Evaluation, Modeling and Optimization of Coverage Enhancement Methods of NB-IoT

Narrowband Internet of Things (NB-IoT) is a new Low Power Wide Area Network (LPWAN) technology released by 3GPP. The primary goals of NB-IoT are improved coverage, massive capacity, low cost, and long battery life. In order to improve coverage, NB-IoT has promising solutions, such as increasing transmission repetitions, decreasing bandwidth, and adapting the Modulation and Coding Scheme (MCS). In this paper, we present an implementation of coverage enhancement features of NB-IoT in NS-3, an end-to-end network simulator. The resource allocation and link adaptation in NS-3 are modified to comply with the new features of NB-IoT. Using the developed simulation framework, the influence of the new features on network reliability and latency is evaluated. Furthermore, an optimal hybrid link adaptation strategy based on all three features is proposed. To achieve this, we formulate an optimization problem that has an objective function based on latency, and constraint based on the Signal to Noise Ratio (SNR). Then, we propose several algorithms to minimize latency and compare them with respect to accuracy and speed. The best hybrid solution is chosen and implemented in the NS-3 simulator by which the latency formulation is verified. The numerical results show that the proposed optimization algorithm for hybrid link adaptation is eight times faster than the exhaustive search approach and yields similar latency

Time Hopping:An Efficient Technique for Reliable Coexistence of TSCH-Based IoT Networks

Escalation in the use of Internet of Things (IoT) devices gives rise to the number of networks operating in the license-free 2.4-GHz frequency band. This prepares the ground for networks to experience interference from coexisting networks and thus performance degradation. Time-slotted channel hopping (TSCH), as an operational medium access mode of the IEEE 802.15.4 technology, was introduced to ensure the reliability of IoT networks when they undergo coexistence. It uses frequency hopping as a protective strategy against long-term packet losses due to interference. However, when several independent TSCH networks coexist, they are prone to interfere with one another. In extreme scenarios, coexisting TSCH networks may block links of one another for an extended duration of time, leading to application failure. In this article, we propose a novel technique called time hopping to secure the reliability of coexisting TSCH networks. The developed technique synchronously and periodically alters the timing of nodes within a TSCH network to avoid coexisting TSCH networks from getting stuck in extreme coexistence scenarios and long-term continuous collisions. We evaluate the effectiveness of the proposed technique through extensive simulations. The results clearly show that the proposed time hopping technique substantially improves the worst case internetwork collision ratio, with as much as 50% improvement in some tested scenarios. The implementation of the technique is very simple, with almost no communication or computation overhead for the constrained wireless nodes; it is done and tested on real nodes for proof of concept

NPTSN:RL-Based Network Planning with Guaranteed Reliability for In-Vehicle TSSDN

To achieve strict reliability goals with lower redundancy cost, Time-Sensitive Software-Defined Networking (TSSDN) enables run-time recovery for future in-vehicle networks. While the recovery mechanisms rely on network planning to establish reliability guarantees, existing network planning solutions are not suitable for TSSDN due to its domain-specific scheduling and reliability concerns. The sparse solution space and expensive reliability verification further complicate the problem. We propose NPTSN, a TSSDN planning solution based on deep Reinforcement Learning (RL). It represents the domain-specific concerns with the RL environment and constructs solutions with an intelligent network generator. The network generator iteratively proposes TSSDN solutions based on a failure analysis and trains a decision-making neural network using a modified actor-critic algorithm. Extensive performance evaluations show that NPTSN guarantees reliability for more test cases and shortens the decision trajectory compared to state-of-the-art solutions. It reduces the network cost by up to 6.8x in the performed experiments

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS:A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications

Neuroradiological manifestations of tuberculous meningitis

Tuberculous meningitis (TBM) represents the most severe form of extra pulmonarytuberculosis (1).The early and exact diagnosis of TBM is important but difficult due to time consuming definitive microbiological procedures (2).Neuroimaging is an important initial investigation in tuberculous meningitis(3).This study was conducted to evaluate the neuroradiological findings in patients with tuberculous meningitis, as a useful modality for itsearly diagnoses and prompt treatment

A Density Functional Theory Study of Raman Modes of Hydrogenated Cadmium Sulphide Nanoparticles

Raman scattering investigations based on density functional theory (DFT) calculations were performed to explore the vibrational modes of wurtzite structured CdS nanoparticles (NPs). The calculations were performed to obtain the Raman spectra for the CdS containing 2, 4, 8 and 12 atoms to study the size dependence. Several vibrational modes indicating stretching and bending features related to Cd and S atoms were observed. Modifications of the frequency and intensity of different Raman modes with an increase in number of atoms in NPs are discussed in detail. It is found that the frequency of the CdS symmetric stretching mode of vibration shows a consistent red shift and that of CdS anti‐symmetric stretching shows a consistent blue shift with the increase in the number of atoms. Hydrogen atoms were added in order to make the closed shell configuration and saturate the NPs as per the requisite for calculating the Raman spectra. This produced some additional modes of vibration related to hydrogen atoms. The SH stretching mode showed a consistent red shift and the CdH stretching mode showed a consistent blue shift with an increase in the number of atoms in NPs. The results generated are found to be in close agreement with the literature. The observed red shift in different modes is assigned to stimulated Raman stretching and blue shift is ascribed to the coherent anti‐stokes Raman scattering

Relation between nodule size and \u3csup\u3e18\u3c/sup\u3eF-FDG-PET SUV for malignant and benign pulmonary nodules.

