Catálogo de publicaciones - libros

The article describes the design of an Internet of Things based platform having as main objective the real-time management of energy consumption in water resource recovery facilities and their integration in a future demand side management environment. The monitoring of several electrical parameters, including energy consumption, is done via a dedicated energy meter, whose design is detailed in the article. The high level data communication from the energy meters to a central platform of the wastewater utility is done via the MQTT protocol. Within the water resource recovery facility, the access network is based either on Wi-Fi or LoRa, which are two enabling technologies for the Internet of Things. The meters are deployed in pilot demonstrators located in two water resource recovery facilities in Lisbon, Portugal.

Traditional reconfigurable devices known as FPGAs utilize a complicated programmable routing network to provide flexibility in connecting different logic elements across the FPGA chip. As such, the routing procedure may become very complicated, especially in the presence of tight timing constraints. Moreover, the routing network itself occupies a large portion of chip area as well as consumes a lot of power. Therefore, limiting their usage in mobile applications or IoT devices with higher performance and lower energy demands. In this paper, we introduce a new reconfigurable architecture which only allows communication between neighboring logic elements. This way, the routing structure and the routing resources become much simpler than traditional FPGAs. Moreover, we present two different method for scheduling and routing in our new proposed architecture. The first method deals with general circuits or irregular computations and is based on integer linear programming. The second method is for regular computations such as convolutional neural networks or matrix operations. We have shown the mapping results on ISCAS benchmark circuits as general irregular computations as well as heuristics to improve the efficiency of mapping for larger benchmarks. Moreover, we have shown results on regular computations including matrix multiplication and convolution operations of neural networks.

A growing number of sensors, embedded in wearables, smart electric meters and other connected devices, is surrounding us and reaching ever deeper into our private lives. While some sensors are commonly regarded as privacy-sensitive and always require user permission to be activated, others are less protected and less worried about. However, experimental research findings indicate that many seemingly innocuous sensors can be exploited to infer highly sensitive information about people in their vicinity. This paper reviews existing evidence from the literature and discusses potential implications for consumer privacy. Specifically, the analysis reveals that certain insufficiently protected sensors in smart devices allow inferences about users’ locations, activities and real identities, as well as about their keyboard and touchscreen inputs. The presented findings call into question the adequacy of current sensor access policies. It is argued that most data captured by smart consumer devices should be classified as highly sensitive by default. An introductory overview of sensors commonly found in these devices is also provided, along with a proposed classification scheme.

The emergence of the Internet of Things (IoT) has brought new improvement and development opportunities to the automotive industry, such as electric vehicles (EVs). EVs are well-known for their short ranges and many studies have reported on the challenges of trip planning and accurate remaining driving range (RDR) estimation. While the demand for connected vehicle applications and its enabling technology has progressed significantly in recent years, there are several constraints for connected and collaborative vehicle application deployments. Data integration issues are currently hindering the development of effective trip planning and RDR estimation solutions for drivers of EVs. Additional constraints have been identified in developing countries, including lack of charging station networks, EV data sources, and software applications. The purpose of this paper is to report on some of the main issues hindering EV data integration, as well as to report on an implementation in South Africa of a Data Integration Platform for EV data using the IoT. The findings show that data integration issues primarily relate to data availability, data quality, and interoperability between devices, IoT platforms, and EV service providers. The paper also identifies enabling technologies, drivers, and future directions for researchers in the IoT and EV domains.

As the Internet of Things (IoT) continues to grow in scope it is bound to pervade an increasing number of firms. To that end it becomes important to understand the challenges and opportunities associated with introducing IoT in the workplace. By studying IoT implementation and usage from the perspective of three different stakeholders cooperating around the same IoT system, we explore how the introduction of IoT in the workplace presents unique opportunities and challenges for both management and individual workers. We conclude that the identified opportunities expressed by the different stakeholders were increased productivity, the ability to monitor performance, and improved customer relations. Challenges encountered were increased stress among some workers and forming a shared understanding of the IoT system’s capabilities amongst different stakeholders.

This paper analyses the water, sanitation and hygiene (WASH) context in South Africa and presents views regarding the possible use of the Internet of Things (IoT) for WASH in South Africa. The views were informed by an analysis of literature related to the WASH domain nationally and internationally. Example case studies of the use of IoT in the WASH sector in developing countries, and where possible Africa, provided further background. As a triangulation exercise, a survey amongst various stakeholders in the WASH domain was conducted. To contextualise the WASH sector in South Africa, value chains were derived. To identity the opportunities for IoT, the findings of the study were combined with insights acquired on the IoT domain in general, both from literature and our experience and learnings.

The present study considered the current state of internet of things in Nigeria, future prospects and challenges to the usage of the technology in Nigerian Agriculture. In Nigeria, IoT has been used to dispense feed and water to chicks, virtual fences for monitoring farmlands and forest trees, cashless sales and purchases of farm produce and input, monitoring and management of staff performances on the farm and e-wallet for input, loan and information accessibility on agricultural issues. However, there is room for improvement in the area of security for the animals (animal tracking), weather forecasting and real-time soil monitoring, livestock and crop health surveillance. Challenges faced in the usage of IoT in Nigeria are inadequate/lack of capital, skilled manpower, facilities. In conclusion, IoT has great potentials to move Nigerian agriculture to an enviable position.

We present IoTutor that is a cognitive computing solution for education of students in the IoT domain. We implement the IoTutor as a platform-independent web-based application that is able to interact with users via text or speech using natural language. We train the IoTutor with selected scientific publications relevant to the IoT education. To investigate users’ experience with the IoTutor, we ask a group of students taking an IoT master level course at the Linnaeus University to use the IoTutor for a period of two weeks. We ask students to express their opinions with respect to the attractiveness, perspicuity, efficiency, stimulation, and novelty of the IoTutor. The evaluation results show a trend that students express an overall positive attitude towards the IoTutor with majority of the aspects rated higher than the neutral value.