IoTBDS 2020 Abstracts


Area 1 - Big Data Research

Full Papers
Paper Nr: 8
Title:

Data-driven Model for Influenza Prediction Incorporating Environmental Effects

Authors:

Yosra Didi, Ahlem Walha and Ali Wali

Abstract: Influenza is one of the most severe and prevalent epidemic that causes mortality and morbidity. The researcher focused on early forecasting to prevent and control the outbreak of the flu disease, which it may reduce their impact on our daily lives. We propose a model based on machine learning methods that is capable of making timely influenza prediction using the impact of many environmental factors such as climatic variables, air pollutants and geographical proximity. Our significant contribution is to incorporate the impact of this environmental factors changes on the spread of the disease with a machine learning method to improve the performance of the influenza prediction models. We use multiple data sources including Illness Like Influenza (ILI) data, climatic factors, air pollutant and geographic proximity that have significant correlation with ILI rate. In this paper, we compare the proposed model with two methods and with the actual value to prove the effectiveness of our approach.

Paper Nr: 68
Title:

Best Fit Missing Value Imputation (BFMVI) Algorithm for Incomplete Data in the Internet of Things

Authors:

Benjamin Agbo, Yongrui Qin and Richard Hill

Abstract: The noticeable growth in the adoption of Internet of Things (IoT) technologies, has led to the generation of large amounts of data usually from sensor devices. When dealing with massive amounts of data, it is very common to observe databases with large amounts of missing values. This is a challenge for data miners because various methods for data analysis only work well on complete databases. A popular way to deal with this challenge is to fill-in (impute) missing values using adequate estimation techniques. Unfortunately, a good number of existing methods rely on all the observed values in the entire dataset to estimate missing values, which significantly causes unfavourable effects (low accuracy and high complexity) on imputed results. In this paper, we propose a novel imputation technique based on data clustering and a robust selection of adequate imputation equations for each missing datapoint. We evaluate our proposed method using six University of California Irvine (UCI) datasets, and relevant comparison with five recently proposed imputation methods. The results presented showed that the performance of the proposed imputation method is comparable with the Local Similarity Imputation (LSI) technique in terms of imputation accuracy, but is significantly less complex than all the existing methods identified.

Short Papers
Paper Nr: 28
Title:

Classifying Big Data Taxonomies: A Systematic Literature Review

Authors:

Daniel Staegemann, Matthias Volk, Alexandra Grube, Johannes Hintsch, Sascha Bosse, Robert Häusler, Abdulrahman Nahhas, Matthias Pohl and Klaus Turowski

Abstract: As big data is a rather young, but growing discipline, lots of confusion about the general nature of this term exists. Consequently, multiple research endeavours to discover unique characteristics, technologies, techniques and their interconnections were conducted, resulting in comprehensive classification approaches. For this purpose, various taxonomies on big data exist in literature. However, due to the multitude of approaches and partial contradictions, no real clarification is achieved. To overcome this issue, a systematic literature review was conducted, which identifies and analyses big data taxonomies. As a result, a classification of those taxonomies is proposed, which additionally tracks sub-domains that are not yet covered by the existing taxonomies so far. Eventually, the publication at hand serves as a starting point for further taxonomy related research endeavours in the big data domain.

Paper Nr: 32
Title:

Predicting SQL Query Execution Time with a Cost Model for Spark Platform

Authors:

Aleksey Burdakov, Viktoria Proletarskaya, Andrey Ploutenko, Oleg Ermakov and Uriy Grigorev

Abstract: The paper proposes a cost model for predicting query execution time in a distributed parallel system requiring time estimation. The estimation is paramount for running a DaaS environment or building an optimal query execution plan. It represents a SQL query with nested stars. Each star includes dimension tables, a fact table, and a Bloom filter. Bloom filters can substantially reduce network traffic for the Shuffle phase and cut join time for the Reduce stage of query execution in Spark. We propose an algorithm for generating a query implementation program. The developed model was calibrated and its adequacy evaluated (50 points). The obtained coefficient of determination R2=0.966 demonstrates a good model accuracy even with non-precise intermediate table cardinalities. 77% of points for the modelling time over 10 seconds have modelling error Δ<30%. Theoretical model evaluation supports the modelling and experimental results for large databases.

Paper Nr: 33
Title:

A Large-scale Replication of Smart Grids Power Consumption Anomaly Detection

Authors:

Bruno Rossi

Abstract: Anomaly detection plays a significant role in the area of Smart Grids: many algorithms were devised and applied, from intrusion detection to power consumption anomalies identification. In this paper, we focus on detecting anomalies from smart meters power consumption data traces. The goal of this paper is to replicate to a much larger dataset a previously proposed approach by Chou and Telaga (2014) based on ARIMA models. In particular, we investigate different model training approaches and the distribution of anomalies, putting forward several lessons learned. We found the method applicable also to the larger dataset. Fine-tuning the parameters showed that adopting an accumulating window strategy did not bring benefits in terms of RMSE. While a 2s rule seemed too strict for anomaly identification for the dataset.

Paper Nr: 36
Title:

Retrieving Similar Software from Large-scale Open-source Repository by Constructing Representation of Project Description

Authors:

Chuanyi Li, Jidong Ge, Victor Chang and Bin Luo

Abstract: The rise of open source community has greatly promoted the development of software resource reuse in all phases of software process, such as requirements engineering, designing, coding, and testing. However, how to efficiently and accurately locate reusable resources on large-scale open source website remains to be solved. Presently, most open source websites provide text-matching-based searching mechanism while ignoring the semantic of project description. For enabling requirements engineers to find software that are similar to the one to be developed quickly at the very beginning of the project, we propose a searching framework based on constructing semantic embedding for software project with machine learning technique. In the proposed approach, both Type Distribution and Document Vector learnt through different neural network language models are used as project representations. Besides, we integrate searching results of different representations with a Ranking model. For evaluating our approach, we compare search results of different searching strategies manually using an evaluating system. Experimental results on a data set consisting of 24,896 projects show that the proposed searching framework, i.e., combining results derived from Inverted Index, Type Distribution and Document Vector, significantly superior to the text-matching-based one.

Paper Nr: 38
Title:

Big Data Processing Tools Navigation Diagram

Authors:

Martin Macak, Hind Bangui, Barbora Buhnova, András J. Molnár and Csaba I. Sidló

Abstract: Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.

