Federico Montori

An interoperable architecture for mobile smart services over the internet of energy

The Internet of Energy (IoE) for Electric Mobility is an European research project that aims at deploying a communication infrastructure to facilitate and support the operations of Electric Vehicles (EVs). In this paper, we present three research contributions of IoE. First, we describe a software architecture to support the deployment of mobile and smart services over an Electric Mobility (EM) scenario. The proposed architecture relies on an ontology-based data representation, on a shared repository of information (Service Information Broker), and on software modules (called Knowledge Processors -KPs) for standardized data access/management. As a result, information sharing among the different stakeholders of the EM scenario (i.e. EVs, EVSEs, City Services, etc) is enabled, and the interoperability of smart services offered by heterogeneous providers is guaranteed by the common ontology. Second, we rely on the proposed architecture to develop a remote charging reservation system, that runs on top of mobile smarthphones, and allows drivers to monitor the current state-of-charge of their EV, and to reserve a charging slot at a specific EVSE. Finally, we validate our architecture through a benchmark framework, that supports the embedding of mobile EV applications and of real KPs into a simulated vehicular scenario, including realistic traffic, wireless communication and battery models. Evaluation results confirm the scalability of our architecture, and the ability to support EVs charging operations on a large-scale scenario (i.e. the downtown of Bologna).

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches.

A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring

The collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as smart cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such Internet of Things (IoT) scenarios need in order to be pervasive. In this paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, mobile crowdsensing management for environmental data, information representation, and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

On the integration of heterogeneous data sources for the collaborative Internet of Things

The Internet of things is foreseen as one of the next imminent Internet revolutions, as many devices will seamlessly communicate together to provide new and exciting services to the end users. One of the challenges that the IoT has to face is about both the heterogeneity of the data available and the heterogeneity of the communication. In this paper we focus on the former, by presenting an architecture able to integrate data coming from different sources, including custom made deployments and government data. New services can be deployed directly by the end users, using reliable or unreliable data sources, and new processed data can be gathered by these services and used by others.

SenSquare: A mobile crowdsensing architecture for smart cities

In the recent years the Smart City paradigm has gained interest worldwide. Services are built on top of data sensed in the city and then analyzed in order to enhance peoples quality of life. Nowadays users are also able to participate in such a data gathering, mostly thanks to a reduction in the cost of the sensing devices. Moreover, smartphones encompass many useful sensors and can be leveraged to obtain data by end users on the move, within the scope of mobile crowdsensing. In this paper, we propose SenSquare, a mobile crowdsensing architecture for Smart Cities, built to embrace both data availability and devices heterogeneity. SenSquare also offers the possibility for stakeholders to reward users sharing their data. Finally, we compare our proposal against a non-smart ideal architecture, showing the benefits and the advantages of a smart architecture such as SenSquare.

Distributed Data Collection Control in Opportunistic Mobile Crowdsensing

Many researchers have nowadays shown a paramount interest in the rising field of Mobile Crowdsensing (MCS). Such paradigm is considered an easy and cost-effective choice for observing phenomena of common interest within the scope of Smart Cities and environmental monitoring. Nevertheless, it brings along many issues, such as fostering participation, reducing the power consumption of end devices and granting coverage. In this paper we focus on the problem of data collection control, which aims to avoid data redundancy and useless power consuming data transfers while assuring a sufficient number of observations for the purpose of coverage. In particular, we design a probabilistic distributed algorithm that aims to achieve a total per-zone number of observations close to a defined amount, while maximizing the fairness among users. We provide both the analytical definition of our algorithm and the performance evaluation through extensive simulations, establishing our algorithm as a good baseline for a poorly investigated problem.

Is WiFi suitable for energy efficient IoT deployments? A performance study

Nowadays billions of connected objects are publishing sensed data everyday and this number is expected to grow exponentially. In many cases IoT objects are battery powered and need to be energy efficient as the most important requirement in order to reduce battery replacement costs. Conversely, WiFi is still the predominant wireless technology, deployed in almost all everyday life environments and thus the easiest network type on top of which build an IoT ecosystem. In this paper we analyze the energy efficiency of constrained devices using WiFi, which is certainly a widely accepted technology, although not specifically designed for constrained devices. To perform our test, we use the ESP-12 SoC, which gained interest recently due to its low cost and capabilities. We test its performance by studying the battery duration of such device under different connectivity conditions, varying its authentication policy, its battery type and its duty cycle. We report results from laboratory tests and show that such devices can be an efficient compromise for low-cost low-energy scenarios using WiFi.

Classification and Annotation of Open Internet of Things Datastreams

The Internet of Things (IoT) is springboarding novel applications and has led to the generation of massive amounts of data that can offer valuable insights across multiple domains: Smart Cities, environmental monitoring, healthcare etc. In particular, the availability of open IoT data streaming from heterogeneous sources constitute a novel powerful knowledge base. However, due to the inherent distributed, heterogeneous and open nature of such data, metadata that describe the data is generally lacking. This happens especially in contexts where IoT data is contributed by users via cloud-based open data platforms, in which even the information about the type of data measured is often missing. Since metadata is of paramount importance for data reuse, there is a need to develop intelligent techniques that can perform automatic annotation of heterogeneous IoT datastreams. In this paper, we propose two novel IoT datastream classification algorithms: CBOS and TKSE for the task of metadata annotation. We validate our proposed techniques through extensive experiments using public IoT datasets and comparing the outcomes with state-of-the-art classification methods. Results show that our techniques bring significant improvements to classification accuracy.

Machine-to-machine wireless communication technologies for the Internet of Things: Taxonomy, comparison and open issues

Abstract Machine-to-Machine (M2M) communication technologies enable autonomous networking among devices without human intervention. Such autonomous control is of paramount importance for several deployments of the Internet of Things (IoT), including smart manufacturing applications, healthcare systems and home automation just to name a few. As a result, several M2M technologies are nowadays available on the market as either proprietary solutions or the effort of standardization initiatives, each targeted for a specific class of IoT applications and characterized by unique features in terms of achievable performance, frequency in use and supported network topologies. In this paper, we aim to organize the existing M2M approaches and technologies into a consistent framework that provides an in-depth vision of the main trends, future directions and open issues. We provide three main contributions in this survey. First, we identify the main use cases and requirements of M2M scenarios and we introduce a multi-layer taxonomy for M2M solutions, taking into account both deployment types and PHY/MAC characteristics. Second, in light of such characteristics, we provide an in-depth review of the existing M2M wireless technologies, considering both proprietary and open/standardized solutions for proximity-based, short-range and large-scale networks. Finally, we perform a critical comparison of the surveyed solutions over different M2M use cases and requirements, and we identify the research directions and open issues that still have to be addressed.

The Curse of Sensing: Survey of techniques and challenges to cope with sparse and dense data in mobile crowd sensing for Internet of Things

Abstract In this paper we present a survey on mobile crowdsensing (MCS) techniques that have been developed to address the Curse of Sensing problem i.e. propensity of MCS applications to generate sparse or dense data that can lead to significant gaps in the extracted knowledge. In order to do so, we identify features, based on the terminologies used in the literature, in order to develop a clear classifications among MCS and crowdsourcing applications and methods. Subsequently, we propose a taxonomy outlining both factors and objectives that need to be considered in designing MCS systems and have a direct impact on MCS applications’ tendency to fall into the Curse of Sensing. We then evaluate the majority of the research proposed in the field of MCS and addressing the Curse of Sensing problem with reference to the proposed taxonomy. Finally, we highlight the existing gaps in the literature and possible directions for future research.