It is the overriding trend of the present-day world that traditional systems and mobile devices are currently transforming into intelligent systems and smart devices. Against this backdrop, cyber-physical systems (CPSs) and Internet-of-Things (IoT) emerge as the times require. To achieve the parallel interactions between the human world and the computer network, IoT along with wireless mobile communication and computing open up some future opportunities as well as challenges for constructing a novel cyber-physical-social system (CPSS) that takes human factors into account during the system operation and management. In this article, a brief comprehensive survey is provided on some of the current research work that contributes to enabling CPSSs. Some crucial aspects of CPSSs are identified, including: the development from CPSs to CPSSs, architecture design, applications, standards, real-world case studies, enabling techniques and networks for CPSSs. To lay a foundation for the development of the upcoming smart world, we further propose a virtualization architecture and an integrated framework of caching, computing and networking for CPSSs. Simulations verify the performance improvement of the proposals. At last, some research issues with challenges and possible solutions are unearthed for researchers in the related research areas.

Cyber-Physical-Social Systems: A State-of-the-Art Survey, Challenges and Opportunities

Human in the Loop

Plug-in electric vehicle (PEV) charging station service capability is physically limited by the charger availability and local transformer capacity. However, the station operation performance has become an increasingly important factor for enhancing charging service accessibility. In this work, we propose an innovative station-level optimization framework to operate charging station with optimal pricing policy and charge scheduling. This model incorporates human behaviors to explicitly and effectively capture drivers’ charging decision process. We propose a menu of price-differentiated charging services, which differ in both the per-unit prices and the energy delivery schedule. Involving human in the loop, the operation model also exploits the capability to alleviate the overstay issue that occurs when a PEV’s charging session has completed. We then propose a muli-block convex transformation methodology to reformulate the resulted non-convex problem via Fenchel-Young Inequality; the Block Coordinate Descent algorithm is applied to solve the overall problem and the efficiency enables real-time implementation. As a result, our simulation demonstrates that the proposed control polices can realize benefits in three aspects: (i) net profits gains, (ii) overstay reduction, and (iii) quality-of-service increase.

Inducing Human Behavior to Maximize Operation Performance at PEV Charging Station

The enormous transmitter power, fully programmable antenna array, and agile frequency generation of the High Frequency Active Auroral Research Program (HAARP) facility in Alaska have allowed the production of unprecedented disturbances in the ionosphere. Using both pencil beams and conical (or twisted) beam transmissions, artificial ionization clouds have been generated near the second, third, fourth, and sixth harmonics of the electron gyrofrequency. The conical beam has been used to sustain these clouds for up to 5 h as opposed to less than 30 min durations produced using pencil beams. The largest density plasma clouds have been produced at the highest harmonic transmissions. Satellite radio transmissions at 253 MHz from the National Research Laboratory TACSat4 communications experiment have been severely disturbed by propagating through artificial plasma regions. The scintillation levels for UHF waves passing through artificial ionization clouds from HAARP are typically 16 dB. This is much larger than previously reported scintillations at other HF facilities which have been limited to 3 dB or less. The goals of future HAARP experiments should be to build on these discoveries to sustain plasma densities larger than that of the background ionosphere for use as ionospheric reflectors of radio signals.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015RS005883

Large ionospheric disturbances produced by the HAARP HF facility

This paper develops an online algorithm to solve a time-varying optimization problem with an objective that comprises a known time-varying cost and an unknown function. This problem structure arises in a number of engineering systems and cyber-physical systems where the known function captures time-varying engineering costs, and the unknown function models user's satisfaction; in this context, the objective is to strike a balance between given performance metrics and user's satisfaction. Key challenges related to the problem at hand are related to (1) the time variability of the problem, and (2) the fact that learning of the user's utility function is performed concurrently with the execution of the online algorithm. This paper leverages Gaussian processes (GP) to learn the unknown cost function from noisy functional evaluation and build pertinent upper confidence bounds. Using the GP formalism, the paper then advocates time-varying optimization tools to design an online algorithm that exhibits tracking of the oracle-based optimal trajectory within an error ball, while learning the user's satisfaction function with no-regret. The algorithmic steps are inexact, to account for possible limited computational budgets or real-time implementation considerations. Numerical examples are illustrated based on a problem related to vehicle platooning.