Paper Nr: 43
Title:

Low Cost Big Data Solutions: The Case of Apache Spark on Beowulf Clusters

Authors:

Marin Fotache, Marius-Iulian Cluci and Valerică Greavu-Şerban

Abstract: With distributed computing platforms deployed on affordable hardware, Big Data technologies have democratised the processing of huge volumes of structured and semi-structured data. Still, the costs of installing and operating even relatively small cluster of commodity servers or the cost of hiring cloud resources could prove inaccessible for many companies and institutions. This paper builds two predictive models for estimating the main drivers of the data processing performance for one of the most popular Big Data system (Apache Spark) deployed on gradually increased number of nodes of a Beowulf cluster. Data processing performance was estimated by randomly generated SparkSQL queries on TPC-H database schema, with variable number of joins (including self-joins), predicates, groups, aggregate functions and subqueries included in FROM clause. Using two machine learning techniques, random forest and extreme gradient boosting, predictive models tried to estimate the query duration on predictors related to cluster setup and query structure and also to assess the importance of predictors for the outcome variability. Results were positive and encouraging for extending the cluster number of nodes and the database scale.

Paper Nr: 47
Title:

Intrinsic Indicators for Numerical Data Quality

Authors:

Milen S. Marev, Ernesto Compatangelo and Wamberto W. Vasconcelos

Abstract: This paper focuses on data quality indicators conceived to measure the quality of numerical datasets. We have devised a set of three different indicators, namely Intrinsic Quality, Distance-based Quality Factor and Information Entropy. The results of quality measures based on these indicators can be used in further data processing, helping to support actual data quality improvements. We argue that the proposed indicators can adequately capture in a quantitative way the impact of different numerical data quality issues including (but not limited to) gaps, noise or outliers.

Paper Nr: 52
Title:

Analysis of Co-authorship Network and the Correlation between Academic Performance and Social Network Measures

Authors:

Qianwen Xu and Victor Chang

Abstract: This project conducted link analysis and graph cluster analysis to analyze the co-authorship network of 166 researchers, mainly from three top universities in Shanghai, China. The publication data of researchers in the area of social science between 2014 and 2016 were collected from Scopus, and the g index was calculated as their performance indicator. For this project, the centrality measures, the efficiency of the egocentric network were calculated as well as authorities and hubs were identified in the link analysis. In addition, clustering algorithms based on betweenness centrality were used to conduct the graph cluster analysis. Finally, in order to identify productive researchers, this project employed the Spearman correlation test to analyze the correlation between a researcher's performance and social network measures. Results from this test indicate that except for closeness centrality and degree centrality, the correlation between g-index and betweenness centrality, eigenvector centrality and efficiency is significant.

Paper Nr: 73
Title:

Determining Potential Failures and Challenges in Data Driven Endeavors: A Real World Case Study Analysis

Authors:

Daniel Staegemann, Matthias Volk, Tuan Vu, Sascha Bosse, Robert Häusler, Abdulrahman Nahhas, Matthias Pohl and Klaus Turowski

Abstract: The utilization of data in general and big data in particular offers large opportunities, but is at the same time accompanied by a huge number of potential causes for failure. To avoid those pitfalls when realizing such undertakings, at the beginning, it is necessary to develop an in-depth understanding of those causes. This contribution analyses twelve real world case studies, from the big data and related domains, which were facing issues. The causes for the experienced problems were extracted and thereupon categorized, facilitating the understanding of practitioners and researchers that are engaged in the big data domain. Furthermore, potential avenues for future research are highlighted.

Paper Nr: 20
Title:

The Suitability of Graph Databases for Big Data Analysis: A Benchmark

Authors:

Martin Macak, Matus Stovcik and Barbora Buhnova

Abstract: Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.

Paper Nr: 26
Title:

Tackling the Six Fundamental Challenges of Big Data in Research Projects by Utilizing a Scalable and Modular Architecture

Authors:

Andreas Freymann, Florian Maier, Kristian Schaefer and Tom Böhnel

Abstract: Over the last decades the necessity for processing and storing huge amounts of data has increased enormously, especially in the fundamental research area. Beside the management of large volumes of data, research projects are facing additional fundamental challenges in terms of data velocity, data variety and data veracity to create meaningful data value. In order to cope with these challenges solutions exist. However, they often show shortcomings in adaptability, usability or have high licence fees. Thus, this paper proposes a scalable and modular architecture based on open source technologies using micro-services which are deployed using Docker. The proposed architecture has been adopted, deployed and tested within a current research project. In addition, the deployment and handling is compared with another technology. The results show an overcoming of the fundamental challenges of processing huge amounts of data and the handling of Big Data in research projects.

Paper Nr: 45
Title:

Discussion on Heterogeneous Converged IoT Networking Scheme of CBN in Hotel Field

Authors:

Jiujun Bai, Yuqing Tong and Xuebo Chen

Abstract: The Internet of Things (IoT) is a business area that the four major domestic operators must compete in the 5G era. How to build a hotel IoT solution based on China Broadcast Network (CBN) without affecting the transmission of video information in the hotel industry is an urgent problem. Under the conditions of internal transmission networks, integrating a networking solution into the CBN IoT is a key for this problem. This paper proposes a heterogeneous networking scheme combining the common transmission medium conditions in the hotel with the conventional network architecture of the IoT, CBN video transmission network and hotel transmission network together. This should be a new idea for the hotel CBN IoT business.

Paper Nr: 66
Title:

Approaching the (Big) Data Science Engineering Process

Authors:

Matthias Volk, Daniel Staegemann, Sascha Bosse, Robert Häusler and Klaus Turowski

Abstract: For many years now, researchers as well as practitioners are harnessing well-known data mining processes, such as the CRISP-DM or KDD, to realize their data analytics projects. In times of big data and data science, at which not only the volume, variety and velocity of the data increases, but also the complexity to process, store and manage them, conventional solutions are often not sufficient and even more sophisticated systems are needed. To overcome this situation, in this positioning paper the (big) data science engineering process is introduced to provide a guideline for the realization of data-intensive systems. For this purpose, using the design science research methodology, existing theory and current literature from relevant subdomains are contextualized, discussed and adapted.

