9,749 research outputs found
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
Twenty-five years of sensor array and multichannel signal processing: a review of progress to date and potential research directions
In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review
With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs
Scheduling Classifiers for Real-Time Hazard Perception Considering Functional Uncertainty
This paper addresses the problem of real-time classification-based machine perception, exemplified by a mobile autonomous system that must continually check that a designated area ahead is free of hazards. Such hazards must be identified within a specified time. In practice, classifiers are imperfect; they exhibit functional uncertainty. In the majority of cases, a given classifier will correctly determine whether there is a hazard or the area ahead is clear. However, in other cases it may produce false positives, i.e. indicate hazard when the area is clear, or false negatives, i.e. indicate clear when there is in fact a hazard. The former are undesirable since they reduce quality of service, whereas the latter are a potential safety concern. A stringent constraint is therefore placed on the maximum permitted probability of false negatives. Since this requirement may not be achievable using a single classifier, one approach is to (logically) OR the outputs of multiple disparate classifiers together, setting the final output to hazard if any of the classifiers indicates hazard. This reduces the probability of false negatives; however, the trade-off is an inevitably increase in the probability of false positives and an increase in the overall execution time required. In this paper, we provide optimal algorithms for the scheduling of classifiers that minimize the probability of false positives, while meeting both a latency constraint and a constraint on the maximum acceptable probability of false negatives. The classifiers may have arbitrary statistical dependences between their functional behaviors (probabilities of correct identification of hazards), as well as variability in their execution times, characterized by typical and worst-case values
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
SWIPT aided Cooperative Communications with Energy Harvesting based Selective-Decode-and-Forward Protocol: Benefiting from Channel Aging Effect
Simultaneous wireless information and power transfer (SWIPT) in radio-frequency (RF) bands enables flexible deployment of battery-powered relays for extending communication coverage. Relays receive downlink RF signals emitted by a source for information decoding and energy harvesting, while the harvested energy is consumed for both information decoding and information forwarding to a destination. An energy harvesting based selective-decode-and-forward (EH-SDF) protocol is proposed, where only the relays having information correctly decoded are activated for information forwarding, while others harvest and store energy for the future use. By considering the channel aging effect, we propose a joint relay selection, power allocation, transmit beamforming and signal splitting design in order to maximise the end-to-end (e2e) throughput of this EH-SDF aided cooperative communication system. Two scenarios with/without direct link between the source and the destination are studied, respectively. The original formulated non-convex optimisation problems with coupled variables are decoupled into three subproblems which are solved by an iterative optimisation algorithm. Numerical results demonstrate that our design with the EH-SDF protocol achieves a higher e2e throughput than the traditional decode-and-forward (DF) counterpart. Moreover, the impact of the channel aging effect on the e2e throughput is also evaluated
Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G
With the advent of the multimodal immersive communication system, people can
interact with each other using multiple devices for sensing, communication
and/or control either onsite or remotely. As a breakthrough concept, a
distributed sensing, computing, communications, and control (DS3C) fabric is
introduced in this paper for provisioning 6G services in multi-tenant
environments in a unified manner. The DS3C fabric can be further enhanced by
natively incorporating intelligent algorithms for network automation and
managing networking, computing, and sensing resources efficiently to serve
vertical use cases with extreme and/or conflicting requirements. As such, the
paper proposes a novel end-to-end 6G system architecture with enhanced
intelligence spanning across different network, computing, and business
domains, identifies vertical use cases and presents an overview of the relevant
standardization and pre-standardization landscape
Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision.
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