A novel model is developed and investigated for a duty cycle wireless sensor network where each sensor node can stay at one of four status (i.e., full-active phase, two semi-active phases each with different functions, and a sleep phase) to make the system more efficient. The explicit result of the joint probability for data packet numbers and the sensor phases is achieved by suitably constructing a multidimensional Markov process. Furthermore, various energy consumption measures, such as energy consumption per unit time in each of the three energy consumptions and energy consumption switching from one phase to another, are obtained. System performance measures, such as the average delay time of a data packet in the system, throughput of the system, and the probability of the sensor node staying at any of the phases, are also achieved. Numerical analyses are provided to validate the model and the analytic results. The proposed model and analysis method are expected to be applied to the design and analysis of WSN models with various phases of the sensor.

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

In up-to-date years deep learning has accrued diverse state-of-the-art results in many fields. However, most of the research efforts chiefly focus on pure technologies or engineering optimizations of deep learning. Inadequate consideration has been paid to it from the panorama of the philosophy of technology. Given the universal Darwinism panorama, technology is regarded as the seventh life form, which, like the other six well-known life forms, has the driving forces of survival and evolution. Similar to genes, temes are the inheritors of technology. If temes do not yield to the law of “survival of the fittest”, they will be discarded by the evolution. In this paper, we refined the essence hidden behind deep learning’s uproar emergence from a technical panorama. We then rendered its importance and philosophical perspective of deep learning. Based on the point of view of “ teme ” (the basis of technology), we moreover analyzed the inherent teme -related defects of deep learning. Finally, we addressed the possible teme-driven directions for deep learning.

In the age of big data, artificial intelligence (AI) algorithms play an important role in demonstrating the value of data, but its negative effects are increasingly prominent. Researchers focus more on the obvious issues like privacy protection these days, and we assume that technologies like AI will make people wiser. But due to the not-so-obvious limits of AI, it may push us to the opposite side–artificial unintelligence. In this paper, a detailed analysis of the process of value creation from data is presented first. Then, based on the above process, the role played by AI algorithms is analyzed. Next, the anti-intelligence features of AI algorithms, such as the tendency of ‘fatuous King’, the generalization of quick thinking, a faster transformation of master/slave at the individual level, and retribalization at the group level, are deeply discussed. Finally, some possilbe ways of circumventing the aforementioned problems are offered.