arXiv subjects
Publications and source records attributed to Charith Perera.
Sensing as a Service (S2aaS) model [1] [2] is inspired by the traditional Everything as a service (XaaS) approaches [3]. It aims to better utilize the existing Internet of Things (IoT) infrastructure. S2aaS vision aims to create 'rentable infrastructure' where interested parties can gather IoT data by paying a fee for the infrastructure owners.
Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.
The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.
This document presents 30 different privacy guidelines that can be used to both design and assess IoT applications and IoT middleware platforms. These guidelines can be broadly categorised into eight categories, namely, MINIMIZE ( ), HIDE ( ), SEPARATE ( ), AGGREGATE ( ), INFORM ( ), CONTROL ( ), ENFORCE ( ), DEMONSTRATE ( ). This document uses the following structure to introduce the each privacy guidelines. First, we describe the philosophy behind each guideline in general. Then, we present the questions that software architects need to think about when designing or assessing an IoT platform or application. The questions slightly vary depending on whether the architect is assessing a platform or an application.
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, specially 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 an sustainable IoT infrastructure for smart cities. In this paper, 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 towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
Location sensing is a key enabling technology for Ubicomp to support contextual interaction. However, the laboratories where calibrated testing of location technologies is done are very different to the domestic situations where `context' is a problematic social construct. This study reports measurements of Bluetooth beacons, informed by laboratory studies, but done in diverse domestic settings. The design of these surveys has been motivated by the natural environment implied in the Bluetooth beacon standards - relating the technical environment of the beacon to the function of spaces within the home. This research method can be considered as a situated, `ethnographic' technical response to the study of physical infrastructure that arises through social processes. The results offer insights for the future design of `seamful' approaches to indoor location sensing, and to the ways that context might be constructed and interpreted in a seamful manner.
The Internet of Things (IoT) [1] envisions the creation of an environment where everyday objects (e.g. microwaves, fridges, cars, coffee machines, etc.) are connected to the internet and make users' lives more convenient. It will also lead users to consume resources more efficiently.
Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.
The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Pareto-optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.
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.
The Internet of Things (IoT) 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.
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.
Internet of Things (IoT) and cloud computing together give us the ability to sense, collect, process, and analyse data so we can use them to better understand behaviours, habits, preferences and life patterns of users and lead them to consume resources more efficiently. In such knowledge discovery activities, privacy becomes a significant challenge due to the extremely personal nature of the knowledge that can be derived from the data and the potential risks involved. Therefore, understanding the privacy expectations and preferences of stakeholders is an important task in the IoT domain. In this paper, we review how privacy knowledge has been modelled and used in the past in different domains. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT. Finally, we discuss major research challenges and opportunities.
Recently, the number of devices has grown increasingly and it is hoped that, between 2015 and 2016, 20 billion devices will be connected to the Internet and this market will move around 91.5 billion dollars. The Internet of Things (IoT) is composed of small sensors and actuators embedded in objects with Internet access and will play a key role in solving many challenges faced in today's society. However, the real capacity of IoT concepts is constrained as the current sensor networks usually do not exchange information with other sources. In this paper, we propose the Visual Search for Internet of Things (ViSIoT) platform to help technical and non-technical users to discover and use sensors as a service for different application purposes. As a proof of concept, a real case study is used to generate weather condition reports to support rheumatism patients. This case study was executed in a working prototype and a performance evaluation is presented.
A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads.
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, i.e. 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 paper 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.
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 has 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 C-MOSDEN ( Context-aware Mobile Sensor Data ENgine) 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 is used to highlight the advantages of context-aware selective sensing.
The idea of an open data market envisions 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 behaviours of data owners and to generate additional business value using different 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 paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.