The design of the gateway for the Cloud of Things

International audienceThe increasing momentum of the Internet of Things (IoT) leaded to the development of a huge number of applications in different domains. Those applications are based on different standards and protocols, making therefore the IoT landscape widely fragmented. In this context, the evolution of Web semantic technologies together with the popularity of Cloud computing represents a solution to enable the horizontal integration of various IoT applications and platforms. This is what the Cloud of Things (CoT) aims to achieve. In this paper, we propose the design of a gateway for the Cloud of Things. The proposed gateway is able to manage semantic-like things and at the same time to act as an end-point for the presentation of data to users. Moreover, thanks to the use of virtualized software -which introduces a negligible impact in terms of performance- the gateway enables a lightweight and dense deployment of services. The paper describes the above technologies and how to combine them in order to design the gateway. Furthermore, we provide information about use cases, hardware, performance evaluation, and future hints to enhance the gateway

Design of a Gateway for the Cloud of Things

International audienceIn the last decades, Sensor Networks (SNs) have played a primary role for the research community. However, the progress focused more on communication and networking aspects between such devices than on the interpretation of the produced data.Just recently, with standards and common frameworks such as Sensor Web Enablement (SWE) and W3C’s Sensor ontology [1], there is an effort to understand data, which comes out, and goes into sensor networks.As stated by Diaz et al. in [2], semantic technologies, such as ontologies and linked data, can be used to solve the problem of scalability – many different devices can be added to the system at a fast pace, by formatting the information –. Interoperability is made easier as well: the data format provided by ontologies makes services equally available; regardless of the device they are being provided.Those technologies, together with the Cloud Computing are at the base of the Cloud of Things (CoT) that we propose in [3]. The CoT aims to better use distributed resources, putting them together and enabling therefore a horizontal integration of various Internet of Things (IoT) platforms.Similar concepts to CoT, such as Capillary Networks [4], and Fog Networks [5] include in their topology, the presence of intelligent nodes that, although often characterized by limited computation resources, can easily manage all the applications running on top of them. Adopting this approach, in this paper we design the architecture of a Gateway for the Cloud of Things. The Gateway is able to manage semantic-like things and to act as an end-point for the dynamic presentation of real world data to consumer applications and users.In order to semantically annotate the properties of the sensors – e.g. measurement capability, position etc. – we embed a light ontology into sensors’ firmware. This will improve the efficiency in the discovery process of sensor properties.Prediction algorithms on data production are used between the Gateway and sensors in order to reduce the number of communications between them, and therefore to lower the battery consumption and interferences.Specifically, in our proposal, we design and develop an efficient gateway through the use of virtualized software and, in particular, by exploiting all the benefits introduced by emerging lightweight virtualization technologies.As exhaustively explained in [6], these technologies introduce an almost negligible overhead and they are modeled in such a way to guarantee a lightweight and dense deployment of services.Those platforms are indeed, not designed to be exclusively used in large data-center or in specific cloud environments, they are equally efficient, even when operating on gateway and/or embedded systems.Container-based solutions implement processes isolation by avoiding the emulation of virtual hardware and therefore reducing the overhead that characterize other virtualization technologies such as hypervisors .Therefore, the advantage of achieving higher density of virtualized processes – which run within each container – is a direct consequence.With specific relation to our scenario, the versatility of container technologies allows a dynamic and optimized usage of the gateway, so that services can be allocated only when needed and according to the functionality of the SN.In conclusion, we show that the combination of all these features can represent a valuable way to convey several advantages – both sensors and gateway side – and noticeable improvements in terms of resource allocation, service management, and energy efficiency and discuss different ways of future investigations

System Design of Internet-of-Things for Residential Smart Grid

Internet-of-Things (IoTs) envisions to integrate, coordinate, communicate, and collaborate real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this paper studies the design of a large scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a backend server, unified home gateway (UHG) at the end users, and user interface for mobile devices. We discuss the features of such IoT system to support a large scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using the XMPP protocol, and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.Comment: 10 pages, 6 figures, journal pape

iGateLink: A Gateway Library for Linking IoT, Edge, Fog and Cloud Computing Environments

In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting as a gateway between IoT devices and Edge/Fog/Cloud Computing environments. Thanks to its pluggable design, modules providing connectivity with a number of devices acting as data sources or Fog/Cloud frameworks can be easily reused for different applications. Using iGateLink in two case-studies replicating previous works in the healthcare and image processing domains, the library proved to be effective in adapting to different scenarios and speeding up the development of gateway applications, as compared to the use of conventional methods

On the Deployment of Healthcare Applications over Fog Computing Infrastructure

Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied delays, Async-HFL employs asynchronous aggregations at both the gateway and the cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection chooses edge devices to trigger local training in real-time while device-gateway association determines the network topology periodically after several cloud epochs, both satisfying bandwidth limitation. We evaluate Async-HFL's convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe robust convergence under unexpected stragglers.Comment: Accepted by IoTDI'2

Sofie: Smart Operating System For Internet Of Everything

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Applications such as cloud offloading, smart home, and smart city are idea area for Edge computing to achieve better performance than cloud computing. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. However, there are still some challenges for applying Edge computing in our daily life. The missing of the specialized operating system for Edge computing is holding back the flourish of Edge computing applications. Service management, device management, component selection as well as data privacy and security is also not well supported yet in the current computing structure. To address the challenges for Edge computing systems and applications in these aspects, we have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating System For Internet Of Everything. SOFIE is the operating system specialized for Edge computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection between cloud and Edge device to handle the data transportation between gateway and Edge devices; to provide service management and data management for Edge applications; to protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices. Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently. To solve the component selection problem in Edge computing paradigm, SOFIE also include our previous work, SURF, as a model to optimize the performance of the system. Finally, we deployed the design of SOFIE on an IoT/M2M system and support semantics with access control

The deployment of an IoT network infrastructure, as a localised regional service

The Internet of things (IoT) is fast evolving with a wide range of technologies being designated specifically as IoT solutions. Studies on such solutions generally reference the specific communication medium while negating the complete architecture of the IoT system. From a system perspective, a complete IoT solution can be separated into three categories, the data collector, the communication method and the cloud platform service. The data collector relates to the embedded system device (or the `things\u27 element) at the source of the application, the communication method relates to the network protocol used to send or receive the data and the cloud platform service relates to the facility used to store and process the data collected. LoRaWan and LoRa are `Long Range\u27 technologies, which define the communication method for such IoT applied systems. LoRa defines the modulation technique, that allows for long range communication, whereas LoRaWan defines the communication and system architecture.This paper presents the design architecture and methodology of a fully functioning LoRaWan based IoT system. Such a system can be provided as a service to a given local region, by utilizing an End Device in conjunction with a LoRa transceiver, a LoRaWan Gateway and a defined cloud platform. The presented IoT system currently serves the region of Tallaght (Dublin, Ireland) and its wider area. As a service, the system has been shown to be capable of supporting a wide range of IoT based applications

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it