Area 2 - Emerging Services and Analytics

Full Papers
Paper Nr: 30
Title:

Online Predicting Conformance of Business Process with Recurrent Neural Networks

Authors:

Jiaojiao Wang, Dingguo Yu, Xiaoyu Ma, Chang Liu, Victor Chang and Xuewen Shen

Abstract: Conformance Checking is a problem to detect and describe the differences between a given process model representing the expected behaviour of a business process and an event log recording its actual execution by the Process-aware Information System (PAIS). However, such existing conformance checking techniques are offline and mainly applied for the completely executed process instances, which cannot provide the real-time conformance-oriented process monitoring for an on-going process instance. Therefore, in this paper, we propose three approaches for online conformance prediction by constructing a classification model automatically based on the historical event log and the existing reference process model. By utilizing Recurrent Neural Networks, these approaches can capture the features that have a decisive effect on the conformance for an executed case to build a prediction model and then use this model to predict the conformance of a running case. The experimental results on two real datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance.

Short Papers
Paper Nr: 11
Title:

A Cloud-based Analytics Architecture for the Application of Online Machine Learning Algorithms on Data Streams in Consumer-centric Internet of Things Domains

Authors:

Theo Zschörnig, Jonah Windolph, Robert Wehlitz and Bogdan Franczyk

Abstract: The increasing number of smart devices in private households has lead to a large quantity of smart homes worldwide. In order to gain meaningful insights into their generated data and offer extended information and added value for consumers, data analytics architectures are essential. In addition, the development and improvement of machine learning techniques and algorithms in the past years has lead to the availability of powerful analytics tools, which have the potential to allow even more sophisticated insights at the cost of changed challenges and requierements for analytics architectures. However, architectural solutions, which offer the ability to deploy flexible, machine learning-based analytics pipelines on streaming data, are missing in research as well as in industry. In this paper, we present the motivation and a concept for machine learning-based data processing on streaming data for consumer-centric Internet of Things domains, such as smart home. This approach was evaluated in terms of its performance and may serve as a basis for further development and discussion.

Area 3 - Big Data for Multi-discipline Services

Short Papers
Paper Nr: 74
Title:

Big Data Analytics as Game Changer in Dealing Impact of Climate Change in Malaysia: Present and Future Research

Authors:

Mohammad F. Abdullah, Mohd Zaki Mat Amin, Zurina Zainol and Marini Mohamad Ideris

Abstract: Data has become a vital and vigorous resource to support the organisation in a data-driven decision-making environment. The emergence of digital transformation has revolutionised data utilisation and management ecosystem, which translated and upscaled the value of data to become a new asset in the organisation. Realisation on the importance of data, Big Data Analytics (BDA) in organisations is part of initiatives to harvest and maximise the potential use of data through data analytics capabilities. N-HyDAA development has encapsulated BDA through integration and analytics of data, information, knowledge and expertise from the expert group in dealing with issues related to the impact of climate change such as water-related disaster and water resources management. Based on N-HyDAA capabilities, there are more potentials and opportunities in the new research area to explore for better cohesion in supporting decision-making.

Paper Nr: 54
Title:

Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building

Authors:

Omar El Midaoui, Btihal El Ghali and Abderrahim El Qadi

Abstract: Due to its specificities and hierarchical structure, a geographical query needs a special process of reformulation by Information Retrieval Systems (IRS). This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy that models’ the notion of adjacency that can be uses in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in the top of the list of retrieved results when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm FP-Growth is applied to this database in his parallel version (PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation based on the taxonomy constructed using our proposed approach improves the precision and the effectiveness of the IRS.

Area 4 - Internet of Things (IoT) Applications

Full Papers
Paper Nr: 37
Title:

IoT-CryptoDiet: Implementing a Lightweight Cryptographic Library based on ECDH and ECDSA for the Development of Secure and Privacy-preserving Protocols in Contiki-NG

Authors:

Eugene Frimpong and Antonis Michalas

Abstract: Even though the idea of transforming basic objects to smart objects with the aid sensors is not new, it is only now that we have started seeing the incredible impact of this digital transformation in our societies. There is no doubt that the Internet of Things (IoT) has the power to change our world and drive us to a complete social evolution. This is something that has been well understood by the research and industrial communities that have been investing significant resources in the field of IoT. In business and industry, there are thousands of IoT use cases and real-life IoT deployments across a variety of sectors (e.g. industry 4.0 and smart factories, smart cities, etc.). However, due to the vastly resource-constrained nature of the devices used in IoT, implementing secure and privacy-preserving services, using, for example, standard asymmetric cryptographic algorithms, has been a real challenge. The majority of IoT devices on the market currently employ the use of various forms of symmetric cryptography such as key pre-distribution. The overall efficiency of such implementations correlates directly to the size of the IoT environment and the deployment method. In this paper, we implement a lightweight cryptographic library that can be used to secure communication protocols between multiple communicating nodes without the need for external trusted entities or a server. Our work focuses on extending the functionalities of the User Datagram Protocol (UDP) broadcast application on the Contiki-NG Operating System (OS) platform.

Paper Nr: 49
Title:

Do Not Tell Me What I Cannot Do! (The Constrained Device Shouted under the Cover of the Fog): Implementing Symmetric Searchable Encryption on Constrained Devices

Authors:

Eugene Frimpong, Alexandros Bakas, Hai-Van Dang and Antonis Michalas

Abstract: Symmetric Searchable Encryption (SSE) allows the outsourcing of encrypted data to possible untrusted third party services while simultaneously giving the opportunity to users to search over the encrypted data in a secure and privacy-preserving way. Currently, the majority of SSE schemes have been designed to fit a typical cloud service scenario where users (clients) encrypt their data locally and upload them securely to a remote location. While this scenario fits squarely the cloud paradigm, it cannot apply to the emerging field of Internet of Things (IoT). This is due to the fact that the performance of most of the existing SSE schemes has been tested using powerful machines and not the constrained devices used in IoT services. The focus of this paper is to prove that SSE schemes can, under certain circumstances, work on constrained devices and eventually be adopted by IoT services. To this end, we designed and implemented a forward private dynamic SSE scheme that can run smoothly on resource-constrained devices. To do so, we adopted a fog node scenario where edge (constrained) devices sense data, encrypt them locally and use the capabilities of fog nodes to store sensed data in a remote location (the cloud). Consequently, end users can search for specific keywords over the stored ciphertexts without revealing anything about their content. Our scheme achieves efficient computational operations and supports the multi-client model. The performance of the scheme is evaluated by conducting extensive experiments. Finally, the security of the scheme is proven through a theoretical analysis that considers the existence of a malicious adversary.

