Aggregate Data Research Articles

Association between abductor tears and hip pathology: A nationwide large cohort study

IntroductionAlthough gluteal tears have been observed in a substantial percentage of total hip arthroplasty (THA) patients and hip osteoarthritis (OA) has been shown to alter the function of the gluteal muscles, the association between gluteal tears and hip OA has not been characterized. Therefore, we evaluated (1) the overlap between hip OA and gluteal tears, (2) the relative risks of gluteal tears in patients who have hip OA, and (3) gluteal tear-free survival after diagnosis or treatment for hip osteoarthritis. MethodsThis retrospective study sourced data from TriNetX, a research network that aggregates data from over 92 million patients. Relative risks for gluteal tears were calculated for known risk factors for gluteal tears, age ≥45 years, female sex, and obesity, as well as for hip OA, hip injections, and THA. A subgroup analysis was performed utilizing a Cox proportional hazard model for patients who were diagnosed with hip OA, received a hip injection, or underwent THA in 2015 to assess gluteal tear-free survival over a 9-year timeframe. ResultsThere was a large degree of overlap between patients with hip OA and gluteal tears, as 17.9% of patients with hip OA and 27.5% of patients with a gluteal tear also had the other pathology. Hip OA was associated with a markedly increased risk of a gluteal tear compared to healthy controls (Relative risk: 26.75, 95% CI: 26.64–26.86). Upon controlling for the established risk factors of gluteal tears, patients with hip OA had a markedly more likely to subsequently be diagnosed with an abductor tear (Hazard ratio: 12.46, 95% CI: 11.75–13.22). ConclusionOverall, these findings suggest a strong association between hip OA and the development of gluteal tears, in which further investigation is merited to determine the biomechanical pathophysiology underlying this potential relationship to inform prevention and treatment strategies.

Matching-Adjusted Indirect Comparison of the Efficacy at Week32 of Tralokinumab and Dupilumab in the Treatment of Moderate-to-Severe Atopic Dermatitis.

Tralokinumab and dupilumab are biological agents licensed for the treatment of moderate-to-severe atopic dermatitis (AD) in adult patients who are candidates for systemic treatment. However, no head-to-head studies of their efficacy have been conducted. This study indirectly compared the efficacy of tralokinumab and dupilumab, both in combination with topical corticosteroids (TCS), at week32. An unanchored matching-adjusted indirect comparison was conducted using individual patient data (IPD) from the ECZTRA3 tralokinumab trial and aggregate data from the LIBERTY AD CHRONOS dupilumab trial. IPD were selected by applying inclusion criteria from LIBERTY AD CHRONOS and weighting to match summary baseline characteristics-age, sex, race, body mass index, disease duration, Eczema Area and Severity Index (EASI), Investigator's Global Assessment (IGA), Dermatology Life Quality Index (DLQI) and SCORing Atopic Dermatitis index-of patients treated with dupilumab. Week32 outcomes of interest were 50%, 75% or 90% improvements in EASI (EASI-50, EASI-75 and EASI-90), IGA scores of 0 or 1 (IGA0/1), ≥ 4-point improvement in worst daily pruritus numerical rating scale (NRS) score, and mean improvements in DLQI and the Patient Oriented Eczema Measure (POEM). After matching, tralokinumab and dupilumab, both in combination with TCS, showed similar efficacy across clinical response endpoints at week32 (IGA0/1, tralokinumab 49.9% vs dupilumab 39.3%; EASI-50, 78.9% vs 77.5%; EASI-75, 71.5% vs 71.9%; EASI-90, 53.3% vs 56.2%). The mean change from baseline in DLQI was statistically significantly larger in the matched tralokinumab plus TCS population than in the dupilumab plus TCS arm (- 12.1 vs - 10.4, p = 0.005). Changes in POEM and worst daily pruritus NRS were similar in the two groups. The results of this analysis demonstrate that, in combination with TCS, tralokinumab and dupilumab have similar efficacy in the treatment of moderate-to-severe AD at 32weeks of therapy.

