Internet of Things

Weber, Rolf H.; Weber, Romana

doi:10.1007/978-3-642-11710-7

Cited by 194 publications

(14 citation statements)

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“…This is consistent with the findings of Weber et al [12] who argue that transparency allows for a certain level of "democratic" legitimisation and predictability through active involvement of citizens as well as through certain control over the decision-making process. Bandara et al [1] proposed a first semantic model for describing devices.…”

Section: Provenance Of Iot Devicessupporting

confidence: 81%

What does this device do?

Pignotti¹,

Beran²,

Edwards³

2014

Proceedings of the the First International Conference on IoT in Urban Space

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This paper describes the Trusted Tiny Things project which is investigating some of the challenges inherit in making the Internet of Things (IoT) more transparent to users. We present a semantic framework for reasoning about the capabilities of IoT devices based on provenance information collected from devices and their associated services. As part of this framework we have developed a semantic model, services and a smartphone app to represent, store and query IoT provenance. The semantic model and app was informed via a series of participatory design activities with users. In this paper we discuss the use of the system with two distinct IoT devices: an NFC tag used at bus stops to provide a means to access real-time bus timetables, and a blackbox device installed into vehicles by insurance companies to track driving behaviour.

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Section: Provenance Of Iot Devicessupporting

confidence: 81%

What does this device do?

Pignotti¹,

Beran²,

Edwards³

2014

Proceedings of the the First International Conference on IoT in Urban Space

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“…A new generation of products, systems and services aims at changing people's behaviour, following and creating trends of quantified self [4], wearables [5] and Internet of Things (IoT) [6]. These new elements are always connected and present in our everyday lives and rely on increasingly more intelligent interactions to make our lives better.…”

Section: Related Workmentioning

confidence: 99%

Data-Enabled Design for Social Change: Two Case Studies

2016

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Smartness in contemporary society implies the use of massive data to improve the experience of people with connected services and products. The use of big data to collect information about people's behaviours opens a new concept of "user-centred design" where users are remotely monitored, observed and profiled. In this paradigm, users are considered as sources of information and their participation in the design process is limited to a role of data generators. There is a need to identify methodologies that actively involve people and communities at the core of ecosystems of interconnected products and services. Our contribution to designing for social innovation in ecosystems relies on developing new methods and approaches to transform data-driven design using a participatory and co-creative data-enabled design approach. To this end, we present one of the methods we have developed to design "smart" systems called Experiential Design Landscapes (EDL), and two sample projects, Social Stairs and [Y]our Perspective. Social Stairs faces the topic of behaviour change mediated by sensing technologies.[Y]our Perspective is a social platform to sustain processes of deliberative democracy. Both projects exemplify our approach to data-enabled design as a social proactive participatory design approach.

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“…IDC (International Data Corporation) predicts that the digital universe is set to explode to an unimaginable 8 Zettabytes by 2015. It can be declared that we are in the era of "Big Data" which is accelerated by the Internet of Things (IoT) [3]. Such "Data Explosions" have led to the next grand challenge in computing known as the 'Big Data' problem [4][5][6][7], which is defined as the practice of collecting and analysing structured and unstructured data sets flowing at a volume and velocity that is too large and too fast to store, process, and interpret manually or using traditional data management applications.…”

Section: Introductionmentioning

confidence: 99%