The primary concern of every individual and organization is the security of sensitive information generated via authorized activities; nonetheless, illicit data drawing and extraction by attackers is prevalent, which may be mitigated by covert approaches. Although cypher techniques give excellent protection, they raise suspicion in the eyes of adversaries, resulting in both passive and active assaults on the information sent. Steganography, on the other hand, helps to reduce third-party suspicion. This method conceals sensitive information on cover data and transports it to the targets without skepticism. However, the issue depends entirely on the effectiveness of the embedding method; it must also satisfy other data concealing features such as embedding capacity. As payload grows, so does skepticism. This article handled this issue to lessen suspicion while maintaining embedding capacity. The article proposes a format-based text concealing algorithm, a traditional way for dealing with embedding capacity and invisibility. The authors compared our results to those of other similar current methods. They discovered that theirs are pretty decent—the present study offered both standard public communication security and medical data protection.

Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the driver’s fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system.

With pervasive of mobile computing and wireless communication, the vehicular ad‐hoc network has become a key technology in modern day's information exchange. Due to the fast growing number of vehicles and saturation of the transport infrastructure, it is inhabitable to come out with better solution considering the available resources. In this study, thorough traffic analysis has been carried out to suggest a better model. Further, an attempt has been made to propose an admission control algorithm based‐on the parameters evolve from the mathematical analysis. Moreover, the performance analysis has been shown to establish the efficacy of the algorithm.