Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science 8-13-2019 Collaborative Edge Computing in Mobile Internet of Things Ravishankar Chamarajnagar Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Recommended Citation Chamarajnagar, Ravishankar, "Collaborative Edge Computing in Mobile Internet of Things." Dissertation, Georgia State University, 2019. https://scholarworks.gsu.edu/cs_diss/153 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. TITLE: COLLABORATIVE EDGE COMPUTING IN MOBILE INTERNET OF THINGS by RAVISHANKAR CHAMARAJNAGAR Under the Direction of Ashwin Ashok Phd. ABSTRACT The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportu- nities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision sys- tems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool in- stead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abun- dance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative comput- ing, collaborative security and collaborative privacy intelligence. The first framework, Op- portunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spa- tial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third frame- work, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user's privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration. INDEX WORDS: Collaborative, Blockchain, Opportunistic, Distributed, Mobile, Secu- rity, Threat, Integrity, Privacy, Intrusion, Smarthome, Dimensions, IoT, Sensing TITLE: COLLABORATIVE EDGE COMPUTING IN MOBILE INTERNET OF THINGS by RAVISHANKAR CHAMARAJNAGAR A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the College of Arts and Sciences Georgia State University 2019 Copyright by Ravishankar Chamarajnagar 2019 TITLE: COLLABORATIVE EDGE COMPUTING IN MOBILE INTERNET OF THINGS by RAVISHANKAR CHAMARAJNAGAR Committee Chair: Ashwin Ashok Committee: Rajshekhar Sunderraman Raheem A. Beyah Zhipeng Cai Electronic Version Approved: Office of Graduate Studies College of Arts and Sciences Georgia State University August 2019 iv DEDICATION This dissertation is dedicated to Georgia State University. v ACKNOWLEDGEMENTS This dissertation work would not have been possible without the support of many people. I want to express my gratitude to my advisor Dr. Ashwin Ashok for the endless hours of discussions and mentoring and to Dr. Rajshekhar Sunderraman for starting me off on this path. I want to thank my parents Venkatadasu and Nagarathna, my brother Shreeram and my sister Savitha for the blessings and the support over the years that have gotten me this far. My wife Vani has been a constant source of inspiration and motivation through the entire journey while my kids Rohan and Anushka, with their belief in me, have pushed me harder every day; they have been an integral part of my journey every step of the way. vi TABLE OF CONTENTS ACKNOWLEDGEMENTS ::::::::::::::::: v LIST OF TABLES :::::::::::::::::::: ix LIST OF FIGURES :::::::::::::::::::: x LIST OF ABBREVIATIONS :::::::::::::::: xii PART 1 INTRODUCTION ::::::::::::::: 1 1.1 Collaborative Edge Computing ..................... 1 1.2 Collaborative Edge Security ....................... 2 1.3 Collaborative Privacy Intelligence ................... 3 PART 2 COLLABORATIVE EDGE COMPUTING: OPPORTUNIS- TIC MOBILE IOT WITH BLOCKCHAIN BASED COL- LABORATION :::::::::::::::: 4 2.1 Towards opportunistic collaboration .................. 5 2.1.1 Blockchain based collaboration . 6 2.1.2 Related Work . 7 2.2 Opportunistic Collaborative Platform design ............ 8 2.2.1 Blockchain Overview . 8 2.2.2 System Overview . 8 2.2.3 System Workflow . 9 2.3 Platform Efficiency ............................ 12 2.3.1 Experiment setup and methodology . 