In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

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The purpose of this paper is to develop a universally accepted measurement scale of Responsible Leadership (RL) based on the triple bottom approach (3BL) which covers environmental, economic, and social aspects at the same time. Systematic literature review of RL conducted along with in-depth interviews conducted with experts of industry and academia. Based on in-depth interviews, a content validity ratio analysis was conducted to examine the content validity of the questionnaire. Explanatory factor analysis using SPSS version 21 and confirmatory factor analysis using AMOS were conducted to test the reliability and validity of the questionnaire. A sample of 329 respondents working in the banking sector was taken for data analysis. Eigenvalues result confirms the triple bottom approach that RL is a three-dimensional construct and measured by economic, social and environmental aspects. These three dimensions explain 66.83% of the total variance explained. Respondents’ demographic shows that the male section is dominant in the banking sector as 83.6% of respondents were male and 16.4% were female. The economic dimension consists of eight items that have a Cronbach alpha value of 0.97, the social dimension has eight items that have a Cronbach alpha value of 0.90, and the environmental dimension consists of nine items that have a Cronbach value of 0.92. Initially, CFA was conducted with 25 items, then implemented modification indices and the final CFA was conducted with 23 items and all indicators of model fitness were found up to standard. The outcome of the present study is in form of a measurement scale based on the triple bottom approach which covers economic, social and environmental aspects of leadership. It is hoped that this scale will be potentially helpful for future researchers to measure RL and check the relationship with other potential variables. This systematic literature review and scale will allow future researchers to explore and conduct empirical studies in banking and other sectors. This paper responded to various calls regarding the universally accepted measurement scale of RL by developing the RL measurement scale.