Network Failures Research Articles

Performance Limitation of Group Testing in Network Failure Detection

Performance Limitation of Group Testing in Network Failure Detection

Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution

Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions.

Novel fault isolation scheme under protection failure in MVDC distribution networks without DC circuit breakers

In medium voltage direct current (MVDC) distribution networks without DC circuit breakers, converters with fault-blocking capability are employed to de-energize the network, which facilitates the disconnection of DC switches. The fault current is cleared rapidly under DC faults, posing difficulties to the reliable fault isolation due to the rapid disappearing fault features. To tackle this problem, a novel isolation scheme based on active control of converters is proposed, where trapezoidal wave current is injected from the converter to the fault point. The faulty line could be located by counting the number of high-level pulses of trapezoidal wave current, and then isolated by the corresponding DC switches during the low-level pulses of trapezoidal wave current. Finally, the healthy parts of the network could be restored to normal operation. Compared with other fault location & isolation methods, the proposed scheme based on the active control of converters could cooperate with DC switches without communication. In addition, the innovative utilization of the number of injected pulses rather than the amplitude or frequency can provide a backup function if the DC switch fails to operate. The proposed scheme further improves the reliability of the MVDC network and is easy to apply, the effectiveness of the proposed scheme is verified in PSCAD/EMTDC.

Feature generation and multi-sequence fusion based deep convolutional network for breast tumor diagnosis with missing MR sequences

In breast tumor diagnostic tasks, joint analysis of multiple sequences of MRI can improve the accuracy of diagnosis. More and more studies focus on the correlation between sequences and try applying multiple MRI sequences to computer-aided diagnosis. The deep neural network plays a vital role in this process because of its good feature extraction ability. However, the inevitable presence of missing or unavailable sequences in clinical can lead to degraded performance or even failure of multi-input networks. The key problem solved in this paper is to ensure the diagnostic accuracy of the network despite the presence of missing input sequences. A new breast tumor diagnosis deep learning method, ESF-NET, is proposed. First, ESF-NET combines the attention mechanism for adversarial learning to obtain the potential mapping and relationship between sequences and completes the generation of features for the missing sequences. Then the extended sequence fusion module with different fusion strategies is designed, which makes the final fusion results benefit from the feature enhancement resulting from all fusion strategies. These strategies can be adaptively weighted. We apply EST-NET to a dataset of MRI images of 98 women at high risk of breast cancer, including 33 benign and 65 malignant lesions. Each patient data contains several two-dimensional slices with different axial planes, the dataset consists of 2245 slices. In the presence of sequence missing, ESF-NET achieves a diagnostic accuracy of 85.61% at the slice level and 89.66% at the patient level, an improvement of 8.39% compared to using only a single sequence.

A Simulation – Optimization Model of Network Failure Prone Manufacturing Systems with a Reliability-Based Maintenance and Revenue Sharing Approach

با توجه به اثر عوامل تصادفی مانند خرابی ماشین‌ها بر قدرت رقابتی سازمان‌های تولیدی، اهمیت برنامه‌ریزی تولید دوچندان شده است. لذا سیستم‌های تولیدی مستعد شکست برای مقابله با این عدم قطعیت، پدید آمده‌اند. جهت حفظ سهم در بازار رقابت و افزایش بهره‌وری، سیستم‌های صنعتی به اسـتراتژی نگهداری و تعمیرات به‌منظور کاهش نرخ خرابی و افزایش قابلیت اطمینان روی آورده‌اند. به‌منظور افزایش ظرفیت تولید، فراهم­آوردن انعطاف‌پذیری بیشتر و اطمینان از رضایت‌مندی مشتری از نظر کمیّت، کیفیت و زمان‌بندی، استفاده از پیمانکاری فرعی با رویکرد اشتراک درآمدها، گزینه‌ای مناسب در این پژوهش است. این پژوهش، سیستمی متشکل از شبکه‌ای از ماشین‌آلات با محدودیت رابطه و زمان خرابی و تعمیر تصادفی می باشد. به‌منظور جلوگیری از کمبود از بافرهای میانی و یک بافر نهایی استفاده می‌شود. پارامتر مؤثر دیگر تعیین دفعات بهینه‌ نگهداری پیشگیرانه است که منتج به حداقل رساندن هزینه نگهداری و تعمیرات پیشگیرانه و اصلاحی می‌شود. هدف تعیین نرخ بهینه تولید و متغیر‌های نگهداری پیشگیرانه و متغیر‌های مربوط به پیمانکاران فرعی است. برای این منظور از شبیه‌سازی گسسته ـ پیشامد استفاده می‌شود. بعد از مدل‌سازی در نرم‌افزار ارنا بهترین مقادیر متغیر‌های تصمیم در بستر Opt Quest به­دست می‌آید که به کاهش 5/22 درصدی هزینه‌های کل سیستم منجر می‌شود.

