Intelligent Big Data Analytics for Internet of Things, Services and People
Submission Deadline: 30 June 2021
IEEE Access invites manuscript submissions in the area of Intelligent Big Data Analytics for Internet of Things, Services and People.
In the envisaged future internet, which consists of billions of digital devices, people, services and other physical objects, people will utilize these digital devices and physical objects to exchange data about themselves and their perceived surrounding environments over a web-based service infrastructure, in what we refer to as the Internet of Things. Because of its openness, multi-source heterogeneity, and ubiquity, interconnecting things, services and people via the internet improves data analysis, boosts productivity, enhances reliability, saves energy and costs, and generates new revenue opportunities through innovative business models. However, the increasing number of IoT users and services leads to fast-growing IoT data, while the quality of service of IoT should also be maintained regardless of the number of IoT users and services. Therefore, the data transmission and processing in IoT should be performed in a more intelligent manner. A large number of computational intelligent technologies such as artificial neural networks, machine learning and data mining can be applied in IoT to improve the IoT data transmission and processing. The adoption of intelligence technologies and big data in handling IoT could offer a number of advantages as big data technology could handle various data effectively, while artificial intelligence technology could further facilitate capturing and structuring the big data.
This Special Section in IEEE Access will focus on intelligent big data analytics for advancing IoT. Novel applications by the integration of big data and artificial intelligence for IoT are particularly welcome.
The topics of interest include, but are not limited to:
- Big-data analytics in IoT
- Machine learning algorithms in IoT
- Scalable/parallel/distributed algorithms in IoT
- Privacy preserving and security approaches for large scale analytics in IoT
- Big data technology for intelligent system
- Artificial intelligence technology for data integration in IoT
- Artificial intelligence technology for data mining in IoT
- Artificial intelligence technology for data prediction in IoT
- Artificial intelligence technology for data storage in IoT
- Artificial intelligence technology for multimedia data processing
- Intelligent optimization algorithms in IoT
- Advances in artificial learning and their applications for information security
- Intelligent big data analytics for prediction and applications in IoT
- Novel applications of intelligent big data analytics for IoT
- Big data technology for intelligent monitoring in IoT
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Zhaoqing Pan, Nanjing University of Information Science and Technology, China
Guest Editors:
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- Yang Xiao, University of Alabama, USA
- Muhammad Khurram Khan, King Saud University, Saudi Arabia
- Markku Oivo, University of Oulu, Finland
- Vidyasagar Potdar, Curtin University, Australia
- Yuan Tian, Nanjing Institute of Technology, China
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zhaoqingpan@nuist.edu.cn.
DL-Tags: DLT and Smart Tags for Decentralized, Privacy-Preserving, and Verifiable Supply Chain Management
Supply chain management enhanced by the Internet of Things (IoT) solutions integrate special tags (e.g., RFID, NFC, and QR-codes) with products to create Smart Tags, in addition to storing supplemental information about a product, which is also used to track products during their lifecycle. However, a product consumer has to implicitly trust the Smart Tag creator and other stakeholders within the supply chain that they are providing authentic data within a product’s tag. The DL-Tags solution steps into this environment to offer a decentralized, privacy-preserving, and verifiable management of Smart Tags during a product’s lifecycle. The solution is based on distributed ledger technology (DLT) and uses the Ethereum blockchain to mediate interactions between the stakeholders during a product’s exchange process. By reaching a consensus on the product’s description and state logged on the blockchain, all involved stakeholders and product consumers can verify the product’s authenticity without revealing their identity. The paper describes the DL-Tags solution and includes a cost analysis of all implemented transactions on the Ethereum blockchain. The proposed solution provides evidence of the product’s origin and its journey across the supply chain while preventing tag duplication and manipulation. It is among the first documented practical solutions using DLT and IoT for supply chain management, which is designed to be distributed ledger agnostic.
View this article on IEEE Xplore
Advanced Internet of Things for Smart Cyber-Physical Infrastructure Systems
Submission Deadline: 20 March 2021
IEEE Access invites manuscript submissions in the area of Advanced Internet of Things for Smart Cyber-Physical Infrastructure Systems.
