Mobile applications are getting a great deal of interest among researchers due to their proliferation and pervasiveness, especially in the context of digital libraries of educational institutes. However, their low acceptance and usage are observed, hence, in-depth investigations are required in order to understand the factors behind low acceptance and intention to use mobile library application (MLA). Therefore, the aim of this work is to empirically explore the acceptance of MLA with a proposed model that is evolved from the technology acceptance model (TAM). The study objects to deliver empirical provision on acceptance of MLA. A self-administrated cross-sectional survey-based study was conducted to gather data from 340 users of MLA. Structural equation model (SEM) with an analysis of moment structure (AMOS) software was conducted to examine quantitative data. Results revealed that perceived usefulness and perceived ease of use are direct significant predictors with the intention to use MLA whereas system quality and habit are the influencing factors toward the usage intention of MLA. The findings help as a guide for effective decision in the design and development of MLA. Further, the outcomes can be utilized in the resource allocation process for ensuring the success of the library's vision and mission.

Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in IoT network. To address the problem, a new framework model is proposed. Firstly, a novel feature selection metric approach named CorrAUC proposed, and then based on CorrAUC, a new feature selection algorithm name Corrauc is develop and design, which is based on wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using AUC metric. Then, we applied integrated TOPSIS and Shannon Entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT dataset and four different ML algorithms. Experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.

Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resourcelimited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this paper, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to longterm constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid meta-heuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function has been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Adaptive Gravitational Search algorithm (AGSA), Whale Optimization Algorithm (WOA). The results prove the superiority of the proposed hybrid approach over existing approaches.