Enabling High-Level Application Development for the Internet of Things

Application development in the Internet of Things (IoT) is challenging because it involves dealing with a wide range of related issues such as lack of separation of concerns, and lack of high-level of abstractions to address both the large scale and heterogeneity. Moreover, stakeholders involved in the application development have to address issues that can be attributed to different life-cycles phases. when developing applications. First, the application logic has to be analyzed and then separated into a set of distributed tasks for an underlying network. Then, the tasks have to be implemented for the specific hardware. Apart from handling these issues, they have to deal with other aspects of life-cycle such as changes in application requirements and deployed devices. Several approaches have been proposed in the closely related fields of wireless sensor network, ubiquitous and pervasive computing, and software engineering in general to address the above challenges. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoT. This paper proposes an integrated approach for addressing the above mentioned challenges. The main contributions of this paper are: (1) a development methodology that separates IoT application development into different concerns and provides a conceptual framework to develop an application, (2) a development framework that implements the development methodology to support actions of stakeholders. The development framework provides a set of modeling languages to specify each development concern and abstracts the scale and heterogeneity related complexity. It integrates code generation, task-mapping, and linking techniques to provide automation. Code generation supports the application development phase by producing a programming framework that allows stakeholders to focus on the application logic, while our mapping and linking techniques together support the deployment phase by producing device-specific code to result in a distributed system collaboratively hosted by individual devices. Our evaluation based on two realistic scenarios shows that the use of our approach improves the productivity of stakeholders involved in the application development

A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration

The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This article surveys e application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the article first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the article surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption, and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability, and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.Peer ReviewedPostprint (author's final draft

From Heterogeneous Sensor Networks to Integrated Software Services: Design and Implementation of a Semantic Architecture for the Internet of Things at ARCES@UNIBO

The Internet of Things (IoTs) is growing fast both in terms of number of devices connected and of complexity of deployments and applications. Several research studies an- alyzing the economical impact of the IoT worldwide identify the interoperability as one of the main boosting factor for its growth, thanks to the possibility to unlock novel commercial opportunities derived from the integration of heterogeneous systems which are currently not interconnected. However, at present, interoperability constitutes a relevant practical issue on any IoT deployments that is composed of sensor platforms mapped on different wireless technologies, network protocols or data formats. The paper addresses such issue, and investigates how to achieve effective data interoperability and data reuse on complex IoT deployments, where multiple users/applications need to consume sensor data produced by heterogeneous sensor networks. We propose a generic three-tier IoT architecture, which decouples the sensor data producers from the sensor data consumers, thanks to the intermediation of a semantic broker which is in charge of translating the sensor data into a shared ontology, and of providing publish-subscribe facilities to the producers/consumers. Then, we describe the real-world implementation of such architecture devised at the Advanced Research Center on Electronic System (ARCES) of the University of Bologna. The actual system collects the data produced by three different sensor networks, integrates them through a SPARQL Event Processing Architecture (SEPA), and supports two front- end applications for the data access, i.e. a web dashboard and an Amazon Alexa voice service

CoAP Infrastructure for IoT

The Internet of Things (IoT) can be seen as a large-scale network of billions of smart devices. Often IoT devices exchange data in small but numerous messages, which requires IoT services to be more scalable and reliable than ever. Traditional protocols that are known in the Web world does not fit well in the constrained environment that these devices operate in. Therefore many lightweight protocols specialized for the IoT have been studied, among which the Constrained Application Protocol (CoAP) stands out for its well-known REST paradigm and easy integration with existing Web. On the other hand, new paradigms such as Fog Computing emerges, attempting to avoid the centralized bottleneck in IoT services by moving computations to the edge of the network. Since a node of the Fog essentially belongs to relatively constrained environment, CoAP fits in well. Among the many attempts of building scalable and reliable systems, Erlang as a typical concurrency-oriented programming (COP) language has been battle tested in the telecom industry, which has similar requirements as the IoT. In order to explore the possibility of applying Erlang and COP in general to the IoT, this thesis presents an Erlang based CoAP server/client prototype ecoap with a flexible concurrency model that can scale up to an unconstrained environment like the Cloud and scale down to a constrained environment like an embedded platform. The flexibility of the presented server renders the same architecture applicable from Fog to Cloud. To evaluate its performance, the proposed server is compared with the mainstream CoAP implementation on an Amazon Web Service (AWS) Cloud instance and a Raspberry Pi 3, representing the unconstrained and constrained environment respectively. The ecoap server achieves comparable throughput, lower latency, and in general scales better than the other implementation in the Cloud and on the Raspberry Pi. The thesis yields positive results and demonstrates the value of the philosophy of Erlang in the IoT space