The Internet of Things (IoT) is rapidly gaining ground as a priority, multidisciplinary research topic in many
academic and industrial disciplines, especially in Cyber-Physical infrastructures, such as renewable
energy generation, smart vehicle management, and water quality monitoring. Due to the rapid
proliferation of smart sensors and meters, wearable devices and smartphones, the Internet of Things-
enabled technology is evolving infrastructure from conventional operation and maintenance business
models to more efficient, sustainable, smart and resilient systems. The successful utilization of IoT
enabled technology in Cyber Physical Infrastructure Systems (CPIS) will enable them to be faster and
more proactive, have a lower overall cost, provide improved business practices and enhance
sustainability. Future IoT enabled infrastructures will be realized to provide timely information
communication and effective decision-making for intelligent society and industry.
There are already IoT smart applications used without human intervention in areas such as traffic
congestion monitoring, household waste recycling, water consumption monitoring and alert systems in
cities. People will play a major role as they generate and use the data coming from these devices. IoT
technology provides potential for optimal feedback and decisions on various infrastructure needs, where it
enables seamless alignments of the local providers with the global potential of wider communities. It
leads to new smart services and increases the efficiency for infrastructure.
However, empowering the utility of IoT enabled technology in Cyber-Physical infrastructure is still
significantly challenging, considering the shortage of cost-effective and accurate smart sensors and
meters, unstandardized IoT system architectures, heterogeneity of connected wearable devices,
multidimensionality and high volume of data generated, and the high demand for interoperability.
From a
user-centric perspective, the successful use of IoT in infrastructure will also need an interoperable IoT
environment for delivery and research, tightly-coupled data mining applications, adequate data and
knowledge standards of self-empowerment, and a sound decision-making foundation. Research will focus
on designing and developing more connected complex systems, dealing with the current lack of
standards, and figuring out ways to analyze the deluge of data. Complexity will be an important factor to
study and control: The behavior of every single node in IoT will need to be considered to determine its
potential impact on the whole CPIS. These challenges and needs provide a lot of opportunities to explore
and investigate new concepts, algorithms and applications in IoT enabled smart infrastructure.
The goal of this Special Section on Internet of Things for smart CPIS is to bring together researchers and
practitioners from both academia and industry into a forum, and to show the state-of-the-art research and applications in utilizing IoT enabled technology for cyber-physical infrastructure, by presenting efficient
scientific and engineering solutions, addressing the needs and challenges for integration with new
technologies, and providing visions for future research and development.
The central theme of the proposed Special Section is on advanced internet of things technologies for
smart cyber-physical infrastructure systems, where smart sensing technologies, IoT architectures,
services, applications, and data analytics for infrastructure applications are the focus areas, and broad
aspects and issues will be well discussed.
This topic should be of great interest to IEEE Access readers.
The theme of the Special Section (SI) is especially focused on the three major aspects of IoT for
infrastructure: (1) intelligent monitoring with increased security and validity in CPS by using a variety of
IoT assets or technologies, including sensors, devices and mobile applications, (2) Interoperability and
data sharing services across IoT infrastructures supporting heterogeneous elements to cooperate
seamlessly to share information, and (3) Creation of ecosystems of “Platforms for Connected Smart
Objects”; integrating the future generations of smart devices (i.e. sensors) and network technologies and
other evolving ICT advances.
The Special Section aims to provide a forum for experts to disseminate their recent advances and views
on future perspectives in the field. The Special Section aims to publish original, significant and visionary
articles which present ideas, innovations, and applications of utilizing IoT enabled technology for
improving the efficiency, sustainability and reliability of infrastructure.
