Syncing a Smart City within an Evolutionary Dynamical Cooperative Environment

Barbara Attanasio, Aurelio La Corte, Marialisa Scatá
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引用次数: 1

Abstract

The cities are evolving in complex systems that compete for space as much as sustainability. In a constantly changing system of networked things and people, the understanding of the way to support the challenges of a smart city is linked to the enabling technologies such as 5G and beyond, automotive Internet of Things (IoT) and Multi Access Edge Computing (MEC). These enable innovative approach to develop sophisticated context and cognitive urban applications, crucial to increase the awareness of the surrounding environment in automotive fields. To this aim, we propose a theoretical approach to investigate a richer structure of the city in terms of depth of knowledge, to unveil hidden urban patterns. This approach is based on two weighted multiplex networks and allows defining and representing cognitive and dynamical interdependence between urban environment and automotive IoT devices (MEC nodes). The evolutionary game theory is applied to study the problem of cooperation of MEC nodes in a multi-service environment, shedding light on the joint impact of its dynamics and the multiplex structure on decreasing the blocking probability of the MEC nodes.
进化动态合作环境下的智慧城市同步
城市在复杂的系统中发展,在争夺空间的同时也在争夺可持续性。在一个不断变化的物联网和人联网系统中,理解如何支持智慧城市的挑战与5G及以后、汽车物联网(IoT)和多接入边缘计算(MEC)等使能技术有关。这使得创新的方法能够开发复杂的环境和认知城市应用,这对于提高汽车领域对周围环境的认识至关重要。为此,我们提出了一种理论方法,从知识的深度来研究更丰富的城市结构,揭示隐藏的城市模式。该方法基于两个加权复用网络,允许定义和表示城市环境和汽车物联网设备(MEC节点)之间的认知和动态相互依存关系。应用进化博弈论研究了多服务环境下MEC节点的合作问题,揭示了其动态性和多路结构对降低MEC节点阻塞概率的共同影响。
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