Short Papers
Paper Nr: 1
Title:

Design and Development of IoT based Computer Network Room Environment Monitoring System

Authors:

Fahd A. Banakhr

Abstract: This paper discusses design and development of internet of thing (IoT) based computer network room monitoring system (NRMS) for twenty five rooms of college campus. Furthermore, it also discusses the benefits of Internet of things offer as compare to traditional method of monitoring which does not cope with rising demand of dynamic power used by servers and networking devices. The NRMS deals with monitoring network room environmental conditions like power, temperature, and relative humidity. The system then sends this information to the IoT cloud and displays the live data on the dashboard. It also sends alert to the authorized persons; records the previous data to find any root cause of the problem if needed. The data updated periodically from the implemented system can be accessible through internet from anywhere in the world.

Paper Nr: 7
Title:

Enabling Predictive and Preventive Maintenance using IoT and Big Data in the Telecom Sector

Authors:

Tahir Mahmood and Kamran Munir

Abstract: Telecom sector has always been working hard to improve network quality to satisfy end user services. Fixing telecom network errors (hardware and software) precisely and quickly is a main factor to improve quality of services. Telecom operators are spending a lot of budget on ad hoc maintenance to fix these errors. This paper presents a framework using internet of things (IoT) and big data to enable predictive and preventive maintenance, which have been applied in the telecom sector. A telecom network consists of radio nodes, transport network, switching centres and civil infrastructure; and in this paper, focus is on the maintenance of Radio Access Network (RAN). A challengeable task for telecom operators has been to maintain radio nodes as these are installed on different locations. This framework for predictive maintenance is modelled using active and historical data from telecom equipment as well as data collected from IoT devices and sensors. The major benefit of implementing this framework has been a control on the time and cost of the maintenance by pre-planning maintenance activities and related budget.

Paper Nr: 9
Title:

The Socio-technical Impact of the Internet of Things: An Exploratory Mixed Methods Research

Authors:

Albert Boonstra and Dustin A. Wiktor-Steffens

Abstract: This study explores how the Internet of Things (IoT) impacts the socio-technical system of organizations. The paper adopts a mixed methods research with a qualitatively driven approach. Data from 21 interviews with experts in the field of IoT and an online survey with 123 IoT professionals were analyzed. Leonardi’s Socio-Technical System Model (2012) was applied as a lens to examine how IoT influences the organizations’ social subsystem and how that, in turn, affects both the materiality of IoT and users’ intentionality in the technical subsystem. The results suggest transformed roles, potentially flattened hierarchies, decreased privacy, and increased transparency to be the main effects. While apparent changes in the social subsystem cause perceived threats that strongly influence users’ intentionality, they do not certainly affect IoT’s materiality. Noteworthy, however, is that irreplaceable users reportedly have the leverage to enforce changes to IoT’s materiality.

Paper Nr: 18
Title:

Unity 3D Simulator of Autonomous Motorway Traffic Applied to Emergency Corridor Building

Authors:

Jurij Kuzmic and Günter Rudolph

Abstract: This paper introduces a 3D simulator made with the game engine Unity to analyse the behaviour of autonomous vehicles in the case of unexpected accidents in motorway traffic. This simulator works towards the removal of current problems with building an emergency corridor on motorways. It is often the case that rescue vehicles cannot reach the scene of an accident and are obstructed by other road users. This means that the help for those involved in the accident may come too late. To prevent this in future with autonomous vehicles and to save human lives, building an emergency corridor for self-driving cars will be simulated and presented with the game engine Unity. Since the autonomous vehicles also have to communicate while driving, the techniques of Vehicle-to-Infrastructure (V2I) Communication, Vehicle-to-Vehicle (V2V) Communication and Infrastructure-to-Infrastructure (I2I) Communication will be reviewed theoretically. Besides, practical methods for lane, distance and rotation detection will be presented. Furthermore, we discuss sensor technology such as position estimator, lidar, radar and video camera. Also, the levels of automation of self-driving cars will be shown. This will make it possible to determine the level of the automated rescue corridor formation. Several experiments prove the simulator’s functionality concerning unexpected accidents and the formation of the rescue corridor. Finally, further research and work in this area will be explained briefly.

Paper Nr: 22
Title:

IoT-based Systems Actuation Conflicts Management Towards DevOps: A Systematic Mapping Study

Authors:

Stéphane Lavirotte, Gérald Rocher, Jean-Yves Tigli and Thibaut Gonnin

Abstract: The Internet of Things (IoT) has long been understood as an infrastructure layer allowing to gather environmental data through sensors. However, it also provides means to physically interact with our living environments through actuators. To the extent that actuation effects are not without risks on safety and trustworthiness, providing the IoT infrastructure layer with merely sensors access control mechanisms is no longer sufficient. It is also required to prevent conflicting (and possibly unsafe) actuation effects to occur in the physical environment and deploy means to resolve them. In this paper, we consider actuation conflicts management as part of the DevOps approach, which aims to harmonize tools and objectives of actors involved in IoT-based systems life cycle from their design to their deployment. In this context, a systematic mapping study (SMS) is conducted to better understand the actuation conflicts management approaches and to what extent they could be integrated into the DevOps life cycle.

Paper Nr: 23
Title:

OKIoT Open Knowledge IoT Project: Smart Home Case Studies of Short-term Course and Software Residency Capstone Project

Authors:

Victor T. Hayashi, Vinicius Garcia, Renato Manzan de Andrade and Reginaldo Arakaki

Abstract: The emergence of smart environments built on top of Internet of Things (IoT) solutions demand new skills and knowledge for developers. Dealing with inherent complexity of IoT architecture, constrained device limitations, communication faults and vendor lock-in can be drawbacks for successful deploy of IoT projects. With the objective of sharing knowledge between IoT developers, OKIoT project focuses on project-oriented education in Short-term and Long-term modes based on Software Engineering methodology. First qualitative results on a 6-week course on MBA subject offering and a case study of an open architecture for smart speaker executed with 1-year mentoring of a capstone project are summarized. Future steps based on case studies insights are presented.

Paper Nr: 53
Title:

Fulfilling the IoT Vision: Are We There Yet?

