IEEE Electrification Magazine - June 2018 - 30
human-in-the-loop simulation that models extensive
interactions among technologies, processes, and humans
in a flexible manner. such an interactive simulation
scheme is helpful for deepening the conceptual understanding of the convergence of technologies, processes,
and humans in a transition toward smart cities. Moreover,
simulations provide empirical results for smart city operations, allow the derivation of guidelines, and facilitate radical transformations of current practices applied to the
design, deployment, and management of smart cities. For
example, simulation results lay foundations for establishing regulations, policies, and technological strategies
applied to smart cities in accordance with specific local
requirements. hence, smart city operations can harness
the power and forces behind sustainable urbanization
more effectively.
30
city authorities for optimizing city operations. Meanwhile,
extensible markup language (XMl) is employed to wrap
operating states initiated from heterogeneous data sources and represent them in a universally standard manner.
when an operating state can be described without a huge
overhead in XMl, each agent is responsible for understanding and parsing XMl files received from the functional components within its own control domain.
the combined utilization of uMl and XMl makes the
operating states of smart city infrastructures more routinely developed. therefore, Mass present a strong position in automating data collection, fusion, and decision
making even for asynchronous interactions. Moreover, the
fidelity of automated interactions can validate theoretical
analyses and empirical observations pertaining to interactions among technology implementation and human
behaviors in smart city operations.
Automated Interactions Enabled by
Multiagent Systems
Human-in-the-Loop Cyberphysical Cosimulation
since the operation of smart city infrastructure is characterized as a highly distributed and evolving process, the
real-time strategic control and management of these
infrastructures can be configured as a multiagent system
(Mas). an Mas is an integration of agents (i.e., computer
systems) that are capable of achieving the assigned goals
without human interventions. in an Mas, each agent
interacts and collaborates with other agents for a global
coherence, and perceives and responds quickly to potential changes in the local working environment to achieve
its design goals.
the automation functionality provided by an Mas is
robust, resilient, flexible and self-organizable. Considering
the high level of flexibility in configuring and coordinating
agents, Mass have proved to be a practical solution for
automated and interactive decision making in complex
and dynamic operating conditions. in particular, Mass
reduce the computation complexity of controlling and
managing smart city operations because each agent is
capable of making locally optimal decisions in near real
time. More specifically, an Mas is deployed with a host of
reliable, robust, and high-performance agents. these
agents manage to efficiently simulate the dynamic decision-making capabilities of functional components in various control domains (e.g., area controllers and smart city
control centers) while fully capturing the behaviors of citizens and city authorities on smart city operations.
Considering the complex interactions among agents
pertinent to distinct smart city infrastructures, universal
modeling language (uMl) is utilized as an object-oriented
language to represent complicated smart city operations
by recognizing citizens' requirements, infrastructure's operation states, and the sequence of events in the operation
of smart city. after converting uMl constructs into executable models, an Mas automates both static interdependencies and dynamic interoperations of smart city
infrastructures as well as interactions among citizens and
smart city infrastructures are complex systems with extensive cyberphysical interdependencies from which exchanges between digital components and physical objects can be
easily abstracted as interactions between agents. when an
Mas considers the behaviors and the activities of citizens
and city authorities in the closed-loop simulation, it opens
the door to human-in-the-loop simulation representing
smart city operations. the human behavior is modeled as
an effective feedback control signal, given the various processes that denote citizen-government collaboration and
human-machine partnership in a cyberphysical representation of smart cities.
the cosimulation of human-in-the-loop and cyberphysical systems consists of three main elements, including
physical elements representing infrastructure components involved in the physical process of city operations,
cyberelements representing communications and computing capabilities, and human elements representing the
human response and intelligence in decision making for
the smart city operation. when cosimulation relies on
existing simulators for various aspects of smart city operations, individual simulation processes are managed within
separate simulators and coordinated with a common simulation goal. Due to inherent differences in the simulation
mechanism (e.g., time-continuous versus event-driven),
simulators are placed in a common platform for realizing
the strict time synchronization and efficient data exchange
among individual simulation processes.
Figure 20 exemplifies the configuration of the cosimulation platform, when municipal solid waste management
relies on electric vehicles in a smart city. the Java agent
Development framework (JaDe) environment is employed
to model the inherent functionality of agents and their
interactions in Mass. oMnet++ is a discrete event simulation environment for representing various means of
communication inside and among smart city infrastructures. openDss is an electric power distribution system
I EEE E l e c t r i f i c a t i on M a gaz ine / j un e 2018
Table of Contents for the Digital Edition of IEEE Electrification Magazine - June 2018
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