In addition to stationary sensors/actuators (nodes), mobile nodes such as vehicles and smartphones are connected to M2M (Machine-to-Machine) systems. Some M2M systems take into account node mobility, while some M2M systems regard vehicles as compute servers. N one of them, however, handle the mobility of nodes that act as compute servers. This paper proposes an MEC (Multi-access Edge Computing) based M2M system called MECM2M, which can handle the mobility of nodes acting as sensors/actuators and compute servers. The design policy of MECM2M is analogous to that of the Internet. The Internet provides two fundamental services: reliable byte stream and unreliable datagram. How data is used is up to applications. MECM2M provides M2M applications with three fundamental services as the M2M services: area-based data acquisition, node-based data acquisition, and node-based action instruction. How sensing data is used is up to M2M applications. MECM2M introduces the virtual nodes as digital twins in the virtual space for the physical nodes. The virtual nodes conceal differences from the physical nodes and provide a uniform interface. The relation among the virtual nodes, the physical nodes, and the locations of the physical nodes is managed by a domain ontology called Onto- Mecm2m,which enables effective processing of the three fundamental services. The evaluation results on an MECM2M emulator show that the response times of the three fundamental services satisfy the latency constraints, i.e., 100 ms, defined in an ETSI (European Telecommunications Standards Institute) document.

In an MEC (Multi-access Edge Computing) environment, it is suitable to compose an application as a set of containers for flexibility in resource allocation. This paper proposes stateless shared containers (SS-Containers) that can be shared by multiple applications simultaneously in an MEC environment, similar to a shared library in an operating system. It is expected that SS-Containers reduce resource consumption, shorten application startup time, and efficiently manage special hardware such as GPU. When 16 applications share an SS-Container with an encryption function, and each application contains the container with the same encryption function, the memory consumption is reduced by approximately 190 MiB. Comparing the case in which nine applications share an SS-Container that accesses a GPU to solve large-scale simultaneous linear equations with the case in which nine applications randomly access a GPU via a container inside them, the response time is reduced approximately by 57 % (39 seconds).

In a Multi-access Edge Computing (MEC) environment, an application will be composed of multiple containers running on edge servers, and a container orchestration method determines which containers should be deployed on which edge servers. This paper proposes a container orchestration method considering shared containers. These containers will be introduced in an MEC environment to reduce application resource usage, similar to a shared library in an operating system. The proposed method adopts a multi-stage grouping of edge servers to aggregate computation resource information and network information of the edge servers for efficient orchestration calculation. The formulation of the proposed orchestration method has been successfully completed, marking a significant milestone in this research. Evaluation will be conducted assuming an environment that contains 2,048 edge servers and 200 user terminals.

The number of IoT devices is increasing, but applications that utilize IoT devices and their data are still developing. One reason is the tight coupling between M2M infrastructure and applications and the difficulty of handling mobile devices in a unified manner. To address these challenges, we developed a versatile M2M system called "MECM2M" that utilizes the MEC paradigm. This paper proposes "MECM2Mv2" by adopting the Pub-Sub model and redesigning modules to improve the latency and efficiency, which were issues in MECM2M. Our future research evaluates the safety of using MECM2Mv2 in autonomous driving applications.