129 LaparoscopiX: Expanding Minimally Invasive Surgery Training in Kenya

OBJECTIVES/GOALS: Open surgery prevails in low- and middle-income countries (LMICs) due to scarcity of laparoscopic equipment and poor access to training. LaparosopiX is a box trainer system designed for teaching hospitals in LMICs; it includes an open-source laser print design and an app to allow surgical trainees to receive feedback from laparoscopic experts. METHODS/STUDY POPULATION: This study aims to assess the usability of Laparoscopix for surgical trainees and mentors at five large teaching hospitals in Kenya. Surgical trainees and mentors who participate in this study will be observed while setting up and using the app to identify natural pain-points. A post-session survey will be conducted to assess immediate perceptions of the platform including ease of navigation and intuitive design. Over three months, aggregate data regarding platform usage at these hospital sites will be collected and analyzed to assess user retention rates, usage and traffic patterns, and skill progression over time. Surveys will be sent out to assess attitudes towards the platform and to elucidate any aspects of the system we can improve. RESULTS/ANTICIPATED RESULTS: We hope to find overall positive impressions towards the LaparoscopiX system during this study. We expect there to be some pain-points that arise during navigation of the app, but we expect no large changes to the application architecture required. We anticipate an immediate increase and eventual plateau of users recruited. We hope to see that surgical trainees are advancing through the app while gaining practice and confidence. We will gather insightful data on which aspects of the app were helpful for trainees, and which can be improved. We also hope to learn what factors may play into trainee and mentor retention in the system. DISCUSSION/SIGNIFICANCE: Through this study, we hope to elucidate ways in which we can improve the LaparoscopiX platform, identify which features to prioritize, and determine the direction of future app development. We believe and hope that LaparoscopiX can expand access to laparoscopic surgical mentorship to improve surgical outcomes and health equity worldwide.

Federated Synchrophasor Data Prediction, Aggregation and Inference Using Deep Learning: A Case of Proactive Control for Short-Term Stability

Federated Synchrophasor Data Prediction, Aggregation and Inference Using Deep Learning: A Case of Proactive Control for Short-Term Stability

Policy Forum: Using Retributive Justice To Ensure Public Trust in Canada's Tax System

To ensure public trust in the tax system, retributive justice must be present. That is, tax offenders must be held accountable for their unethical behaviour and punished in accordance with tax laws. In this article, the authors first summarize recent academic research about the deterrent effect of knowledge of others' tax-crime punishments. They classify this research into two categories: archival "naming-and-shaming" research and experimental tax ethics research. Next, they report the results of a survey of adult Canadian taxpayers in which they assess the perceived severity of tax-crime punishments. They compare these survey results with aggregate tax-crime punishment data in Canada and show that there is an expectations gap between what Canadians believe constitutes appropriate punishment for tax crimes and actual punishments. Finally, the authors discuss enforcement and other challenges for tax authorities in bringing about retributive justice, and consider other ways in which tax authorities might make tax offenders accountable for their crimes. Their ultimate suggestion is that the Canada Revenue Agency needs to increase its perceived power by increasing the frequency of actual prosecutions for tax evasion. Recent proposals for reform of the general anti-avoidance rule may lead to more prosecutions.

Federated data acquisition market: Architecture and a mean-field based data pricing strategy

With the increasing global mobile data traffic and daily user engagement, technologies, such as mobile crowdsensing, benefit hugely from the constant data flows from smartphone and IoT owners. However, the device users, as data owners, urgently require a secure and fair marketplace to negotiate with the data consumers. In this paper, we introduce a novel federated data acquisition market that consists of a group of local data aggregators (LDAs); a number of data owners; and, one data union to coordinate the data trade with the data consumers. Data consumers offer each data owner an individual price to stimulate participation. The mobile data owners naturally cooperate to gossip about individual prices with each other, which also leads to price fluctuation. It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario. Hence, we propose a data pricing strategy based on mean-field game (MFG) theory to model the data owners’ cost considering the price dynamics. We then investigate the interactions among the LDAs by using the distribution of price, namely the mean-field term. A numerical method is used to solve the proposed pricing strategy. The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme. The result further demonstrates that the influential LDAs determine the final price distribution. Last but not least, it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.