System for monitoring and supporting the treatment of sleep apnea using IoT and big data

[EN] Sleep apnea has become in the sleep disorder that causes greater concern in recent years due to its morbidity and mortality, higher medical care costs and poor people quality of life. Some proposals have addressed sleep apnea disease in elderly people, but they have still some technical limitations. For these reasons, this paper presents an innovative system based on fog and cloud computing technologies which in combination with IoT and big data platforms offers new opportunities to build novel and innovative services for supporting the sleep apnea and to overcome the current limitations. Particularly, the system is built on several low-power wireless networks with heterogeneous smart devices (i.e, sensors and actuators). In the fog, an edge node (Smart IoT Gateway) provides IoT connection and interoperability and pre-processing IoT data to detect events in real-time that might endanger the elderly's health and to act accordingly. In the cloud, a Generic Enabler Context Broker manages, stores and injects data into the big data analyzer for further processing and analyzing. The system's performance and subjective applicability are evaluated using over 30 GB size datasets and a questionnaire fulfilled by medicals specialist, respectively. Results show that the system data analytics improve the health professionals' decision making to monitor and guide sleep apnea treatment, as well as improving elderly people's quality of life. (C) 2018 Elsevier B.V. All rights reserved.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's "Horizon 2020'' research and innovation program as part of the ACTIVAGE project under Grant 732679 and the Interoperability of Heterogeneous IoT Platforms project (INTER-IoT) under Grant 687283.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive and Mobile Computing. 50:25-40. https://doi.org/10.1016/j.pmcj.2018.07.007S25405

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

Split and Migrate: Resource-Driven Placement and Discovery of Microservices at the Edge

Microservices architectures combine the use of fine-grained and independently-scalable services with lightweight communication protocols, such as REST calls over HTTP. Microservices bring flexibility to the development and deployment of application back-ends in the cloud. Applications such as collaborative editing tools require frequent interactions between the front-end running on users\u27 machines and a back-end formed of multiple microservices. User-perceived latencies depend on their connection to microservices, but also on the interaction patterns between these services and their databases. Placing services at the edge of the network, closer to the users, is necessary to reduce user-perceived latencies. It is however difficult to decide on the placement of complete stateful microservices at one specific core or edge location without trading between a latency reduction for some users and a latency increase for the others. We present how to dynamically deploy microservices on a combination of core and edge resources to systematically reduce user-perceived latencies. Our approach enables the split of stateful microservices, and the placement of the resulting splits on appropriate core and edge sites. Koala, a decentralized and resource-driven service discovery middleware, enables REST calls to reach and use the appropriate split, with only minimal changes to a legacy microservices application. Locality awareness using network coordinates further enables to automatically migrate services split and follow the location of the users. We confirm the effectiveness of our approach with a full prototype and an application to ShareLatex, a microservices-based collaborative editing application

Smartphone as an Edge for Context-Aware Real-Time Processing for Personal e-Health

The medical domain is facing an ongoing challenge of how patients can share their health information and timeline with healthcare providers. This involves secure sharing, diverse data types, and formats reported by healthcare-related devices. A multilayer framework can address these challenges in the context of the Internet of Medical Things (IoMT). This framework utilizes smartphone sensors, external services, and medical devices that measure vital signs and communicate such real-time data with smartphones. The smartphone serves as an “edge device” to visualize, analyze, store, and report context- aware data to the cloud layer. Focusing on medical device connectivity, mobile security, data collection, and interoperability for frictionless data processing allows for building context-aware personal medical records (PMRs). These PMRs are then securely transmitted through a communication protocol, Message Queuing Telemetry Transport (MQTT), to be then utilized by authorized medical staff and healthcare institutions. MQTT is a lightweight, intuitive, and easy-to-use messaging protocol suitable for IoMT systems. Consequently, these PMRs are to be further processed in a cloud computing platform, Amazon Web Services (AWS). Through AWS and its services, architecting a customized data pipeline from the mobile user to the cloud allows displaying of useful analytics to healthcare stakeholders, secure storage, and SMS notifications. Our results demonstrate that this framework preserves the patient’s health-related timeline and shares this information with professionals. Through a serverless Business intelligence interactive dashboard generated from AWS QuickSight, further querying and data filtering techniques are applied to the PMRs which identify key metrics and trends