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Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay.

This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL.

This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs.

In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.

Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.

In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.

The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.

The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.

The purpose of this paper is to highlight the application of Interpretive Structural Modeling (ISM) to analyze the barriers in implementation of Total Productive Maintenance (TPM). TPM is explained in brief with emphasis on maintenance programs to improve quality of products, reliability of processes and reduction in cost. Barriers in implementation of TPM are also discussed. Concept of ISM and steps in developing ISM are described in detail. The authors then illustrate the research methodology which involves applying ISM to analyze barriers in TPM.

The paper starts off by describing the concepts of TPM and ISM. Barriers in implementation of TPM are discussed. It explains ISM as a methodology to understand the underlying interrelationship among the inhibiting factors. The authors draw up an action plan to carry out research on the usage of ISM to study the TPM inhibitors, to develop an integrated model to establish the relationship among the different TPM inhibiting factors and to suggest action plan to mitigate these factors.

Interpretive Structural Modeling (ISM) can be used to analyze the driving and dependence power of the variables inhibiting implementation of TPM. The barriers to implement TPM are described with detailed explanation. The complexity of the problem and the degree of interconnection among the variables can be found out. This will help Managers take action on mitigating the barriers.

By analyzing the interrelationships among the barriers and their strengths, management can chalk out the strategy to implement TPM in an organization. Management will become aware of the barriers which have the maximum influence and then can act accordingly to mitigate these barriers. This will help in implementing TPM faster and in an organized manner.

Many authors have used ISM to study various issues. A couple of authors have used ISM to determine barriers in implementation of TPM. The authors feel that most of the papers describe ISM in brief making it slightly difficult for readers to understand. This paper aims to explain elaborately step-by-step on how to develop an ISM making it easier for researchers to understand the ISM concept. Even though there are papers on TPM and difficulties in implementation of TPM, this paper explains the barriers in implementing TPM based on the experience of the corresponding author having worked in the refinery industry.

Presents a theoretical approach to analysing the effects of minimum wages on employment which is intended to conform more with the functioning of actual labour markets than do other popular models traditionally used to analyse the likely effects of minimum wages on employment. The model has the desirable property of not only allowing for the negative effect predicted by conventional models, but also permiting a non‐negative impact which is consistent with several recent empirical pieces of work. Examines the employment effects of the industry‐level system of minimum wages which operated in the UK until September 1993. Results reported are not in line with the orthodox model as they suggest a neutral or positive impact of Wages Council minimum wages on employment between 1978 and 1990.

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level.

In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years.

From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies.

The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company.

In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

This paper investigates herding behavior and its persistence among foreign institutional investors (FIIs) in the individual stocks of the energy sector of Indian stock exchange by focusing on post turmoil period. The study also examines the relation of herding with the individual return, market return, trading volume and conditional volatility of individual and market return.

The presence of herding is investigated by Lakonishok et al. (1992) model, value-based and count-based herd ratio measure among FIIs in individual stock of energy sector post turmoil period. Further, run test was employed to check the persistency in herding and multivariate distributed lag to investigate the relationship with the market determinant.

The result indicates the existence of herding in most of the companies and strong persistence in all the companies. The intensity of buy side herding is higher than sell side. Herding and individual return both are significant driving forces of FIIs herding, while trading volume and market volatility in few companies exhibit inverse relationship.

This study is limited to investigation of energy sector stock.

Stock market is significantly influenced by FIIs and their propensity to herd may generate instability in the stock market. Therefore, regulatory authority should continuously monitor the flow of fund by FIIs.

Herding in the individual stock of the energy sector was not previously performed.