Adaptive personalized privacy-preserving data collection scheme with local differential privacy

Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In most recent works, it is assumed that the privacy parameter is determined solely by collectors and then dispatched to all participants. However, it is inelegant and unpromising for all participants to accept the same level of privacy preservation due to their personalized preferences. Here, an adaptive data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods are adaptively chosen by data participants according to their personalized privacy preferences. The adaptive boundary based on the minimum mean square error (MSE) is theoretically and accurately derived to allow participants to adaptively choose the best perturbation method. Then, a weighted combination method is demonstrated to do effective data aggregation from multiple privacy groups. Moreover, an expanded data strategy (EDS) with multiple privacy perturbations is presented to equivalently increase the sample size without harming others privacy, thereby further improving the accuracy of statistics. Finally, the experiments show that the proposed scheme performs better than the previous proposal in terms of MSE and average error rate (AER), especially using the EDS method.

Rapid Production Method of Massive Thematic Maps Based on Geospatial Knowledge Extraction

<p>Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.</p> <p> </p>

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

EPri-MDAS: An efficient privacy-preserving multiple data aggregation scheme without trusted authority for fog-based smart grid

With the increasingly pervasive deployment of fog servers, fog computing extends data processing and analysis to network edges. At the same time, as the next-generation power grid, the smart grid should meet the requirements of security, efficiency, and real-time monitoring of user energy consumption. By utilizing the low-latency and distributed properties of fog computing, it can improve communication efficiency and user service satisfaction in smart grids. For the sake of providing adequate functionality for the power grid, various schemes have been proposed. Whereas, many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats. In this paper, we propose the EPri-MDAS: an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem, which achieves both data integrity verification and data source authentication with the most efficient block cipher-based authenticated encryption algorithm. It performs well in energy efficiency with strong security. Especially, the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses; it also allows for fault tolerance while protecting personal privacy. The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

Sectoral Herding During Global Rare Events: Evidence from the Indonesian Capital Market

Research Aims: This research aims to examine the effects of increased levels of herding on abnormal returns during rare events.Design/Methodology/Approach: Time series regression including all stocks across 9 sectors in the Indonesia Stock Exchange from 1997 to 2020, totaling 5,615 observations is used. The primary model is predicated on three factors derived from Fama-French and prospect theory to incorporate herding as a primary risk factor in assessing the impact of on abnormal returns during rare events.Research Results: The results show that various events produce impacts on herding behavior across different sectors. During bearish market conditions, this behavior manifests significant negative effect leading to greater abnormal returns. Conversely, positive and significant anti-herding behavior is observed in bullish market conditions. Rare events do not necessarily induce herding behavior but may lead to anti-herding behavior.Theoretical Contribution/Originality: In this research, the variables are developed from the Efficient Market Hypothesis, Capital Asset Pricing Model, prospect theory, and market integration theory. The estimation model is grounded in prospect theory and the contribution addresses the research gaps.Practitioners/Policy Implications: The provision of insights to stakeholders in the capital market regarding the impact of rare events on financial behaviors influences investors' decision-making processes in stock investments.Research Limitations/Implications: The measurement of herding refers to Chang et al. (2000) due to the availability of aggregate data from the Indonesian Stock Exchange. Comprehensive micro-level data is not unavailable and the accessibility of complete micro-level data can be conducted. The presence of these data in the capital markets of other countries should be investigated.

Optimizing IoT Data Aggregation: Hybrid Firefly-Artificial Bee Colony Algorithm for Enhanced Efficiency in Agriculture

The data aggregation process in this study has been enhanced by the hybrid firefly-artificial bee colony algorithm (HFABC) by increasing the average packet delivery ratio, end-to-end delay, and lifespan computation. In this study, HFABC and Multi Hop LEACH are two algorithms that are used to aggregate IoT data. Their performance is compared using evaluation criteria including average End-to-End Delay, PDR, and network lifetime. The HFABC method reduces average End-to-End Delay more effectively than Multi Hop LEACH, with gains of 2.20 to 8.66 %. This demonstrates how well it works to reduce the lag times for data transfer in IoT networks. With improvements ranging from 3.45% to 45.39%, HFABC has a greater success rate than Multi Hop LEACH in effectively delivering packets. HFABC increases network lifetime by 0.047 to 2.286 percent, indicating that it helps keep IoT networks operating for longer. For effective data aggregation in IoT networks, HFABC is a superior solution that decreases delays, improves packet delivery, and lengthens network lifetime.

