The evolution of computing and networking technologies has opened the era of cloud computing, and the advent of the Internet of Things (IoT) paradigm has been questioning its limitations. Owing to advances in computer networks, cloud computing is improving, and the most promising technology is fog computing. Although fog computing is recognized as the most appropriate computing model for the IoT, it has not yet been widely used, and the major reasons are as follows. The replacement of the firmware and hardware of network equipment is inevitable; however, the operator in charge of carrying out this expensive task is unclear, and even if the operator is selected, the reason may be not rational. In addition, although fog computing is based on collaboration between several infrastructure operators and service providers, it is not clear who operates and manages the infrastructure. Furthermore, there is still a resource allocation problem for a fog service instance. In this paper, we propose a user participatory fog computing architecture and its management schemes to address the above problems related to its feasibility. In the proposed architecture, fog service instance placement optimization is performed based on service usage of participating users, which is formulated into a mixed-integer non-linear programming problem and then linearized. The proposed architecture and the fog service placement method are evaluated based on simulation, taking into account actual parameters of the IoT services and devices.INDEX TERMS Fog computing, Internet of Things, optimization, software defined networking.

No abstract

A smart city is an urban area that collects data from various devices to effectively manage urban resources. The smart city IoT infrastructure connects numerous devices to an Internet-protocol-based low-power wireless network, shares massive amounts of data, and facilitates the development of new services. Message queuing telemetry transport (MQTT), a lightweight exchange protocol for the IoT environment, uses a publish and subscribe structure via a centralized broker to share data. The extent of edge computing provides distributed and closer resources to the data source while maintaining low transmission costs. However, a centralized MQTT data broker is unsuitable for distributed edge resources and could result in high latency, traffic, and bottleneck risk. Therefore, we proposed a distributed MQTT broker optimized architecture. A distributed MQTT broker for edge resources could reduce network traffic and data delivery latency by only managing consumed topics in the network. We formulate an integer non-linear program to optimize container placement and avoid wasting edge computing resources. We compared our proposed architecture to the existing distributed MQTT middleware architecture with greedy and random container placement through extensive simulation. Our methods show better performance in lowering deployment failure ratio, power consumption, network usage, and synchronization overhead.

No abstract

No abstract