Distributed task offloading optimization with queueing dynamics in multi-agent mobile-edge computing networks

Task offloading decision-making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet-of-Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multi-agent network without any centralized communication and computing coordination. In this paper, we investigate the distributed task offloading optimization problem with consideration of the upper-layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent’s threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method

Joint mobility, communication and computation optimization for UAVs in air-ground cooperative networks

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency

Aqueous electrosynthesis of an electrochromic material based water-soluble EDOT-MeNH2 hydrochloride

2\u27-Aminomethyl-3,4-ethylenedioxythiophene (EDOT-MeNH2) showed unsatisfactory results when its polymerization occurred in organic solvent in our previous report. Therefore, a water-soluble EDOT derivative was designed by using hydrochloric modified EDOT-MeNH2 (EDOT-MeNH2·HCl) and electropolymerized in aqueous solution to form the corresponding polymer with excellent electrochromic properties. Moreover, the polymer was systematically explored, including electrochemical, optical properties and structure characterization. Cyclic voltammetry showed low oxidation potential of EDOT-MeNH2·HCl (0.85 V) in aqueous solution, leading to the facile electrodeposition of uniform the polymer film with outstanding electroactivity. Compared with poly(2′-aminomethyl- 3,4-ethylenedioxythiophene) (PEDOT-MeNH2), poly(2′-aminomethyl-3,4-ethylenedioxythiophene salt) (PEDOT-MeNH3 +A-) revealed higher efficiencies (156 cm2 C-1), lower bandgap (1.68 eV), and faster response time (1.4 s). Satisfactory results implied that salinization can not only change the polymerization system, but also adjust the optical absorption, thereby increase the electrochromic properties

Weighted energy efficiency maximization for a UAV-assisted multi-platoon mobile edge computing system

With the rapid development of mobile computing, mobile edge computing has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this paper, we studied a UAV-assisted mobile edge computing system with multi-platoon vehicles. Our paper aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a two-dimensional path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the non-convexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming method. The simulation results show that the proposed method significantly surpasses conventional schemes

Decentralized robust control for vehicle platooning subject to uncertain disturbances via super-twisting second-order sliding-mode observer technique

Platoon-based vehicular cyber-physical systems (VCPSs) have attracted much attention due to their potential to improve road capacity and energy efficiency. However, the comprehensive effect of the mismatched modeling dynamics and unknown disturbances can impose a great challenge on the convergence and stability of vehicle platooning. In this paper, we propose a novel decentralized robust control approach to address the external disturbances in vehicle platooning. Specifically, by combining a super-twisting second-order sliding mode (SOSM) strategy and a disturbance observer (DO), we design a super-twisting SOSMDO platoon controller. We also derive some design conditions of the controller and observer gains. Using the Lyapunov methodology, we theoretically prove under the design conditions the finite-time convergence of the super-twisting SOSMDO to the platooning equilibrium state and its closed-loop stability to the disturbances. Extensive simulations have been conducted and the results demonstrate the superior performance of the proposed control approach in terms of inter-vehicle spacing, velocity tracking, and platoon robustness

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency

Robust min-max model predictive vehicle platooning with causal disturbance feedback

Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) condition for guaranteeing the recursive feasibility and input-to-state practical stability (ISpS) of the platoon system. Finally, simulation results are provided to verify the effectiveness and advantage of the proposed MPC in terms of constraint satisfaction, platoon stability and robustness against different external disturbances

Inhalation of electronic cigarettes slightly affects lung function and inflammation in mice

Electronic cigarettes have become increasingly popular, but the results of previous studies on electronic cigarette exposure in animals have been equivocal. This study aimed to evaluate the effects of electronic cigarette smoke (ECS) and cigarette smoke (CS) on lung function and pulmonary inflammation in mice to investigate whether electronic cigarettes are safer when compared to cigarettes. 32 specific pathogen-free BALB/c male mice were randomly grouped and exposed to fresh air (control), mint-flavored ECS (ECS1, 6 mg/kg), cheese-flavored ECS (ECS2, 6 mg/kg), and CS (6 mg/kg). After 3 weeks exposure to ECS or CS, we measured lung function (PIF and Penh) and blood oxygen saturation. The levels of TNF-α and IL-6 in the bronchoalveolar lavage fluid (BALF) and serum were measured using ELISA. HE staining was performed to observe the pathological changes in the lung tissues. The levels of IL-6 in BALF and serum, and TNF-α in BALF, were elevated similarly in the ECS and CS groups compared to the control group. Significant elevation was observed in serum TNF-α levels in the CS group. The total count of cells in BALF were increased after ECS1 exposure and CS exposure. PIF and oxygen saturation decreased, and Penh increased markedly in the CS group but not in the ECS groups. Compared with the ECS groups, mice in the CS group had widened lung tissue septa and increased inflammatory cell infiltration. However, we did not detect significant differences between mint-flavored and cheese-flavored e-cigarettes in our study. Overall, our findings suggested that both ECS and CS impair lung function and histopathology while promoting inflammation. In contrast, ECS has a less negative impact than CS

Field Emission Properties and Fabrication of CdS Nanotube Arrays

A large area arrays (ca. 40 cm2) of CdS nanotube on silicon wafer are successfully fabricated by the method of layer-by-layer deposition cycle. The wall thicknesses of CdS nanotubes are tuned by controlling the times of layer-by-layer deposition cycle. The field emission (FE) properties of CdS nanotube arrays are investigated for the first time. The arrays of CdS nanotube with thin wall exhibit better FE properties, a lower turn-on field, and a higher field enhancement factor than that of the arrays of CdS nanotube with thick wall, for which the ratio of length to the wall thickness of the CdS nanotubes have played an important role. With increasing the wall thickness of CdS nanotube, the enhancement factorβdecreases and the values of turn-on field and threshold field increase

Aqueous electrosynthesis of an electrochromic material based water-soluble EDOT-MeNH2 hydrochloride

2\u27-Aminomethyl-3,4-ethylenedioxythiophene (EDOT-MeNH2) showed unsatisfactory results when its polymerization occurred in organic solvent in our previous report. Therefore, a water-soluble EDOT derivative was designed by using hydrochloric modified EDOT-MeNH2 (EDOT-MeNH2·HCl) and electropolymerized in aqueous solution to form the corresponding polymer with excellent electrochromic properties. Moreover, the polymer was systematically explored, including electrochemical, optical properties and structure characterization. Cyclic voltammetry showed low oxidation potential of EDOT-MeNH2·HCl (0.85 V) in aqueous solution, leading to the facile electrodeposition of uniform the polymer film with outstanding electroactivity. Compared with poly(2′-aminomethyl- 3,4-ethylenedioxythiophene) (PEDOT-MeNH2), poly(2′-aminomethyl-3,4-ethylenedioxythiophene salt) (PEDOT-MeNH3 +A-) revealed higher efficiencies (156 cm2 C-1), lower bandgap (1.68 eV), and faster response time (1.4 s). Satisfactory results implied that salinization can not only change the polymerization system, but also adjust the optical absorption, thereby increase the electrochromic properties