## 1 Introduction  
Recent advances in mobile technologies have enabled a new class of smart city and fifth-generation (5G) network applications, such as smart homes and real-time situation analyses. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to the Internet of Things (IoT) devices. To address the requirements of these applications, several edge computing systems, such as cloudlet computing, mobile edge computing, and fog computing, were proposed [1, 2, 3, 4]. The deployment of edge computing systems requires the addition of new infrastructure or the updating of existing infrastructure. Edge computing systems also do not utilize the capabilities of end devices, such as smartphones, mobile robots, and smart vehicles, which are equipped with multicore central and graphical processing units, several sensors, or multiple wireless communication technologies. A heterogeneous private edge cloud system is proposed to overcome the drawbacks of edge computing systems. A heterogeneous private edge cloud system [1] is a small-scale cloud data center in a local physical area, such as a house or an office. It consists of various stationary and mobile devices, such as personal computers, mobile robots, smartphones, and sensors, which are interconnected through single or multiple infrastructure-based or infrastructures-less wireless local area networks (LANs). A heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. The efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A middleware platform is required to manage and utilize the heterogeneous private edge cloud system infrastructure efficiently. Such a platform is responsible for the discovery, monitoring, allocation, and management of resources. Compared with those of conventional systems, such as mobile ad hoc clouds [2] or edge clouds [4], the design of a middleware platform for a heterogeneous private edge cloud system is more challenging due to (1) the presence of heterogeneous devices, such as sensors, smartphones, mobile robots, and personal computers, and (2) a multilayer network environment. Existing middleware platforms are divided into two categories: middleware platforms for IoT systems and middleware platforms for distributed computing systems. Middleware platforms for IoT systems provide access to and control physical devices. They also support data collection, data analysis, or application composition services. These platforms, however, do not provide computing services, do not utilize network-level information, such as link quality and link lifetime, and are not designed for complex multilayer network environments. Most middleware platforms for distributed computing systems provide computing or storage services but do not support data collection and actuation services; they are also not designed for heterogeneous multilayer network environments. Therefore, they do not efficiently utilize heterogeneous routes, simultaneous transmission on multiple communication technologies, and several network-level parameters, such as link quality and lifetimes. The current study presents the design of an intelligent middleware platform that aims to utilize the characteristics efficiently and address the challenges of heterogeneous computing and a multilayer network environment to (1) manage heterogeneous computing and network resources efficiently, and (2) provide task processing, data collection, and data storage services to support emerging smart city and 5G network applications. The new platform consists of two layers: a software-defined network (SDN) and a machine learning-based multi-network management layer and a machine learning-based resource management layer.