The topics of interest include, but are not limited to:
- Intelligent sensing and monitoring techniques and applications for CPIS
- Challenges and issues of intelligent sensing and data collection techniques in CPIS
- Integrated communication and distributed computing design for CPIS
- Internet of Things architecture design for CPIS
- Theoretical computing foundation and models for CPIS
- Intelligent real time data analytics for CPIS
- Security and privacy issues in the distributed computing infrastructure of CPIS
- Machine learning techniques for CPIS
- Co-design of distributed computing and physical systems in CPIS
- Large-scale data analysis in CPIS
- Scalable data and resource management in CPIS
- Robotics and autonomous systems for CPS
- Human in the loop robotics and interaction tech for CPS
- Innovative and cutting-edge technologies for CPIS
- Applications of Cyber-physical system
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Po Yang, Sheffield University, UK
Guest Editors:
-
- Wenyan Wu, Birmingham City University, UK
- Zofia Lukzos, Delft University of Technology, Netherlands
- Zahid Akhtar, State University of New York Polytechnic Institute, USA
Relevant IEEE Access Special Sections:
- Big Data Technology and Applications in Intelligent Transportation
- Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
- Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: po.yang@sheffield.ac.uk.
Towards Smart Cities with IoT Based on Crowdsensing
Submission Deadline: 31 December 2020
IEEE Access invites manuscript submissions in the area of Towards Smart Cities with IoT Based on Crowdsensing.
The proliferation of Internet of Things (IoT) has paved the way for the future of smart cities. The large volume data over IoT can enable decision-making for various applications, such as smart transportation, smart parking and smart lighting. The key to the success of smart cities is data collection and aggregation over IoT. Recently, crowdsensing has become a new data collection paradigm over IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with the microphones on smartphones. Despite the advantages of crowdsensing and IoT, there are many challenges to utilize crowdsensing over IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users. This Special Section aims to solicit original research works that address the challenging problems in utilizing crowdsensing over IoT for smart cities.
The topics of interest include, but are not limited to:
- Collaborative sensing and computing
- Sparse mobile crowdsensing over IoT
- Task allocation/selection in crowdsensing
- Fair/long-term/quality-oriented incentive in crowdsensing
- Data collection/aggregation in crowdsensing
- Truth discovery in crowdsensing
- Security, trust and privacy protection for IoT devices
- Privacy-preserving data collection for crowdsensing
- Fog computing and edge computing
- IoT device monitoring and scheduling in smart city
- Analysis for IoT-based Big Data
- Vehicular crowdsensing networks in smart city
- Resource management of IoT devices for smart city
- Quality measurement of crowdsourced data for smart city
- Crowdsourced data enabled applications in smart city
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor:
Kun Wang, University of California, Los Angeles, USA
Zhibo Wang, Wuhan University, China
Guest Editors:
-
- Ye-Qiong Song, University of Lorraine, France
- Dejun Yang, Colorado School of Mines, USA
- Shibo He, Zhejiang University, China
- Wei Wang, Amazon Inc, USA
Relevant IEEE Access Special Sections:
- Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
- Data Mining for Internet of Things
- Security, Privacy, and Trust Management in Smart Cities
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zbwang@whu.edu.cn.
Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments
Submission Deadline: CLOSED
IEEE Access invites manuscript submissions in the area of Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments.
Mobile cooperative sensing is becoming a popular paradigm to collect information and outsource tasks to mobile users. Detected events are then reported continuously through the integration and cooperation of sensors, actuators, controllers, and artificial intelligence. Mobile cooperative sensing refers to estimating the event information and simulating the situation of smart environments through the analysis of data from sensor networks deployed in the environment. The systems are able to actively obtain information of occupancy, such as identification, activity, and even gestures with mobile cooperative sensing. For instance, intrusion detection and human identification help to detect intruders automatically. In smart environments, light and temperature can be adjusted according to the occupancy, which conserves the energy. For children and the elderly, activities such as falling can be monitored to prevent potential hazards. Mobile cooperative sensing is becoming a vital part of the fvarious emerging applications in smart environments, such as energy consumption estimation, security surveillance, and human behavior analysis. Generally speaking, mobile cooperative sensing provides an active detected input for smart systems, so it can make judgment or feedback accordingly.
