# PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications
###### Abstract  
The cloud-based Internet of Things (IoT-Cloud) combines the advantages of the IoT and cloud computing, which not only expands the scope of cloud computing but also enhances the data processing capability of the IoT. Users always seek affordable and efficient services, which can be completed by the cooperation of all available network resources, such as edge computing nodes. However, current solutions exhibit significant security and efficiency problems that must be solved. Insider attacks could degrade the performance of the IoT-Cloud due to its natural environment and inherent open construction. Unfortunately, traditional security approaches cannot defend against these attacks effectively. In this paper, a novel practical edge computing service architecture (PECSA), which integrates a trust management methodology with dynamic cost evaluation schemes, is proposed to address these problems. In the architecture, the edge network devices and edge platform cooperate to achieve a shorter response time and/or less economic costs, as well as to enhance the effectiveness of the trust management methodology, respectively. To achieve faster responses for IoT-based requirements, all the edge computing devices and cloud resources cooperate in a reasonable way by evaluating computational cost and runtime resource capacity in the edge networks. Moreover, when cooperated with the edge platform, the edge networks compute trust values of linked nodes and find the best collaborative approach for each user to meet various service requirements. Experimental results demonstrate the efficiency and the security of the proposed architecture. edge computing; cloud; Internet of Things (IoT); efficiency; trust +
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data transmission and data integration. Furthermore, users can obtain shorter responses from the edges than from the Cloud. Edge computing nodes are aware of users' location by analyzing the data received from IoT devices. With edge networks, data can be transmitted between the user and EC nodes without the participation of the core network [5]. Since the IoT nodes and edge nodes move randomly, tasks need to be dynamically assigned to the EC nodes that are adjacent to the users. To minimize the service delay, many solutions tasks should be offloaded to different EC nodes that are adjacent to the users [6; 7; 8; 9; 10]. It is inefficient for IoT applications to solve these requirements by simply allocating tasks to available resources; further, to constantly improve hardware configurations is impractical. In addition, a high-configured computing platform is usually associated with more costs. Recently, a lot of studies have paid attention to offloading strategies for high service quality and/or low costs [11; 12; 13; 14; 15]. In the literature, there is a common assumption that tasks are only assigned to a single 4G or 5G network. However, for the edge computing network that integrates both 4G and 5G devices, such operation may be not suitable because the quality of the service depends on a single network. An important part of service costs is energy consumption, regardless of network structure. To minimize the service costs, most of the literature focuses on ways to minimize energy consumption [4; 9; 15; 16; 17]. Unfortunately, in the literature, researchers often separate operating costs from user prices. The authors in [4] investigate both running time and total costs. However, their optimization expression considers the weighting of cost and latency but ignores the requirements of the users who need minimum service price and of those who need a minimum service latency. Furthermore, many data generated on the IoT require security protection [18; 19; 20; 10]. For instance, the health data of patients need to be transmitted and processed under privacy protection. For all the aforementioned works, the authors do not take into account the costs of security measures, such as encryption and digital signatures.