Data-centric Internet Of Things Research Articles

A Robust Energy Optimization and Data Reduction Scheme for IoT Based Indoor Environments Using Local Processing Framework

The extensive growth in popularity of Internet of Things (IoT) has led to the generation of massive amount of data from several heterogeneous sensory devices. This has also led to the increase in energy consumption by these connected devices. Smart buildings are one such platform which are equipped with several micro-controllers and sensors, generating a huge amount of redundant information at their data acquisition level. As a result, real-time applications may not be efficiently executed due to latency delays at the cloud service end. This requires several devices at cloud service end to execute the massive amount of data generated by these sensors, which does not satisfy green computing criteria. In this context, a novel local processing mechanism (LPM) is proposed, which favors an improved IoT service architecture for smart buildings. From the perspective of green computing, the proposed LPM framework facilitates reduction of manifolds at data acquisition level of sensor nodes. This paper also addresses the concept of optimal use of sensors in a wireless sensor network (WSN) and estimates costs corresponding to non-Poisson and Poisson arrival of data packets at local processor using the well-known queuing model. We also provide an efficient algorithm for smart buildings using our expert Markov switching (EMS) model, which is a well known probabilistic model in the field of artificial intelligence (AI) for subjectively validating real sensory data sets (viz., temperature, pressure, and humidity). Further, it has been analyzed that the proposed EMS algorithm outperforms several other algorithms conventionally used for determining the state of large-scale dynamic sensor networks. The service cost of proposed model has been compared with conventional model under various stress conditions viz., arrival rate, service rate, and number of clusters. It is observed that the proposed model operates well by leveraging green computing criteria. Thus, in the aforementioned context, this paper provides thing-centric, data-centric, and service-oriented IoT architecture.

A Data-Centric Internet of Things Framework Based on Azure Cloud

Internet of Things (IoT) has been found pervasive use cases and become a driving force to constitute a digital society. The ultimate goal of IoT is data and the intelligence generated from data. With the progress in public cloud computing technologies, more and more data can be stored, processed and analyzed in cloud to release the power of IoT. However, due to the heterogeneity of hardware and communication protocols in the IoT world, the interoperability and compatibility among different link layer protocols, sub-systems, and back-end services have become a significant challenge to IoT practices. This challenge cannot be addressed by public cloud suppliers since their efforts are mainly put into software and platform services but can hardly be extended to end devices. In this paper, we propose a data-centric IoT framework that incorporates three promising protocols with fundamental security schemes, i.e., WiFi, Thread, and LoRaWAN, to cater to massive IoT and broadband IoT use cases in local, personal, and wide area networks. By taking advantages of the Azure cloud infrastructure, the framework features a unified device management model and data model to conquer the interoperability challenge. We also provide implementation and a case study to validate the framework for practical applications.

Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud

The convergence of Internet of Things (IoT) and the Cloud has significantly facilitated the provision and management of services in large-scale applications, such as smart cities. With a huge number of IoT services accessible through clouds, it is very important to model and expose cloud-based IoT services in an efficient manner, promising easy and real-time delivery of cloud-based, data-centric IoT services. The existing work in this area has adopted a uniform and flat view to IoT services and their data, making it difficult to achieve the above goal. In this article, we propose a software framework, Context-driven And Real-time IoT (CARIoT) for real-time provisioning of cloud-based IoT services and their data, driven by their contextual properties. The main idea behind the proposed framework is to structure the description of data-centric IoT services and their real-time and historical data in a hierarchical form in accordance with the end-user application’s context model. CARIoT features design choices and software services to realize this service provisioning model and the supporting data structures for hierarchical IoT data access. Using this approach, end-user applications can access IoT services and subscribe to their real-time and historical data in an efficient manner at different contextual levels, e.g., from a municipal district to a street in smart city use cases. We leverage a popular cloud-based data storage platform, called Firebase, to implement the CARIoT framework and evaluate its efficiency. The evaluation results show that CARIoT’s hierarchical structure imposes no additional overhead with less data notification delay as compared to existing flat structures.

Possible implementations of the cloud-based data-centric IoT pilot platforms for smart cities and agriculture

The Internet of Things (IoT) is starting to appear everywhere in many shapes and forms. But security is one of the most crucial thing that could trip up the growth of the IoT. Following security principles used in enterprise computing can help clear that issue. Already there are more connected devices than people on the planet, according to leading researchers in this area. By 2020, there will be 50 billion connected devices, outnumbering people by more than 6 to 1. Most of these devices will be controllable over the Internet, and they will increasingly be responsible for collecting and transmitting sensitive data. Today consumers might own an app that collects information on their exercise routine. In a few years, those same people might have an Internet-enabled medical device that continually delivers data to their doctor. In the wrong hands, data from home management systems could be used to assess user’s whereabouts. Likewise, businesses could be vulnerable when they connect things like HVAC, irrigation, or commercial appliances.

Fog Computing: A Primer

Fog computing (FC) was proposed in 2012 by Cisco as the ideal computing model for providing real-time computing services and storage to support the resource-constrained Internet of Things (IoT) devices. Thus, FC may be regarded as the convergence of the IoT and the Cloud, combining the data-centric IoT services and pay-as-you-go characteristics of clouds. This paper provides a brief introduction of fog computing.

A Roadmap to the Programmable World: Software Challenges in the IoT Era

The Internet of Things (IoT) represents the next significant step in the evolution of the Internet and software development. Although most IoT research focuses on data acquisition, analytics, and visualization, a subtler but equally important transition is underway. Hardware advances are making it possible to embed fully fledged virtual machines and dynamic language runtimes virtually everywhere, leading to a Programmable World in which all our everyday things are connected and programmable dynamically. The emergence of millions of remotely programmable devices in our surroundings will pose significant software development challenges. A roadmap from today's cloud-centric, data-centric IoT systems to the Programmable World highlights the technical challenges that deserve to be part of developer education and deserve deeper investigation beyond those IoT topics that receive the most attention today.

When things matter: A survey on data-centric internet of things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.