Abstract The most common semiquantitative method of evaluation of pulmonary lesions using 18F-FDG PET is FDG standardized uptake value (SUV). An SUV cutoff of 2.5 or greater has been used to differentiate between benign and malignant nodules. The goal of our study was to investigate the correlation between the size of pulmonary nodules and the SUV for benign as well as for malignant nodules. Methods Retrospectively, 173 patients were selected from 420 referrals for evaluation of pulmonary lesions. All patients selected had a positive CT and PET scans and histopathology biopsy. A linear regression equation was fitted to a scatter plot of size and SUVmax for malignant and benign nodules together. A dot diagram was created to calculate the sensitivity, specificity, and accuracy using an SUVmax cutoff of 2.5. Results The linear regression equations and (R2)s as well as the trendlines for malignant and benign nodules demonstrated that the slope of the regression line is greater for malignant than for benign nodules. Twenty-eight nodules of group one (≤ 1.0 cm) are plotted in a dot diagram using an SUVmax cutoff of 2.5. The sensitivity, specificity, and accuracy were calculated to be 85%, 36% and 54% respectively. Similarly, sensitivity, specificity, and accuracy were calculated for an SUVmax cutoff of 2.5 and found to be 91%, 47%, and 79% respectively for group 2 (1.1–2.0 cm); 94%, 23%, and 76%, respectively for group 3 (2.1–3.0 cm); and 100%, 17%, and 82%,, respectively for group 4 (\u3e 3.0 cm). The previous results of the dot diagram indicating that the sensitivity and the accuracy of the test using an SUVmax cutoff of 2.5 are increased with an increase in the diameter of pulmonary nodules. Conclusion The slope of the regression line is greater for malignant than for benign nodules. Although, the SUVmax cutoff of 2.5 is a useful tool in the evaluation of large pulmonary nodules (\u3e 1.0 cm), it has no or minimal value in the evaluation of small pulmonary nodules (≤ 1.0 cm)

Relation between nodule size and 18F-FDG-PET SUV for malignant and benign pulmonary nodules.

The most common semiquantitative method of evaluation of pulmonary lesions using 18F-FDG PET is FDG standardized uptake value (SUV). An SUV cutoff of 2.5 or greater has been used to differentiate between benign and malignant nodules. The goal of our study was to investigate the correlation between the size of pulmonary nodules and the SUV for benign as well as for malignant nodules

Polyneuropathy associated with iga Paraproteinemia: a case report and literature Review.

Paraproteinemia is precipitated by an accumulation of monoclonal plasma cells or B lymphocytes. Idiopathic neuropathies that are associated with paraproteinemia account for only 10% of the neuropathies. Paraprotein acts like an antibody and is targeted against myelin and axons present in the peripheral nerves. Despite being of interest for quite a long time, the caudal relationship between paraproteinemias and peripheral neuropathies still remains a sorcery. We report a case of a middle aged male who presented with pain and parasthesias in both arms and legs. His workup revealed him to be having a paraproteinemic neuropathy consistent with IgA Lambda chains that account for being the most rare type of monoclonal gammopathy than IgM or IgG having the potential to progress to smouldering multiple myeloma

Mobility and Dispersion Optimization of Nano Zerovalent Iron (nZVI) in Disinfection of Urban Wastewater with Pneumatic Nitrogen Gas Injection

Zero iron nanoparticle is considered as a universal enhancement agent. Its stabilization in aqueous environments with different coatings, reduces the efficiency of nanoparticles to a great extent. This study aimed to optimize the mobility and dispersion of nanoparticles to increase the inactivation efficiency of heterotrophic bacteria in urban sewage effluents. The experiment was carried out on Response Surface Methodology (RSM) and Central Composite Design (CCD) using Design Expert 10 software. Iron nanoparticles were synthesized in two types of carboxymethyl cellulose-coated and simple type. B-nZVI  was introduced into the effluent with by pneumatic injection of nitrogen gas. CMC-nZVI was also mixed with a mixer in the effluent. Comparison of the results was done with two HPC and cellular molecular techniques (Genetic sequencing of 16s rRNA bacteria). The highest inactivation efficiency (90%) was observed in minute 23 for pneumonic injection of B-nZVI at a flow rate of 10 L / min.  Finally, with the improvement of gas pressure and flow rate, the inactivation efficiency was recorded at 95.6% at 32 minutes. Final model obtained from this process agreed with the quadratic equation. General forecasting of the model was expressed by the correlation coefficient (R2=0.9447) that made good fitness for the response data. The statistical significance was determined using Fisher's statistics (F-value=13.29). For optimal use of nZVI in the inactivation of urban wastewater heterotrophic bacteria, nZVI can be injected into the wastewater by pneumatic injection in two steps with an inert gas such as nitrogen. In the nZVI pneumatic injection, the efficiency of deactivating bacteria in urban wastewater treatment plants was about 17% to 39% better than that of the coated-nZVI such as CMCs