The edge of the network plays a vital role in an Internet of Things (IoT) system, serving as an optimal site to perform an operation on data before transmitting it over the network. We present the ...fog-specific decomposition of multivariate linear regression as the predictive analytic model in our work using the statistical query model and summation form. The decomposition method used is not the contribution, but applying the decomposition method to the analytics model to run in a distributed manner in the fog-enabled IoT deployments is the contribution. What is novel is the decomposition made on a fog-based distributed setting. To test the performance, our proposed approach has been applied to a real-world dataset and evaluated using a fog computing testbed. The proposed method avoids sending raw data to the cloud and offers balanced computation in the infrastructure. The results show an 80% reduction in the amount of data transferred to the cloud using the proposed fog-based distributed data analytics approach compared with the conventional cloud-based approach. Furthermore, by adopting the proposed distributed approach, we observed a 98% drop in the time taken to arrive at the final result compared with the cloud-centric approach. We also present the results on the quality of analytics solution obtained in both approaches, and they suggest that the fog-based distributed analytics approach can serve as equally as the traditional cloud-centric approach.
Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application ...architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.
Fog computing is an extension of cloud computing right to the edge of the network, and seeks to minimize service latency and average response time in applications, thereby enhancing the end-user ...experience. However, there still is the need to define where the service should run for attaining maximum efficiency. By way of the work proposed in this paper, we seek to develop a resource-aware placement of data analytics platform in fog computing architecture, that would adaptively deploy the analytic platform to run either on the cloud, or the fog, thus reducing the network costs and response time for the user.
Summary
Internet of Things (IoT), fog computing, cloud computing, and data‐driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by ...getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end‐to‐end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices‐oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6‐month mature real‐world deployment, wherein the data from wearables on cows is sent to a fog‐based platform for data classification and analysis, which includes decision‐making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog‐based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud‐based approach.
•Design and development of an IoT application leveraging the fog computing paradigm.•Validation of the application in IoT enabled smart dairy farming scenario.•A real-world smart farm setup with a ...full dairy herd of 150 cows.•Blended clustering & classification ML model for early lameness detection in cattle.•Early lameness detection window of 3 days before visual signs, with 87% accuracy.
Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage.
The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach.
This work investigates the impact of entrepreneurial experiential learning (EL) on students' entrepreneurial intention (EI), through the lens of entrepreneurial self-efficacy (ESE). To provide a ...comprehensive viewpoint, the direct impact of entrepreneurial attitude (EA) on entrepreneurial intention has also been looked at. This study, has adopted existing scales to measure entrepreneurial intention, entrepreneurial attitude, and entrepreneurial self-efficacy. For experiential learning, a structured scale has been designed on the basis of its existing scales. Data were collected from 669 students, enrolled in entrepreneurial courses/programs. Punjab, Haryana, and National capital region (NCR) are the three Indian regions considered for the survey. The results highlight that there is a positive influence of experiential learning on entrepreneurial intention though β-value is low (0.042). While the β-value with mediation influence of entrepreneurial self-efficacy between experiential learning and entrepreneurial intention was 0.090 (0.697*0.130), which states that entrepreneurial self-efficacy mediates and enhances the relationship between experiential learning and entrepreneurial intention. It was also found that there is a direct and strong influence of entrepreneurial attitude on entrepreneurial intention (β-value, 0.872). Thus, there is a need to focus on entrepreneurial attitude, as it strongly influences the intentions of the students. Overall results have a bearing that experiential learning; entrepreneurial self-efficacy and entrepreneurial attitude emerged as important predictors of entrepreneurial intention and explained 89.6% of variations in the model. Moreover, experiential learning with the mediation of entrepreneurial self-efficacy had more impact on entrepreneurial intention. Thus, there is a need to focus on entrepreneurial self-efficacy as well as experiential learning.
Experiential learning has been regarded as an effective tool for grasping entrepreneurial knowledge. The current study examines the influence of Experiential Learning (EL) with sub-variables (i.e., ...Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualisation (AC), & Active Experimentation (AE) on the Entrepreneurial self-efficacy (ESE) of the male and female Higher educational institution (HEI) students possessing high Entrepreneurial Intention. The study uses a Liñán and Chen (2009) tool to identify students with high Entrepreneurial Intentions (EI), selecting 323 (195 male and 128 female) respondents possessing 4–5 scores on a scale of 5. The survey considered three growing Indian regions: Punjab, Haryana, and NCR (National capital region). For EL, a self-structured questionnaire and for the ESE scale of Mcgee, Peterson, Mueller, and Sequeira (2009) was adopted. The results highlight that in the case of male and female students, all sub-variables of EL, i.e., CE, RO, AC & AE of EL, strongly predicted ESE. However, there is a difference in the degree of influence of such dimensions based on Gender. In the case of male degree of influence is maximum with RO followed by AE, CE, AC, but for females the sequence of degree of influence is different as RO is followed by CE, AC and AE. The findings can be helpful to design and support experiential learning system concerning entrepreneurship students of higher educational institutes.
•Experiential learning (EL) has been regarded as an effective tool for grasping entrepreneurial knowledge. The current study examines the influence of EL with sub-variables (i.e., Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualisation (AC), & Active Experimentation (AE) on the Entrepreneurial self-efficacy (ESE) of the male and female Higher educational institution (HEI) students possessing high Entrepreneurial Intention.•The results highlight that in the case of male and female students, all sub-variables of EL, i.e., CE, RO, AC & AE of EL, strongly predicted ESE. However, there is a difference in the degree of influence of such dimensions based on Gender.•The findings can be helpful to design and support experiential learning system concerning entrepreneurship students of higher educational institutes in the nation.
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