Data Requests Research Articles

Nonspecific Effects of the Bacillus Calmette-Guérin Vaccine in Portuguese Children Under 5 Years of Age: Protocol for a Population-Based Historical Birth Cohort Study.

The Bacillus Calmette-Guérin vaccine (BCG) against tuberculosis (TB) shows beneficial nonspecific effects, which are likely related to innate immune training. Until 2016, a single BCG dose was administered to all newborns in Portugal. In July 2016, a clinical guideline established that only children under 6 years belonging to high-risk groups should receive BCG. This might have prevented nonvaccinated children from developing trained immunological responses as effectively as BCG-vaccinated children. This study aims to investigate if there is variation in TB-related and all-cause mortality, and severe, moderate, or mild morbidity in children under 5 years of age, and whether such variation might be explained by the BCG vaccination policy change in 2016. This population-based historical birth cohort study includes children under 5 years of age born in Portugal between July 1, 2010, and June 30, 2021. Newborns with low birth weight, premature status, or known or suspected HIV infection are excluded. The follow-up period is until the completion of 5 years of age or the end of follow-up (June 30, 2021). The study will use secondary data from the National Health Service user registry, death certificate database, vaccination registry, communicable diseases surveillance system, TB surveillance system, diagnosis-related group information system for hospital admissions and emergency department visits, and primary health care information system. The data will be linked. Primary outcomes include person-time incidence rates of death (all causes and TB), TB diagnosis, and all causes and some specific causes of severe, moderate, or mild morbidity, and the incidence rate ratio of nonvaccinated to BCG-vaccinated children. We will compare the probability of surviving the first and fifth years of life or of not having severe, moderate, or mild morbidity during the follow-up period according to exposure (BCG vaccinated or nonvaccinated, number of doses, and time from birth until the first dose), using the log-rank test for assessing differences in survival rates between exposed and nonexposed children and hazard ratios for quantifying the differences. Moreover, we will perform a proportional hazards regression analysis. Ethics approval has been obtained. In March 2022, database owners were contacted to present the project and discuss the request for data. A unique identifier will be used. In July 2023, a process of redefinition of the variables per database was initiated. Data were received in October and November 2023. In November 2023, further work was conducted. By April 2024, we expect to start analyzing the full data set. The results will contribute to the accumulating body of knowledge and might have relevance to guide global BCG vaccination policy. Data linkage can contribute to a swifter mechanism to use available health data to conduct population-based studies and inform policy decision-making. ClinicalTrials.gov NCT05471167; https://clinicaltrials.gov/study/NCT05471167. DERR1-10.2196/55332.

Public Influence on the Ethnic Disparity in Stop-and-Search Statistics in Four London Boroughs

The literature on police stop-and-search events over the last two decades has heavily criticised the police for being racially disproportionate towards young black men compared to their white counterparts, resulting in the police being labelled as institutionally racist. However, none of the literature considers the fact that the public reporting of incidents may have had a major and direct influence on police stop-and-search outcomes. This significant issue remains a hidden and under-researched area, even though the disproportionality of police stop-and-search incidents is the leading cause of the negative relationship between the police and black and minority ethnic groups. This study addresses this gap by examining public bias in reporting incidents that result in police stopping and searching an individual. This study addresses this gap by examining public bias in reporting incidents that result in police stopping and searching an individual. A mixed method approach has been taken and primary data has been collected through freedom of information requests from the four borough command units (BCU) of the Metropolitan Police with densely populated areas with Black and Asian minority groups to investigate the link between public calls or reports of incidents and the initiation of police stop-and-search events. This data has been measured against national stop and search statistics in line with demographic data gathered from National Census data for the areas. The research concludes that there is discrimination against black and minority ethnic groups in members of the public reporting concerns. Freedom of information request for quantitative data from the Metropolitan Police reveals that public calls for reported incidents are on average 8.4 times more likely to describe the perpetrator as black European rather than white and 23.9 times more likely in the Central East borough when describing Asian ethnicity.

Facilitating an ecosystem approach through open data and information packaging

Abstract Open data that can be easily incorporated into analyses are essential for developing ecosystem approaches to marine ecological management: a common goal in fisheries policy in many countries. Although it is not always clear what constitutes an ecosystem approach, it always involves scientists working with a large variety of data and information, including data from physical and oceanographic sampling, multispecies surveys, and other sources describing human pressures. This can be problematic for analysts because these data, even when available, are often held in disparate datasets that do not necessarily correspond at appropriate temporal and spatial scales. Data can often only be obtained by specific requests to individuals in governmental agencies who are delivering on an increasing number of data requests as interest grows in practical ecosystem approach implementation. This data access model is not sustainable and hinders the momentum for ecosystem approach development. We describe a data bundling R package that makes data and climate projections available at appropriate scales to facilitate development of an ecosystem approach for the Gulf of St. Lawrence, Canada. This approach integrates closely with the present workflow of most government analysts, academics in fisheries, and scientists in private industry. The approach conforms with open data initiatives and makes data easily available globally while relieving some of the burden of data provision that can fall to some individuals in government laboratories. The structure and approach are generic, adaptable, and transferable to other regions and jurisdictions.

