ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources

Edge and fog computing have grown popular as IoT deployments become wide-spread. While application composition and scheduling on such resources are being explored, there exists a gap in a distributed data storage service on the edge and fog layer, instead depending solely on the cloud for data persistence. Such a service should reliably store and manage data on fog and edge devices, even in the presence of failures, and offer transparent discovery and access to data for use by edge computing applications. Here, we present Elfstore, a first-of-its-kind edge-local federated store for streams of data blocks. It uses reliable fog devices as a super-peer overlay to monitor the edge resources, offers federated metadata indexing using Bloom filters, locates data within 2-hops, and maintains approximate global statistics about the reliability and storage capacity of edges. Edges host the actual data blocks, and we use a unique differential replication scheme to select edges on which to replicate blocks, to guarantee a minimum reliability and to balance storage utilization. Our experiments on two IoT virtual deployments with 20 and 272 devices show that ElfStore has low overheads, is bound only by the network bandwidth, has scalable performance, and offers tunable resilience.Comment: 24 pages, 14 figures, To appear in IEEE International Conference on Web Services (ICWS), Milan, Italy, 201

Quantum scattering cross sections of O(3P^3P) + N2_2 collisions for planetary aeronomy

"Hot atoms", which are atoms in their excited states, transfer their energy to the surrounding atmosphere through collisions. This process of energy transfer is known as thermalization, and it plays a crucial role in various astrophysical and atmospheric processes. Thermalization of hot atoms is mainly governed by the amount of species present in the surrounding atmosphere and the collision cross-section between the hot atoms and surrounding species. In this work, we investigated the elastic and inelastic collisions between hot oxygen atoms and neutral N$_2$ molecules, relevant to oxygen gas escape from the martian atmosphere and for characterizing the chemical reactions in hypersonic flows. We conducted a series of quantum scattering calculations between various isotopes of O($^3P$) atoms and N$_2$ molecules across a range of collision energies (0.3 to 4 eV), and computed both their differential and collision cross-sections using quantum time$-$independent coupled-channel approach. Our differential cross-section results indicate a strong preference for forward scattering over sideways or backward scattering, and this anisotropy in scattering is further pronounced at higher collision energies. By comparing the cross-sections of three oxygen isotopes, we find that the heavier isotopes consistently have larger collision cross-sections than the lighter isotopes over the entire collision energy range examined. However, for all the isotopes, the variation of collision cross-section with respect to collision energy is the same. As a whole, the present study contributes to a better understanding of the energy distribution and thermalization processes of hot atoms within atmospheric environments. Specifically, the cross$-$sectional data presented in this work is directly useful in improving the accuracy of energy relaxation modeling of O and N$_2$ collisions over Mars and Venus atmospheres.Comment: 7 pages, 5 figures, 4 tables, submitted to MNRA

Green Approach for Next Generation Computing: A Survey

In the past few years, the Information and Communication Technology sector have made a significant advancement in Cloud Computing technology. The world wide acceptance of Cloud technology can be credited to the various benefits a Cloud offers to its users. The Cloud technology helps in preventing resource wastage to a much greater extent. The authors of this paper wish to explore the concept behind cloud computing, its strategy, and benefits. This paper presents some facts and figures related to cloud and green computing which will help in obtaining the gist of Cloud Computing and in understanding the need of going green in computing

Emission Factors for Continuous Fixed Chimney Bull Trench Brick Kiln (FCBTK) in India

Uncertainty in emissions from brick manufacturing is a major concern and more primary monitoring based datasets are required. This study presents latest emission factors for continuous fixed chimney bull trench brick kilns (FCBTK), which is the main technology used for brick production in India. Stack monitoring of kilns in a typical brick manufacturing cluster in India is carried out to monitor emissions of pollutants like PM, SO2 and CO. Average concentrations of PM, SO2 and CO in the stacks are measured to be 172±76, 114±47 and 484±198 mg/Nm3, respectively. Monitored stack concentrations are used to compute emission factors based on brick production and fuel consumption activities in the cluster. The computed emission factors across different kilns ranged between 0.81-1.18, 0.57-0.71 and 2.07-2.80g/kg of fired bricks for PM, SO2 and CO, respectively. Corresponding emission factors per unit of coal used in brick kilns are found to be in the range of 13-29, 9-15, 40-56 g /kg for PM, SO2 and CO, respectively. The differences in emission factors are mainly due to variations in the quality of coal used by different kilns. Good correlations were observed between changing calorific values, ash and sulphur content of coal and emissions monitored in the kilns. These new factors can be used for improvement in emission inventories and thereafter modelling results for the region

