The Internet of Things (IoT) is undergoing rapid growth in the IT industry, but, it continues to be associated with several security and privacy concerns as a result of its massive scale, decentralised topology, and resource-constrained devices. Blockchain (BC), a distributed ledger technology used in cryptocurrency has attracted significant attention in the realm of IoT security and privacy. However, adopting BC to IoT is not straightforward in most cases, due to overheads and delays caused by BC operations. In this paper, we apply a BC technology known as Hyperledgder Fabric, to an IoT network. This technology introduces an execute-order technique for transactions that separates the transaction execution from consensus, resulting in increased efficiency. We demonstrate that our proposed IoT-BC architecture is sufficiently secure with regard to fundamental security goals i.e., confidentiality, integrity, and availability. Finally, the simulation results are highlighted that shows the performance overheads associated with our approach are as minimal as those associated with the Hyperledger Fabric framework and negligible in terms of security and privacy.

Smart city is synchronized with digital environment and its transportation system is vitalized with RFID sensors, Internet of Things (IoT) and Artificial Intelligence. However, without user's behavioral assessment of technology, the ultimate usefulness of smart mobility cannot be achieved. This paper aims to formulate the research framework for prediction of antecedents of smart mobility by using SEM-Neural hybrid approach towards preliminary data analysis. This research undertook smart mobility service adoption in Malaysia as study perspective and applied the Technology Acceptance Model (TAM) as theoretical basis. An extended TAM model was hypothesized with five external factors (digital dexterity, IoT service quality, intrusiveness concerns, social electronic word of mouth and subjective norm). The data was collected through a pilot survey in Klang Valley, Malaysia. Then responses were analyzed for reliability, validity and accuracy of model. Finally, the causal relationship was explained by Structural Equation Modeling (SEM) and Artificial Neural Networking (ANN). The paper will share better understanding of road technology acceptance to all stakeholders to refine, revise and update their policies. The proposed framework will suggest a broader approach to investigate individual-level technology acceptance.

With a rapid growth in the IT industry, Internet of Things (IoT) has gained a tremendous attention and become a central aspect of our environment. In IoT the things (devices) communicate and exchange the data without the act of human intervention. Such autonomy and proliferation of IoT ecosystem make the devices more vulnerable to attacks. In this paper, we propose a behavior monitor in IoT-Blockchain setup which can provide trust-confidence to outside networks. Behavior monitor extracts the activity of each device and analyzes the behavior using deep auto-encoders. In addition, we also incorporate Trusted Execution Technology (Intel SGX) in order to provide a secure execution environment for applications and data on blockchain. Finally, in evaluation we analyze three IoT devices data that is infected by mirai attack. The evaluation results demonstrate the ability of our proposed method in terms of accuracy and time required for detection.

Healthcare workers in Pakistan are still fighting at the frontline to control the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and have been identified as the earliest beneficiaries for COVID-19 vaccination by the health authorities of the country. Besides, the high vaccination rates of frontline healthcare workers (FHWs) are essential to overcome the ongoing pandemic and reduce the vaccines hesitancy among the general population. The current research employed the theory of planned behavior (TPB) to investigate the COVID-19 vaccination behavior among FHWs in Pakistan as well as the predictors of such behavior. Following the epidemic control and prevention policies, a sample of 680 FHWs were accessed to fill in the questionnaire evaluating the components of the TPB. Moreover, the potential role of anticipated regret (AR) and perceived susceptibility (PS) on COVID-19 vaccination behavior was also assessed. The partial least square structural equation modeling (PLS-SEM) results revealed that the TPB components, as well as the AR, have positive associations with the COVID-19 vaccination behavior. The results further confirmed that PS positively affects the anticipated regret, attitude (ATT), and subjective norm (SN) to vaccinate against SARS-CoV-2. The perceived susceptibility also has a positive association with COVID-19 vaccination behavior through the mediation of anticipated regret, ATT, and SN. Our findings highlighted the importance of COVID-19 vaccination among healthcare workers, which can be applied to reduce vaccine hesitancy among the general public.

The primary objective of the present research is to examine the impact of touch and gustatory stimuli on airline image and repurchase intention. Furthermore, the present research examines the moderating role of price fairness and mediation impact of airline image. The researchers gather data from the customers of PIA and Airblue travelling abroad from Islamabad International Airport, Karachi International airport and Lahore International airport by employing multi-stage sampling technique. Total of 576 questionnaires was distributed among the respondents, and the response rate was 68.9%. For the analysis of data received, the researcher employed PLS-SEM. The finding of the study confirmed the significant impact of touch and gustatory stimuli on airline image and repurchase intention. Findings of the study revealed the mediating role of airline image among touch, gustatory and repurchase intention was significant as well. At the end, perceived price fairness also moderated the relationship of airline image and repurchase intention. The present study fills the gap of limited studies conducted in the past regarding sensory stimuli in the context of the airline industry, the role of airline image as a mediator under SOR model and moderating impact of perceived price. Findings of the present study are helpful for policymakers and practitioners of the airline industry in Pakistan to develop the strategy by which they can retain their customers on international routes.

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A smart home is grounded on the sensors that endure automation, safety, and structural integration. The security mechanism in digital setup possesses vibrant prominence and the biometric facial recognition system is novel addition to accrue the smart home features. Understanding the implementation of such technology is the outcome of user behavior modeling. However, there is the paucity of empirical research that explains the role of cognitive, functional, and social aspects of end-user's acceptance behavior towards biometric facial recognition systems at homes. Therefore, a causal research survey was conducted to comprehend the behavioral intention towards the use of a biometric facial recognition system. Technology Acceptance Model (TAM) was implied with Perceived System Quality (PSQ) and Social Influence (SI) to hypothesize the conceptual framework. Data was collected from 475 respondents through online questionnaires. Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) were employed to analyze the surveyed data. The results showed that all the variables of the proposed framework significantly affected the behavioral intention to use the system. The PSQ appeared as the noteworthy predictor towards biometric facial recognition system usability through regression and sensitivity analyses. A multi-analytical approach towards understanding the technology user behavior will support the efficient decision-making process in Human-centric computing.

Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.