Rule based complex event processing for IoT applications: Review, classification and challenges

AbstractIn recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope.

Utility Scores for Risk-Reducing Mastectomy and Risk-Reducing Salpingo-Oophorectomy: Mapping to EQ-5D.

Risk-reducing mastectomy (RRM) and risk-reducing salpingo-oophorectomy (RRSO) are the most effective breast and ovarian cancer preventive interventions. EQ-5D is the recommended tool to assess the quality of life and determine health-related utility scores (HRUSs), yet there are no published EQ-5D HRUSs after these procedures. These are essential for clinicians counselling patients and for health-economic evaluations. We used aggregate data from our published systematic review and converted SF-36/SF-12 summary scores to EQ-5D HRUSs using a published mapping algorithm. Study control arm or age-matched country-specific reference values provided comparison. Random-effects meta-analysis provided adjusted disutilities and utility scores. Subgroup analyses included long-term vs. short-term follow-up. Four studies (209 patients) reported RRM outcomes using SF-36, and five studies (742 patients) reported RRSO outcomes using SF-12/SF-36. RRM is associated with a long-term (>2 years) disutility of -0.08 (95% CI -0.11, -0.04) (I2 31.4%) and a utility of 0.92 (95% CI 0.88, 0.95) (I2 31.4%). RRSO is associated with a long-term (>1 year) disutility of -0.03 (95% CI -0.05, 0.00) (I2 17.2%) and a utility of 0.97 (95% CI 0.94, 0.99) (I2 34.0%). We present the first HRUSs sourced from patients following RRM and RRSO. There is a need for high-quality prospective studies to characterise quality of life at different timepoints.

Critical Thinking in the Discourse of Russian and World Science: a Review of Research

Introduction. The study of the considered problems is relevant in the context of the digitalization of modern society, where critical thinking becomes an integral attribute of the formed personality. It allows you to analyze and assess the reliability of information, prioritize data, reflexively assess the situation and consciously communicate with surrounding individuals both offline and online. This article considers the definition of the concept and scientific approaches to the study of “critical thinking”. The independence (a developed broad outlook and the presence of logic), emotionality and flexibility (the ability to interpret, analyze and compare), as well as depth and integrity (observation, a detailed approach to the study of the issue, a vision of the general, not just the particular) are stood out among the key characteristics of this phenomenon.Objective. There is an aggregation, analysis and interpretation of empirical and theoretical data of Russian and foreign specialists in the field of psychology, philosophy, pedagogy and sociology on the topic of critical thinking.Concepts of critical thinking. Approximate scientific concepts in the field of studying the phenomenon of critical thinking are considered. The distinctive features of philosophical, psychological, pedagogical and communicative approaches are determined.Approaches to the study of critical thinking. The analysis of the research experience of the scientific community on the problem of critical thinking is carried out. The key features and groups of requirements for critical thinking are highlighted.Discussion. The analysis of the research are conducted by the authors of native and foreign authors on the problem of critical thinking made it possible to form an idea of the study of each of the presented concepts, determining the further potential for work at the junction of psychological and communicative approaches. This method will allow us to consider the phenomenon of critical thinking from the point of view of the importance of the skill in terms of communicative interaction in social networks and exposure to provocative actions on the part of users in a naturally and artificially given context.

The effects of pre-loading on structural behavior of reinforced concrete beams under fire condition: a review

PurposeThis review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.Design/methodology/approachThis review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.FindingsThe review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.Originality/valueThe review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

Prevalence of functional disorders across Europe: a systematic review and meta-analysis.

