To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the platform. Secondly, we model the problem of service placement and data management in the proposed MEC-based solution taking into account the dependencies at the data level between IoT services and sensing resources. Our model also considers that caches can be deployed on MEC hosts, to allow the sharing of the same data between different IoT services with overlapping geographical scope, and provides support for IoT services with heterogeneous QoS requirements, such as different frequencies of periodic task execution. Due to the complexity of the optimisation problem, a heuristic algorithm is proposed using linear relaxation and rounding techniques. Extensive simulation results demonstrate the efficiency of the proposed approach, especially when traffic demands generated by the service requests are not uniform.

为了满足对数据密集型和低延迟IoT应用不断增长的需求，多访问边缘计算（MEC））正在成为一种有效的边缘计算方法，从而能够执行对最终用户比较近的对延迟敏感的处理任务。但是，MEC系统中有关资源分配和服务放置的大多数现有工作都忽略了新物联网用例的独特特征。例如，许多物联网应用程序要求在实时数据流上定期执行计算任务，这些实时数据流源自散布在​​广泛区域中的设备。因此，请求IoT服务的用户通常与数据生产者相距遥远。为了填补这一空白，这项工作的贡献是双重的。首先，我们提出了一种符合MEC的架构解决方案，以通过通用的MEC平台部署来支持多个IoT服务提供商的运营，从而可以指导和塑造平台内的IoT数据传输。其次，我们在提出的基于MEC的解决方案中对服务放置和数据管理问题进行了建模，同时考虑到了IoT服务与感知资源之间在数据级别的依赖性。我们的模型还考虑了可以在MEC主机上部署缓存，以允许在具有重叠地理范围的不同IoT服务之间共享相同的数据，并为具有不同QoS要求的IoT服务提供支持，例如周期性任务执行的不同频率。由于优化问题的复杂性，提出了一种使用线性松弛和舍入技术的启发式算法。大量的仿真结果证明了该方法的有效性，尤其是当服务请求生成的流量需求不一致时。