Optimizing Utility-Energy Efficiency for the Metaverse over Wireless Networks under Physical Layer Security

The Metaverse, an emerging digital space, is expected to offer various services mirroring the real world. Wireless communications for mobile Metaverse users should be tailored to meet the following user characteristics: 1) emphasizing application-specific perceptual utility instead of simply the transmission rate, 2) concerned with energy efficiency due to the limited device battery and energy intensiveness of some applications, and 3) caring about security as the applications may involve sensitive personal data. To this end, this paper incorporates application-specific utility, energy efficiency, and physical-layer security (PLS) into the studied optimization in a wireless network for the Metaverse. Specifically, after introducing utility-energy efficiency (UEE) to represent each Metaverse user's application-specific objective under PLS, we formulate an optimization to maximize the network's weighted sum-UEE by deciding users' transmission powers and communication bandwidths. The formulated problem belongs to the sum-of-ratios optimization, for which prior studies have demonstrated its difficulty. Nevertheless, our proposed algorithm 1) obtains the global optimum for the weighted sum-UEE optimization, via a transform to parametric convex optimization problems, 2) applies to any utility function which is concave, increasing, and twice differentiable, and 3) achieves a linear time complexity in the number of users (the optimal complexity in the order sense). Simulations confirm the superiority of our algorithm over other approaches. We explain that our technique for solving the sum-of-ratios optimization is applicable to other optimization problems in wireless networks and mobile computing

Support Vector Machine for Behavior-Based Driver Identification System

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described

Microfluidic Electrical Sorting of Particles Based on Shape in a Spiral Microchannel

Shape is an intrinsic marker of cell cycle, an important factor for identifying a bioparticle, and also a useful indicator of cell state for disease diagnostics. Therefore, shape can be a specific marker in label-free particle and cell separation for various chemical and biological applications. We demonstrate in this work a continuous-flow electrical sorting of spherical and peanut-shaped particles of similar volumes in an asymmetric double-spiral microchannel. It exploits curvature-induced dielectrophoresis to focus particles to a tight stream in the first spiral without any sheath flow and subsequently displace them to shape-dependent flow paths in the second spiral without any external force. We also develop a numerical model to simulate and understand this shape-based particle sorting in spiral microchannels. The predicted particle trajectories agree qualitatively with the experimental observation. (C) 2014 AIP Publishing LLC

Viscoelastic effects on electrokinetic particle focusing in a constricted microchannel

Focusing suspended particles in a fluid into a single file is often necessary prior to continuous-flow detection, analysis, and separation. Electrokinetic particle focusing has been demonstrated in constricted microchannels by the use of the constriction-induced dielectrophoresis. However, previous studies on this subject have been limited to Newtonian fluids only. We report in this paper an experimental investigation of the viscoelastic effects on electrokinetic particle focusing in non-Newtonian polyethylene oxide solutions through a constricted microchannel. The width of the focused particle stream is found NOT to decrease with the increase in DC electric field, which is different from that in Newtonian fluids. Moreover, particle aggregations are observed at relatively high electric fields to first form inside the constriction. They can then either move forward and exit the constriction in an explosive mode or roll back to the constriction entrance for further accumulations. These unexpected phenomena are distinct from the findings in our earlier paper [Lu et al., Biomicrofluidics 8, 021802 (2014)], where particles are observed to oscillate inside the constriction and not to pass through until a chain of sufficient length is formed. They are speculated to be a consequence of the fluid viscoelasticity effects. (c) 2015 AIP Publishing LLC

Thermo-economic evaluation and optimization of solar-driven power-to-chemical systems with thermal, electricity, and chemical storage

This paper evaluates the thermo-economics of power-to-chemicals using solar energy, with the chemicals being methane, methanol, and gasoline. In addition to the optimal technology sizing and heat cascade utilization, this paper also considers the optimal molten-salt solar power tower (MSPT) design, as the MSPT significantly affects the levelized product cost. A bi-level optimization is proposed, employing mixed-integer linear programming at the lower level with heat and mass integration for optimizing sizes and operating strategies of technologies, and with heat cascade utilization and a genetic algorithm at the upper level for optimizing the MSPT design. In the upper level, the full-load storage hours, design direct normal irradiance, solar multiple, and sizes of the MSPT are optimized. The electricity sources considered are the MSPT, photovoltaic (PV) with daily electricity storage, and the electrical grid as a complementary technology to satisfy the targeted daily product demand. Cost-competitiveness of solar-driven chemical synthesis is thoroughly assessed via considering sensitivity analysis on 1) regional solar resource endowments and actual local demands; 2) electricity sources, that is, PV vs. MSPT; and 3) the scale effect represented by different chemicals’ yield. The results show that the levelized methane cost ranges from 4.5 to 8.5 €/kg, depending on the location, plant size, and annual power contribution of concentrated solar power. Due to the larger mass production, the levelized cost of methanol and gasoline is lower: 1.5–2.2 €/kg for methanol and 4–6 €/kg for gasoline. The findings highlight the significance of location choice, that is, natural endowment of solar radiation and carbon sources. Using the syngas co-electrolysis pathway and direct solar radiation 100 kWh/m2 higher, the methane production cost is decreased by 2.4 €/kg. Sensitivity analysis performed on plant scale reveals that a compact, small-scale system is far too expensive. The levelized cost of methane could be decreased by 1.2 €/kg when the plant is scaled up from 4,000 to 20,000 kg/day H2. Due to its expensive electricity storage and limited working hours, PV is typically not chosen as a power source. Overall, solar fuels are unlikely to be cost-competitive in the near future when compared to market prices for all three compounds under consideration