Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power ...Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers.
Purpose
Currently reported computer‐aided detection (CAD) approaches face difficulties in identifying the diverse pulmonary nodules in thoracic computed tomography (CT) images, especially in ...heterogeneous datasets. We present a novel CAD system specifically designed to identify multisize nodule candidates in multiple heterogeneous datasets.
Methods
The proposed CAD scheme is divided into two phases: primary phase and final phase. The primary phase started with the lung segmentation algorithm and the segmented lungs were further refined using morphological closing process to include the pleural nodules. Next, we empirically formulated three subalgorithms modules to detect different sizes of nodule candidates (≥3 and <6 mm; ≥6 and <10 mm; and ≥10 mm). Each subalgorithm module included a multistage flow of rule‐based thresholding and morphological processes. In the final phase, the nodule candidates were augmented to boost the performance of the classifier. The CAD system was trained using a total number of nodule candidates = 201,654 (after augmentation) and nonnodule candidates = 731,486. A rich set of 515 features based on cluster, texture, and voxel‐based intensity features were utilized to train a neural network classifier. The proposed method was trained on 899 scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC‐IDRI). The CAD system was also independently tested on 153 CT scans taken from the AAPM–SPIE–LungX Dataset and two subsets from the Early Lung Cancer Action Project (ELCAP and PCF).
Results
For the LIDC−IDRI training set, the proposed CAD scheme yielded an overall sensitivity of 85.6% (1189/1390) and 83.5% (1161/1390) at 8 FP/scan and 1 FP/scan, respectively. For the three independent test sets, the CAD system achieved an average sensitivity of 68.4% at 8 FP/scan.
Conclusion
The authors conclude that the proposed CAD system can identify dissimilar nodule candidates in the multiple heterogeneous datasets. It could be considered as a useful tool to support radiologists during screening trials.
This paper proposes two general active-passive two-way ranging (TWR) methods: AP1-TWR and AP2-TWR. The proposed methods rely on 2 types of anchors: active-passive and passive-only. The first type ...actively takes part in packet exchange and listens to transmissions of other active-passive anchors, and the second type only listens. Pairing these concepts with active single-sided (SS), symmetrical double-sided (SDS), and alternative double-sided (AltDS) TWR methods provides a total of six different active-passive methods. As a result of assigning different numbers of the two anchor types, the range estimation root-mean-square-error (RMSE), or the air time efficiency, or both, can be improved. Simulation results show that AP1-TWR surpasses the performance of the best active two-way ranging method by employing 10 active-passive anchors, while AP2-TWR surpasses the same mark with only 6 active-passive anchors. Further results validate and show that, compared to AP1-TWR, the AP2-TWR gives a relative improvement of range estimation RMSE about 10 to 20% in every configuration, making AP2-TWR the overall better performing method. Without a loss in the number of available range estimates, both methods could also increase the air time efficiency by keeping the number of active-passive anchors to a minimum while increasing the amount of passive anchors.
Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet ...computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.
IEEE 802.15.6 is a wireless body area network (WBAN) standard proposed to facilitate the exponentially growing interest in the field of health monitoring. This standard is flexible and outlines ...multiple basic medium-access control (MAC) protocols that are contention based and collision free to meet the WBAN quality-of-service (QoS) challenges. Typically, current research trends in WBAN MAC focus on designing a hybrid MAC that is a combination of basic MAC protocols. In this paper, we provide a first detailed survey of existing hybrid MAC protocols based on the IEEE 802.15.6, which would be useful for the related research community. First, this paper lists the design challenges of a WBAN MAC. Second, it highlights the significance of hybrid MAC protocols in meeting the design challenges while comparing them to standard MAC protocols. Third, a critical and thorough comparison of existing hybrid MAC protocols is presented in terms of network QoS and WBAN specific parameters. Finally, we identify key open research areas that are often neglected in hybrid MAC design and further propose some possible directions for future research.
