Report on the First International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT 2014)

International audienceThe 1st International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT), held in conjunction with VLDB 2014, aims at sparking research on data analytics, shifting the focus from business to consumers services. While much of the public and academic discourse about personal data has been dominated by a focus on the privacy concerns and the risks they raise to the individual, especially when they are seen as the new oil of the global economy. PDA@IOT focus on how persons could effectively exploit the data they massively create in CyberPhysicalworlds. We believe that the full potential of the IoT goes far beyond connecting “things” to the Internet: it is about using data to create new value for people. In a People-centric computing paradigm, both small scalepersonal data and large scale aggregated data should be exploited to identify unmet needs and proactively offerthem to users. PDA@IOT seeks to address current technology barriers that impede existing personal dataprocessing and analytics solutions to empower people in personal decision making.The PDA@IOT ambition is to provide a unique forum for researchers and practitioners that approach personal data from different angles, ranging from data management and processing, to data mining and human-data interaction, as well as to nourish the interdisciplinary synergies required to tackle the challenges and problems emerging in People-centric Computing

Big Data Analytics in Healthcare Internet of Things

Nowadays, wearable medical devices play a vital role in many environments such as continuous health monitoring of individuals, road traffic management, weather forecasting, and smart home. These sensor devices continually generate a huge amount of data and stored in cloud computing. This chapter proposes Internet of Things (IoT) architecture to store and process scalable sensor data (big data) for healthcare applications. Proposed architecture consists of two main sub-architecture, namely, MetaFog-Redirection (MF-R) and Grouping & Choosing (GC) architecture. Though cloud computing provides scalable data storage, it needs to be processed by an efficient computing platforms. There is a need for scalable algorithms to process the huge sensor data and identify the useful patterns. In order to overcome this issue, this chapter proposes a scalable MapReduce-based logistic regression to process such huge amount of sensor data. Apache Mahout consists of scalable logistic regression to process large data in distributed manner. This chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor data generated by the wearable medical devices

Personal Data Stores (PDS): A Review

Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms’ development and widespread adoption

Consensus-Based Data Management within Fog Computing For the Internet of Things

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality

Data science on industrial data -- Today's challenges in brown field applications

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls

Smartphone: The Ultimate IoT and IoE Device

Internet of Things (IoT) and Internet of Everything (IoE) are emerging communication concepts that will interconnect a variety of devices (including smartphones, home appliances, sensors, and other network devices), people, data, and processes and allow them to communicate with each other seamlessly. These new concepts can be applied in many application domains such as healthcare, transportation, and supply chain management (SCM), to name a few, and allow users to get real-time information such as location-based services, disease management, and tracking. The smartphone-enabling technologies such as built-in sensors, Bluetooth, radio-frequency identification (RFID) tracking, and near-field communications (NFC) allow it to be an integral part of IoT and IoE world and the mostly used device in these environments. However, its use imposes severe security and privacy threats, because the smartphone usually contains and communicates sensitive private data. In this chapter, we provide a comprehensive survey on IoT and IoE technologies, their application domains, IoT structure and architecture, the use of smartphones in IoT and IoE, and the difference between IoT networks and mobile cellular networks. We also provide a concise overview of future opportunities and challenges in IoT and IoE environments and focus more on the security and privacy threats of using the smartphone in IoT and IoE networks with a suggestion of some countermeasures