At present, with the increasing mobile cooperative sensing technology in the field of Mobile Internet of Things (M-IoT) and the continuous improvement and upgrading of the current internet infrastructure, application and business model innovations are constantly emerging. Mobile cooperative sensing is further penetrating traditional fields, such as finance, transportation, medical treatment, education, etc… Mobile cooperative sensing technology can already be found in the fields of intelligent transportation, internet finance, and intelligent medical treatment, for example. In the future, some preliminary application results can be expected to be used in other fields, especially in the application areas of smart environments (e.g., smart homes and cities), which will have a far-reaching impact. Mobile cooperative sensing technology is not just about the detection via sensor networks; it is also about enabling a wide range of new capabilities, architectures, and service paradigms. The development of mobile cooperative sensing is set to have major economic, social, and environmental impacts, the intersection of which forms future sustainable growth.
As a result, the development of mobile cooperative sensing in smart environments is fundamentally important; however, many problems still remain.. For example, how to construct and improve the framework and platform for mobile cooperative sensing; how to collect, measure, process, analysis, and optimize mobile cooperative sensing data; and how to improve the effectiveness and decrease the energy in mobile cooperative sensing. These are all vital but unavoidable problems for mobile cooperative sensing in smart environments.
This Special Section is intended to provide a specific opportunity to argue the state-of-the-art technologies around mobile cooperative sensing in smart environments and invite researchers in the relevant fields to share the latest progress, novel methodologies, and potential research topics.
The topics of interest include, but are not limited to:
- Construction and improvement of framework and platform for mobile cooperative sensing
- Collection, measurement, processing, analysis, and optimization of mobile cooperative sensing data
- Improvement of effectiveness and decrease of energy in mobile cooperative sensing
- Evaluation of performance relevant to mobile cooperative sensing systems
- Adaptability of mobile cooperative sensing systems in complex and variable environments
- Emerging technologies in smart environments
- Artificial intelligence in smart environments
- Smart environment-based machine learning for mobile cooperative sensing
- Security and privacy of mobile cooperative sensing in smart environments
- Quality of Service (QoS) and Mobile Internet of Things (M-IoT) services in mobile cooperative sensing systems
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Mu Zhou, Chongqing University of Posts and Telecommunications, China
Guest Editors:
-
- Hongying Meng, Brunel University London, UK
- Haiying Wang, Ulster University, UK
- Lei Chen, Georgia Southern University, USA
- Kunjie Xu, Intel Corporation, USA
Relevant IEEE Access Special Sections:
- Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
- Intelligent Information Services
- Advances in Machine Learning and Cognitive Computing for Industry Applications
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zhoumu@cqupt.edu.cn.
Reliability in Sensor-Cloud Systems and Applications (SCSA)
Submission Deadline: 28 February 2021
IEEE Access invites manuscript submissions in the area of Reliability in Sensor-Cloud Systems and Applications (SCSA).
The sensor-cloud system (SCS) integrates sensors, sensor networks, and cloud for managing sensors, collecting data, and decision-making. The integration provides flexible, low-cost, and reconfigurable platforms for monitoring and controlling the applications, including emerging applications of the Internet of things (IoT), machine to machine, and cyber-physical systems. Though the SCS has received tremendous attention from both academia and industry because of its numerous exciting applications, it suffers from issues with reliability.
The reliability of the SCS is the ability to perform required functions at or below a given failure rate for a given period of time. The required functions are identified as correctly providing measurement results. Sensor data can be faulty due to system faults or security attacks during the sensor data acquisition and collection, and data processing/decision-making on the cloud. There is also the possibility of not receiving the data at all or received data is compromised. Sensor data is also susceptible to errors and interference such as noise. Furthermore, to save sensor resources, end-users are becoming more dependent on cloud processing and decision-making. This enables significant interactions between sensors and between sensors and the cloud. As a result, the required functions can be extended to cover more concerns, including system design and integration, system continuity, real-time responsiveness, data security, and prevention of privacy intrusions, etc. This is why practitioners and engineers must also be able to measure imprecision and true reliability for both the data and the system that deals with the data. When designing and developing the SCS for real applications, the reliability verification at any level is required.
This Special Section aims to collect and publish state-of-art research results to solve the reliability concerns of sensor-cloud systems.
