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The purpose of this study is to verify that if applications categories are segmented and resources are allocated based on their specific category, how effective scheduling can be done?.

This paper proposes a scheduling framework for IoT application jobs, based upon the Quality of Service (QoS) parameters, which works at coarse grained level to select a fog environment and at fine grained level to select a fog node. Fog environment is chosen considering availability, physical distance, latency and throughput. At fine grained (node selection) level, a probability triad (C, M, G) is anticipated using Naïve Bayes algorithm which provides probability of newly submitted application job to fall in either of the categories Compute (C) intensive, Memory (M) intensive and GPU (G) intensive.

Experiment results showed that the proposed framework performed better than traditional cloud and fog computing paradigms.

The proposed framework combines types of applications and computation capabilities of Fog computing environment, which is not carried out to the best of knowledge of authors.

This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files.

To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies.

Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system.

The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.

Purpose: This study examines how corporate social responsibility (CSR) affects Tata enterprises’ financial performance. Numerous studies have examined how CSR affects company FP, with mixed results. The large variety of outcomes may have been due to erroneous analysis or insignificantly controlled variables, but the most likely explanation is that different research utilised different approaches. This study examines the relationship between CSR and financial performance in India using Tata Group companies listed on the BSE100.

Methodology: The BSE100-listed Tata companies were chosen for investigation because Tata's are pioneer in philanthropy and CSR. The present investigation relies on data obtained from annual reports and sustainability reports of the respective companies for a period of 10 years (2013–2022). Regression analysis was performed using Stata version 14 to evaluate the relationship between CSR spending and financial performance.

Research limitation: The study is confined to 7 Tata companies indexed in the BSE100 for 10 years (FY2013–FY2022).

Social implication: Tata companies’ ethics and philanthropy activities are landmarks in Indian society that can be used to motivate the business stakeholders to contribute more to CSR. The tourism companies can use Tata's CSR model to grow their financial performance.

Findings: The findings of the study depict that financial performance is positively impacted by the amount spent on CSR by the companies. Companies that invest more in CSR have higher profitability, ROA, ROE, EPS, MB Ratio and MR_Daily.

Originality: This chapter will add comprehensive knowledge about the relationship of CSR and financial performance.