An Embedded Cloud Design for Internet-of-Things

Internet-of-Things (IoT) consists of interconnected heterogeneous devices that ubiquitously interact with physical world. The devices are often resource constrained in terms of energy, computation, and communication resources. Distributing processing between these heterogeneous devices could yield to better performance and sharing, and extending resources of the devices could yield to more intelligent ubiquitous applications. Such a design can be called as “embedded cloud”, which is defined in this paper. An embedded cloud design is presented that consists of distributable Process Description Language (PDL), Distributed Middleware (DiMiWa), and an infrastructure. As a result, PDL can execute distributed processes and share resources as services over heterogeneous IoT devices with help of DiMiWa and the infrastructure. The design is evaluated with a prototype implementation, where PDL and DiMiWa are executed on a small 8-bit microcontroller-based IoT device. The implementation requires only 5122？B of program memory (4% of the available), consumes under 1？ms of CPU time per process in the worst case, and allows over 100 simultaneous services per device. 1. Introduction Internet-of-Things (IoT) consists of heterogeneous networked embedded devices that communicate through wired or wireless links or the Internet to create new ubiquitous, mash up, context aware, and information-based applications [1–3]. Often these embedded devices measure or interact with physical world ubiquitously, and they consist of wired sensors, RFIDs, Wireless Sensor Networks (WSNs), and/or mobile devices. Typically, IoT devices have limited communication and processing capabilities due to the energy consuming communication, small physical factor, battery operation, and long lifetime expectations. Distributing and networking tens or hundreds of IoT devices enable intelligent ubiquitous applications. Distributing the processing or the application logic over the networked IoT devices is an important feature [3], since in-network processing can improve energy efficiency and data delivery reliability due to the reduced communication and congestion, especially, on resource-constrained WSNs [4–7]. Current IoT abstractions that hide device heterogeneity from end-user application development do not take distributed or in-network processing into account. The processing is done in the infrastructure, and the IoT devices are mainly used as heterogeneous data providers that are homogenized for the end-user applications [3, 4, 8]. The heterogeneity of the IoT devices makes distributing processing