Fog and Edge Oriented Embedded Enterprise Systems Patterns: Towards Distributed Enterprise Systems That Run on Edge and Fog Nodes

Enterprise software systems enable enterprises to enhance business and management reporting tasks in enterprise settings. Internet of Things (IoT) focuses on making interactions possible between a number of network-connected physical devices. Prominence of IoT sensors and multiple business drivers have created a contemporary need for enterprise software systems to interact with IoT devices. Business process implementations, business logic and microservices have traditionally been centralized in enterprise systems. Constraints like privacy, latency, bandwidth, connectivity and security have posed a new set of architectural challenges that can be resolved by designing enterprise systems differently so that parts of business logic and processes can run on fog and edge devices to improve privacy, minimize communication bandwidth and promote low-latency business process execution. This paper aims to propose a set of patterns for the expansion of previously-centralized enterprise systems to the edge of the network. Patterns are supported by a case study for contextualization and analysis

Intelligent IoT and Dynamic Network Semantic Maps for more Trustworthy Systems

As technology evolves, the Internet of Things (IoT) concept is gaining importance for constituting a foundation to reach optimum connectivity between people and things. For this to happen and to allow easier integration of sensors and other devices in these technologic environments (or networks), the configuration is a key process, promoting interoperability between heterogeneous devices and providing strategies and processes to enhance the network capabilities. The optimization of this important process of creating a truly dynamic network must be based on models that provide a standardization of communication patterns, protocols and technologies between the sensors. Despite standing as a major tendency today, many obstacles still arise when implementing an intelligent dynamic network. Existing models are not as widely adopted as expected and semantics are often not properly represented, hence resulting in complex and unsuitable configuration time. Thus, this work aims to understand the ideal models and ontologies to achieve proper architectures and semantic maps, which allow management and redundancy based on the information of the whole network, without compromising performance, and to develop a competent configuration of sensors to integrate in a contemporary industrial typical dynamic network

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

When Things Matter: A Data-Centric View of the 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. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, 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 article surveys 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

Platforms and Protocols for the Internet of Things

Building a general architecture for the Internet of Things (IoT) is a very complex task, exacerbated by the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we identify the main blocks of a generic IoT architecture, describing their features and requirements, and analyze the most common approaches proposed in the literature for each block. In particular, we compare three of the most important communication technologies for IoT purposes, i.e., REST, MQTT, and AMQP, and we also analyze three IoT platforms: openHAB, Sentilo, and Parse. The analysis will prove the importance of adopting an integrated approach that jointly addresses several issues and is able to flexibly accommodate the requirements of the various elements of the system. We also discuss a use case which illustrates the design challenges and the choices to make when selecting which protocols and technologies to use