Personalized Optimization with User's Feedback

This paper investigates dynamical systems where system inputs are induced by human behavior. In particular, we consider linear time-invariant systems with a stochastic discrete choice actuation model. We are motivated by increasingly important cyber-physical-social systems (CPSS), such as smart mobility, smart energy, and smart cities. Existing literature regarding random dynamical systems (RDS) predominantly considers additive noise models with well-defined probability distributions. Furthermore, the role of human interactions is usually considered a disturbance. The closed-loop system must not be designed explicitly for this disturbance, but must be robust to it instead. This paper adds two original contributions to the existing literature. First, we integrate Discrete Choice Models (DCM) from behavioral economics into dynamical systems to incorporate human decision making, yielding a Dynamical System with Discrete Choice Models (DSDCM). System inputs are triggered by human actuators, who act selfishly by taking actions that maximize their own utility functions. Second, we formulate a convex optimization problem for DSDCM that seeks to incentivize human decision making to achieve a system-wide objective. Finally, we apply DSDCM in the context of demand response and provide potential directions for future work.

System Analysis and Optimization of Human-Actuated Dynamical Systems

Modeling & Control of Human Actuated Systems

Nonlinear interactions of high power HF radio waves in the ionosphere provide aeronomers with a unique space-based laboratory capability. The High-Frequency Active Auroral Research Program (HAARP) in Gakona, Alaska is the world’s largest heating facility, yielding effective radiated powers in the gigawatt range. New results are present from HAARP experiments using a “twisted beam” excitation mode. Analysis of twisted beam heating shows that the SEE results obtained are identical to more traditional patterns. One difference in the twisted beam mode is the heating region produced is in the shape of a ring as opposed to the more traditional “solid spot” region from a pencil beam. The ring heating pattern may be more conducive to the creation of stable artificial airglow layers because of the horizontal structure of the ring. The results of these runs include artificial layer creation and evolution as pertaining to the twisted beam pattern. The SEE measurements aid the interpretation of the twisted beam interactions in the ionosphere.

“Twisted Beam” SEE Observations of Ionospheric Heating from HAARP

A microwave driven resonator is as an electron/ion cloud generator for illumination and plasma source applications. A sustained porous cavity resonator (PCR) glow discharge is externally excited using a resonant frequency electromagnetic (EM) wave that excites large internal electric fields. The resonator with a Q of 300 amplifies the incident electric field by factors of about 100 causing a breakdown of the neutral gas inside the sphere. The rise time of the fields inside the sphere is Q times the wave period. A glowing plasma ball is sustained around the point where the maximum electric fields exceed the plasma-discharge threshold for the low-pressure gas inside the resonator. After the EM pump field is removed, the plasma light source is rapidly quenched. The externally driven spherical PCR was fabricated from a theoretical design of the PCR for a laboratory demonstration of the plasma ball generation system. Using both theory and experiment, basic research has been applied to understand: 1) the EMs of resonator excitation and amplification; 2) the generation of light by glow radio frequency discharge; and 3) the effect of background neutral density of the size and intensity of the glowing plasma clouds. For this study, plasma resonators were constructed for the spherical TM101 and TE101 modes and a TE011 circular-cylindrical cavity. All PCR structures provide the ability to confine a plasma into a desired spatial shape without magnetic fields.

Sang Min Han

Papers

System Analysis and Optimization of Human-Actuated Dynamical Systems

Modeling & Control of Human Actuated Systems

“Twisted Beam” SEE Observations of Ionospheric Heating from HAARP

Visible Plasma Clouds With an Externally Excited Spherical Porous Cavity Resonator