13 2.3.2 App Execution latency . 14 2.3.3 Resource Utilization Efficiency . 15 vii 2.4 Takeaways and Future work ....................... 17 PART 3 COLLABORATIVE EDGE SECURITY: INTEGRITY THREAT IDENTIFICATION FOR DISTRIBUTED IOT IN PRECI- SION AGRICULTURE ::::::::::::: 19 3.1 Towards Collaborative Security ..................... 20 3.1.1 Related Work . 22 3.1.2 Background on Mesh Network . 24 3.2 Spatio-Temporal locality based Threat detection . 25 3.2.1 Local Outlier Factor Algorithm . 26 3.2.2 Spatio-Temporal locality based Threat detection algorithm . 27 3.3 Precision Agriculture based Threat Detection . 33 3.3.1 Evaluation of the platform . 36 3.3.2 Thoreau Dataset Analysis . 40 3.4 Takeaways and Future work ....................... 42 PART 4 COLLABORATIVE PRIVACY INTELLIGENCE: USER PRIVACY INTRUSION MODELING USING MACHINE LEARNING OF SMART HOME IOT SENSORS DATA 45 4.1 Towards Collaborative Privacy Intelligence . 46 4.1.1 Privacy Concerns and Dimensions . 47 4.1.2 Related Work . 49 4.2 Privacy Dimensional Modeling ..................... 52 4.2.1 Localization Overview . 53 4.2.2 Emotional Dimension Overview . 55 4.3 Privacy Intrusion Quantification .................... 58 4.3.1 Experimental Setup and Methodology . 59 4.3.2 Rule based Localization Evaluation . 63 4.3.3 V/A/D based Emotional Evaluation . 66 viii 4.4 Takeaways and Future work ....................... 68 PART 5 CONCLUSION :::::::::::::::: 70 REFERENCES ::::::::::::::::::::: 72 APPENDICES :::::::::::::::::::::: 79 Appendix A SURVEY: THREAT PERCEPTION BY PRIVACY DI- MENSIONS ::::::::::::::::: 79 Appendix B SURVEY: EMOTIONAL DIMENSION CLASSIFICATION 82 ix LIST OF TABLES Table 2.1 HW Configuration of IoT Nodes . 13 Table 2.2 Resource Usage before deploying apps { percentage of total capacity of the node . 16 Table 2.3 Resource Usage after deploying apps{ percentage of total capacity of the node . 16 Table 3.1 Mean-Variance table. This is an example table for 3 physical sensing attributes (classes). A new column is appended for each new class. Each table represents the ensemble mean in time window Td. Here, mean and variance of sensor data from each of 11 nodes are collected within the time window. 31 Table 3.2 LOF of values from Mean-Variance table in time-window Td. The row and column definitions are same as the mean-variance table. 32 x LIST OF FIGURES Figure 2.1 Opportunistic Collaborative IoT using Blockchains . 5 Figure 2.2 System Workflow. Lines in Red are the contributions of this approach 10 Figure 2.3 App Execution Latency . 14 Figure 2.4 Collaborative Edge Scaling . 18 Figure 3.1 Precision Agriculture Data Integrity . 20 Figure 3.2 Our proposed integrity threat identification framework . 25 Figure 3.3 Local Outlier Factor (LOF) algorithm pseudocode . 27 Figure 3.4 Results from Local Outlier Factor (LOF) algorithm . 28 Figure 3.5 Sensor Network . 29 Figure 3.6 Geometry of sensor placements in a precision agriculture setting (left) and sample LOFs from simulated sensor data for the specific geometry (right) . 34 Figure 3.7 Integrity Threat Detection feasibility through LOFs for real sensor data from a wireless mesh network. 37 Figure 3.8 Number of threats detected compared to ground truth (blue line) at multiple threshold levels. 38 Figure 3.9 Threat detection performance for 100 cycle experiment for 50 nodes (6 nodes physically attacked and 44 nodes virtually attacked by noise addition). 39 Figure 3.10 Integrity Threat Detection Classification Effectiveness using ROC curve. 40 Figure 3.11 Integrity Threat Detection Classification Performance with Machine Learning . 41 Figure 3.12 Thoreau Data Set: Threats by Season . 42 Figure 3.13 Threats on 4/1/2017 from 9:30 PM to 11:59 PM . 43 xi Figure 3.14