Modeling the resumption of work and production of enterprises during COVID-19: An SIR-based quantitative framework.

The ongoing COVID-19 pandemic has evolved beyond being a public health crisis as it has exerted worldwide severe economic impacts, triggering cascading failures in the global industrial network. Although certain powerful enterprises can remain its normal operation during this global shock, what's more likely to happen for the majority, especially those small- and medium-sized firms, is that they are experiencing temporary suspension out of epidemic control requirement, or even permanent closure due to chronic business losses. For those enterprises that sustain the pandemic and only suspend for a relatively short period, they could resume work and production when epidemic control and prevention conditions are satisfied and production and operation are adjusted correspondingly. In this paper, we develop a novel quantitative framework which is based on the classic susceptible-infectious-recovered (SIR) epidemiological model (i.e., the SIR model), containing a set of differential equations to capture such enterprises' reactions in response to COVID-19 over time. We fit our model from the resumption of work and production (RWP) data on industrial enterprises above the designated size (IEDS). By modeling the dynamics of enterprises' reactions, it is feasible to investigate the ratio of enterprises' state of operation at given time. Since enterprises are major economic entities and take responsibility for most output, this study could potentially help policy makers better understand the economic impact caused by the pandemic and could be heuristic for future prevention and resilience-building strategies against suchlike outbreaks of public health crises.

HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments.

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

Default Mode Network failure is associated with increased tau PET uptake

AbstractBackgroundThe accumulation of neurofibrillary tangles contributes to a neurodegenerative cascade toward Alzheimer’s disease. Focal tau deposition may impair signal integrity within a broader network of functionally connected brain regions. 18F‐Flortaucipir PET allows visualization of tau pathology in vivo, and functional compensation between Default Mode Network (DMN) subsystems is an fMRI‐based biomarker of network failure. We sought to characterize the relationship between network failure in the DMN and tau burden throughout the brain.Method620 ADNI3 subject visits with concurrent fMRI and flortaucipir PET scans were selected from the database (http://adni.loni.ucla.edu) for analysis. ADNI PET core PVC values referencing inferior cerebellar cortex were used to index SUVR across the entire FreeSurfer parcellation, and the ADNI MRI core Network Failure Quotient (NFQ) was used as a summary metric of fMRI connectivity strength in the DMN. Linear partial correlation coefficients were calculated for NFQ and SUVR in each brain region, controlling for age. Lasso logistic regression also returned penalized maximum‐likelihood fitted coefficients for a generalized linear model estimating NFQ from regional SUVR; regions with non‐zero coefficients in the sparsest model were included in a composite. A subset of 131 subjects with paired visits at one year follow‐up were included in longitudinal analysis.ResultA significant age‐corrected association was observed between higher NFQ, reflecting worsening DMN failure, and higher flortaucipir SUVR in Braak 1 (p < 0.02) and meta‐temporal composite ROIs (p < 0.03). Age‐corrected partial correlations achieved marginal significance (p < 0.05) in bilateral entorhinal, inferior and middle temporal, superior parietal and caudal middle frontal cortices, left posterior cingulate and right isthmus cingulate. Lasso regression revealed a subset of non‐zero coefficients including frontal, temporal, cingulate and subcortical brain regions. In the longitudinal analysis, age‐corrected baseline tau load in Braak 5/6 and a set of frontoparietal subregions was marginally significantly associated with change in NFQ after 1 year.ConclusionFunctional network failure in the DMN is associated with greater tau burden inside and outside the network. These results suggest the NFQ is a viable fMRI‐based biomarker for tau pathophysiology and disease state.

МЕТОД ДОСЯГНЕННЯ КОНСЕНСУСУ ВРОЗПОДІЛЕНИХ СЕРВІСНИХ СИСТЕМАХ

This article examines the problems of distributed algorithms and proposes their solution using temporal analysis. There are many things that can go wrong in distributed systems that can cause the system to crash. The solution to this is to build a system that can withstand the problems that arise during its operation. It turns out that having an algorithm capable of reaching consensus is extremely important for systems that want to function properly despite network failures. Although consensus is omitted in performance-oriented systems, they still rely heavily on systems that implement consensus algorithms for them (such as Zookeeper, etc.) to handle the consensus-reduced task, while at the same time having some weaker consistency model. In turn, the algorithms available today have several problems, the solution of which will significantly increase the performance of the algorithms and, as a result, the systems that use them. This article discusses the problems that arise in existing implementations and presents a data analysis technique and model for solving one of the algorithm problem.

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf.