Functional Disorders (FD) refer to persistent somatic symptoms caused by changes in the functioning of bodily processes. Previous findings suggest that FD are highly prevalent, but overall prevalence rates for FD in European countries are scarce. Therefore, the aim of the present work was to estimate the point prevalence of FD in adult general populations. PubMed and Web of Science were searched from inception to June 2022. A generalized linear mixed-effects model for statistical aggregation was used for statistical analyses. A standardized quality assessment was performed, and PRISMA guidelines were followed. A total of 136 studies were included and systematically synthesized resulting in 8 FD diagnoses. The large majority of studies was conducted in the Northern Europe, Spain, and Italy. The overall point prevalence for FD was 8.78% (95% CI from 7.61 to 10.10%) across Europe, with the highest overall point prevalence in Norway (17.68%, 95% CI from 9.56 to 30.38%) and the lowest in Denmark (3.68%, 95% CI from 2.08 to 6.43%). Overall point prevalence rates for specific FD diagnoses resulted in 20.27% (95% CI from 16.51 to 24.63%) for chronic pain, 9.08% (95% CI from 7.31 to 11.22%) for irritable bowel syndrome, and 8.45% (95% CI from 5.40 to 12.97%) for chronic widespread pain. FD are highly prevalent across Europe, which is in line with data worldwide. Rates implicate the need to set priorities to ensure adequate diagnosis and care paths to FD patients by care givers and policy makers.

A lab-centric, workflow-based data management system for environmental DNA research

The adoption of environmental DNA approaches as a standard tool for biodiversity monitoring leads to the increase in the number of eDNA-based species occurrence records; however, considerable disparity remains in the nature and quality of associated information, much of it unpublished and/or poorly parametrised. A robust system for tracking biological materials from their point of origin through laboratory analyses is required to connect inferred taxon occurrences with analytical history and provenance data. The bulk of eDNA research is currently driven by small-scale operations where the tasks of digitisation, organisation and cross-referencing field records with laboratory analytical data and biomaterial sample location, are often performed manually and disconnected. We present an integrative, full-stack data management solution that provides a structured ontological concept, a minimalist data schema for eDNA research and a software application prototype designed to facilitate real-time digitisation, parsing, annotation and archival of eDNA data. The system tracks the provenance and analytical history of biological samples through a structured hierarchy of events, linked with associated digital file attachment archives, such as images and raw sequence files, and with inferred taxonomic occurrence records. The data entry process is compartmentalised and incorporated into the corresponding stages of standard operations used in fieldwork, biological collection management and laboratory analysis. Resulting data records can be integrated into various output formats required for large-scale analytics, publication and/or submission to global data aggregators. The prototype is implemented on the Microsoft 365 platform as a relational database (Access) linked to cloud-based data tables (SharePoint) and a set of associated data conversion spreadsheets (Excel). The system is designed primarily around the data management needs of small research labs; however, it is scalable to larger institutions and inter-institutional academic networks.

LFTDA: A lightweight and fault-tolerant data aggregation scheme with privacy-enhanced property in fog-assisted smart grid

In recent years, the data aggregation scheme was widely adopted in smart grids (SG) to protect data privacy while ensuring data availability. Meanwhile, asymmetric homomorphic encryption is a popular technique that guarantees data aggregation and accurate computing results. However, it often brings heavy computation overheads and costs, which increases burdens to SG. In order to lighten SG’s burden, in this paper, a lightweight and fault-tolerant data aggregation scheme (LFTDA) with privacy-enhanced property is proposed. Unlike existing schemes, our proposed scheme uses a lightweight symmetric encryption algorithm and a skillful mathematical structure to achieve data aggregation. In addition, in the proposed scheme, smart meters are allowed to dynamically join and exit from the group while guaranteeing the correctness, security, and efficiency of aggregation. More importantly, our LFTDA is fault-tolerant, which means that the data collection from other devices will not be affected even if some of the smart meters or fog nodes do not work. From the view of security, LFTDA can defend against eavesdropping attack and collusion attack. Finally, we evaluate its performance along with other related schemes in terms of aggregation, decryption, and communication costs, which shows that LFTDA is very competitive and hence is suitable for many practical SG applications.