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Vampire attacks are considered to be the most vulnerable resource draining attack that is potential in disabling the connectivity of the network by draining mobile node's energy at a faster rate. This vampire attack is generic as they exploit the characteristic features of the base protocol used for enabling communication in mobile ad hoc networks (MANETs). The core objective of this paper is an attempt to formulate an energy forecasting mechanism using grey theory that ensures reliable network connectivity that gets influenced through the vampire behaviour of mobile nodes under active communication. This Semi-Markov chain-based grey prediction-based mitigation (SMCGPM) is an enhanced Markov chain model that integrates the characteristic features of stochastic theory and grey theory for improving the efficacy in detecting a specific kind of vampire attack called as stretch attack. In this technique, the elucidated data from each mobile node are initially modeled based on Grey model. Then, the residual error is calculated between the forecasted and observed values of energy possessed by the mobile nodes based on their packet forwarding rates. SMCGPM has the capability of predicting the possible transition behaviour of mobile nodes through the estimated residual error derived from the Markov chain matrices. Simulation results confirm that SMCGPM is predominant than the baseline prediction schemes by facilitating an effective detection rate of 29% as they achieve correctness and accuracy in prediction through Semi-Markov chain stochastic properties inspired energy factor prediction. [ABSTRACT FROM AUTHOR]

PCM (phase-change memory) is a memory innovation that has gained prominence as a capacity-class memory for computer systems. It is made up of a tiny functional amount of phase-change material that is located in the middle of two electrodes. In PCM, data is kept by utilizing the difference in electrical resistance between a crystalline phase, which has a high resistance, and its amorphous phase, which has a low resistance. Using electrical pulses, the phase-change material would be shifted from a high to the low conductive region and conversely. However, the device's material science concerned with PCM has been generally studied, and questions remain regarding their electrical, warm, and fundamental aspects since its publication in the 1960s. One major downside of PCM is its low heat conductivity, which causes delays in the energy charging/discharging procedure and hardware efficiency. As a result of this, one of the primary focuses of PCM studies has been the improvement of PCM's heat conductivity through the utilization of nanotechnology and nanomaterials. Nanotechnology has been developing ultrasmall nanoparticles to improve traditional PCMs' thermophysical characteristics. These nanomaterials, such as metal, metal oxide, and carbon, will significantly boost PCM's thermal properties, including supercooling, viscosity, and heat capacity. An overview of PCM devices is presented in this article, which underlies perusing and composition activities. Consequently, we offered novel PCM devices and materials. Therefore, the total study is qualitative, and no machine learning approach is used. Because of this, we cannot say that the data is quantitative. Our work includes both test representations of the specific features observed in nanoscale PCM devices and material science demonstrations. In the end, we provide a point of view on some remaining open inquiries and possible future exploration directions. [ABSTRACT FROM AUTHOR]