事情(IoT ) 的因特网针对启用物理世界和电子空间的互联和集成。它代表未来联网的趋势，并且带信息产业革命的第三个波浪。在这篇文章，我们首先介绍某背景和 IoT 的相关技术并且讨论 IoT 的概念和目的。然后，我们在场挑战和关键科学问题在 IoT 包含了开发。而且，我们介绍中国(973 程序) 的国家基本研究程序支持的当前的研究工程。最后，我们构画出未来研究方向。

Internet of Things： Objectives and Scientific Challenges

The Internet of Things (IoT) is the latest example of the System of Systems (SoS), demanding for both innovative and evolutionary approaches to tame its multifaceted aspects. Over the years, different IoT methodologies, frameworks, platforms, and tools have been proposed by industry and academia, but the jumbled abundance of such development products have resulted into a high (and disheartening) entry-barrier to IoT system engineering. In this survey, we steer IoT developers by: 1) providing baseline definitions to identify the most suitable class of development products—methodologies, frameworks, platforms, and tools–for their purposes and 2) reviewing seventy relevant products through a comparative and practical approach, based on general SoS engineering features revised in the light of main IoT systems desiderata (i.e., interoperability, scalability, smartness, and autonomy). Indeed, we aim to lessen the confusion related to IoT methodologies, frameworks, platforms, and tools as well as to freeze their current state, for eventually easing the approach towards IoT system engineering.

Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools

Software-intensive cyber-physical systems have to deal with massive numbers of components, featuring complex interactions among components and with humans and other systems. Often, they are designed to operate in open and non-deterministic environments, and to dynamically adapt to new requirements, technologies and external conditions. This class of systems has been named ensembles and new engineering techniques are needed to address the challenges of developing, integrating, and deploying them. In the paper, we briefly introduce SCEL (Software Component Ensemble Language), a kernel language that takes a holistic approach to programming autonomic computing systems and aims at providing programmers with a complete set of linguistic abstractions for programming the behavior of autonomic components and the formation of autonomic components ensembles, and for controlling the interaction among different components.

A formal approach to autonomic systems programming: the SCEL language.

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

The Internet of things (IoT) is rapidly growing, and many security issues relate to its wireless technology. These security issues are challenging because IoT protocols are heterogeneous, suit different needs, and are used in different application domains. From this assessment, we identify the need to provide a homogeneous formalism applying to every IoT protocols. In this survey, we describe a generic approach with twofold challenges. The first challenge we tackle is the identification of common principles to define a generic approach to compare IoT protocol stack. We base the comparison on five different criteria: the range, the openness of the protocol, the interoperability, the topology and the security practices of these IoT protocols. The second challenge we consider is to find a generic way to describe fundamental IoT attacks regardless of the protocol used. This approach exposes similar attacks amongst different IoT protocols and is divided into three parts: attacks focusing on packets (passive and active cryptographic attacks), attacks focusing on the protocol (MITM, Flooding, Sybil, Spoofing, Wormhole attacks) and attacks focusing on the whole system (Sinkhole, Selective forwarding attacks). It also highlights which mechanisms are different between two protocols to make both of them vulnerable to an attack. Finally, we draw some lessons and perspectives from this transversal study.

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A survey of IoT protocols and their security issues through the lens of a generic IoT stack

The international community has largely recognized that the Earth’s climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.

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A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

The Internet of Things (IoT) has a great potential to change our lives Billions of heterogeneous, distributed, intelligent, and sometimes mobile devices, will be connected and offer new types of applications and ways to interact The dynamic environment of the IoT, the involvement of the human in the loop, and the runtime interactions among devices and applications, put additional requirements on the systems' architecture In this paper, we use the Emergent Configurations (ECs) concept as a way to engineer IoT systems and propose an architecture for ECs More specifically, we discuss (i) how connected devices and applications form ECs to achieve users' goals and (ii) how applications are run and adapted in response to runtime context changes including, eg, the sudden unavailability of devices, by exploiting the Smart Meeting Room case

Architecting Emergent Configurations in the Internet of Things

During the last decade, a large number of different definitions and taxonomies of Internet of Things (IoT) systems have been proposed. This has resulted in a fragmented picture and a lack of consensus about IoT systems and their constituents. To provide a better understanding of this issue and a way forward, we have conducted a Systematic Mapping Study (SMS) of existing IoT System taxonomies. In addition, we propose a characterization of IoT systems synthesized from the existing taxonomies, which provides a more holistic view of IoT systems than previous taxonomies. It includes seventeen characteristics, divided into two groups: elements and quality aspects. Finally, by analyzing the results of the SMS, we draw future research directions.

Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study

Internet of Things (IoT) systems are becoming ubiquitous, and their spread has had a significant impact on all aspects of society. Software is a key aspect of IoT systems, from firmware to cloud infrastructures. For this reason, Software Engineering (SE) is crucial to design, develop, deploy, and maintain high-quality IoT systems. Despite the high relevance of these systems, by analysing the literature from a Software Engineering perspective little emerges about their key elements and characteristics, including qualities as perceived by experts working in the IoT field. For this reason, the aim of this work is to understand from the practice, the main characteristics of IoT systems to improve the SE support for their development. We carried out a survey and received 433 practitioners answers from 53 countries across the world. By analysing the collected data, we found that so far: (i) experts working on (industrial) IoT systems only acknowledge in practice some of the main elements and characteristics of IoT systems that can be found in the literature; (ii) most IoT systems require human intervention while advanced learning and self-adaption features are not widely adopted yet; (iii) Smart Industry, Smart City, Smart Building, and Smart Home are by far the most relevant IoT systems application domains; (iv) deployment choices of IoT systems largely favour the Cloud for what concerns the computation (and thus the software); and finally (v) the most relevant quality attributes for IoT systems are reliability, availability, performance, scalability, and security.

What are IoT systems for real? An experts’ survey on software engineering aspects

Engineering Internet of Things (IoT) systems is a challenging task partly due to the dynamicity and uncertainty of the environment including the involvement of the human in the loop. Users should be able to achieve their goals seamlessly in different environments, and IoT systems should be able to cope with dynamic changes. Several approaches have been proposed to enable the automated formation, enactment, and self-adaptation of goal-driven IoT systems. However, they do not address deployment issues. In this paper, we propose a goal-driven approach for deploying self-adaptive IoT systems in the Edge-Cloud continuum. Our approach supports the systems to cope with the dynamicity and uncertainty of the environment including changes in their deployment topologies, i.e., the deployment nodes and their interconnections. We describe the architecture and processes of the approach and the simulations that we conducted to validate its feasibility. The results of the simulations show that the approach scales well when generating and adapting the deployment topologies of goal-driven IoT systems in smart homes and smart buildings.

Fahed Alkhabbas

Papers

A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

Architecting Emergent Configurations in the Internet of Things

Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study

What are IoT systems for real? An experts’ survey on software engineering aspects

A Goal-Driven Approach for Deploying Self-Adaptive IoT Systems