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4s, which has been identified on boundary condition of Pdf function.

A global functional network biomarker across aging and dysexecutive Alzheimer’s disease

AbstractBackgroundDisruption of the default mode network (DMN) is thought to be a common feature of aging and across Alzheimer’s disease (AD) clinical phenotypes, while syndromic variability could be reflected in selective degeneration of macro‐scale networks optimally supporting specific mental abilities (Fig 1). As part of a collaborative effort between Mayo Clinic and the Human Connectome Project‐Aging (HCP‐A), we aimed to delineate a biomarker of global network failure across aging and the dysexecutive (dAD) and amnestic phenotypes of AD, and biomarkers of focal degeneration specific to each AD phenotype.MethodWe used a regression framework to assess the relationships between a marker of DMN dysfunction, the network failure quotient (NFQ), and factors relevant to AD such as age, cortical thickness (global cortical thickness, inferior parietal lobe), hippocampal volume, and cognition (episodic memory, cognitive flexibility) in the HCP‐A cohort, which consists of 725 non‐demented older adults. We then fit centile curves to the HCP‐A cohort to assess whether the NFQ and focal degeneration markers could distinguish dAD (n=10) and amnestic AD (n=8) patients from the HCP‐A cohort at the group‐ and individual‐level. Participants were scanned using the HCP‐A protocol, allowing for the acquisition of high‐resolution multi‐echo structural images and longer resting‐state connectivity acquisition time compared to traditional protocols.ResultThe NFQ related to age, cortical thickness, hippocampal volume, and cognition in the HCP‐A cohort. It also strongly discriminated dAD and amnestic AD from the HCP‐A cohort but did not discriminate between AD phenotypes. In contrast, markers of focal degeneration were phenotype‐specific; parieto‐frontal areas yielded largest effect sizes for dAD patients, and hippocampal and temporal areas best discriminated patients with amnestic AD (Fig 2).ConclusionThe NFQ may reflect shared system‐level pathophysiological mechanisms across aging and the dysexecutive and amnestic phenotypes of AD whereas focal degeneration markers highlight distinct pathognomonic processes across AD phenotypes. These findings suggest that inter‐individual variability in cognitive impairment in AD would be closely tied to modular, phenotype‐specific network degeneration combined with a common global functional disruption of the DMN. Such information has crucial implications for clinical biomarkers and to provide a deeper understanding of heterogeneity in AD.

Research on emergency repair strategy based on load grade and economy

Distribution network plays the role of distributing electric energy in the power system, whether it can run normally relates to people’s production and life. In recent years, frequent natural disasters have caused large-scale failures of distribution networks, resulting in the interruption of a large number of load power supply, which has a huge impact on people’s lives and post-disaster recovery work. Therefore, it is very important to formulate scientific and efficient emergency repair strategies. This paper will study the power supply recovery and multi-fault rush repair of distribution network respectively from the two aspects of load grade and the economy of power grid operation, establish mathematical model, and use particle swarm optimization algorithm to solve, so as to provide a more efficient emergency plan for power supply recovery after disaster.

Network Identifiability: Advances in Separating Systems and Networking Applications

This letter discusses the parallelism between the concept of network identifiability introduced by Boolean Network Tomography (BNT), and the theory of separating systems, highlighting applications of interest to research in networking. This letter evidences how recent results of BNT have direct implications to the formulation of new bounds to the size of separating systems over finite sets. Grounding on these theoretical results, we provide an efficient algorithm for the design of separating systems that meet the bound tightly. We extend the proposed results, bounds and algorithm, to networking applications, including network failure assessment, robust network design and compressed sensing.

Voltage profile assessment in smart distribution grids considering BESS and communication network failures

• Sequential monte carlo simulation for assessing voltage profile of smart grids. • modeling failures in the BESS systems and the communication network (CN). • BESS optimization models for day ahead scheduling and voltage profile correction. • The results shows that communication failures have impact on the voltage profile. Battery energy storage systems (BESSs) are expected to play a major role in voltage control of smart distribution grids (SDGs) due to the high penetration of intermittent renewable energy. However, neither these devices nor the communication infrastructure needed for their control, are fully reliable. Due to this, BESS unavailability might occur, degrading voltage profile. This paper presents a new fixed-time sequential Monte Carlo simulation for assessing indices of SDGs’ long-duration voltage variations (LDVVs) considering both failures in BESS systems and in the communication network (CN). Results show that these failures have significant impact on the LDVV indices of the SDG.

