Firmware Over-the-air Programming Techniques for IoT Networks - A Survey

The devices forming Internet of Things (IoT) networks need to be re-programmed over the air, so that new features are added, software bugs or security vulnerabilities are resolved, and their applications can be re-purposed. The limitations of IoT devices, such as installation in locations with limited physical access, resource-constrained nature, large scale, and high heterogeneity, should be taken into consideration for designing an efficient and reliable pipeline for over-the-air programming (OTAP). In this work, we present a survey of OTAP techniques, which can be applied to IoT networks. We highlight the main challenges and limitations of OTAP for IoT devices and analyze the essential steps of the firmware update process, along with different approaches and techniques that implement them. In addition, we discuss schemes that focus on securing the OTAP process. Finally, we present a collection of state-of-the-art open-source and commercial platforms that integrate secure and reliable OTAP.

Revised D-A-CH reference values for the intake of biotin

Purpose The reference values for biotin intake for Germany, Austria and Switzerland lead back to a report in 2000. Following a timely update process, they were revised in 2020. Methods For infants aged 0 to < 4 months, adequate biotin supply via human milk was assumed and in consequence the reference value reflects the amount of biotin delivered by human milk. For infants aged 4 to < 12 months, biotin intake was extrapolated from the reference value for younger infants. Due to missing data on average requirement, the reference values for biotin intake for children, adolescents and adults were derived based on observed intake levels. The reference value for lactating women considered in addition biotin losses via human milk. Results The reference value for biotin intake for infants aged 0 to < 4 months was set at 4 µg/day and for infants aged 4 to < 12 months at 6 µg/day. In children and adolescents, the reference values for biotin intake ranged from 20 µg/day in children 1 to < 4 years to 40 µg/day in youths 15 to < 19 years. For adults including pregnant women, 40 µg/day was derived as reference value for biotin intake. For lactating women, this value was set at 45 µg/day. Conclusions As deficiency symptoms of biotin do not occur with a usual mixed diet and the average requirement cannot be determined, reference values for an adequate biotin intake for populations from Germany, Austria and Switzerland were derived from biotin intake levels assessed in population-based nutrition surveys.

Publication and Impact of Preprints Included in the First 100 Editions of the CDC COVID-19 Science Update (Preprint)

BACKGROUND Preprints are publicly available manuscripts posted to various servers that have not been peer-reviewed. Although preprints have existed since 1961, they have gained increased popularity and credibility during the COVID-19 pandemic due to the need for immediate, relevant information. OBJECTIVE The inclusion of preprints in the CDC COVID-19 Science Update, a weekly publication that provides brief summaries of new COVID-19-related studies, is an opportunity to evaluate the publication rate and impact (Altmetric Attention Score and citation count) of selected preprints and assess the performance of the Science Update to select impactful preprints. METHODS All preprints in the first 100 editions (April 1, 2020 – July 30, 2021) of the Science Update were included in the study. Preprints that were not published were categorized as “unpublished preprints”. Preprints that were subsequently published exist in two versions (in a peer-reviewed journal and on the original preprint server) which were analyzed separately and referred to as “peer-reviewed preprint” and “original preprint”, respectively. Time-to-publish was the time interval between the date on which a preprint was first posted to the date on which it was first available as a peer-reviewed article. Impact was quantified by Altmetric Attention Score and citation count for all available manuscripts on August 6, 2021. Preprints were analyzed by publication status, rate, and time to publication. RESULTS Among 275 preprints included in the CDC COVID-19 Science Update during the study period, most came from three servers: medRxiv (n=201), bioRxiv (n=41), and SSRN (n=25), with eight coming from other sources. More than half (55.3%) were eventually published. The median time-to-publish was 2.31 months (IQR 1.38-3.73). When preprints posted in the last 2.31 months were excluded (to account for the time-to-publish), the publication rate was to 67.8%. Seventy-six journals published at least one preprint from the CDC COVID-19 Science Update and 18 journals published at least three. The median Altmetric Attention Score for unpublished preprints (n=123) was 146 (IQR 22-552) and median citation count of 2 (IQR 0-8); for original preprints (n=152) these values were 212 (IQR 22-1164) and 14 (IQR 2-40), respectively. For peer-review preprints, these values were 265 (IQR 29-1896) 19 (IQR 3-101), respectively. CONCLUSIONS Prior studies of COVID-19 preprints found publication rates between 5.4% and 21.1%. Preprints included in the CDC COVID-19 Science Update were published at a higher rate than overall COVID-19 preprints, and those that were ultimately published were published within months and received higher attention scores than unpublished preprints. These findings indicate that the Science Update process for selecting preprints appears have done so with high fidelity in terms of their likelihood to be published and impactful. Incorporation of high-quality preprints into the CDC COVID-19 Science Update improves this activity’s capacity to inform meaningful public health decision making.

