Detailed knowledge about the electrical power consumption in industrial production environments is a prerequisite to reduce and optimize their power consumption. Today's industrial production sites are equipped with a variety of sensors that, inter alia, monitor electrical power consumption in detail. However, these environments often lack an automated data collation and analysis. We present a system architecture that integrates different sensors and analyzes and visualizes the power consumption of devices, machines, and production plants. It is designed with a focus on scalability to support production environments of various sizes and to handle varying loads. We argue that a scalable architecture in this context must meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency. As a solution, we propose a microservice-based architecture augmented by big data and stream processing techniques. Applying the fog computing paradigm, parts of it are deployed in an elastic, central cloud while other parts run directly, decentralized in the production environment. A prototype implementation of this architecture presents solutions how different kinds of sensors can be integrated and their measurements can be continuously aggregated. In order to make analyzed data comprehensible, it features a single-page web application that provides different forms of data visualization. We deploy this pilot implementation in the data center of a medium-sized enterprise, where we successfully monitor the power consumption of 16 servers. Furthermore, we show the scalability of our architecture with 20,000 simulated sensors.
We did not find any monitoring approaches for production environments designed in a microservices architecture. However, microservice-based approaches exist for other applications of the Internet of Things, such as @cite_22 and @cite_9 . As we propose in our architecture, these approaches intend to deploy microservices decentralized for flexibility and extendibility. Moreover, they use an asynchronous messaging bus for the exchange of sensor data as in our approach. Both approaches do not focus on scalability and, therefore, do not evaluate this.
@cite_9 @cite_22
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Industrial Internet of things (IIoTs) relies on different devices working together, gathering and sharing data using multiple communication protocols. This heterogeneity becomes a hindrance in the development of architectures that can support applications operating independently of the underlying protocols. Therefore in this paper, we proposed a modular and scalable architecture based on lightweight virtualization. The modularity provided by the proposed architecture combined with lightweight virtualization orchestration supplied by Docker simplifies management and enables distributed deployments. Availability and fault-tolerance characteristics are ensured by distributing the application logic across different devices where a single microservice or even device failure can have no effect on system performance. The proposed architecture is instantiated and tested on a simple time-dependent use case. The obtained results validates that the proposed architecture can be used to deploy services on demand at different architecture layers. This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings. Abstract In recent years a considerable effort has been spent by research and industrial communities in the digitalization of production environments with the main objective of achieving a new automation paradigm, more flexible, responsive to changes, and safe. This paper presents the architecture, and discusses the benefits, of a distributed middleware prototype supporting a new generation of smart-factory-enabled applications with special attention paid to simulation tools. Devised within the scope of MAYA EU project, the proposed platform aims at being the first solution capable of empowering shop-floor Cyber-Physical-Systems (CPSs), providing an environment for their Digital Twin along the whole plant life-cycle. The platform implements a microservice IoT-Big Data architecture supporting the distributed publication of multidisciplinary simulation models, managing in an optimized way streams of data coming from the shop-floor for real-digital synchronization, ensuring security and confidentiality of sensible data. In the context of the Internet of Things, an increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common device-centric approach limit the flexibility of these platforms: there are no means to incorporate people and their subjective impressions about the collected data. In order to build the Internet of Things and People and ultimately the Internet of Everything, we aim at providing an extendable concept of data which allows to enrich existing data points with any kind of additional information. This enables us to gain semantic and user specific context by attaching subjective data to objective values. For this end we support data ranging from text-based formats like JSON to images and video footage. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform. The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standardization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, micro service architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the micro service architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces. This paper presents our architectural approach to building a collaborative Industry 4.0 platform that enables IoT-based real-time monitoring, optimization and negotiation in manufacturing supply chains. The platform is called NIMBLE and is currently being developed in a European research project. The presented approach utilizes microservice technology, and implements the core business functionality of the platform through a composition of decentralized, scalable services. The communication among services, and with platform users, manufacturers, suppliers, sensors and Web resources, is supported through simple protocols and lightweight mechanisms. Core business services of the implemented architecture are released as open source software, enabling multiple prospective platform-providers to establish B2B marketplaces for collaboration within their own industrial sector or region. To demonstrate microservices in practice, we present two scenarios, both related to manufacturing of wooden home buildings: one is IoT-based data sharing in a supply chain, and the other deals with