Balanced Deep CCA for Bird Vocalization Detection

Event detection improves when events are captured by two different modalities rather than just one. But to train detection systems on multiple modalities is challenging, in particular when there is abundance of unlabelled data but limited amounts of labeled data. We develop a novel self-supervised learning technique for multi- modal data that learns (hidden) correlations between simultaneously recorded microphone (sound) signals and accelerometer (body vibration) signals. The key objective of this work is to learn useful embeddings associated with high performance in downstream event detection tasks when labeled data is scarce and the audio events of interest — songbird vocalizations — are sparse. We base our approach on deep canonical correlation analysis (DCCA) that suffers from event sparseness. We overcome the sparseness of positive labels by first learning a data sampling model from the labelled data and by applying DCCA on the output it produces. This method that we term balanced DCCA (b-DCCA) improves the performance of the unsupervised embeddings on the down-stream supervised audio detection task compared to classsical DCCA. Because data labels are frequently imbalanced, our method might be of broad utility in low-resource scenarios

Resource-Efficient Quantum Circuits for Molecular Simulations: A Case Study of Umbrella Inversion in Ammonia

We conducted a thorough evaluation of various state-of-the-art strategies to prepare the ground state wavefunction of a system on a quantum computer, specifically within the framework of variational quantum eigensolver (VQE). Despite the advantages of VQE and its variants, the current quantum computational chemistry calculations often provide inaccurate results for larger molecules, mainly due to the polynomial growth in the depth of quantum circuits and the number of two-qubit gates, such as CNOT gates. To alleviate this problem, we aim to design efficient quantum circuits that would outperform the existing ones on the current noisy quantum devices. In this study, we designed a novel quantum circuit that reduces the required circuit depth and number of two-qubit entangling gates by about 60%, while retaining the accuracy of the ground state energies close to the chemical accuracy. Moreover, even in the presence of device noise, these novel shallower circuits yielded substantially low error rates than the existing approaches for predicting the ground state energies of molecules. By considering the umbrella inversion process in ammonia molecule as an example, we demonstrated the advantages of this new approach and estimated the energy barrier for the inversion process.Comment: 7 pages, 8 figure

Ecohydrological and hydrogeological dynamics of groundwater springs in Eastern Himalaya, India

Groundwater springs are critical to achieving Sustainable Development Goals (SDG 6, access to clean water) in the Himalaya and remain highly vulnerable to climate change and land-use and land cover change. In a first from Eastern Himalaya, we analysed the relative controls of land-use, precipitation, soil properties, and hydrogeology on the diel and seasonal variability in three representative springs using high-frequency discharge monitoring. Kamrang spring is a high-discharge depression spring fed by a homogenous aquifer, whereas Mamley and Gaddi show dual-flow characteristics attributed to primary matrix-based flows and secondary conduit (karst) or unconsolidated storage-based flows, respectively. The first reports of strong diel fluctuations in springflows show significantly higher amplitude in the depression spring (22 ± 41 l min−1) than the fracture (15 ± 26 l min−1) and karst springs (12 ± 24 l min−1), attributed to evapotranspiration and hydrogeology, respectively. The forest spring (Gaddi, low soil hydraulic conductivity, Ksat) showed a faster response at intense precipitation (>30 mm h−1), whereas the agriculture springs (Kamrang and Mamley, high Ksat) showed the lowest lags at low-moderate intensities (<20 mm h−1). The depression spring showed high recharge potential, whereas the karst and fracture springs were constrained by their relatively smaller recharge area and low Ksat, respectively. The per capita daily water availability was barely sufficient to support the minimum (20 l) and mandated (55 l) requirements for 30–70% and 2–47% of days a year, respectively. Thus, future precipitation intensification and land-use change will disproportionately impact the >5th-order karst and fracture springs. The study provides an integrated analytical framework for understanding Himalayan springs, which are critical for achieving SDG 6 (access to clean water) and a baseline for developing appropriate springshed models for effective management of freshwater ecosystems (SDG 15) against future climate change impacts (SDG 13), as well as informing the water security assessment in the Himalaya