The European Joint Programme on Radioactive Waste Management EURAD: update of its strategic research agenda

The European Joint Programme on Radioactive Waste Management EURAD brings together various research actors, namely waste management organisations (WMO), technical support organisations (TSO) and research entities (RE), to work on a joint strategic research agenda (SRA) focusing on deep geological disposal of radioactive waste. In total, 116 project partners from 23 countries have worked jointly since 2019 in collaborative RD&amp;D work packages, strategic studies and various knowledge management activities. EURAD research is driven by the need for implementation of a deep geological repository and its safety, while aiming for scientific excellence. EURAD has developed a roadmap which is seen as a representation of a generic radioactive waste management (RWM) programme. The content is focused on what knowledge and competencies (including infrastructures) are considered most critical for RWM and implementation of deep geological disposal, in alignment with the EURAD vision. Here, the current SRA update process will be outlined from the perspective of Europe's research entities contributing to EURAD. In this context, the international network of research entities EURADSCIENCE plays a key role. EURADSCIENCE addresses – and will address during decades to come – scientific excellence in (the full lifecycle of) radioactive waste management from cradle to grave. As an independent, cross-disciplinary and inclusive organization, its overarching aim is to ensure scientific excellence and credibility in decision-making on RWM, regardless of national implementation status, waste type or national inventory. To this end, EURADSCIENCE will define and update its own SRA. The approach here is to maintain a holistic view of scientific disciplines and provide scientific excellence to advance progress of national radioactive waste management programmes, and to ensure scientific credibility of waste management concepts as well as addressing fundamental requirements related to knowledge management. More generally speaking, EURADSCIENCE aims to bring forward a vision that assures that scientific excellence and ever-developing scientific advances are integrated at any given time into the multigenerational implementation process of geological disposal. Similarly, the respective WMO and TSO networks, IGD-TP and SITEX, have developed their SRAs based on their specific roles and perspectives. Ultimately, the overlap between these SRAs will define the envelope for future European RD&amp;D activities in the context of RWM. The update process has recently been consolidated after consultations between the three actor groups. Ultimately, the EURAD general assembly will have to approve the SRA update process regarding its alignment with the EURAD roadmap, the development of the seven existing SRA themes, the development of future RD&amp;D activities via an EURAD exchange forum and the focus of RD&amp;D planning for the next 10 years.

DRG grouping by machine learning: from expert-oriented to data-based method

Background Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost. Methods This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping. Results Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time. Conclusions As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process.

Firmware Update Using Multiple Gateways in LoRaWAN Networks

The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway.

ITOC: An Improved Trie-Based Algorithm for Online Packet Classification

With the development of SDN, packet classifiers nowadays need to be provided with low update latency besides fast lookup performance because switches need to respond to update control messages from controllers in time to guarantee real-time service in SDN implementations. Classification in this scenario is called online packet classification. In this paper, we put forward an improved trie-based algorithm for online packet classification (ITOC), in which we provide a trie selection strategy to avoid occasional high update latency in the update process of online trie-based algorithms. Experiments are conducted to validate the effectiveness of our optimization and compare the performance of ITOC with the offline methods, DPDK ACL. Experimental results demonstrate that ITOC has the same level of lookup speed with DPDK ACL and greatly decreased the update latency as well. The update latency of ITOC is only 6.85% of DPDK ACL library in the best case.

Diversity Evolutionary Policy Deep Reinforcement Learning

The reinforcement learning algorithms based on policy gradient may fall into local optimal due to gradient disappearance during the update process, which in turn affects the exploration ability of the reinforcement learning agent. In order to solve the above problem, in this paper, the cross-entropy method (CEM) in evolution policy, maximum mean difference (MMD), and twin delayed deep deterministic policy gradient algorithm (TD3) are combined to propose a diversity evolutionary policy deep reinforcement learning (DEPRL) algorithm. By using the maximum mean discrepancy as a measure of the distance between different policies, some of the policies in the population maximize the distance between them and the previous generation of policies while maximizing the cumulative return during the gradient update. Furthermore, combining the cumulative returns and the distance between policies as the fitness of the population encourages more diversity in the offspring policies, which in turn can reduce the risk of falling into local optimal due to the disappearance of the gradient. The results in the MuJoCo test environment show that DEPRL has achieved excellent performance on continuous control tasks; especially in the Ant-v2 environment, the return of DEPRL ultimately achieved a nearly 20% improvement compared to TD3.

AUTOMATIC CHANGE DETECTION OF DIGITAL MAPS USING AERIAL IMAGES AND POINT CLOUDS

In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change detection based on traditional classification algorithms is difficult due to the high level of noise in the data, such as introduced by temporary changes (e.g. cars being parked). This paper describes a method to detect changes between two time steps using 2.5D data and to transfer these insights to a digital map. For every polygon in the map, several attributes are collected from the input data, which are used to train a machine-learning model based on gradient boosting. A case study in Haarlem, in the Netherlands, was conducted to test the performance of this proposed approach. Results show that this methodology can recognize a substantial amount of changes and can support – and speed up – the manual updating process.

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.