In future remote healthcare monitoring system, it is necessary to constantly monitor the patients physiological parameters. For example, a pregnant woman parameters such as blood pressure and heart ...rate of the woman and heart rate and movements of fetal to control their health condition. To support the high-intensity and short-lived demands of these emerging applications, Narrowband Internet of Things (NB-IoT) is a promising technology that provides long-range communications at a low data rate for sensors with reduced device processing complexity and long battery lifetime. This paper aims to investigate the realistic performance of NB-IoT in terms of effective throughput, patient served per cell and latency in healthcare monitoring system with both in-band and stand-alone deployment.
In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical ...damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.
NB-IoT is a promising cellular technology for enabling low cost, low power, long-range connectivity to IoT devices. With the bandwidth requirement of 180 kHz, it provides the flexibility to deploy ...within the existing LTE band. However, this raises serious concerns about the performance of the technology due to severe interference from multi-tier 5G HetNets. Furthermore, as NB-IoT is based on HD-FDD, the symmetric allocation of spectrum band between the downlink and uplink results in underutilization of resources, particularly in the case of asymmetric traffic distribution. Therefore, an innovative RRM strategy needs to be devised to improve spectrum efficiency and device connectivity. This article presents the detailed design challenges that need to be addressed for the RRM of NB-IoT and proposes a novel framework to devise an efficient resource allocation scheme by exploiting cooperative interference prediction and flexible duplexing techniques.
Integration of the Cyber–Physical System (CPS) concept with bio-analytical devices is highly desirable to enable device automation as well as to improve diagnostic and analytical capabilities. ...However, the modeling must account for entity interactions, system dynamics, and non-functional aspects required for proper device functionality.
This paper presents a model-based system architecture that builds upon an extended timed automata-based formal technique. In contrast to prior works that utilized SysML or UML-based models, this allows for the wireless control of bio-analytical instruments. Using this formal method, the UPPAAL tool is used to model and test a case study called “A droplet flow cytometer for testing bacteria’s susceptibility to antibiotics”. The study shows the implications of formal techniques for the design and verification of wireless automation of high-throughput laboratory setups in Model-Based System Engineering (MBSE). Moreover, the paper extends the above aspects by adding the possibility to model multi-system interaction. This is used to analyze the trade-off between centralized and decentralized information flow strategies for better system performance under delay and bandwidth constraints. UPPAAL Stratego is used to analyze strategies for achieving specific delays and bandwidth consumption while avoiding packet losses in the event of network congestion. The results show that under strict delay constraints and high traffic, the use-case system selects the decentralized strategy over the centralized strategy. In low-traffic scenarios, the centralized strategy is more effective at ensuring the reliable operation of systems.
This paper first presents a comprehensive analysis of Non-Line-of-Sight (NLoS) error cases in the Ultra-Wideband (UWB) Active-Passive Two-Way Ranging (AP-TWR) protocol. Based on this analysis, we ...then propose the Adaptive Extended Kalman Filter (A-EKF) positioning method, utilizing variances calculated from AP-TWR range estimates, which are adapted based on the distance and intermittency of the range estimates. The proposed method needs no training data, nor any additional information about the environment the system is deployed in and does not yield any additional time delays. Based on experiments conducted in an industrial environment, the results show that the proposed method outperforms standard non-adaptive AP-TWR and active-only Single-Sided Two-Way Ranging (SS-TWR) methods in both stationary and movement tests. The stationary tests show that on average the proposed A-EKF method provides more than three times lower Root-Mean-Square-Error (RMSE) than the next best method (AP-TWR) in 3D positioning, while SS-TWR consistently performs worse by about 0.4 m in the z-axis. Additionally, the movement tests confirm the findings of the stationary tests and show that the challenging propagation conditions of the testing environment cause maximum errors at about 4.5 m for AP-TWR and SS-TWR, whereas the proposed A-EKF managed to mitigate these effects and reduce the error by 9 times, resulting in a maximum error of 0.5 m.
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