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

SummaryIn this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS.

Responding to unusual government request for user data: How tech companies make sense of human rights

Data sharing practices between governments and the private sector are characterized by a lack of transparency which has potential implications for human rights. Minimal scholarship exists investigating how companies address human rights risks stemming from government requests for user data. Understanding corporate response processes to government requests is central to advancing human rights research at the intersection of tech company conduct. This becomes even more pressing as emerging technologies gather increasing amounts of data. Scholarship demonstrates that transparency reporting cannot assist in analyzing data sharing practices between the private and the public sectors due to a variety of constraints. Using semi-structured interviews with senior staff at technology companies, this paper presents an empirical analysis of how technology company representatives and external advisors seek to align their processes when responding to government requests for user data. It describes a set of six themes using human rights terminology which company representatives aim to employ when responding to requests.

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Abstract. Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

Privacy-preserving and verifiable data aggregation for Internet of Vehicles

With the development of Internet of Vehicles (IoV), organizations or institutions are increasingly interested in aggregating vehicle perception data to perform data analysis. Nevertheless, the existing solutions are difficult to meet the comprehensive requirements of IoV scenario, such as low latency, privacy preservation and rich functions. Additionally, it is more challenging that the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. To this end, we propose a Privacy-Preserving and Verifiable Data Aggregation Scheme for IoV (PPVDA), which allows the efficient aggregation for inner product over multi-dimensional data, and the data requester can verify the correctness and integrity of the aggregation results. By exploiting homomorphic MAC and secret sharing techniques, we construct a lightweight and verifiable mechanism for results as well as data privacy protection. We also conduct a comprehensive security analysis of PPVDA under the malicious security model. Moreover, extensive theoretical analyses and experimental evaluations demonstrate that PPVDA performs efficiently while retaining more desired properties. When dealing with 4×104 VNs, 3000 RSUs and 500-dimensional vectors, each EN side only takes about 74.5 ms and the DR side only takes about 3.8 ms, is therefore suitable for practical IoV scenarios.

Navigating Gene Therapy Access: The Case of Bulgaria in the Context of the EU Regulatory Landscape.

Gene therapies (GTs) have recently emerged as revolutionary personalized therapeutic options. Despite their promising potential, challenges such as uncertainty regarding long-term health benefits and safety, along with extreme price tags, pose significant obstacles to patient access. Within the EU, the European Medicines Agency plays a pivotal role with regards to GT market authorization. However, national authorities are responsible for pricing and reimbursement, which results in fragment patient access within the EU. This study aimed to provide an overview of the complex landscape of post-market authorization accessibility for GT products in Bulgaria, comparing it with neighboring EU countries. We applied a mixed-methods approach, including desk research, public data requests, and list price comparisons. As of 1 April 2023, 14 GTs had a valid market authorization at the EU level. In Bulgaria, Kymriah® was the only GT included in the Positive Drug List (PDL), with an official list price of EUR 335,636.94. Similar results were found in Romania, whereas five GTs were included in Greece's PDL. Additionally, Zolgensma® was found accessible in Bulgaria through an alternative individual access scheme at an estimated price of EUR 1,945,000.00. In conclusion, this study emphasized targeted policy interventions to address health inequalities and to ensure timely access to GTs within the EU.

TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities

The popularity of fog-enabled smart cities is increasing due to the advantages provided by modern communication and information technologies, which contribute to an improved quality of life. Wireless networks make them more vulnerable when the network is under malicious attacks that cause a collision in the medium. Furthermore, diverse applications of smart cities demand a contention-free medium access control (MAC) protocol to meet adaptive data requirements. In this work, a time-slot-based medium access control protocol to meet adaptive data requirements (TMPAD) for IoT nodes in fog-enabled smart cities is proposed. TMPAD proposes a trust mechanism to differentiate malicious and legitimate data requests. In addition, it accommodates more legitimate data-requesting nodes to transfer their data during a session by applying the technique for order performance by similarity to ideal solution (TOPSIS) and 0/1 knapsack algorithm. The performance of TMPAD is compared with well-known techniques such as first come first serve (FCFS), shortest job first (SJF), and longest job first (LJF) in different prospective scenarios. The results show that TMPAD scrutinizes more data-requesting nodes in slot allocation, allowing more data transmission in a session, with better mean trust value, as compared to other algorithms.

Deep-MAD: Deep learning based multiple attack detection for secure device- to-device communication in FOG environment1

An exponential growth of users demands ubiquitous connectivity, which requires the integration of new technology. Therefore, Device to Device (D2D) communication has been considered a promising technology that utilizes effective and efficient communication. Even though numerous studies have been conducted for establishing secure D2D communication, however, existing techniques face challenges like privacy, security threats, and poor generality. To overcome these issues a novel Deep-MAD model is proposed to preserve data privacy along with its access control in the D2D network and multiple attack detection in a fog environment. A Fully Homomorphic Elliptic Curve Cryptography (FHECC) is introduced to transmit data securely in a D2D network. The data owner uses FHECC algorithm to encrypt the plain text into cipher text before storing it on the fog. Whenever the user requests data from the fog, the fog service provider confirm the user’s access control. Furthermore, the deep learning-based Bi-LSTM is used to differentiate the device as an authorized or unauthorized user. If the IP address is genuine then the inverse FHECC is used to decrypt the data for authorized users. Otherwise, the particular device is blocked and it is sent for further verification for classifying the types of attacks. The effectiveness of the proposed strategy is examined using several parameters, such as computational complexity, scalability, accuracy, and Execution time. The proposed technique improves the overall computational overhead of 31.77, 9.34, and 4.67 better than AKA protocol, lightweight cipher, and FHEEP respectively.

ECKOchain: A FAIR blockchain‐based database for long‐term ecological data

Abstract Open data practices in ecology are increasingly accepted, yet primary long‐term ecological data remain hard to find. Barriers preventing open long‐term ecological data include social and economic constructs such as a sense of data proprietorship and fear of misuse of complex datasets. To incentivise open primary ecological data and ensure long‐term preservation, we propose a decentralised data management approach using blockchain technology. The blockchain‐based database is governed by transparent and immutable data management protocols, agreed on by members of the network. Specialised protocols ensure agreement in the network before new data is accepted, and no entity can single‐handedly alter existing data. We introduce the ECKOchain, a ‘proof of concept’ ecological blockchain‐based database created with the Hyperledger Fabric framework. While metadata and access policies are distributed to all network members, primary data remains with data owners and are served on‐demand to approved parties according to specified usage licences. Details of data requests are preserved indefinitely on the blockchain and serve as auditable data usage agreements. With the distributed blockchain‐based database we advocate for open science and transparency in long‐term management of ecological data. The ECKOchain is also suitable for other scientific fields where auditability and transparency are considerations to long‐term data management.

Design of Student Service Information System to Print Grade Transcripts in the Industrial Engineering Study Program in Universitas Sebelas Maret Surakarta

The student service system at Universitas Sebelas Maret Surakarta (UNS), especially the print transcript of grades currently running, students can apply through three methods. The first method is done by filling out an application for a printed transcript. The second method is to use the Whatsapp application. The third method is done by using E-mail. Due to the large number of media used to apply for printed transcripts, the administrative unit of the study program cannot process all requests for print transcripts quickly. Sometimes there is a request to print a grade transcript that is not processed because the request data is overwritten with the new request data, so it is not readable by the study program administration unit. Based on the description of the background, it can be formulated that the problem to be solved in this research is "How to design a student service information system for printing transcripts in the Industrial Engineering study program of UNS". Limitations The problem that the author discusses in the study focuses more on the process of applying for transcripts of grades in the scope of the Industrial Engineering study program of UNS. The method used to design an information system for the print out of transcripts is using the System Development Life Cycle (SDLC) with the Waterfall model. The programming language used to design an information system for printing transcripts is PHP Native and MySQL database as data storage media. With the design of the information system for the application of printed transcripts, it is hoped that it can improve the quality of service to students, especially in the scope of the Industrial Engineering study program of UNS.

Retraction: Comparison between 200, 400 and 600 microgram rectal misoprostol before cesarian section: A randomized clinical trial.

The above article, published online on January 7th, 2019 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editor-in-Chief, Hiroaki Kajiyama, Japan Society of Obstetrics and Gynecology, and John Wiley & Sons, Australia, Ltd. The retraction has been agreed on following concerns about the validity of the presented data raised by a third party. As part of the investigation, an independent statistical expert concluded that there are inconsistencies in the data that could only be verified by examining the underlying clinical trial data. The authors have not responded to repeated requests for the original data and have not provided any explanation into the overlap between this study and one published in 2018 in a different journal. Furthermore, while the institute where the research was conducted confirmed that this clinical study was approved by an institutional ethics committee, documentation of the approval is not available. The authors have not responded when asked to agree to this retraction.

The Impact of Multiple API Connection Request to An Open-Source Android-based Currency Tracker's Performance and The Mitigation

High traffic data requests through APIs can significantly impact the performance of mobile applications, affecting both the user interface and overall system performance. The user's perception and experience while performing tasks within the application are closely tied to the perceived performance of the user interface. To understand the impact of connection requests on application performance, a study is necessary, with responsiveness being a critical factor. This study aims to investigate the potential effects on the user interface when each unit or feature in the application requires a data connection through the API. The study will examine code patterns and conduct multiple data requests through APIs. Performance profiling will be the main method used to test each unit or block of code related to data request and reception via the API. The obtained results from the testing process will reveal the performance of the currency tracker application, specifically in areas directly associated with data request and reception via the API. To optimize the request and data calling process, best practices such as AsyncTask and Coroutine will be implemented. The implementation of these best practices will be assessed based on a predefined matrix to measure improvements needed to enhance applications’ performance. Therefore, the results of this study will demonstrate the influence of data request and reception processes within applications on network activity. Furthermore, the implementation of best practices, specifically AsyncTask and Coroutine, will enhance the overall performance of the application.

Продовольственные отходы в социальных учреждениях: потери средств бюджетного финансирования

Circular agriculture considered as one of the forms of sustainable development of the agricultural sector. The essence of the proposed new approach in agriculture based on the reduction of agricultural waste and the ability to use a closed production cycle to increase economic, social and environmental efficiency. The author has previously developed an experimental business model for the circular economy of agriculture and the agri-food system using the example of the Komi Republic. The study revealed that the main problem inherent in the circular economy of agriculture, as the disposal of animal waste, is absent in the region. Taking into account the peculiarities of the region, other significant problems were found: the disposal of containers from food imported from other regions and the amount of discarded food purchased for state social institutions under Federal Law No. 44 «On the contract system in the field of procurement of goods, works, services to meet state and municipal needs». Based on surveys, it was revealed that in social institutions where free meals provided at the expense of the republican budget, the amount of food thrown away by students and patients ranges from 25 to 60%. The scientific novelty of the work lies in the fact that for the Republic of Komi, for the first time, the losses of budget funds allocated for free meals in public institutions in the context of municipalities of the region were calculated. The article calculates the losses of budget funding from discarded food (food) in the areas of education, social protection and healthcare for 2017-2022. The calculations based on the data of requests, both to the relevant ministries of the Komi Republic, and to the administration of municipalities of urban districts and municipal districts. It is revealed that the loss of budget funding due to discarded food for 2017-2022 range from 1024.3 to 3073 million rubles. The practical significance of the introduction of this model in the regional economy of the republic will ensure a reduction in food losses, a decrease in the formation of food and non-food waste, which will increase the level of self-sufficiency in basic foodstuffs, the rational use of resources in the agricultural sector and the effective use of budgetary funds.

Camguard: Preventing unauthorised camera access for women’s saftey

Online harassment is a serious problem that can have significant negative impacts on the mental health and well-being of its victims. To address this issue, an Android app has been developed that is accessible to a wide range of users worldwide. The app is built on a script backend process, providing a stable and efficient environment for data management and request processing. The app's API is integrated into the script backend process, ensuring seamless and efficient operation.During the installation process, the app requires users to go through a verification process that includes providing their Google Account, mobile number, and an OTP. This ensures that only legitimate users have access to the app's features and functions. The app also requires users to create a strong master password, which is directly connected to the Google bot, enhancing its security. Biometrics are recommended to protect the master password, ensuring that only authorized users can access their data.The app's security measures are crucial in ensuring the privacy and protection of user data. The use of biometrics and direct connection to the Google bot enhances security, and the app's API provides access to relevant information and enhances the user experience. Users can change their password securely, ensuring that only authorized users can access their data.Overall, the app is a reliable and efficient solution for managing and protecting user data. Its compatibility with a wide range of devices and seamless integration of API make it accessible to a large percentage of Android users worldwide. The app's verification process, use of biometrics, and direct connection to the Google bot enhance its security, ensuring that only authorized users have access to the app's functionalities. The development of this app is a critical step in addressing the issue of online harassment and protecting the privacy and well-being of its users.

Cleaning Up the Neighborhood: White Influx and Differential Requests for Services

Visible signs of disorder serve as markers of difference across urban space. Sociological theory suggests that variation in collective social control efforts contributes to variation in physical disorder. However, how structural characteristics shape differences in informal social control remains underexplored because of limited data on disorder and social control. Using city service request data and a novel dataset drawing on Google Street View imagery and computer vision methods, the authors examine the neighborhood characteristics associated with propensities to request trash-related services across five large U.S. cities. The authors find that socioeconomically advantaged neighborhoods and those with fewer minority and foreign-born residents have higher propensities. However, an increase in White residents, but not necessarily an increase in high–socioeconomic status residents, is strongly associated with greater propensities. The authors argue that incoming White residents introduce unique dynamics of social control that are not necessarily collective, thereby affecting spatial inequality and power relations within their new neighborhoods.

Human Eye Aberrometry Data Generation Using Generative Adversarial Neural Network

It’s obvious that for development and improvement of methods and apparatus for diagnosis and treatment of optical flaws of human eye at the modelling stage, it’s necessary to have sets of real measurements. However, data requests to clinics are accompanied by substantial amount of bureaucracy procedures and, at the same time, acquired dataset may be too small, which can be critical, for example, for training of neural networks. According to the analysis of existing publications, publicly available datasets of aberrometry data (sets of eye’s refractive flaws) are rare and consist of relatively low number of measurements. But, due to current development state of neural networks, it is possible to generate data based on real measurements. The most common solutions are methods based on the usage of the Generative Adversarial Networks (GAN). This tendency is also relevant for the modern ophthalmology, but no publications aimed at aberrometry data synthesis were found. For this reason, objective of this work is development of solution for generation of sets of human eye’s refractive errors using neural networks. Proposed solution includes generator and critic networks trained according to the Wasserstein GAN with Gradient Penalty (WGAN GP) algorithm. In order to improve training, the method of data augmentation called Data Augmentation Optimized for GAN (DAG) was used, moreover, the possibility of augmentation of aberrometry data in two forms was implemented — for both Zernike coefficient vectors and wavefront pixel images. According to the result’s evaluation, generated data has the distribution close to the real sample (Fréchet distance equals 0.7) and, at the same time, it is neither a copy of real measurements (92% creativity rate) nor duplication of a few aberration sets (diversity metric equals 3.64 which is close to the optimal 3.83). The direction of further improvement includes enhancement of existing architectures of generator and critic, search or creation of bigger training dataset and refinement of data augmentation technics.

Patient participation in research projects in Community Pharmacies: An exploratory study

Introduction: Over the last years there has been an increase in community pharmacy participation in research projects. Patient recruitment plays a key role in the research project success. Pharmacists’ barriers and enablers of recruitment have been identified, but little is known about the influence of research project-related factors. The aim of this paper is to explore patient participation in different studies conducted in community pharmacies and to identify study-specific factors that may be associated with it. Method: An experimental multicenter study was performed in 12 community pharmacies participating in 4 research projects. The number of patients who accepted/refused to participate was recorded. Variables related to each offered study and the project were collected. A bivariate analysis using Pearson’s Chi-Square test and a risk analysis were performed. Results: Participation rate was 90.44 % (n=558). Study type (OR=2.64; 95 % CI=1.47-4.75; cross-sectional vs pragmatic), the type of measurement applied (OR=2.47; 95 % CI=1.43-4.36), the use of a personalized care area (PCA) (OR=2.49; 95 % CI=1.44-4.39), and personal data request (OR=2.53; 95 %CI=1.47-4.42) showed association with participation in the RP (p<0.05). Conclusions: Patient participation in research projects is high and appears to rely on study-specific factors.

Energy-efficient non-anchor channel allocation in NB-IoT cellular networks

In Narrowband Internet of Things (NB-IoT), a base station can provide multiple non-anchor channels to release the burden of anchor channels. NB-IoT devices can use non-anchor channels to execute data transmission to save energy. However, we creatively observe a new important property in the NB-IoT; allocating more non-anchor channels to a base station may even cause an increase in the energy consumption of devices during the data transmission procedure. Unlike traditional channel assignment problems, this new property in the NB-IoT leads to the non-anchor channel allocation being a non-convex problem. This article studies the energy-efficient non-anchor channel allocation in the NB-IoT to minimize the energy consumption of devices to finish their data requests. Because of the non-convex property, this article proposes a dynamic programming algorithm without considering frequency reuse to determine how many non-anchor channels each base station should use. We prove that the dynamic programming algorithm can get an optimal solution. Then, we propose an energy-efficient non-anchor channel reuse algorithm to minimize the energy consumption of devices. Compared with two baselines, the simulation results show that the proposed algorithms reduce the energy consumption by 66%, and blind decoding consumes the most energy of a device.