Charith Perera

Big Data Privacy in the Internet of Things Era

Over the last few years, weve seen a plethora of Internet of Things (IoT) solutions, products, and services make their way into the industrys marketplace. All such solutions will capture large amounts of data pertaining to the environment as well as their users. The IoTs objective is to learn more and better serve system users. Some IoT solutions might store data locally on devices (things), whereas others might store it in the cloud. The real value of collecting data comes through data processing and aggregation on a large scale, where new knowledge can be extracted. However, such procedures can lead to user privacy issues. This article discusses some of the main challenges of privacy in the IoT as well as opportunities for research and innovation. The authors also introduce some of the ongoing research efforts that address IoT privacy issues.Over the last few years, we have seen a plethora of Internet of Things (IoT) solutions, products and services, making their way into the industrys market-place. All such solution will capture a large amount of data pertaining to the environment, as well as their users. The objective of the IoT is to learn more and to serve better the system users. Some of these solutions may store the data locally on the devices (‘things’), and others may store in the Cloud. The real value of collecting data comes through data processing and aggregation in large-scale where new knowledge can be extracted. However, such procedures can also lead to user privacy issues. This article discusses some of the main challenges of privacy in IoT, and opportunities for research and innovation. We also introduce some of the ongoing research efforts that address IoT privacy issues.

Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.

A knowledge-based resource discovery for Internet of Things

In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.

Fog Computing for Sustainable Smart Cities: A Survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things IoT has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.

Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms

The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart homes and smart cities. Thus far, privacy concerns have not been explicitly considered by IoT applications and middleware platforms. This is partly due to the lack of systematic methods for designing privacy that can guide the software development process in IoT. In this paper, we propose a set of guidelines, a privacy by-design framework, that can be used to assess privacy capabilities and gaps of existing IoT applications as well as middleware platforms. We have evaluated two open source IoT middleware platforms, namely OpenIoT and Eclipse SmartHome, to demonstrate how our framework can be used in this way.

Feed me, feed me: an exemplar for engineering adaptive software

The Internet of Things (IoT) promises to deliver improved quality of life for citizens, through pervasive connectivity and quantified monitoring of devices, people, and their environment. As such, the IoT presents a major new opportunity for research in adaptive software engineering. However, there are currently no shared exemplars that can support software engineering researchers to explore and potentially address the challenges of engineering adaptive software for the IoT, and to comparatively evaluate proposed solutions. In this paper, we present Feed me, Feed me, an exemplar that represents an IoT-based ecosystem to support food security at different levels of granularity: individuals, families, cities, and nations. We describe this exemplar using animated videos which highlight the requirements that have been informally observed to play a critical role in the success or failure of IoT-based software systems. These requirements are: security and privacy, interoperability, adaptation, and personalisation. To elicit a wide spectrum of user reactions, we created these animated videos based on the ContraVision empirical methodology, which specifically supports the elicitation of end-user requirements for controversial or futuristic technologies. Our deployment of ContraVision presented our pilot study subjects with an equal number of utopian and dystopian scenarios, derived from the food security domain, and described them at the different level of granularity. Our synthesis of the preliminary empirical findings suggests a number of key requirements and software engineering research challenges in this area. We offer these to the research community, together with a rich exemplar and associated scenarios available in both their textual form in the paper, and as a series of animated videos (http://sead1.open.ac.uk/fmfm/)

End-to-End Privacy for Open Big Data Markets

Establishing an open data market would require the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviors of data owners and to generate additional business value using techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This article discusses why privacy matters in the IoT domain in general and especially in open data markets, and then surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end-to-end privacy for open data markets. It also highlights some of the major research challenges that must be addressed to make the vision of open data markets a reality through ensuring the privacy of stakeholders.

Valorising the IoT databox : creating value for everyone

The Internet of Things is expected to generate large amounts of heterogeneous data from diverse sources including physical sensors, user devices and social media platforms. Over the last few years, significant attention has been focused on personal data, particularly data generated by smart wearable and smart home devices. Making personal data available for access and trade is expected to become a part of the data-driven digital economy. In this position paper, we review the research challenges in building personal Databoxes that hold personal data and enable data access by other parties and potentially thus sharing of data with other parties. These Databoxes are expected to become a core part of future data marketplaces. Copyright

Applying Seamful Design in Location-Based Mobile Museum Applications

The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of power-efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences. However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems. Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength. These issues are rendering a seamless functioning of any location-based application practically impossible. Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, that is, to reveal and exploit these problems and turn them into a part of the actual experience. In order to demonstrate the viability of this approach, we designed, implemented, and evaluated the Ghost Detector—an educational location-based museum game for children. By presenting a qualitative evaluation of this game and by motivating our design decisions, this article provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.