Big data security in Internet of Things

The Internet-of-Things (IoT) paradigm is an emerging twenty-first century technological revolution, a concept that facilitates to communicate with objects, devices, and machines at unprecedented scale. Nowadays, IoT is extensively applied to numerous applications such as intelligent transportation, smart security, smart grid, and smart home. Now, considering that in the near future, millions of devices will be interconnected and will be producing enormous data, the privacy and security of data going to be challenged and private information may leak at any time. This chapter presents an overview of the IoT and security concerns on big data while we discuss privacy and security approaches for big data with reference to infrastructure, application, and data

A novel meta-heuristic for green computing on VFI-NoC-HMPSoCs

The number of processors has increased significantly on multiprocessor system therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. Heterogeneous VFI, Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) i.e. VFI-NoC-HMPSoCs are widely adopted in computational extensive applications due to their higher performance and an exceptional Quality-of-Service (QoS). Proper task scheduling using search-based algorithms on multiprocessor architectures can significantly improve the performance and energy-efficiency of a battery-constrained embedded system. In this paper, unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of 15% and 20% over CA-TMES-Search and CA-TMES-Quick respectively

Energy efficient heuristic algorithm for task mapping on shared-memory heterogeneous MPSoCs

Existing research mostly reduce mapping time and inter-processors communication energy of the multiprocessor system-on-chips (MPSoCs). Unlike other approaches in this paper we have explored energy efficient task mapping on shared-memory heterogeneous MPSoCs considering the energy performance profile of the processors. We propose mitosis heterogeneous-genetic algorithm (MH-GA) for energy aware task mapping on DVFS-enabled processors in order to maximally exploit the inherent heterogeneity in the MPSoC platform while satisfying the application deadline restriction. The proposed heuristic mapping approach has an integrated list scheduler that assigns priority to the tasks with lower deadlines. The experiments are conducted on 4 synthetic and 4 real-world task graphs (TGs) acquired from embedded systems synthesis benchmarks (E3S). The experimental results are compared with the greedy algorithm and our proposed heuristic algorithm achieves maximum energy efficiency of ~53.2% while reduces the average energy consumption ~21.5%

Energy optimization of streaming applications in IoT on NoC based heterogeneous MPSoCs using re-timing and DVFS

The Multiprocessor System-on-Chip (MPSoC) computing architectures are widely adopted in modern embedded systems for real-time applications due to their high performance, reliability, and Quality-of-Service (QoS). Green computing or energy-efficient task scheduling is a critical technological challenging facet in an energy constrained embedded systems because higher energy consumption limits the lifetime of the computing platform and causes an increased carbon footprint. In this paper, we investigate energy-aware task scheduling on Dynamic Voltage and Frequency Scaling (DVFS) enabled Network-on-Chip (NoC) based Heterogeneous MPSoCs (HMPSoCs). We transform the intra-data dependencies into inter-data dependencies of the tasks with precedence constraints represented by Directed Acyclic Graph (DAG). We further implement Energy-efficient Task Scheduling Heuristic (ETSH) algorithm embedded with a list scheduler to perform energy-aware task scheduling while considering the energy performance profiles of the processors and task deadlines. The observed results on 5 real-world and 5 synthetic Task Graphs (TGs) adopted from Embedded Systems Synthesis (E3S) benchmarks suit demonstrate that ETSH outperforms state-of-the-art technique. Concisely, it achieves 20% and 38% average energy-efficiency with and without using coarse-grained software pipelining respectively

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation