IoTBD 2016 Abstracts

Area 1 - Big Data Research
Full Papers Short Papers
Area 2 - Emerging Services and Analytics
Full Papers Short Papers
Area 3 - Internet of Things (IoT) Applications
Full Papers Short Papers
Area 4 - Internet of Things (IoT) Fundamentals
Full Papers Short Papers

Area 1 - Big Data Research

Full Papers
Paper Nr: 5
Title:

Reliable Virtual Data Center Embedding Across Multiple Data Centers
Authors:

Gang Sun, Sitong Bu, Vishal Anand, Victor Chang and Dan Liao
Abstract: Cloud computing has become a cost-effective paradigm for deploying online service applications in large data centers in recent years. Virtualization technology enables flexible and efficient management of physical resources in cloud data centers and improves the resource utilization. A request for resources to a data center can be abstracted as a virtual data center (VDC) request. Due to the use of a large number of resources and at various locations, reliability is an important issue that should be addressed in large and multiple data centers. However, most research focuses on the problem of reliable VDC embedding in a single data center. In this paper, we study the problem of reliable VDC embedding across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized, while satisfying the reliability requirement of each VDC request. We model the problem by using mixed integer linear programming (MILP) and propose a heuristic algorithm to address this NP-hard problem efficiently. Simulation results show that the proposed algorithm performs better in terms of lowering physical resource consumption and VDC request blocking ratio compared with existing solution.
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Paper Nr: 18
Title:

Subgraph Isomorphism Search in Massive Graph Databases
Authors:

Chemseddine Nabti and Hamida Seba
Abstract: Subgraph isomorphism search is a basic task in querying graph data. It consists to find all embeddings of a query graph in a data graph. It is encountered in many real world applications that require the management of structural data such as bioinformatics and chemistry. However, Subgraph isomorphism search is an NP-complete problem which is prohibitively expensive in both memory and time in massive graph databases. To tackle this problem, we propose a new approach based on concepts widely different from existing works. Our approach relies on a summarized representation of the graph database that minimizes both the amount space required to store data graphs and the processing time of querying them. Experimental results show that our approach performs well compared to the most efficient approach of the literature.
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Paper Nr: 19
Title:

Parallel Real Time Seizure Detection in Large EEG Data
Authors:

Laeeq Ahmed, Ake Edlund, Erwin Laure and Stephen Whitmarsh
Abstract: Electroencephalography (EEG) is one of the main techniques for detecting and diagnosing epileptic seizures. Due to the large size of EEG data in long term clinical monitoring and the complex nature of epileptic seizures, seizure detection is both data-intensive and compute-intensive. Analysing EEG data for detecting seizures in real time has many applications, e.g., in automatic seizure detection or in allowing a timely alarm signal to be presented to the patient. In real time seizure detection, seizures have to be detected with negligible delay, thus requiring lightweight algorithms. MapReduce and its variations have been effectively used for data analysis in large dataset problems on general-purpose machines. In this study, we propose a parallel lightweight algorithm for epileptic seizure detection using Spark Streaming. Our algorithm not only classifies seizures in real time, it also learns an epileptic threshold in real time. We furthermore present “top-k amplitude measure” as a feature for classifying seizures in the EEG, that additionally assists in reducing data size. In a benchmark experiment we show that our algorithm can detect seizures in real time with low latency, while maintaining a good seizure detection rate. In short, our algorithm provides new possibilities in using private cloud infrastructures for real time epileptic seizure detection in EEG data.
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Paper Nr: 24
Title:

Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis
Authors:

Seungwoo Jeon, Bonghee Hong, Juhyeong Kim and Hyun-jik Lee
Abstract: Stock price prediction is extremely difficult owing to irregularity in stock prices. Because stock price sometimes shows similar patterns and is determined by a variety of factors, we present a novel concept of finding similar patterns in historical stock data for high-accuracy daily stock price prediction with potential rules for simultaneously selecting the main factors that have a significant effect on the stock price. Our objective is to propose a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use hierarchical clustering to easily find similar patterns in the layer adjacent to the current pattern according to the hierarchical structure. Second, we select the determinants that are most influenced by the stock price using feature selection. Moreover, we generate an artificial neural network model that provides numerous opportunities for predicting the best stock price. Finally, to verify the validity of our model, we use the root mean square error (RMSE) as a measure of prediction accuracy. The forecasting results show that the proposed model can achieve high prediction accuracy for each stock by using this measure.
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Paper Nr: 39
Title:

Utilization of the Internet of Things for Real-time Data Collection and Storage of Big Data as it Relates to Improved Demand Response
Authors:

Shawyun Sariri, Volker Schwarzer and Reza Ghorbani
Abstract: Demand response programs are viewed as a solution to counter the increasing demand in energy consumption, as well as a way to combat the stochastic nature of renewable sources within the current grid infrastructure. In order to apply an efficient demand response program, it is first necessary to understand the power consumption behaviours within a power grid system. Obtaining large quantities of consumer power consumption data will al-low the ability to tailor a demand response program to efficiently implement control decisions in real-time. The programs are a cost effective alternative to high priced spinning reserves and energy storage. The focus of data collection will be on dense urban environments, which provide a number of factors that can be evaluated as they relate to an efficient demand response program. The island of Oahu was the location of a pilot program to test the feasibility of large data collection and storage. A smart metering device collected high resolution data, which was transmitted to a server where load forecasting and peak shaving decisions could be calculated. The design of the pilot system and initial results of the large data collection are discussed.
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Paper Nr: 43
Title:

Key based Reducer Placement for Data Analytics across Data Centers Considering Bi-level Resource Provision in Cloud Computing
Authors:

Jiangtao Zhang, Lingmin Zhang, Hejiao Huang, Zeo L. Jiang and Xuan Wang
Abstract: Due to the distribution characteristic of the data source, such as astronomy and sales, or the legal prohibition, it is not always practical to store the world-wide data in only one data center (DC). Hadoop is a commonly accepted framework for big data analytics. But it can only deal with data within one DC. The distribution of data necessitates the study of Hadoop across DCs. In this situation, though we can place mapper in the local DCs, where to place reducers is a great challenge, since each reducer almost needs to process all map output across all involved DCs. Aiming to reduce costs, a key based scheme is proposed which can respect the locality principle of traditional Hadoop as much as possible while realizing deployment of reducers with lower cost. Considering both data center level and server level resource provision, a bi-level programming is used to formalize the problem and it is solved by a tailored two level group genetic algorithm (TLGGA). Extensive simulations demonstrate the effectiveness of TLGGA. It can outperform both the baseline and the state-of-the-art mechanisms by 49% and 40%, respectively.
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Paper Nr: 48
Title:

Users’ Willingness to Share Data on the Internet: Perceived Benefits and Caveats
Authors:

Martina Ziefle, Julian Halbey and Sylvia Kowalewski
Abstract: One of the major challenges of the ongoing digitalization and the ubiquitous usage of pervasive computing in all fields of our lives is to steer a sensible balance between benefits and drawbacks of using the Internet and to implement an appropriate data handling when using digital media. The broad availability of data, in line with the enormous velocity of information retrieval, is open to abuse and malpractice, with privacy threats as the most serious barrier. The consumers and their attitudes and behaviors when using the Internet play an important role in the discussion about privacy protection. The aim of the current study was to analyze Internet usage behaviors and users’ willingness to share their data when using digital services and social network sites. In a two step empirical approach, we first explore users’ perceptions of privacy in the context of Internet usage and social network sites by means of a focus group approach. In a second step, a quantitative study was carried out. Using a conjoint measurement approach, user scenarios were created from combinations of different levels of anonymization extent, data type, and benefits from sharing the data. The respondents’ task was to decide under which conditions they would be willing to share their data. 80 volunteers (50,6% women) between 14 and 60 years of age participated in the conjoint study.
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Paper Nr: 68
Title:

CoAP Option for Capability-Based Access Control for IoT-Applications
Authors:

Borting Chen, Mesut Güneş and Yu-Lun Huang
Abstract: Access control is critical for many applications of the Internet of Things (IoT) since the owner of an IoT device (and application) may only permit one user to access a subset of the resources of the device. To provide access control for an IoT network, recent work adopted the capability-based access control (CBAC) model, which allows an IoT device to decide on the authorization by itself based on a capability token. However, the existing approaches based on CBAC directly attach the capability token at the end of CoAP when sending a request message. For the receiver, it is not easy to retrieve the capability token from the request message if the CoAP payload is present, because CoAP does not have a length field to indicate the size of its payload. To counter this problem, we propose a CoAP option, Cap-Token, to encapsulate a capability token when sending request messages. Because a CoAP option is independent from other CoAP fields, a receiver can get the capability token from the Cap-Token option of the request message without ambiguity. We also provide a compression mechanism to reduce the size of the Cap-Token option. Our evaluation shows that the compression mechanism can save the size of the option by 60%. Adding a compressed Cap-Token option to a request message increases the IP datagram size by 45 bytes, which is only 41% of the increase when directly attaching the capability token at the end of CoAP.
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Short Papers
Paper Nr: 2
Title:

Image Semantic Distance Metric Learning Approach for Large-scale Automatic Image Annotation
Authors:

Cong Jin and Shu-Wei Jin
Abstract: Learning an effective semantic distance measure is very important for the practical application of image analysis and pattern recognition. Automatic image annotation (AIA) is a task of assigning one or more semantic concepts to a given image and a promising way to achieve more effective image retrieval and analysis. Due to the semantic gap between low-level visual features and high-level image semantic, the performances of some image distance metric learning (IDML) algorithms only using low-level visual features is not satisfactory. Since there is the diversity and complexity of large-scale image dataset, only using visual similarity to learn image distance is not enough. To solve this problem, in this paper, the semantic labels of the training image set participate into the image distance measure learning. The experimental results confirm that the proposed image semantic distance metric learning (ISDML) can improve the efficiency of large-scale AIA approach and achieve better annotation performance than the other state-of-the art AIA approaches.
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Paper Nr: 4
Title:

Behavior Analysis based DNS Tunneling Detection and Classification with Big Data Technologies
Authors:

Bin Yu, Les Smith, Mark Threefoot and Femi Olumofin
Abstract: Domain Name System (DNS) is ubiquitous in any network. DNS tunnelling is a technique to transfer data, convey messages or conduct TCP activities over DNS protocol that is typically not blocked or watched by security enforcement such as firewalls. As a technique, it can be utilized in many malicious ways which can compromise the security of a network by the activities of data exfiltration, cyber-espionage, and command and control. On the other side, it can also be used by legitimate users. The traditional methods may not be able to distinguish between legitimate and malicious uses even if they can detect the DNS tunnelling activities. We propose a behaviour analysis based method that can not only detect the DNS tunnelling, but also classify the activities in order to catch and block the malicious tunnelling traffic. The proposed method can achieve the scale of real-time detection on fast and large DNS data with the use of big data technologies in offline training and online detection systems.
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Paper Nr: 6
Title:

Unified Cloud Orchestration Framework for Elastic High Performance Computing in the Cloud
Authors:

Lukasz Miroslaw, Michael Pantic and Henrik Nordborg
Abstract: The demand for computational power and storage in industry and academia is continuously increasing. One of the key drivers of this demand is the increased use of numerical simulations, such as Computational Fluid Dynamics for product development. This type of simulations generates huge amounts of data and demands massively parallel computing power. Traditionally, this computational power is provided by clusters, which require large investments in hardware and maintenance. Cloud computing offers more flexibility at significantly lower costs but the deployment of numerical applications is time-consuming, error-prone and requires a high level of expertise. The purpose of this paper is to demonstrate the SimplyHPC framework that automatizes the deployment of the cluster in the cloud, deploys and executes large scale and parallel numerical simulations, and finally downloads the results and shuts down the cluster. Using this tool, we have been able to successfully run the widely accepted solvers, namely PETSc, HPCG and ANSYS CFX, in a performant and scalable manner on Microsoft Azure. It has been shown that the cloud computing performance is comparable to on-premises clusters in terms of efficiency and scalability and should be considered as an economically viable alternative.
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Paper Nr: 7
Title:

Using Location-Labeling for Privacy Protection in Location-Based Services
Authors:

Dan Liao, Hui Li, Vishal Anand, Victor Chang, Gang Sun and Hongfang Yu
Abstract: The developments in positioning and mobile communication technology have made applications that use location-based services (LBS) increasingly popular. For privacy reasons and due to lack of trust in the LBS provider, k-anonymity and l-diversity techniques have been widely used to preserve user privacy in distributed LBS architectures. However, in reality, there exist scenarios where the user locations are identical or similar/near each other. In such a scenario the k locations selected by k-anonymity technique are the same and location privacy can be easily compromised or leaked. To address the issue of privacy protection, in this paper, we propose the concept of location-labels to distinguish mobile user locations to sensitive locations and ordinary locations. We design a location-label based (LLB) algorithm for protecting location privacy while minimizing the query response time of LBS. We also evaluate the performance and validate the correctness of the proposed algorithm through extensive simulations.
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Paper Nr: 14
Title:

Knowledge Management as a Service - When Big Data Meets Knowledge Management
Authors:

Thomas Ochs and Ute Riemann
Abstract: Purpose – Nowadays the world is getting more technological savvy. The collection of data is becoming a hype which is a phenomenon is called “big data”. Companies seeking for these data collections and data analytics assuming valuable insights. As for now, these valuable insights are perishable to a high degree - perishable because the insights are only valuable if you can detect and act on them (The Forrester Wave, Q3 2014, p2). In our article, we propose to take advantage of big data analytics while introducing a service-oriented knowledge management discipline that will allow gaining the full value of big data. Herein, we focus on the benefit aspect of big data linked to the service approach of knowledge management, which may increase the value of big data. Findings –In fact, big data analytics offer value and the use of big data has the potential to transform business in itself. However, there are greater opportunities beyond big data analytics once we turn data from information into a knowledge linked to business strategy, easy accessible and consume. With the introduction of knowledge management-as-a-service to the concept of big data, we provide justification for bringing proven knowledge management strategies and tools into the cloud sphere to bear on big data and business analytics. With the introduction of pre-defined service to knowledge management, we open the ability for increased competitiveness as a final consequence (Thuraisingham and Parikh, 2008) and the value of any company (Bertino et al., 2006). Originality/Value – Our article outlines the previously underestimated strong link of big data and knowledge management and how the delivery of data-driven intelligence is supported with the appliance of a cloud-based service model. When big data and cloud-based knowledge management are combined are able to not only uncover a new revenue stream but also create a true competitive advantage.
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Paper Nr: 25
Title:

Towards Intelligent Data Analysis: The Metadata Challenge
Authors:

Besim Bilalli, Alberto Abelló, Tomàs Aluja-Banet and Robert Wrembel
Abstract: Once analyzed correctly, data can yield substantial benefits. The process of analyzing the data and transforming it into knowledge is known as Knowledge Discovery in Databases (KDD). The plethora and subtleties of algorithms in the different steps of KDD, render it challenging. An effective user support is of crucial importance, even more now, when the analysis is performed on Big Data. Metadata is the necessary component to drive the user support. In this paper we study the metadata required to provide user support on every stage of the KDD process. We show that intelligent systems addressing the problem of user assistance in KDD are incomplete in this regard. They do not use the whole potential of metadata to enable assistance during the whole process. We present a comprehensive classification of all the metadata required to provide user support. Furthermore, we present our implementation of a metadata repository for storing and managing this metadata and explain its benefits in a real Big Data analytics project.
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Paper Nr: 33
Title:

DataCommandr: Column-oriented Data Integration, Transformation and Analysis
Authors:

Alexandr Savinov
Abstract: In this paper, we describe a novel approach to data integration, transformation and analysis, called DataCommandr. Its main distinguishing feature is that it is based on operations with columns rather than operations with tables in the relational model or operations with cells in spreadsheet applications. This data processing model is free of such typical set operations like join, group-by or map-reduce which are difficult to comprehend and slow at run time. Due to this ability to easily describe rather complex transformations and high performance on analytical workflows, this approach can be viewed as an alternative to existing technologies in the area of ad-hoc and agile data analysis.
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Paper Nr: 59
Title:

Joins vs. Links or Relational Join Considered Harmful
Authors:

Alexandr Savinov
Abstract: Since the introduction of the relational model of data, the join operation is part of almost all query languages and data processing engines. Nowadays, it is not only a formal operation but rather a dominating pattern of thought for the concept of data connectivity. In this paper, we critically analyze properties of this operation, its role and uses by demonstrating some of its fundamental drawbacks in the context of data processing. We also analyze an alternative approach which is based on the concept of link by showing how it can solve these problems. Based on this analysis, we argue that link-based mechanisms should be preferred to joins as a main operation in data model and data processing systems.
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Paper Nr: 60
Title:

A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice
Authors:

Przemyslaw Woznowski, Rachel King, William Harwin and Ian Craddock
Abstract: Human Activity Recognition (AR) is an area of great importance for health and well-being applications including Ambient Intelligent (AmI) spaces, Ambient Assisted Living (AAL) environments, and wearable healthcare systems. Such intelligent systems reason over large amounts of sensor-derived data in order to recognise users’ actions. The design of AR algorithms relies on ground-truth data of sufficient quality and quantity to enable rigorous training and validation. Ground-truth is often acquired using video recordings which can produce detailed results given the appropriate labels. However, video annotation is not a trivial task and is, by definition, subjective. In addition, the sensitive nature of the recordings has to be foremost in minds of the researchers to protect the identity and privacy of participants. In this paper, a hierarchical ontology for the annotation of human activity recognition in the home is proposed. Strategies that support different levels of granularity are presented enabling consistent, and repeatable annotations for training and validating activity recognition algorithms. Best practice regarding the handling of this type of sensitive data is discussed.
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Paper Nr: 61
Title:

Sampling and Evaluating the Big Data for Knowledge Discovery
Authors:

Andrew H. Sung, Bernardete Ribeiro and Qingzhong Liu
Abstract: The era of Internet of Things and big data has seen individuals, businesses, and organizations increasingly rely on data for routine operations, decision making, intelligence gathering, and knowledge discovery. As the big data is being generated by all sorts of sources at accelerated velocity, in increasing volumes, and with unprecedented variety, it is also increasingly being traded as commodity in the new “data economy” for utilization. With regard to data analytics for knowledge discovery, this leads to the question, among various others, of how much data is really necessary and/or sufficient for getting the analytic results that will reasonably satisfy the requirements of an application. In this work-in-progress paper, we address the sampling problem in big data analytics and propose that (1) the problem of sampling the big data for analytics is “hard”specifically, it is a theoretically intractable problem when formal measures are incorporated into performance evaluation; therefore, (2) heuristic, rather than algorithmic, methods are necessarily needed in data sampling, and a plausible heuristic method is proposed (3) a measure of dataset quality is proposed to facilitate the evaluation of the worthiness of datasets with respect to model building and knowledge discovery in big data analytics.
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Paper Nr: 69
Title:

Detecting Data Stream Dependencies on High Dimensional Data
Authors:

Jonathan Boidol and Andreas Hapfelmeier
Abstract: Intelligent production in smart factories or wearable devices that measure our activities produce on an ever growing amount of sensor data. In these environments, the validation of measurements to distinguish sensor flukes from significant events is of particular importance. We developed an algorithm that detects dependencies between sensor readings. These can be used for instance to verify or analyze large scale measurements. An entropy based approach allows us to detect dependencies beyond linear correlation and is well suited to deal with high dimensional and high volume data streams. Results show statistically significant improvements in reliability and on-par execution time over other stream monitoring systems.
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Paper Nr: 70
Title:

Security Incident Information Exchange for Cloud Services
Authors:

Christian Frøystad, Erlend Andreas Gjære, Inger Anne Tøndel and Martin Gilje Jaatun
Abstract: The complex provider landscape in cloud computing makes incident handling difficult, as Cloud Service Providers (CSPs) with end-user customers do not necessarily get sufficient information about incidents that occur at upstream CSPs. In this paper, we argue the need for commonly agreed-upon incident information exchanges between providers as a means to improve accountability of CSPs. The discussion considers several technical challenges and non-technical aspects related to improving the situation for incident response in cloud computing scenarios. In addition, we propose a technical implementation which can embed standard representation formats for incidents in notification messages, built over a publish-subscribe architecture, and a web-based dashboard for handling the incident workflow.
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Paper Nr: 71
Title:

I’ll Trust You – for Now
Authors:

Martin Gilje Jaatun
Abstract: The pervasiveness of cloud computing paired with big data analytics is fueling privacy fears among the more paranoid users. Cryptography-based solutions such as fully homomorphic encryption and secure multiparty computation are trying to address these fears, but still do not seem to be ready for prime time. This paper presents an alternative approach using encrypted cloud storage by one provider, supplemented by cloud processing of cleartext data on one or more different cloud providers.
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Paper Nr: 10
Title:

Big Data in Cloud Computing: Features and Issues
Authors:

Pedro Caldeira Neves, Bradley Schmerl, Javier Cámara and Jorge Bernardino
Abstract: The term big data arose under the explosive increase of global data as a technology that is able to store and process big and varied volumes of data, providing both enterprises and science with deep insights over its clients/experiments. Cloud computing provides a reliable, fault-tolerant, available and scalable environment to harbour big data distributed management systems. Within the context of this paper we present an overview of both technologies and cases of success when integrating big data and cloud frameworks. Although big data solves much of our current problems it still presents some gaps and issues that raise concern and need improvement. Security, privacy, scalability, data governance policies, data heterogeneity, disaster recovery mechanisms, and other challenges are yet to be addressed. Other concerns are related to cloud computing and its ability to deal with exabytes of information or address exaflop computing efficiently. This paper presents an overview of both cloud and big data technologies describing the current issues with these technologies.
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Paper Nr: 21
Title:

Semantic Agent in the Context of Big Data - Usage in Ontological Information Retrieval in Scientific Research
Authors:

Caio Saraiva Coneglian, Elvis Fusco and José Eduardo Santarem Segundo
Abstract: The evolution of information technology caused an expansion in the amount of data available on the internet. Moreover, such developments demanded that new tools were created to allow processing at high velocity, trying various informational sources. In this context, in flocking to the three V (Velocity, Variety and Volume), emerged the phenomenon called Big Data. From the emergence of this phenomenon, the need to generate new architectures that allow that users, enjoy the high volume of data spread throughout the Web. One way to improve the processes carried out, insert the question of semantic information processing, in which the use of domain ontologies can expand as computational agents interpret the meaning of the data. Thus, this paper aims to present a proposal for architecture that places the elements of Big Data and semantic, seeking to insert a model that is adapted to the current computing needs. As proof of concept performed the implementation of the architecture, exploring the question of scientific research, where a user is assisted to find relevant information in academic databases. Through the implementation, it was found that the use ontologies in a Big Data architecture, significantly improves the recovery of information performed by computational agents.
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Paper Nr: 34
Title:

Feature Driven Survey of Big Data Systems
Authors:

Cigdem Avci Salma, Bedir Tekinerdogan and Ioannis N. Athanasiadis
Abstract: Big Data has become a very important driver for innovation and growth for various industries such as health, administration, agriculture, defence, and education. Storing and analysing large amounts of data are becoming increasingly common in many of these application areas. In general, different application domains might require different type of big data systems. Although, lot has been written on big data it is not easy to identify the required features for developing big data systems that meets the application requirements and the stakeholder concerns. In this paper we provide a survey of big data systems based on feature modelling which is a technique that is utilized for defining the common and variable features of a domain. The feature model has been derived following an extensive literature study on big data systems. We present the feature model and discuss the features to support the understanding of big data systems.
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Paper Nr: 53
Title:

Cryptography Arbitration: Security Complexities of Cloud Enabled IoT in Europe and Beyond
Authors:

Morgan Eldred, Hassan Alnoon and Sultan AlTamimi
Abstract: The global nature of the Internet of Things and cloud has and will result in emerging challenges, such as whom is liable for data protection and security breaches of personal data. This paper puts forward the concept of ‘cryptography arbitration’ and the need to design and architect legally compliant solutions. As the world becomes more interconnected we are likely to see more example of technology practices sweeping the globe and raising further data protection challenges; much like the fault lines between tectonic plates. This paper provides contribution by capturing some emerging impacts and challenges and how they relate to the internet of things and the need for solutions to have the appropriate cryptography safeguards.
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Area 2 - Emerging Services and Analytics

Full Papers
Paper Nr: 13
Title:

Energy Cost Minimization with Risk Rate Constraint for Internet Data Center in Deregulated Electricity Markets
Authors:

Zhongjin Li, Jidong Ge, Chuanyi Li, Hongji Yang, Haiyang Hu and Bin Luo
Abstract: With the large-scale development of internet data center (IDC), the energy cost is increasing significantly and has attracted a great deal of attention. Moreover, existing scheduling optimization methods for cloud computing applications disregard the security services. In this paper, we propose a long-term energy cost minimization (ECM) algorithm with risk rate constraint for an internet data center in deregulated electricity markets. First, we formulate the stochastic optimization problem taking the temporal diversity of electricity price and risk rate constraint into account. Then, an operation algorithm is designed to solve the problem by Lyapunov optimization framework, which offers provable energy cost and delay guarantees. Extensive evaluation experiments based on the real-life electricity price demonstrate the effectiveness of proposed algorithm.
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Short Papers
Paper Nr: 38
Title:

Security in the Industrial Internet of Things - The C-SEC Approach
Authors:

Jose Romero-Mariona, Roger Hallman, Megan Kline, John San Miguel, Maxine Major and Lawrence Kerr
Abstract: A revolutionary development in machine-to-machine communications, the “Internet of Things” (IoT) is often characterized as an evolution of Supervisory Control and Data Acquisition (SCADA) networks. SCADA networks have been used for machine-to-machine communication and controlling automated processes since before the widespread use of the Internet. The adoption of open internet protocols within these systems has created unforeseen security vulnerabilities. In this paper we detail the Cyber-SCADA Evaluation Capability (C-SEC), a US Department of Defense research effort aimed at securing SCADA networks. We also demonstrate how the C-SEC framework could enhance the security posture of the emerging IoT paradigm.
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Paper Nr: 55
Title:

A Provenance Framework for Policy Analytics in Smart Cities
Authors:

Barkha Javed, Richard McClatchey, Zaheer Khan and Jetendr Shamdasani
Abstract: Sustainable urban environments require appropriate policy management. However, such policies are established as a result of underlying, potentially complex and long-term policy making processes. Consequently, better policies require improved and verifiable planning processes. In order to assess and evaluate the planning process, transparency of the system is pivotal which can be achieved by tracking the provenance of policy making process. However, at present no system is available that can track the complete cycle of urban planning and decision making. We propose to capture the complete process of policy making and to investigate the role of Internet of Things (IoT) provenance to support design-making for policy analytics and implementation. The environment in which this research will be demonstrated is that of Smart Cities whose requirements will drive the research process.
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Paper Nr: 56
Title:

Detection of Damage and Failure Events of Critical Public Infrastructure using Social Sensor Big Data
Authors:

Iris Tien, Aibek Musaev, David Benas, Ameya Ghadi, Seymour Goodman and Calton Pu
Abstract: Public infrastructure systems provide many of the services that are critical to the health, functioning, and security of society. Many of these infrastructures, however, lack continuous physical sensor monitoring to be able to detect failure events or damage that has occurred to these systems. We propose the use of social sensor big data to detect these events. We focus on two main infrastructure systems, transportation and energy, and use data from Twitter streams to detect damage to bridges, highways, gas lines, and power infrastructure. Through a three-step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geolocated tweets identifying failure events. Applying the strategy to real-world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures.
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Paper Nr: 67
Title:

A Perspective on Industry 4.0: From Challenges to Opportunities in Production Systems
Authors:

Ateeq Khan and Klaus Turowski
Abstract: Industry 4.0 and smart factory are the terms frequently used for next generation production systems. Advancement of Information technologies paved the way for evolution of production systems. To remain competitive in the market, enterprises want to utilize these technological advancements in order to solve current challenges and serve customers in new ways which were not imagined before. In order to provide new services quickly, new methods and technologies have to be introduced at manufacturing level. The paper briefly discusses industry 4.0 and settings (arrangements) for co-innovation. This paper also describes what are the current challenges faced by companies with the help of a survey. The paper proposes an approach from strategical to operational level for the implementation of industry 4.0. In this paper, we also provide new opportunities, scenarios, and applications enabled by introducing new tools and technologies for industry 4.0. At the end, the paper provides summary and glimpse of the future work.
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Area 3 - Internet of Things (IoT) Applications

Full Papers
Paper Nr: 36
Title:

The Internet of Speaking Things and Its Applications to Cultural Heritage
Authors:

Fiammetta Marulli, Remo Pareschi and Daniele Baldacci
Abstract: The initial driver for the development of an Internet of Things (IoT) was to provide an infrastructure capable of turning anything into a sensor that acquires and pours data into the cloud, where they can be aggregated with other data and analysed to extract decision-supportive information. The validity of this initial motivation still stands. However, going in the opposite direction is at least as useful and exciting, by exploiting Internet to make things communicate and speak, thus complementing their capability to sense and listen. In this work we present applications of IoT aimed to support the Cultural Heritage environments, but also suitable for Tourism and Smart Urban environments, that advance the available user-experience based on smart devices via the interaction with speaking things. In the first place we describe a system architecture for speaking things, comprehensive of the basic communication protocols for carrying information to the user as well as of higher-level functionalities for content generation and dialogue management. We then show how this architecture is applied to make artworks speak to people. Finally, we introduce speaking holograms as a yet more advanced and interactive application.
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Paper Nr: 49
Title:

Sensor Network for Real-time In-situ Seismic Tomography
Authors:

Lei Shi, Wen-Zhan Song, Fan Dong and Goutham Kamath
Abstract: Most existing seismic exploration or volcano monitoring systems employ expensive broadband seismometer as instrumentation. At present raw seismic data are typically collected at central observatories for post processing. With a high-fidelity sampling, it is virtually impossible to collect raw, real-time data from a large-scale dense sensor network due to severe limitations of energy and bandwidth at current, battery-powered sensor nodes. At some most threatening and active volcanoes, only tens of nodes are maintained. With a small network and post processing mechanism, existing system do not yet have the capability to recover physical dynamics with sufficient resolution in real-time. This limits our ability to understand earthquake zone or volcano dynamics. To obtain the seismic tomography in real-time and high resolution, a new sensor network system for real-time in-situ seismic tomography computation is proposed in this paper. The design of the sensor network consists of hardware, sensing and data processing components for automatic arrivaltime picking and tomography computation. This system design is evaluated both in lab environment for 3D tomography with real seismic data set and in outdoor field test for 2D surface tomography.
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Paper Nr: 72
Title:

Evaluation of an Arduino-based IoT Person Counter
Authors:

Bruno F. Carvalho, Caio C. M. Silva, Alessandra M. Silva, Fábio Buiati and Rafael Timóteo
Abstract: The IoT devices can provide a wide range of information, which can be used to infer the behavior patterns with a large semantic bias. In this sense, an IoT network has the ability to use trafficked information to perform its own management. One type of information that can be used by an IoT network is the amount of people in a certain place. This information, combined with others, can help IoT-based systems discover characteristics about the environment in which it is deployed. Thus, the integration of the data captured provides the achievement of manifold applications, such as air conditioning regulation, security access and people management in a working environment. In this research, it is proposed the implementation of an IoT person counter. Two different technologies were used, aiming to verify the best option to design a counter device with low-cost microcontrollers and sensors. Experimental results shows that in controlled environments the IoT person counter has a satisfactory accuracy. Some limitations were also identified in order to clarify the scenarios where those devices can be used.
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Paper Nr: 74
Title:

Resilient Metro-scale Smart Structures: Challenges & Future Directions
Authors:

Mike Burmester and Jorge Munilla
Abstract: Smart structures are highly inter-connected adaptive systems that are coordinated by cyber systems to optimize specific system objectives. In this paper we consider the challenges for securing metro-scale smart structures. We use a threat model that allows for untrusted behavior to capture realistic IoT scenarios, and discuss vulnerabilities, exploits and attack vectors. Resilience is defined in terms of stability, resistance to damage and self-healing. To illustrate the challenges of capturing resilience we consider two very different applications: supply chain logistics and smart grids. Both are mixed latency and throughput sensitive, each in their own particular way. The first involves scanning RFID tagged objects in pallets. An untrusted RFID reader is given a one-time authenticator to inspect a pallet and identify any missing objects; and, if there are no missing objects, compile a proof of integrity. The reader should not be able to trace objects via unauthorized inspections (privacy). This application uses RS erasure codes that are more appropriate for memory constrained RFID tags. The second application involves securing industrial substation automation systems. These are particularly vulnerable to cyber attacks, and HIL testbeds are used for real-time multilayer vulnerability analysis. For metro-scale applications we propose virtualized testbeds that are portable and suitable for onsite incidence response. For each application we show how metro-scale analytics are used to capture resiliency.
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Paper Nr: 75
Title:

Security and Privacy for Emerging Smart Community Infrastructures
Authors:

Bogdan Copos, Karl Levitt, Jeff Rowe, Parisa Kianmajd, Chen-Nee Chuah and George Kesidis
Abstract: Smart communities of the future have features that make them susceptible to novel forms of cyber-attack and a potential loss of privacy for the citizens they serve. We view these communities as metro level wide area control systems with sensors and actuators located in residences, the workplace, in mobile vehicles and even worn on the body. In addition, this distributed system may not be subject to centralized control. It needs to be responsive to the individual needs of citizen owners yet still maintain the ability to coordinate actions across a neighborhood, or larger metropolitan area. The question we wish to address is, as frameworks emerge to handle these unique challenges, how can we provide security and privacy for such and open and decentralized environment? We suggest ways to add security and privacy to low level IoT devices, to a cloudlet based application platform, to a wide area SDN for coordination, and to negotiation protocols for citizen coordination.
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Short Papers
Paper Nr: 17
Title:

Intelligent Farm Relaxation for Smart City based on Internet of Things: Management System and Service Model
Authors:

Lina Yu, Sha Tao, Wanlin Gao, Ganghong Zhang and Kequan Lin
Abstract: Farm relaxation is a type of city tourism. Currently, this type of tourism has demonstrated a series of problems, including blind spots in the service channel, simple one-sided service content and passive service delivery. To address these issues, here the concept of “intelligent farm relaxation” was proposed. In addition, an intelligent farm management system IEFMS was developed based on key techniques from the Internet of Things (IoT) as well as a related service model. This system has five layers, which are, from top to bottom: the presentation layer, the application layer, the application support layer, the data layer, and the infrastructure layer. Based on this, the intelligent farm was divided into four sections and a service model proposed: planting areas, a management services centre, a logistics distribution centre and a data centre. This service model is characterized by digital dynamic management and customized whole-process proactive services. The results of this study will help improve intelligent farm management services for smart city, likewise providing technical and application support for the intelligentization, automation and diversification of intelligent farm relaxation service management, and also to promote adding cultural, ecological, technological and service value to intelligent farm relaxation.
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Paper Nr: 50
Title:

A Low-cost Intelligent Car Break-in Alert System - Using Smartphone Accelerometers for Detecting Vehicle Break-ins
Authors:

Reinhold Behringer, Muthu Ramachandran and Victor Chang
Abstract: Smartphones provide sensors and online data connectivity which makes them suitable for individual alert systems. A prototype for a car break-in alert system has been developed which can detect activity through smartphone accelerometers. The main goal of this system is to provide a low-cost alternative to expensive embedded systems with similar functionality. It can be controlled remotely solely through text messages from another mobile phone, which provides the option to use SMS instead of internet data access, in case of high cost of internet data connection (international roaming charges). In case of a detected break-in the system will send a text message to the user’s second phone. The user also can query information and request the location of the vehicle. The prototype has been tested in various situations, and data have been collected to distinguish different scenarios. The system has been programmed with MIT App Inventor and will be made available for free on the Google Play Store.
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Paper Nr: 62
Title:

Bringing Dynamics to IoT Services with Cloud and Semantic Technologies - An Innovative Approach for Enhancing IoT based Services
Authors:

Sébastien Dupont, Amel Achour, Fabrice Estiévenart, Laurent Deru and Nikolaos Matskanis
Abstract: In this paper, we present an innovative software architecture that brings dynamics to the world of interconnected small devices and sensors by mixing cloud services, semantics and border router technologies. Dynamic aspects can be enabled both in the way that the devices are deployed or managed as well as in the manner in which the data can be combined or interpreted to form additional services. We got inspired from the architecture of mediators and wrappers in databases and services systems and adapt them to the IoT world. We illustrate our purposes with a use case scenario that involves different actors from the energy and smart cities domains.
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Paper Nr: 26
Title:

The Implementation of a Wireless-remote Arduino-compatible Programmable Logic Controller with Smart User Interface Towards Industrial Internet of Things
Authors:

Tossaporn Wetsiri and Wimol San-Um
Abstract: The Industrial Internet of Things (IIoT) typically enables remote communications via wireless IP devices and interactions between human and manufacturing processes through sensors and actuators, resulting in the enhancement of performances and efficiency as well as the reduction in manufacturing costs. This paper presents a complete IIoT platform, involving all physical, communication and application layers. The universal Programmable Logic Controller (PLC) is controlled by an open software Arduino microcontroller via a high-voltage peripheral board. The communication between PLC and end users is achieved by Wi-Fi technology and Message Queue Telemetry Transport (MQTT) protocol that allow data storage and monitoring in cloud server. The Graphic User Interface (GUI) is designed for both a web-based display and an Android application. Demonstrations on the pneumatic control system are also included. The proposed system offers a low-cost high efficiency implementation of IIoT for Small-and-Medium Enterprises (SMEs) in ASEAN countries. CCS Concepts: CCS → Hardware → Communication hardware, interfaces and storage → Wireless integrated network sensors.
Paper Nr: 29
Title:

Extracting Usage Patterns from Power Usage Data of Homes’ Appliances in Smart Home using Big Data Platform
Authors:

Ali Reza Honarvar and Ashkan Sami
Abstract: Advances in sensing techniques and IoT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study we investigate this issue and extracted valuable sequence pattern from real appliances’ power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

Area 4 - Internet of Things (IoT) Fundamentals

Full Papers
Paper Nr: 35
Title:

Recognizing Compound Events in Spatio-Temporal Football Data
Authors:

Keven Richly, Max Bothe, Tobias Rohloff and Christian Schwarz
Abstract: In the world of football, performance analytics about a player’s skill level and the overall tactics of a match are supportive for the success of a team. These analytics are based on positional data on the one hand and events about the game on the other hand. The positional data of the ball and players is tracked automatically by cameras or via sensors. However, the events are still captured manually, which is time-consuming and error-prone. Therefore, this paper introduces an approach to identify compound events by analyzing the positional data of football matches. We trained and aggregated the machine learning algorithms Support Vector Machine, KNearest Neighbors and Random Forest, based on features, which were calculated on the basis of the positional data. To validate the feasibility of our approach we evaluated the quality of the results by comparing recall and precision. We demonstrated that it is possible to detect compound events from spatio-temporal football data. Nevertheless, the choice of a specific algorithm has a significant impact on the quality of the predicted results.
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Paper Nr: 42
Title:

A Utility Paradigm and Roadmap of Internet-of-Things in Thailand for Digital Economy Development towards ASEAN Economic Community
Authors:

Jeerana Noymanee, Wimol San-Um and Thanaruk Theeramunkong
Abstract: While the ASEAN Economic Community (AEC) is on the rise in global economy, a digital economy is consequently an effective and efficient policy, which is necessary to stimulate a rapid growth in an average Gross Domestic Product (GDP). Internet of Things (IoT), in which physical perceptions, cyber interactions, social correlations, and cognitive process can be united through ubiquitous interconnections, potentially enables a success in digital economy policy. Thailand as a part of AEC has realized the importance of the design and implementation of IoT ranging from physical layer to application layer. This paper presents the roadmap of IoT in Thailand towards AEC. In accordance to Thai environments and possible application platforms, the current status of IoT is described in terms of Internet-of-Device (IoO), Internet-of-Service (IoS), Internet-of-People (IoP), and Internet-of-Intelligence (IoI). The roadmap of IoT for Thailand until the year 2020 and beyond is suggested as for a perspective on an opportunity in international trading and investments. Challenges in major IoT implementation issues in Thailand such as security, standardization, and interoperability are also discussed. This paper offers new perspectives, utility paradigm, social and economic impacts of IoT implementation in Thailand as a potential country in terms of markets and production hubs in South East Asia region.
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Short Papers
Paper Nr: 11
Title:

Cassandra for Internet of Things: An Experimental Evaluation
Authors:

André Duarte and Jorge Bernardino
Abstract: The proliferation of the Internet of Things (IoT) increases the amount of data that is being produced. Therefore it is extremely important to find the best possible storage engine to process these huge amounts of data. With the intent of discovering which database engine better supports the characteristics of an IoT system it is essential to analyse the existing databases and test them in a practical context. With this objective we decided to use one of the most popular databases, Cassandra, to evaluate it on an IoT environment. We evaluate the querying processing time of Cassandra using queries of an IoT real time environment and comparing it with different types of data architectures. The main focus of this work is to investigate if Cassandra can provide good performance in an IoT system.
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Paper Nr: 28
Title:

Internet of Bicycles - Tracking and Monitoring Life-cycle Information using GS1
Authors:

Miyeon Lee, Sunghoon Lee, Jaehyung Choi, Seongsik Kim and Daeyoung Kim
Abstract: Social phenomena such as the rise of cyclists, the expansion of public bicycle systems and the increase of bicycle thefts highlight the needs of tracking a bicycle’s life-cycle and implementing new services based on information from a bicycle’s life-cycle. We suggest Global Bicycle Information Architecture to describe and save a bicycle’s life-cycle. We extend the GS1 EPCglobal architecture for Global Bicycle Information Architecture to identify bicycles and capture and share information. Global Bicycle Information Architecture enables stakeholders to gather information that can be used to formulate public policies or for protection of property – in this study, bicycles. We verify the availability of Global Bicycle Information Architecture with the implementation of a bicycle tracking system.
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Paper Nr: 32
Title:

Supporting Application Requirements in Cloud-based IoT Information Processing
Authors:

Sabrina De Capitani di Vimercati, Giovanni Livraga, Vincenzo Piuri, Pierangela Samarati and Gerson A. Soares
Abstract: IoT infrastructures can be seen as an interconnected network of sources of data, whose analysis and processing can be beneficial for our society. Since IoT devices are limited in storage and computation capabilities, relying on external cloud providers has recently been identified as a promising solution for storing and managing IoT data. Due to the heterogeneity of IoT data and applicative scenarios, the cloud service delivery should be driven by the requirements of the specific IoT applications. In this paper, we propose a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing. Our solution allows a subject with an authority over an IoT infrastructure to formulate conditions that the provider must satisfy in service provisioning, and computes a SLA based on these conditions while accounting for possible dependencies among them. We also illustrate a CSP-based formulation of the problem of computing a SLA, which can be solved adopting off-the-shelves CSP solvers.
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Paper Nr: 40
Title:

Converging Future Internet, “Things”, and Big Data: A Specification Following NovaGenesis Model
Authors:

Antonio M. Alberti, Eduardo S. dos Reis, Rodrigo da R. Righi, Víctor M. Muñoz and Victor Chang
Abstract: The convergence of Internet of “things” (IoT) with big data platforms and cloud computing is already happening. However, the vast majority, if not all the proposals are based on the current Internet technologies. The convergence of IoT, big data and cloud in “clean slate” architectures is an unexplored topic. In this article, we discuss this convergence considering the viewpoint of a “clean slate” proposal called NovaGenesis. We specify a set of NovaGenesis services to publish sensor device’s data in distributed hash tables employing selfverifying addresses and contract-based trust network formation. IoT devices capabilities and configurations are exposed to software-controllers, which control their operational parameters. The specification covers how the “things” sensed information are subscribed by a big data service and injected in Spark big data platform, allowing NovaGenesis services to subscribe data analytics from Spark. Future work include implementation of the proposed specifications and further investigation of NovaGenesis services performance and scalability.
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Paper Nr: 57
Title:

An Agent Architecture to Enable Self-healing and Context-aware Web of Things Applications
Authors:

Rafael Angarita, Maude Manouvrier and Marta Rukoz
Abstract: The Internet of Things (IoT) paradigm promises to connect billions of objects in an Internet-like structure. Applications composed from connected objects in the IoT are expected to have a huge impact in the transportation and logistics, healthcare, smart environments, and personal and social domains. The world of things is much more complex, dynamic, mobile, and failure prone than the world of computers, with contexts changing rapidly and unpredictably. The growing complexity of IoT applications will be unmanageable, and will hamper the creation of new services and applications, unless the systems will show “self-*” functionality such as self-management, self-healing and self-configuration. The Web of Things (WoT) builds on top of the IoT to create applications composed of smart things relying on standard and well-known Web technologies. In this paper, we present a new agent architecture to enable self-healing and context-aware WoT applications.
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Paper Nr: 63
Title:

A New Distributed MIKEY Mode to Secure e-Health Applications
Authors:

Mohammed Riyadh Abdmeziem, Djamel Tandjaoui and Imed Romdhani
Abstract: Securing e-health applications in the context of Internet of Things (IoT) is challenging. Indeed, resources scarcity in such environment hinders the implementation of existing standard based protocols. Among these protocols, MIKEY (Multimedia Internet KEYing) aims at establishing security credentials between two com- municating entities. However, the existing MIKEY modes fail to meet IoT specificities. In particular, the pre-shared key mode is energy efficient, but suffers from severe scalability issues. On the other hand, asymmetric modes such as the public key mode are scalable, but are highly resource consuming. To address this issue, we combine two previously proposed approaches to introduce a new distributed MIKEY mode. Indeed, relying on a cooperative approach, a set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the pre-shared mode is used in the constrained part of the network, while the public key mode is used in the unconstrained part of the network. Preliminary results show that our proposed mode is energy preserving whereas its security properties are kept safe.
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Paper Nr: 64
Title:

Orchestrating the Cognitive Internet of Things
Authors:

Chung-Sheng Li, Frederica Darema, Verena Kantere and Victor Chang
Abstract: The introduction of pervasive and ubiquitous instrumentation within Internet of Things (IoT) leads to unprecedented real-time visibility of the power grid, traffic, transportation, water, oil & gas. Interconnecting those distinct physical, people, and business worlds through ubiquitous instrumentation, even though still in its embryonic stage, has the potential to create intelligent IoT solutions that are much greener, more efficient, comfortable, and safer. An essential new direction to materialize this potential is to develop comprehensive models of such systems dynamically interacting with the instrumentation in a feed-back control loop. We describe here opportunities in applying cognitive computing on interconnected and instrumented worlds (CIoT) and call out the system-of-systems trend on interconnecting these distinct but interdependent worlds, and methods for advanced understanding, analysis, and real-time decision support capabilities with the accuracy of full-scale models.
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