Authors:

Daniel Del Gaudio and Pascal Hirmer

Abstract: The vision of the Internet of Things is enabling self-controlled and decentralized environments, in which hardware devices, equipped with sensors and actuators communicate with each other trough standardized internet protocols to reach common goals. The device-to-device communication should be decentralized and should not necessarily require human interaction. However, enabling such complex IoT applications, e.g., connected cars, is a big challenge, since many requirements need to be fulfilled. These requirements include, for example, security, privacy, timely data processing, uniform communication standards, or location-awareness. Based on an intensive literature review, in this overview paper, we define requirements for such environments and, in addition, we discuss whether they are fulfilled by state-of-the-art approaches or whether there still has to be work done in the future. We conclude this paper by illustrating research gaps that have to be filled in order to realize the IoT vision.

Paper Nr: 55
Title:

Preparatory Reflections on Safe Context-adaptive Software (Position Paper)

Authors:

Dominik Grzelak and Uwe Aßmann

Abstract: Mobile technology and the Internet of Things promise to deepen the interaction between people, services, and physical devices. Digital solutions for these prospective computing systems are not only radically changing the user experience but also the software engineering process. Without a doubt, software complexity enormously increases, and prospective systems become challenging to develop, maintain, and verify. The user’s reliance on safety-critical software systems is a serious element in any software engineering process where the absence of bugs must be ensured, and malfunction ruled out. Software that is not safe, i.e., the software’s behavior does not comply with a specification, could cause loss of profits or, in the worst-case, harm people. Software safety is an ongoing but mostly academic research field incorporating formal methods to prove the correctness of a program using mathematical methods. In this spirit, we examine the promising context-aware computing and model-driven development paradigms that have directed the development of fog computing and IoT platforms alike. Furthermore, we aggregate viable requirements for computational context models to be employed both for computation and also reasoning about the correctness of applications.

Paper Nr: 57
Title:

Self-adaptive Sensing IoT Platform for Conserving Historic Buildings and Collections in Museums

Authors:

Rita Tse, Marcus Im, Su-Kit Tang, Luís F. Menezes, Alfredo G. Dias and Giovanni Pau

Abstract: As historic buildings and collections in museums are normally of deteriorated structure or materials, any sudden change of weather or environment, such as oxygen level, temperature, humidity, air quality, etc., may cause damages to them and it may not be recoverable. Internet of Things (IoT) is common in solving problems by collecting environmental data using sensors. The data is live and immediate for visualizing the environment, which is suitable for conserving the buildings and collections. However, there is no one-for-all IoT solution for this conservation problem. In this paper, we propose the design of the sensor device in the IoT platform for conserving historic buildings and collections in museums. The sensor device is self-adaptive, running continuously without any interruption causing by the instability of power and network connection. The platform is currently implemented for the conservation project in the Science museum, University of Coimbra, Portugal. It has been running over a year and the conservation work is going well.

Paper Nr: 40
Title:

Overview of Enterprise IoT Security System based on Edge Computing

Authors:

Liyun Lan, Jiujun Bai and Xuebo Chen

Abstract: The topic of security runs through the development of human society, and it exists in all aspects of our lives and work. In order to better ensure the safety of the employees in enterprises, the enterprise IoT security system should be established. With the development of technology, the application of IoT in enterprise security has made some progress. Since more and more devices are connected to the Internet and generate big data, cloud computing is no longer sufficient to process and analyse IoT devices in real time. Especially the data is generated by different digital platforms involving enterprise production, management and safety. Therefore, edge computing can be taken into account. This paper briefly introduces the development status of Internet of Things technology and edge computing technology comparing with cloud computing. It should be helpful for us that edge computing is proposed to apply to enterprise IoT security system.

Paper Nr: 63
Title:

Scalable Logistic Cell RFID Witness Model

Authors:

Bernhard Heiden, Volodymyr Alieksieiev and Bianca Tonino-Heiden

Abstract: This paper describes a scalable logistic cell Radio Frequency IDentification (RFID) Witness Model. First, a scalable logistic cell analysis is done which can be applied to the logistics of any size-scale and application. This model is then implemented into Witness and simulated, for different cases. To show practicability, the model is mirrored in a physical Internet of Things (IoT) device in form of an Arduino micro-controller board which is attached to an RFID-Reader, together with a model-warehouse / forklift truck unit. The specific challenge of this work is to design a universal logistic model, for demonstration of all possible logistic applications with one simple cell, together with a single step IoT connection, and that can be easily built as well as a physical, as a computer simulation model.

Area 5 - Internet of Things (IoT) Fundamentals

Full Papers
Paper Nr: 12
Title:

Skeleton-based Action Recognition for Industrial Packing Process

Authors:

Zhenhui Chen, Haiyang Hu, Zhongjin Li, Xingchen Qi, Haiping Zhang, Hua Hu and Victor Chang

Abstract: The applications of action recognition in real-world scenarios are challenging. Although state-of-the-art methods have demonstrated good performance on large scale datasets, we still face complex practical problems and inappropriate models. In this work, we propose a novel local image directed graph neural network (LI-DGNN) to solve a real-world production scenario problem which is the completeness identification of accessories during the range hood packing process in a kitchen appliance manufacturing workshop. LI-DGNN integrates skeleton-based action recognition and local image classification to make good use of both human skeleton data and appearance information for action recognition. The experimental results demonstrate the high recognition accuracy and good generalization ability on the range hood packing dataset (RHPD) which is generated in the industrial packing process. The results can meet the recognition requirements in the actual industrial production process.

Paper Nr: 13
Title:

A Life Cycle Method for Device Management in Dynamic IoT Environments

Authors:

Daniel D. Gaudio, Maximilian Reichel and Pascal Hirmer

Abstract: In the Internet of Things, interconnected devices communicate with each other through standardized internet protocols to reach common goals. By doing so, they enable building complex, self-organizing applications, such as Smart Cities, or Smart Factories. Especially in large IoT environments, newly appearing devices as well as leaving or failing IoT devices are a great challenge. New devices need to be integrated into the application whereas failing devices need to be dealt with. In a Smart City, newly appearing actors, for example, smart phones or connected cars, appear and disappear all the time. Dealing with this dynamic is a great issue, especially when done automatically. Consequently, in this paper, we introduce A Life Cycle Method for Device Management in Dynamic IoT Environments. This method enables integrating newly appearing IoT devices into IoT applications and, furthermore, offers means to cope with failing devices. Our approach is evaluated through a system architecture and a corresponding prototypical implementation.