The topics of interest include, but are not limited to:
- Reliability theory, standardization, modeling, and architecture for SCSA
- Reliability in sensor design and integration in SCSA
- Reliability in communication and networking in SCSA
- Reliability in sensor data privacy processing, computing, and control in SCSA
- Reliability in applying machine learning models and methods for SCSA
- Reliability through data security, privacy, and trust for SCSA
- Reliability concerns in monitoring diverse applications of SCSA
- Reliability through QoS in application monitoring in SCSA
- Reliability issues (e.g., cloud storage, computing, decision-making) in SCSA
- Reliability in fog and edge computing in SCSA
- Reliability in mobile sensing, offloading and tracking in SCSA
- Reliability assessment, measures, and testbeds for SCSA
- Reliability levels and relations, metrics, and measures for SCSA
- Data quality assurance and quality control in SCSA
- Effective network design and development of SCSA
- Smartphone-based and heterogeneous design of SCSA
- Reliability through fault-tolerance and reliability in SCSA
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Md Zakirul Alam Bhuiyan, Fordham University, USA
Guest Editors:
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- Arafatur Rahman, University Malaysia Pahang, Malaysia
- Liu Qin, Hunan University, China
- Xuyun Zhang, Macquarie University, Australia
- Taufiq Asyhari, Coventry University, UK
Relevant IEEE Access Special Sections:
- Additive Manufacturing Security
- Advanced Sensor Technologies on Water Monitoring and Modeling
- The convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zakirulalam@gmail.com.
Emerging Trends of Energy and Spectrum Harvesting Technologies
Submission Deadline: 31 December 2020
IEEE Access invites manuscript submissions in the area of Emerging Trends of Energy and Spectrum Harvesting Technologies.
Billions of low-end wireless devices, such as sensors, are permeating into almost every aspect of personal life, such as in vehicles, washing machines, air conditioners, etc. These miniaturized and low-end devices are a promising solution to collect information and assist users for interaction with real-world objects. Frost & Sullivan reported that the global market of miniaturized devices is forecast to increase from 1.4 billion to 3.26 billion from 2014 to 2024. Unfortunately, the performance of miniaturized devices, which generally operate with limited battery power and transmit data over an unlicensed spectrum, is highly deteriorated due to the resource scarcity issues in terms of energy and spectrum. The energy scarcity issue limits the longevity of devices, and requires the operator to manually replace the depleted battery, which results in considerable maintenance costs. Even with sufficient energy supply, data transmission of devices conflicts with other networks that coexist in the unlicensed spectrum band, which creates the spectrum scarcity issue. To alleviate these energy and spectrum scarcity issues, numerous energy and spectrum harvesting technologies emerge, such as mini solar panel, piezoelectric transducers, and cognitive radio. By embedding these modules, the devices can harvest energy from the ambient energy sources and explore the idle licensed spectrum for data transmission, leading to energy and spectrum harvesting-enabled devices.
The ESH technologies have received much attention from the industry. Numerous commercial products have emerged on the global market, such as self-powered miniaturized devices produced by EnOcean, battery-free Bluetooth tags by Wiliot, and PolarFusion digital radio architecture for extreme low power by Innophase.
However, issues remain in the application of the ESH-enabled devices. First, empowering devices with ESH capabilities increases the manufacturing cost. How can one design cost-efficient, ESH-enabled, embedded architecture with promising performance? Second, considering that both the spectrum sensing and data transmission consume energy, the management of energy and spectrum resource both impact the system performance, which makes resource allocation challenging in ESH-enabled devices. Third, the energy harvesting process and activities of primary users, who own the licensed spectrum, exhibit high dynamics over time. How can one customize the communication protocol to adapt to such high dynamics and guarantee the delivery ratio?
To address these issues, this Special Section solicits original research and practical contributions in ESH technologies. We would like to provide a chance to share ideas and solutions for the enabling techniques which empower the devices with ESH capability. We also highly welcome submissions related to the joint management of energy and spectrum resources.
