Interdisciplinary cross-cultural and cross-organizational research offers great opportunities for innovative breakthroughs in the field of smart cities, yet it also presents organizational and knowledge development hurdles. Smart cities must be large towns able to sustain the needs of their citizens while promoting environmental sustainability. Smart cities foment the widespread use of novel information and communication technologies (ICTs); however, experimenting with these technologies in such a large geographical area is unfeasible. Consequently, smart campuses (SCs), which are universities where technological devices and applications create new experiences or services and facilitate operational efficiency, allow experimentation on a smaller scale, the concept of SCs as a testbed for a smart city is gaining momentum in the research community. Nevertheless, while universities acknowledge the academic role of a smart and sustainable approach to higher education, campus life and other student activities remain a mystery, which have never been universally solved. This paper proposes a SC concept to investigate the integration of building information modeling tools with Internet of Things- (IoT)-based wireless sensor networks in the fields of environmental monitoring and emotion detection to provide insights into the level of comfort. Additionally, it explores the ability of universities to contribute to local sustainability projects by sharing knowledge and experience across a multi-disciplinary team. Preliminary results highlight the significance of monitoring workspaces because productivity has been proven to be directly influenced by environment parameters. The comfort-monitoring infrastructure could also be reused to monitor physical parameters from educational premises to increase energy efficiency.

The creation of prosumer communities leveraging the Smart Grid domain to improve energy production and consumption patterns has been proposed in the literature. We propose to use a novel iot paradigm, namely the siot, to enable appliance-level metering (sub-metering) and to create prosumer communities dynamically. We use the term sioe to refer to the integration between prosumers and devices via social relationships. This paper explores the diverse technologies that will enable the sioe. On the one hand, we explore the different aspects and value propositions of the siot, applying this novel paradigm to the creation of prosumer communities. On the other hand, we explore dsm, its synergy with siot and the properties and challenges that may arise. Concretely, in this paper we aim at grouping prosumers by their energy load shape compatibility and then reschedule intra-group load shapes. We propose a clustering model and we develop two clustering heuristics to distribute prosumers among pcg and one par prediction heuristic in order to reduce the par and the amplitude of energy peaks per cluster. Then, on the basis of a real dataset, we perform simulations and analyze and discuss the results, paving the way for further research.

Sensor networks and the Internet of Things have driven the evolution of traditional electric power distribution networks towards a new paradigm referred to as Smart Grid. However, the different elements that compose the Information and Communication Technologies (ICTs) layer of a Smart Grid are usually conceived as isolated systems that typically result in rigid hardware architectures which are hard to interoperate, manage, and to adapt to new situations. If the Smart Grid paradigm has to be presented as a solution to the demand for distributed and intelligent energy management system, it is necessary to deploy innovative IT infrastructures to support these smart functions. One of the main issues of Smart Grids is the heterogeneity of communication protocols used by the smart sensor devices that integrate them. The use of the concept of the Web of Things is proposed in this work to tackle this problem. More specifically, the implementation of a Smart Grid’s Web of Things, coined as the Web of Energy is introduced. The purpose of this paper is to propose the usage of Web of Energy by means of the Actor Model paradigm to address the latent deployment and management limitations of Smart Grids. Smart Grid designers can use the Actor Model as a design model for an infrastructure that supports the intelligent functions demanded and is capable of grouping and converting the heterogeneity of traditional infrastructures into the homogeneity feature of the Web of Things. Conducted experimentations endorse the feasibility of this solution and encourage practitioners to point their efforts in this direction.

The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.

Contrary to the rapid evolution experienced in the last decade of Information and Communication Technologies and particularly the Internet of Things, electric power distribution systems have remained exceptionally steady for a long time. Energy users are no longer passive actors; the prosumer is expected to be the primary agent in the Future Grid. Demand Side Management refers to the management of energy production and consumption at the demand side, and there seems to be an increasing concern about the scalability of Demand Side Management services. The creation of prosumer communities leveraging the Smart Grid to improve energy production and consumption patterns has been proposed in the literature, and several works concerned with scalability of Demand Side Management services group prosumers to improve Demand Side Management services scalability. In our previous work, we coin the term Social Internet of Energy to refer to the integration between devices, prosumers and groups of prosumers via social relationships. In this work, we develop an algorithm to coordinate the different clusters we create using the clustering method by load profile compatibility (instead of similarity). Our objective is to explore the possibilities of the cluster-by-compatibility heuristic we proposed in our previous work. We perform experiments using synthetic and real datasets. Results show that we can obtain a global reduction in Peak-to-Average Ratio with datasets containing up to 200 rosumers and creating up to 6 Prosumer Community Groups, and imply that those Prosumer Community Groups can perform load rescheduling semi-autonomously and in parallel with each other.