CSF tau phosphorylation at sites 217 and 181 are associated with preclinical and biphasic alterations in resting‐state functional connectivity in Autosomal Dominant AD

AbstractBackgroundIn autosomal dominant AD (ADAD), elevated cerebrospinal fluid (CSF) soluble phosphorylated tau (pT181) increases shortly after amyloid positron emission tomography. Recent advancements have identified additional phosphorylated tau sites, such as phosphorylated tau 217 and 205 (pT217 and pT205), which are associated with amyloid deposition and neuronal dysfunction, respectively. Degradation of cortical brain network resting‐state functional connectivity (RS‐FC) is typically observed at the onset of cognitive impairment and is most prevalent in regions where amyloid and tau accumulate. However, it is unknown how specific phosphorylated tau sites are associated with alterations in brain networks and contribute to the transition from asymptomatic to symptomatic AD.MethodCSF tau phosphorylation occupancy on 217, 181 and 205 residues and RS‐FC were measured in 93 individuals from the Dominantly Inherited Alzheimer Network (amyloid‐negative asymptomatic mutation carrier [(aMCA‐); n=26]; amyloid‐positive asymptomatic mutation carriers [a(MCA+); n=36]; and amyloid‐positive symptomatic mutation carriers [(sMCA+); n=31]). Regression models were used to determine within‐group associations between CSF phosphorylated tau sites and within‐network RS‐FC measures.ResultP‐tau associated RS‐FC alterations began during preclinical stages and continuously changed throughout disease progression. Specifically, within aMCA‐, pT217, pT181, and pT205 were associated with decreased RS‐FC in salience and ventral attention networks, while in aMCA+, pT217 and pT181 were associated with increased RS‐FC in the fronto‐parietal network. In sMCSA+ elevated p‐tau sites were associated with decreased RS‐FC in the cingulo‐opercular, dorsal attention, default mode, and memory networks.ConclusionElevated phosphorylation at specific tau sites is associated with a biphasic pattern of RS‐FC which begins during asymptomatic stages. P‐tau‐ associated RS‐FC alterations were consistent with proposed models of cascading network failure and AD pathophysiology. These results suggest that in ADAD, phosphorylation of tau is associated with widespread and dynamic RS‐FC alterations which begin before the transition to symptomatic AD.

Multichannel S-ALOHA-Enabled Autonomous Self-Healing in Industrial IoT Networks

For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.

Cascading failures in EV charging network

With the rapid increase of Electric vehicle (EV) penetration, EV charging network become an important infrastructure. This paper studies the configuration of EV charging network from a safety perspective. It is found that cascading failures may happen if one charging station is out of work and its load has to be redistributed to its neighboring stations. The cascading failure model borrowed from network science area is used in the study, and the results show the effects of system parameters on the network robustness. The results indicate that in order to achieve a good performance, it is better to assign extra load based on neighbors’ degree when a node is overloaded. On the other hand, the extra load redistribution should not consider the distance between the overloaded node and its neighbors.

Adaptive Emergency Call Service for Disaster Management

Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims’ devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER.

"The Internet Is Keeping Me from Dying from Boredom": Understanding the Management and Social Construction of the Self Through Middle-Class Indian Children's Engagement with Digital Technologies During the COVID-19 Lockdown.

This paper unpacks how everyday lives of urban middle-class children were mediated by digital technologies during the COVID-19 national lockdown in India. In contemporary India, children's engagements with digital technologies are structured by their social class, gender, and geographical locations. The resultant disparities between "media-rich" and "media-poor" childhoods in India are stark (Banaji 2017). In this paper, we argue that the national lockdown in the wake of the COVID-19 pandemic exposed India's "media-rich" children to particular threats and obstacles. Based on semi-structured interviews and mapping exercises with 16- to 17-year-old urban middle-class young people, we explore how being confined to their homes for an extended period when their schools shifted to online delivery of teaching and learning; young people negotiated risks and sought digital opportunities in the management and social construction of the self (Callero 2003, 2014). While the majority of existing studies focus on societal anxieties around children's digital media use, in almost a medicalized and pathological fashion, and its impact on parenting practices (Lim 2020; Livingstone and Blum-Ross 2020), we shift the attention to study this social phenomenon to help understand how children reflect on their engagement with technology and shape their own well-being through social construction of the self. Our findings demonstrate that children are reflexive users of digital technologies, as they navigate network failure issues, the demands of online classrooms, their own mental health and social relationships, and deploy the affordances of digital technologies to combat loneliness, nurture contact with friends, and explore educational and career resources. These strategies, in the management and social construction of the self, play out within the discourse of pedagogized middle-class childhood in India, which is imbued with notions of academic success and failure (Kumar 2016; Sen 2014). Media-rich middle-class young people's management and social construction of the self, in the context of crisis and uncertainty, helps promote our understanding of the relationship between social structure, self-structure, and behavior choices, implications of this for child well-being, and reproduction of social inequality in society.