The topics of interest include, but are not limited to:
- Architecture design for ESH-enabled systems
- Data-driven modeling of energy and spectrum harvesting processes
- Communication protocol design for ESH-enabled networks
- Hardware design and prototyping of ESH-enabled systems
- Key scenarios/applications for ESH-enabled IoT devices
- Convergence of energy and spectrum harvesting
- Security, privacy, and trust in ESH-enabled systems
- Reliable data delivery in ESH-enabled systems
- Simulation platforms for ESH-enabled systems
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Guangjie Han, Hohai University, China
Guest Editors:
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- Deyu Zhang, Central South University, China
- Ning Zhang, Texas A&M University at Corpus Christi, USA
- Song Guo, The Hong Kong Polytechnic University, Hong Kong, China
- Geyong Min, University of Exeter, United Kingdom
- Shengrong Bu, University of Glasgow, United Kingdom
- Kanke Gao, Magna International Inc, USA
Relevant IEEE Access Special Sections:
- Energy Harvesting and Scavenging: Technologies, Algorithms, and Communication Protocols
- Intelligent and Cognitive Techniques for Internet of Things
- Energy Efficient Wireless Communications with Energy Harvesting and Wireless Power Transfer
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: hanguangjie@gmail.com.
Edge Intelligence for Internet of Things
Submission Deadline: 31 December 2020
IEEE Access invites manuscript submissions in the area of Edge Intelligence for Internet of Things.
Internet of Things (IoT) is a key enabler for many modernized applications, from marine monitoring to outer space exploration. In recent years, we have witnessed the proliferation of edge/mobile computing and IoT, where billions of mobile and IoT devices are connected to the Internet, generating a huge volume of data at the network edge. Driven by this trend, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, analysis and control closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability and energy efficiency, as well as alleviate the network traffic.
However, with the emergence of diverse IoT applications (e.g., smart home, smart city, industrial automation, connected vehicles), it becomes challenging for edge computing to deal with these heterogeneous IoT environments with edge big data. Motivated by the success of artificial intelligence (AI) in a wide spectrum of fields, it is envisaged that AI-powered edge computing can overcome the emerging challenges by fully unleashing the potential of the edge big data. The resulting new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence for IoT is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. There are still many fundamental challenges faced in how to improve the architecture of IoT while fully utilizing these techniques (communication, big data processing, and computing etc.,) Some open issues require immediate review, such as:
- How can we utilize advanced capabilities of IoT, such as in-network storage and caching, to offload the IoT data in order to release the traffic scale in the cellular networks and provide low-latency IoT services via edge intelligence?
- Can we leverage recent advances in computing and machine learning to develop IoT?
- How can we design novel security schemes such as lightweight encryption and blockchain, to reduce the energy consumption of a secure IoT network?
- How can we support good system observability and controllability with the employment of edge intelligence in the IoT platform?
- How can we realize big data analysis at the edge of IoT to promote traditional IoT to content-centric IoT integrated with 5G/6G?
The aim of this Special Section is to solicit original submissions related to edge intelligence for IoT including but not limited to:
- Advanced system modeling, including computation modeling, content modeling, threat/security modeling, and energy consumption modeling
- Novel transmission technologies for learning-based applications at the network edge
- Scheduling schemes for efficient training, inference for edge learning/edge intelligence
- Timely data acquisition mechanisms to support delay sensitive edge processing
- Big data analytics for edge intelligence
- Coded computing for edge intelligence
- Enabling technologies, e.g., SDN, NFV, CRAN, D2D, cloud/fog computing and networking
- Emerging applications via edge intelligence: vehicular networking, massive IoT, smart grid, healthcare, intelligent manufacturing
- Novel network architecture: convergence of computing, communications and caching, content/information-centric network, cognitive computing and networking, big data analytics
- Context-aware schemes: incentive mechanism for computing and caching, pricing, game-theoretic approach, network economics, caching placement and delivery
- Mobility management for mobile edge computing and proactive caching, i.e., exploiting mobility for more computing and caching opportunities
- Energy efficiency aspects: energy harvesting, energy storage, energy transfer
- Novel pricing schemes for edge computing, communications, energy, AI model, data resources
- AR/VR applications
- Tactile Internet
- Novel security technologies such as adapted blockchain, differential privacy, intrusion detection, traceability, etc.
- Novel applications for edge intelligence such as autonomous driving, Industry 4.0, networked robots, networked UAV, smart grid, energy Internet, etc.
- Prototyping, test-beds and field trials
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Zhenyu Zhou, North China Electric Power University, China
Guest Editors:
-
- Takuro Sato, Professor, Waseda University, Japan
- Zheng Chang, Associate Professor, University of Jyvaskyla, Finland
- Sherali Zeadally, Associate Professor, University of Kentucky, USA
- Haris Gacanin, Department Head, Nokia Bell Labs, Belgium
Relevant IEEE Access Special Sections:
- Edge Computing and Networking for Ubiquitous AI
- Communication and Fog/Edge Computing Towards Intelligent Connected Vehicles (ICVs)
- Cloud-Fog-Edge Computing in Cyber-Physical-Social Systems (CPSS)
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zhenyu_zhou@ncepu.edu.cn.
Lightweight Security and Provenance for Internet of Health Things
Submission Deadline: 31 October 2020
IEEE Access invites manuscript submissions in the area of Lightweight Security and Provenance for Internet of Health Things.
As an extension of the Internet of Things (IoT), the Internet of Health Things (IoHT), play an important role in the remote exchange of data of different physical processes such as patient monitoring, treatment progress, observation and consultation. In IoHT, the connectivity, integration, computation and interoperability are enabled through various sensors, actuators, and controllers, thereby providing seamless connectivity with efficient utilization of resources. In emergency situations, when a patient is being shifted to a hospital, seamless connectivity between Ambulance to Hospital (A2H), Hospital to Hospital (H2H) and Hospital to Ambulance (H2A), is very critical. With advances in tele-medicine, telesurgery, and other health-care applications, streaming has become an essential part of IoHT. The data traffic in IoHT applications, such as interactive multimedia streaming, traffic generated from faulty sensors, and vital signs, can tolerate packet loss but have stringent delay requirements. On the other hand, video streaming applications cannot tolerate jitter. Similarly, the low-power devices are sensitive to packet loss, and the periodic physiological traffic of medical traffic can tolerate delay, or jitter, but not packet loss. Routing data in different IoHT applications has varying quality of service (QoS) requirements in terms of delay, packet loss, jitter, and throughput. Most of the algorithms used today to secure the data and cryptography techniques in IoHT contain high computational complexities with high energy consumption. However, due to the energy limitations of low-power embedded devices, traditional cryptographic solutions are not viable for most of the IoHT applications. Less computational complexity, less space acquisition and energy-efficient security primitives are key building blocks for end-to-end content protection, user authentication, and consumer confidentiality in the IoHT. Once the data is gathered from different applications, it must be accurate and information about its origin should also be known. Due to scalability, tiny devices installed in IoHT are not usually physically protected. Data security and provenance therefore serve as the backbone for implementing IoHT applications.
This Special Section targets original technical articles with novel contributions on the improvement of security of IoHT, in particular by finding the correct lightweight solution. Review articles of high quality that provide thorough overview of the subject will also be considered. The topics of interest include, but are not limited to:
- Lightweight security for IoHT
- Low energy IoHT systems
- Low energy security algorithms for A2H, H2H, and H2A in IoHT
- Lightweight solutions for data forensics in IoHT
- Lightweight routing algorithms for data provenance in IoHT
- Secure lightweight protocols for A2H, H2H, and H2A in IoHT
- Security framework and architecture for IoHT
- Lightweight video streaming mechanism for IoHT
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Muhammad Tariq, FAST National University of computer & Emerging Sciences, Pakistan and Princeton University, USA
Guest Editors:
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- Takuro Sato, Waseda University, Waseda University, Tokyo, Japan
- Gautam Srivastava, Brandon University, Canada
- Vuk Marojevic, Mississippi State University, USA
- Mario Goldenbaum, Bremen University, Bremen, Germany
Relevant IEEE Access Special Sections:
- Secure Communication for the Next Generation 5G and IoT Networks
- Deep Learning: Security and Forensics Research Advances and Challenges
- Emerging Approaches to Cyber Security
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: mtariq@princeton.edu.
Blockchains and Smart Contracts for the Internet of Things
Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts-scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.
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