product driven logistics planning. The further development of the platform will be driven by the requirements of at least four different use cases throughout Europe, and by incorporating advanced business models to support the growth of powerful network effects of the platform. Abstract In today's manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision-makers to prioritize green manufacturing. The Internet of Things paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from manufacturing processes can be collected easily, and then analyzed, to improve energy-aware decision-making. Relying on a comprehensive literature review and on experts' insight, this paper contributes to the understanding of energy-efficient production management practices that are enhanced and enabled by the Internet of Things technology. In addition, it discusses the benefits that can be obtained thanks to adopting such management practices. Eventually, a framework is presented to support the integration of gathered energy data into a company's information technology tools and platforms. This is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage on such data in order to improve energy efficiency, and therefore competitiveness, of manufacturing companies. With the outcomes of this paper, energy managers can approach the Internet of Things adoption in a benefit-driven manner, addressing those energy management practices that are more aligned with company maturity, measurable data and available information systems and tools. In this paper we address the demand for flexibility and economic efficiency in industrial autonomous guided vehicle (AGV) systems by the use of cloud computing. We propose a cloud-based architecture that moves parts of mapping, localization and path planning tasks to a cloud server. We use a cooperative longterm Simultaneous Localization and Mapping (SLAM) approach which merges environment perception of stationary sensors and mobile robots into a central Holistic Environment Model (HEM). Further, we deploy a hierarchical cooperative path planning approach using Conflict-Based Search (CBS) to find optimal sets of paths which are then provided to the mobile robots. For communication we utilize the Manufacturing Service Bus (MSB) which is a component of the manufacturing cloud platform Virtual Fort Knox (VFK). We demonstrate the feasibility of this approach in a real-life industrial scenario. Additionally, we evaluate the system's communication and the planner for various numbers of agents. We introduce a novel control network protocol, try-once-discard (TOD), for multiple-input-multiple-output (MIMO) networked control systems (NCSs), and provide an analytic proof of global exponential stability for both the new protocol and the more commonly used (statically scheduled) access methods. Our approach is to first design the controller using established techniques and considering the network transparency, and then analyze the effect of the network on closed-loop system performance. When implemented, an NCS consists of multiple independent sensors and actuators competing for an access to the network, with no universal clock available to synchronize their actions. Since the nodes act asynchronously, we allow access to the network at anytime, but assume each access occurs before a prescribed deadline, known as the maximum allowable transfer interval. Only one node may access the network at a time. This communication constraint imposed by the network is the main focus of the paper. The performance of the new TOD protocol and the statically scheduled protocols are examined in simulations of an automotive gas turbine and an unstable batch reactor. Recent advances in ICT enable the evolution of the manufacturing industry to meet the new requirements of the society. Cyber-physical systems, Internet-of-Things (IoT), and Cloud computing, play a key role in the fourth industrial revolution known as Industry 4.0. The microservice architecture has evolved as an alternative to SOA and promises to address many of the challenges in software development. In this paper, we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyber-physical microservice as the key construct. The manufacturing plant processes are defined as compositions of primitive cyber-physical microservices adopting either the orchestration or the choreography pattern. IoT technologies are used for system integration and model-driven engineering is utilized to semi-automate the development process for the industrial engineer, who is not familiar with microservices and IoT. Two case studies demonstrate the feasibility of the proposed approach. Pervasive computing and Internet of Things (IoTs) paradigms have created a huge potential for new business. To fully realize this potential, there is a need for a common way to abstract the heterogeneity of devices so that their functionality can be represented as a virtual computing platform. To this end, we present novel semantic level interoperability architecture for pervasive computing and IoTs. There are two main principles in the proposed architecture. First, information and capabilities of devices are represented with semantic web knowledge representation technologies and interaction with devices and the physical world is achieved by accessing and modifying their virtual representations. Second, global IoT is divided into numerous local smart spaces managed by a semantic information broker (SIB) that provides a means to monitor and update the virtual representation of the physical world. An integral part of the architecture is a resolution infrastructure that provides a means to resolve the network address of a SIB either using a physical object identifier as a pointer to information or by searching SIBs matching a specification represented with SPARQL. We present several reference implementations and applications that we have developed to evaluate the architecture in practice. The evaluation also includes performance studies that, together with the applications, demonstrate the suitability of the architecture to real-life IoT scenarios. In addition, to validate that the proposed architecture conforms to the common IoT-A architecture reference model (ARM), we map the central components of the architecture to the IoT-ARM. Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI’s, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard. Established approaches to data aggregation in wireless sensor networks (WSNs) do not cover the variety of new use cases developing with the advent of the Internet of Things (IoT). In particular, the current push toward fog computing, in which control, computation, and storage are moved to nodes close to the network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms. Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks. We consider optimization of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes. Although aggregation only accounts for less than 15 of total energy usage in all cases tested, it provides substantial energy savings. Our results show more than 13 times greater energy usage for 40-node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions. Enforcing in code privacy laws, internal company rules and principles like Privacy by Design is recognized as a challenge for the IT industry. In this paper we analyze the steps required and propose a guide towards this major goal. Our proposal is to emphasize the need to overcome the limits of service orchestration and create strong privacy and security enabling architectures based on two main ideas. The first idea is to use a semantic firewall that is capable to check privacy properties for the communication between applications and cloud and between cloud's sub-systems. The second idea is to improve current SOA architectures with architectures based on executable choreographies that can be formally verified. In this paper we identify three types of executable choreographies. New types of abstraction which machines can verify and humans can trust are enabled by executable choreographies that act like truly verifiable environments for cloud applications. Due to their low cost and small form factors, a large number of sensor nodes can be deployed in redundant fashion in dense sensor networks. The availability of redundant nodes increases network lifetime as well as network fault tolerance. It is, however, undesirable to keep all the sensor nodes active at all times for sensing and communication. An excessive number of active nodes lead to higher energy consumption and it places more demand on the limited network bandwidth. We present an efficient technique for the selection of active sensor nodes in dense sensor networks. The active node selection procedure is aimed at providing the highest possible coverage of the sensor field, i.e., the surveillance area. It also assures network connectivity for routing and information dissemination. We first show that the coverage-centric active nodes selection problem is NP-complete. We then present a distributed approach based on the concept of a connected dominating set (CDS). We prove that the set of active nodes selected by our approach provides full coverage and connectivity. We also describe an optimal coverage-centric centralized approach based on integer linear programming. We present simulation results obtained using an ns2 implementation of the proposed technique. Given a set of sensor nodes V where each node wants to broadcast a message to all its neighbors that are within a certain broadcasting range, the local broadcasting problem is to schedule all these requests in as few timeslots as possible. In this paper, assuming the more realistic physical interference model and no knowledge of the topology, we present three distributed local broadcasting algorithms where the first one is for the asynchronized model and the other two are for the synchronized model. Under the asynchronized model, nodes may join the execution of the protocol at any time and do not have access to a global clock, for which we give a distributed randomized algorithm with approximation ratio O(log2 n). This improves the state-of-the-art result given in [14] by a logarithmic factor. For the synchronized model where communications among nodes are synchronous and nodes can perform physical carrier sensing, we propose two distributed deterministic local broadcasting algorithms for synchronous and asynchronous node wakeups, respectively. Both algorithms have approximation ratio O(log n). Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68 . Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances. This work addresses the problem of balancing the trade-off between the energy cost due to communication and the accuracy of the tracking-based trajectories’ detection and representation in Wireless Sensor Networks (WSNs) settings. We consider some of the approaches used by the Moving Objects Databases (MOD) and Computational Geometry (CG) communities, and we demonstrate that with appropriate adaptation, they can yield significant benefits in terms of energy savings and, consequently, lifetime of a given WSN. Towards that, we developed distributed variations of three approaches for spatio-temporal data reduction – two heuristics (Dead-Reckoning and the Douglas-Peuker algorithm), and a variant of a CG-based optimal algorithm for polyline reduction. In addition, we examine different policies for managing the buffer used by the individual tracking nodes for storing the partial trajectory data. Lastly, we investigated the potential benefits of combining the different data-reduction approaches into ”hybrid” ones during tracking of a particular object’s trajectory. Our experiments demonstrate that the proposed methodologies can significantly reduce the network-wide energy expenses due to communication and increase the network lifetime. A large fraction of today's Internet applications are internally publish subscribe in nature; the current architecture makes it cumbersome and inept to support them. In essence, supporting efficient publish subscribe requires data-oriented naming, efficient multicast, and in-network caching. Deployment of native IP-based multicast has failed, and overlay-based multicast systems are inherently inefficient. We surmise that scalable and efficient publish subscribe will require substantial architectural changes, such as moving from endpoint-oriented systems to information-centric architectures. In this paper, we propose a novel multicast forwarding fabric, suitable for large-scale topic-based publish subscribe. Due to very simple forwarding decisions and small forwarding tables, the fabric may be more energy efficient than the currently used ones. To understand the limitations and potential, we provide efficiency and scalability analysis via simulations and early measurements from our two implementations. We show that the system scales up to metropolitan WAN sizes, and we discuss how to interconnect separate networks.