Paper Nr: 70
Title:

A Systematic Mapping of Patterns and Architectures for IoT Security

Authors:

Tanusan Rajmohan, Phu H. Nguyen and Nicolas Ferry

Abstract: We have entered a vast digital revolution of the IoT era when everything is connected. The popularity of IoT applications makes security for IoT of paramount importance. Security patterns are based on domain-independent time-proven security knowledge and expertise. Can they be applied to IoT? We aim to draw a research landscape of patterns and architectures for IoT security by conducting a systematic mapping study. From more than a thousand of relevant papers, we have systematically identified and analyzed 24 papers that have been published around patterns for IoT security (and privacy). Our analysis shows that there is a rise in the number of publications addressing security patterns in the two recent years. However, there are gaps in this research area that can be filled in to promote the use of patterns for IoT security and privacy.

Short Papers
Paper Nr: 19
Title:

A Reinforcement Learning QoS Negotiation Model for IoT Middleware

Authors:

Itorobong S. Udoh and Gerald Kotonya

Abstract: A large number of heterogeneous and mobile devices interacting with each other, leading to the execution of tasks with little human interference, characterizes the Internet of Things (IoT) ecosystem. This interaction typically occurs in a service-oriented manner facilitated by an IoT middleware. The service provision paradigm in the IoT dynamic environment requires a negotiation process to resolve Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences. This paper proposes a negotiation model that allows negotiating agents to dynamically adapt their strategies using a model-based reinforcement learning as the QoS preferences evolve and the negotiation resources changes due to the changes in the physical world. We use a simulated environment to illustrate the improvements that our proposed negotiation model brings to the QoS negotiation process in a dynamic IoT environment.

Paper Nr: 21
Title:

Stocks Prices Prediction with Long Short-term Memory

Authors:

Zinnet D. Akşehir, Erdal Kılıç, Sedat Akleylek, Mesut Döngül and Burak Coşkun

Abstract: It is a difficult problem to predict the one-day next closing price of stocks since there are many factors affecting stock prices. In this study, by using data from November 29, 2010 to November 27, 2019 and stocks for the closing price of the next day are predicted. The long short-term memory method, a type of recurrent neural networks, is preferred to develop the prediction model. The set of input variables created for the proposed model consists of stock price data, 29 technicals and four basic indicators. After the set of input variables is created, the one-day next closing prices of AKBNK and GARAN stocks are developed the model to predict. The model's prediction performance is evaluated with Root Mean Square Error(RMSE) metric. This value is calculated as 0.482 and 0.242 for GARAN and AKBNK stocks respectively. According to the results, the predictions realized with the set of input variables produced are sufficiently successful.

Paper Nr: 27
Title:

Application Lifecycle Management for Industrial IoT Devices in Smart Grid Use Cases

Authors:

Stephan Cejka, Florian Kintzler, Lisa Müllner, Felix Knorr, Marco Mittelsdorf and Jörn Schumann

Abstract: Complex cyber-physical systems like the Smart Grid, in which Industrial Internet of Things (IIoT) technology is used, require advanced software maintenance mechanisms to remain dependable and secure. In this paper, requirements and tasks for an application lifecycle management for IIoT use cases, with special focus on the domains of Smart Grid and Smart Buildings, are defined and state-of-the-art software deployment processes from IoT use cases are evaluated for usage in those domains. As there is no suitable framework, an approach for the deployment of OSGi components is described. On top of such software deployment tools, a knowledge-based software management framework that utilizes domain specific knowledge to create and execute software rollout plans will be presented. Thus, dependencies can be managed on device, system and domain level.

Paper Nr: 67
Title:

IIot Platform for Agile Manufacturing in Plastic and Rubber Domain

Authors:

Ilaria Bosi, Jure Rosso, Enrico Ferrera and Claudio Pastrone

Abstract: In recent years, the concept of integration as a key to digital transformation has also been associated with the interconnection of hardware, software, data and information in Industry 4.0. One of the greatest challenges of Industry 4.0 is to be able to ingest massive amounts of data coming out from machines: the eFactory platform enable users to exploit innovative functionalities, experiment with disruptive approaches and develop custom solutions to maximise connectivity, interoperability and efficiency across the supply chains. To achieve this goal, it is necessary to work on standard communication protocols and architectures. By leveraging Industrial Internet of Things (IIoT) technologies, this feasibility study focuses on the design and implementation of an open source platform for plastic and rubber industry, that abstract data and functionalities provided by on- board machinery sensors, exposing relevant services outside the machines to external cloud-based applications. The federation of this new services related to the industrial scenario is supported by an interoperable 'Data Spine' that simplifies cross-platform communication and securely capture information on the multi-tier supply chain. The intent is to make the production process more automated, interconnected and moreover to support a Zero-Defect strategy thanks to digital technologies involved in the project.

Paper Nr: 75
Title:

BRAIN-IoT: Paving the Way for Next-Generation Internet of Things

Authors:

Enrico Ferrera, Xu Tao, Davide Conzon, Victor S. Pombo, Miquel Cantero, Tim Ward, Ilaria Bosi and Mirko Sandretto

Abstract: Nowadays, the adoption of the Internet of Things is drastically increasing in different domains and is contributing to the fast digitalization of several different critical sectors. In the near future, next generation of IoT-based systems will become more complex to be designed and managed. An opportunity for the development of flexible smart IoT-based systems that drive the business decision-making is to take more precise and accurate decisions at the right time, collecting real-time IoT generated data. This involves a set of challenges including the complexity of IoT-based systems and the management of large-scale systems scalability. With respect to these challenges, we propose to automate the management of IoT-based systems mainly based on an autonomic computing approach; these systems should implement cognitive capabilities that allow them learning and generating decisions at the right time. Consequently, we propose a model-driven methodology for designing smart IoT-based systems. With this objective, BRAIN-IoT paves the way to develop and demonstrate novel IoT concepts and solutions to underpin the Next Generation Internet of Things vision and architecture, that focusing on self-aware and semi-autonomous IoT systems, as well as on moving away from centralized cloud-computing solutions towards distributed intelligent edge computing systems.

