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.
Shrouf and Miragliotta @cite_2 report on different approaches for energy management enabled by Internet of Things (IoT) technologies. Based on literature, expert interviews, and reports of manufactures, they summarize different IoT architectures for power monitoring and present a general abstraction of them. The resulting architecture primarily focuses on network interconnections and integration of other systems. As in our approach, it respects real-time data processing and the challenge of integrating data of different sensors and data formats. However, data is only processed in a cloud or local server infrastructure and does not follow fog computing paradigms. The architecture represents a general approach and is therefore too abstract to offer a reference implementation.
@cite_2
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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 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. 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. In the near future, all our everyday things and objects will be both connected to the Internet and equipped with enough sensing, acting and processing capabilities to exploit the full potential benefits of the so called Internet of Things (IoT) paradigm. Even the simplest objects will become smart because they will be interconnected to other objects to share and collect data from the environments in which they are placed thus paving the way to novel application services, computing and communication scenarios.In this context, interoperability among different standards and communication technologies is still a significant challenge that we have started to address by proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet. The presented unified, high-level and extendible software architecture supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.A specific testbed on common smartphones with different hardware and software capabilities was deployed to evaluate the real feasibility of the designed solution measuring the system performance in terms of energy consumption, memory and CPU usage in high and low load scenarios. According to the obtained results, the implemented software architecture for multi-standard and multi-technology interoperation presents a reduced use of hardware resources in front of a relatively high energy consumption value, mostly due to the simultaneously active radio interfaces combined with a small battery capacity, that limits the smartphone lifetime. Nevertheless, the presented general approach is still remarkable because this latter aspect will most likely be exceeded, in a short time, thanks to daily technological advancements in both batteries and radio interfaces. 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. 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. 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. Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account. 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 an Internet of Things (IoT) platform for a smart building which provides human care services for occupants. The individual health profiles of the occupants are acquired by the IoT-based smart building, which uses the accumulated knowledge of the occupants to provide better services. To ensure the thermal comfort of the occupants inside the building, we propose a dynamic thermal model of occupants. This model is based on the heat balance equation of human body and thermal characteristics of the occupants. We implement this model in two smart building models with heaters controlled by a temperature and thermal comfort index using MATLAB Simulink®. The simulation results show that the thermal comfort-based control is more effective to maintaining occupants’ thermal satisfaction and is therefore recommended for use providing human care services using IoT platforms in smart buildings. To smartly control the massive electrical appliances in buildings to save energy, the real-time on off states of the electrical appliances are critically required as the fundamental information. However, it is generally a very difficult and costly problem, because N appliances have 2N states and the appliances are massively in modern buildings. This paper propose a novel compressive sensing model for monitoring the massive appliances' states, in which the sparseness of on off switching events within a short observation interval is exploited. Based on such a temporal sparseness feature, a lightweight state tracking framework is proposed to track the on off states of N appliances by deploying only m smart meters on the power load tree, where m L N. Particularly, it firstly presents an online state decoding algorithm based on a Hidden Markov Model of sparse state transitions. It reduces the traditional O(t22N) complexity of Viterbi decoding to polynomial complexity of O(tnU+1) where n 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. By bridging the physical and the virtual worlds, the Internet of Things (IoT) impacts a multitude of application domains, among which smart cities, smart factories, resource management, intelligent transportation, health and well-being to name a few. However, leveraging the IoT within software applications raises tremendous challenges from the networking up to the application layers, in particular due to the ultra-large scale, the extreme heterogeneity and the dynamics of the IoT. This paper more specifically explores how the service-oriented architecture paradigm may be revisited to address challenges posed by the IoT for the development of distributed applications. Drawing from our past and ongoing work within the MiMove team at Inria Paris, the paper discusses the evolution of the supporting middleware solutions spanning the introduction of: probabilistic protocols to face scale, cross-paradigm interactions to face heterogeneity, and streaming-based interactions to support the inherent sensing functionality brought in by the IoT. The Internet of Things (IoT) sparks a whole new world of embedded applications. Most of these applications are based on deeply embedded systems that have to operate on limited or unreliable sources of energy, such as batteries or energy harvesters. Meeting the energy requirements for such applications is a hard challenge, which threatens the future growth of the IoT. Software has the ultimate control over hardware. Therefore, its role is significant in optimizing the energy consumption of a system. Currently, programmers have no feedback on how their software affects the energy consumption of a system. Such feedback can be enabled by energy transparency, a concept that makes a program’s energy consumption visible, from hardware to software. This letter discusses the need for energy transparency in software development and emphasizes on how such transparency can be realized to help tackle the IoT energy challenge. The focus of the Internet has recently shifted from current computers and mobile devices to everyday objects, people, and places; consequently, the Internet of Things (IoT) promises to be not only a compelling vision but the actual driving force of the upcoming fourth Industrial Revolution. Novel cyber-physical, customized, and highly pervasive services are impacting our lives, involving several stakeholders and fostering an unseen globally interconnected ecosystem. However, IoT system development is a multifaceted process that’s complex, error-prone, and time-consuming. Although modeling and simulation are crucial aspects that could effectively support IoT system development, an integrated approach synergistically providing both of them is lacking. The authors propose a hybrid approach that uses agents for IoT modeling and OMNeT for simulation, providing mapping guidelines between the agent paradigm and the OMNeT simulator abstractions. The proposed approach has been applied in small-, medium-, and large-scale IoT scenarios, where relevant performance indexes of IoT entities communication have been measured and analyzed. Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge. The transformation of traditional energy networks to smart grids can assist in revolutionizing the energy industry in terms of reliability, performance and manageability. However, increased connectivity of power grid assets for bidirectional communications presents severe security vulnerabilities. In this letter, we investigate Chi-square detector and cosine similarity matching approaches for attack detection in smart grids where Kalman filter estimation is used to measure any deviation from actual measurements. The cosine similarity matching approach is found to be robust for detecting false data injection attacks as well as other attacks in the smart grids. Once the attack is detected, system can take preventive action and alarm the manager to take preventative action to limit the risk. Numerical results obtained from simulations corroborate our theoretical analysis. In the Urban Internet of Things (IoT), devices and systems are interconnected at the city scale to provide innovative services to the citizens. However, the traffic generated by the sensing and processing systems may overload local access networks. A coexistence problem arises where concurrent applications mutually interfere and compete for available resources. This effect is further aggravated by the multiple scales involved and heterogeneity of the networks supporting the urban IoT. One of the main contributions of this paper is the introduction of the notion of content- oriented cognitive interference control in heterogeneous local access networks supporting computing and data processing in the urban IoT. A network scenario where multiple communication technologies, such as Device-to- Device and Long Term Evolution (LTE), is considered. The focus of the present paper is on city monitoring applications, where a video data stream generated by a camera system is remotely processed to detect objects. The cognitive network paradigm is extended to dynamically shape the interference pattern generated by concurrent data streams and induce a packet loss trajectory compatible with video processing algorithms. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of interfering applications for a target accuracy of the monitoring application. The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage, physical, environmental and human systems in real-time. The inherent closedloop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are becoming the vital engine for real-time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as reusable micro-benchmarks. Further, we propose four IoT application benchmarks composed from these tasks, and that leverage various dataflow semantics of DSPS. The applications are based on common IoT patterns for data pre-processing, statistical summarization and predictive analytics. These are coupled with four stream workloads sourced from real IoT observations on smart cities and fitness, with peak streams rates that range from 500 to 10000 messages sec and diverse frequency distributions. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public Cloud, and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, and by IoT practitioners to evaluate DSPS platforms. Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.