, but also significantly increases the transmission rate. This feature is of particular importance to Internet of Things (IoT) with trillions of low-cost devices. In particular, for e-health IoT applications, an efficient
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HARCI: A Two-Way Authentication Protocol for Three Entity Healthcare IoT Networks IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-01 Tejasvi Alladi; Vinay Chamola; Naren
With the recent use of IoT in the field of healthcare, a lot of patient data is being transmitted and made available online. This necessitates sufficient security measures to be put in place to prevent the possibilities of cyberattacks. In this regard, several authentication techniques have been designed in recent times to mitigate these challenges, but the physical security of the healthcare IoT devices
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Efficient Identity-Based Distributed Decryption Scheme for Electronic Personal Health Record Sharing System IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-01 Yudi Zhang; Debiao He; Mohammad S. Obaidat; Pandi Vijayakumar; Kuei-Fang Hsiao
The rapid development of the Internet of Things (IoT) has led to the emergence of more and more novel applications in recent years. One of them is the e-health system, which can provide people with high-quality and convenient health care. Meanwhile, it is a key issue and challenge to protect the privacy and security of the user’s personal health record. Some cryptographic methods have been proposed
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A Multi-Stage Stochastic Programming-Based Offloading Policy for Fog Enabled IoT-eHealth IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-01 Long Zhang; Bin Cao; Yun Li; Mugen Peng; Gang Feng
To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers
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Remote Monitoring of Physical Rehabilitation of Stroke Patients Using IoT and Virtual Reality IEEE J. Sel. Area. Comm. (IF 11.42) Pub Date : 2020-09-01 Octavian Postolache; D. Jude Hemanth; Ricardo Alexandre; Deepak Gupta; Oana Geman; Ashish Khanna
The statistics highlights that physical rehabilitation are required nowadays by increased number of people that are affected by motor impairments caused by accidents or aging. Among the most common causes of disability in adults are strokes or cerebral palsy. To reduce the costs preserving the quality of services new solutions based on current technologies in the area of physiotherapy are emerging