The critical environment of the underground mines is a risky zone for mining applications and it is very hazardous to engage the miners without a sophisticated communication system. The existing wired networks are susceptible to damage and the wireless radio systems experience severe fading that restricts the complete access to the entire assembly of a mine. Wireless optical communication is a better approach that can be incorporated in the erratic atmosphere of underground mines to overcome such issues, as lights are already used to illuminate the mine galleries. This study is focused on investigating the characteristics of visible light communication (VLC) in an underground coal mine. The entire scope of VLC is elaborated along with the influence of coal dust particles and the scattering model. The impact of coal dust clouds on visibility and attenuation is analyzed for visible light transmission. The shadowing effect generated by the pillars and mining machinery is estimated by employing the bimodal Gaussian distribution (BGD) approach in coal mines. The characteristic model of VLC for underground coal mines is presented by classifying the area of the mine into mine gallery and sub-galleries. The transmission links of VLC are categorized as the line of sight (LOS) link for direct propagation and the non-LOS (NLOS) link for reflected propagation. The scenarios of LOS and NLOS propagation are considered for each evaluating parameter. Furthermore, the performance of the proposed framework is examined by computing the received signal power, path loss, delay spread (DS), and signal to noise ratio (SNR).

In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.

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In the last five years, demand for cloud computing among businesses and individual users is increasing immensely because of numerous reasons including, improved productivity, efficiency and speed, cost savings, performance, and most importantly security. Machine learning techniques are making progress in a variety of domains of cloud computing to resolve security concerns and manage data efficiently. In cloud security, a relatively novel approach is Artificial Neural Networks (ANN). We propose a new security design using neural network and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the information in an encrypted form without disclosing the data of the provider party to secure important information. We recommend a solution based on fully homomorphic encryption (FHE) to handle sensitive information without revealing the original data. The encryption technique we considered is matrix operation-based randomization and encipherment (MORE), which allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead. In this paper, we examined the speech and voice recognition problem and the performance of the proposed method has been validated in MATLAB simulation. Results showed that applying neural network training with MORE improved accuracy, runtime, and performance. These results highlight the potential of the proposed neural network and encryption technique to protect the privacy and providing high accuracy in a reasonable period when compared to other state of the art techniques.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.