Paper Nr: 39
Title:

A Review of Edge Computing Nodes based on the Internet of Things

Authors:

Yunqi Dong, Jiujun Bai and Xuebo Chen

Abstract: Due to the limitations of resources on the IoT device side, it is necessary to provide users with services not only by means of a long-distance cloud computing center node, but also by some edge computing nodes. If all the data on the device side is transmitted to the cloud center node, it will be returned to the device side after unified processing. This transmission method will bring great pressure to the network link and data center, and it will also cause the cloud center node to overload and refuse service. In order to speed up the data processing and reduce delay, we briefly summarize the edge computing node model in this paper. Firstly, considering the properties of privacy, security, trust and resource scheduling, etc., the edge computing is analyzed. Then, based on these properties, the definition, architecture, and collaboration with cloud-edge-net of edge computing are discussed. We also introduce the current key technologies used in edge computing, such as network, virtualization, isolation, deep learning, and access control technologies. Finally, we give a prospect of the possible application of edge computing in the future.

Area 6 - IoT Technologies

Full Papers
Paper Nr: 46
Title:

A Real-time Temperature Anomaly Detection Method for IoT Data

Authors:

Wei Liu, Hongyi Jiang, Dandan Che, Lifei Chen and Qingshan Jiang

Abstract: Temperature control plays a vital part in medical supply management, of which effective monitoring and anomaly detection ensure that the medication storage is maintained properly to meet health and safety requirements. In this paper, an unsupervised temperature anomaly detection method, called DTAD (Dynamic Threshold Anomaly Detection), is proposed to detect anomalies in real-time temperature time series. The DTAD sets dynamic thresholds based on the Smoothed Z-Score Algorithm, rather than set fixed thresholds of a temperature range by experience. The comparative evaluation is performed on the DTAD and four other commonly employed methods, the results of which shows that the DTAD reaches a higher accuracy and a better time efficiency. The DTAD is fully automated and can be used in developing a real-time IoT temperature anomaly detection system for medical equipment.

Short Papers
Paper Nr: 59
Title:

cloud.iO, An Open-source W3C WoT Compliant Framework

Authors:

Lucas Bonvin, Dominique Gabioud and Michael Clausen

Abstract: The Internet of Things (IoT) is gaining more and more popularity. It is therefore not surprising that the number of IoT cloud-based solutions grows accordingly. Most of these solutions have their own semantics and syntax, hence creating a heterogeneous landscape with a lack of interoperability. W3C works on the Web of Things (WoT) standardization with the goal of bringing interoperability across IoT solutions. This paper presents how the interoperability of the open-source IoT solution cloud.iO has been enhanced by making it compliant with the W3C WoT recommendations.

Paper Nr: 6
Title:

Safety in Distributed Sensor Networks: A Literature Review

Authors:

Tobias Altenburg, Sascha Bosse and Klaus Turowski

Abstract: The connectivity megatrend dominates the current social change. The number of networked devices and the resulting amount of data is constantly increasing worldwide. For this reason, the dependability of computer systems is becoming increasingly relevant. Especially in the context of civil infrastructures, the constant availability of computer systems is of great importance. This paper provides a structured overview of the current literary status of safety in distributed sensor networks. Most approaches from the literature focus on the design phase. By the following connection with the existing dependability theory, the potential for the optimization of dependability could be proven.

Area 7 - Security, Privacy and Trust

Full Papers
Paper Nr: 10
Title:

Experimental Evaluation of Forward Secure Dynamic Symmetric Searchable Encryption using the Searchitect Framework

Authors:

Ines Kramer, Silvia Schmidt, Manuel Koschuch and Mathias Tausig

Abstract: In this work we present a prototype implementation of a framework for searchable encryption (SE), “Searchitect”. Our framework can be used to extend applications with search functionality over encrypted data in a protocol agnostic approach, hopefully paving the way for a broader and easier adoption of this promising privacy enhancing technology. Furthermore, it allows for easy comparison and evaluation of different implementations of SE schemes. We discuss dynamic searchable encryption schemes, supporting efficient updates of an encrypted index, as well as forward secure schemes that guarantee additional security properties, which resist file injection attacks. We evaluate the performance characteristics of two implementations of existing forward secure schemes, DynRH and Sophos. Our results show that the DynRH implementation is outperforming Sophos in terms of efficiency in the execution time of the search and update protocol, but needs more bandwidth for a search request. In addition, we augment an existing cloud-storage application with SE functionality using our framework, showing the negligible additional effort required by the implementers to accomplish this.

Paper Nr: 14
Title:

ZigBee IoT Intrusion Detection System: A Hybrid Approach with Rule-based and Machine Learning Anomaly Detection

Authors:

Fal Sadikin and Sandeep Kumar

Abstract: The Internet of Things (IoT) is an emerging technology with potential applications in different domains. However these IoT systems introduce new security risks and potentially open new attack vector never seen before. In this article, we show various methods to detect known attacks, as well as possible new types of attacks on ZigBee based IoT systems. To do so, we introduce a novel Intrusion Detection System (IDS) with hybrid approach by combining the human-crafted rule-based and machine learning-based anomaly detection. Rule-based approach is used to provide accurate detection mechanism for known attacks, but the rule-based approach introduces complexity in defining precise rules for accurate detection. Therefore, machine learning approach is specifically used to create a complex model of normal behaviour that is used for anomaly detection. This paper outlines the IDS implementation that cover various types of detection methods both to detect known attacks, as well as potential new type of attacks in the ZigBee IoT systems.

Paper Nr: 15
Title:

Decepti-SCADA: A Framework for Actively Defending Networked Critical Infrastructures

Authors:

Nicholas Cifranic, Jose Romero-Mariona, Brian Souza and Roger A. Hallman

Abstract: Supervisory Control and Data Acquisition (SCADA) networks, which enable virtual components of critical infrastructures to connect to physical components, like the electrical grid, for example, are susceptible to cyber threats. This introductory paper discusses the application of deception as a technique for improving the cybersecurity posture of a network by using decoys to obfuscate the network and in turn make it harder for a potential adversary to find the real components. The Decepti-SCADA framework is introduced, which demonstrates multiple improvements over previous implementations of cyber deception strategies for SCADA systems. Decepti-SCADA has developed SCADA-specific decoys that can be used in a critical infrastructure environment. We detail Decepti-SCADA’s architecture, decoy generation and distribution, and ultimately explore what else can be done with cyber deception for critical infrastructures through early results.

Paper Nr: 16
Title:

Machine Learning based Malware Traffic Detection on IoT Devices using Summarized Packet Data

Authors:

Masataka Nakahara, Norihiro Okui, Yasuaki Kobayashi and Yutaka Miyake

Abstract: As the number of IoT (Internet of Things) devices increases, the countermeasures against cyberattacks caused by IoT devices become more important. Although mechanisms to prevent malware infection to IoT devices are important, such prevention becomes hard due to sophisticated infection steps and lack of computational resource for security software in IoT devices. Therefore, detecting malware infection of devices is also important to suppress malware spread. As the types of IoT devices and malwares are increasing, advanced anomaly detection technology like machine learning is required to find malware infected devices. Because IoT devices cannot analyze own behavior by using machine learning due to limited computing resources, such analysis should be executed at gateway devices to the Internet. This paper proposes an architecture for detecting malware traffic using summarized statistical data of packets instead of whole packet information. As this proposal only uses information of amount of traffic and destination addresses for each IoT device, it can reduce the storage space taken up by data and can analyze number of IoT devices with low computational resources. We performed the malware traffic detection on proposed architecture by using machine learning algorithms of Isolation Forest and K-means clustering, and show that high accuracy can be achieved with the summarized statistical data. In the evaluation, we collected the statistical data from 26 IoT devices (9 categories), and obtained the result that the data size required for analysis is reduced over 90% with keeping high accuracy.

Short Papers
Paper Nr: 24
Title:

Condition Elements Extraction based on PCA Attribute Reduction and Xgboost

Authors:

Luzhe Cao, Jinxuan Cao, Haoran Yin, Yongcheng Duan and Xueyan Wu

Abstract: In order to solve the problems of high data redundancy, unsatisfactory classification effect and low precision rate of situation elements extraction in large-scale network, a algorithm that extraction of situation elements based on PCA attribute reduction and Xgboost is proposed. Firstly, PCA is used to reduce the attributes of the data set, and then Xgboost classifier is constructed to classify and train the data after dimension reduction. In order to verify the effectiveness of the proposed algorithm, NSL-KDD data set was used to test the proposed algorithm. Through experiments, this algorithm is compared with SVM and other five algorithms. The experimental results show that the precision rate of the algorithm is greatly improved and the extraction of situation elements is effectively improved.

Paper Nr: 58
Title:

Self-recovery Service Securing Edge Server in IoT Network against Ransomware Attack

Authors:

In-San Lei, Su-Kit Tang, Ion-Kun Chao and Rita Tse

Abstract: Edge server takes an important role in IoT networks that distributes the computing power in the network and serves as a temporary storage for IoT devices. If there is any ransomware attack to the edge server, its network segment will be paralyzed, raising the data integrity and accuracy issues in the IoT system. In this paper, we propose a self-recovery method, called Self-Recovery Service (SRS), which can detect ransomware signature and recover victim files automatically. No interruption to the operation of edge server would be caused by ransomware. SRS is evaluated in the simulation test and the result shows that SRS takes insignificant system resources for its operation that does not degrade the performance of the edge server.

Paper Nr: 62
Title:

Investigating Trusted Records for Employment and Education

Authors:

Rayan M. Ghamri, Nawfal F. Fadhel and Gary B. Wills

Abstract: Employment uses trusted records such as award certificates as a part of hiring procedures. Some countries practices for employment history reference; either a service certificate or systems based on pension records which are based on number of years of employment that are also can be used for pension eligibility. However, there is an increase on the number of issued award certificates which are, in most cases, do not follow an agreed standard, which increases challenges for an authenticated record sharing. Moreover, employment systems are not required to share employment history nor to accommodate record requests educational institutes. Additionally, public employment systems are by design isolated from interaction with private sector. The research suggest that distributed systems would reduce costs for verifying and authenticating records while being flexible on allocating trusted records such as employment history, awarded certificates and resumes.

Paper Nr: 76
Title:

Environmental Aware Vulnerability Scoring

Authors:

Andreas Eitel

Abstract: When assessing the CVSS value of a vulnerability, the Environmental Metrics are often ignored. There are several reasons for this. However, this score is essential for the prioritization of vulnerabilities. The author proposes an approach that should generate the environmental score systematically and highly automated. For this purpose, various information about the systems and the network is needed, which should be managed in a model. An algorithm uses the linked information to automatically determine the Environmental Metrics. Experts without a security background should thus be able to determine this score in the same way as experts. The results should also be repeatable and independent of the evaluator.

Paper Nr: 48
Title:

A Framework for Data Sharing between Healthcare Providers using Blockchain

Authors:

Ahmed G. Alzahrani, Ahmed Alenezi, Hany F. Atlam and Gary Wills

Abstract: The healthcare data are considered as a highly valuable source of information that can improve healthcare systems to be more intelligent and improve the quality of the provided services. However, due to security and privacy issues, sharing data between healthcare organisations is challenging. This has led to data shortage in the healthcare sector which is considered as a significant issue not only in the Kingdom of Saudi Arabia (KSA) but also worldwide. The primary objective of conducting this paper is to investigate the various factors that enable secure sharing and exchange of healthcare information between different healthcare providers in the KSA. It starts by discussing the current literature and frameworks for managing healthcare data information and the challenges that health providers encounter, particularly when it comes to issues such as data security, patient privacy, and healthcare information exchange. These challenges in managing healthcare data have necessitated the need for implementing a solution that can allow medical providers to have access to updated healthcare information. Attention in the healthcare sector has been drawn to blockchain technology as a part of the solution, especially after the technology was successfully applied in the financial sector to improve the security of financial transactions, particularly involving digital currencies such as Bitcoin. Therefore, a framework based on the blockchain technology has been proposed to achieve the goals of the present research.

Paper Nr: 72
Title:

What3Words Geo Encryption: An Alternative Approach to Location based Encryption

Authors:

Julian Dreyer and Ralf Tönjes

Abstract: Identity Based Encryption (IBE) is a steadily emerging field of research in the cryptographical domain. A special flavor of IBE called Location Based Encryption (LBE) includes a given location attribute to add additional access control to the encrypted entity. The main goal is to allow an entity to decrypt the ciphertext only and only if the correct location information is provided. This allows to control the access based on the position of the data user. Existing solutions for LBE make use of the conventional Global Positioning System (GPS). Though, conventional GPS solutions are known to be influenced by an artificially added error, resulting in inaccuracy of the location data. This will consequently require the LBE scheme to include a level of tolerance, as the GPS coordinates may slightly diverge between different points in time. In order to mitigate this problem by design, an alternative approach to LBE is proposed to add additional tolerance. The approach presented in this paper makes use of the What3Words location system, which offers the required tolerance for the decryption and thereby mitigating the problem of GPS inaccuracy. A following study then evaluates the real-world performance of the new encryption algorithm.