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Human Activity Inference Systems for Context-aware Internet of Things Applications

Human Activity Inference Systems for Context-aware Internet of Things Applications
Alternative Title
상황 인지 기반 사물인터넷 어플리케이션을 위한 사람 행동 추론 시스템
Park, Ho Min
DGIST Authors
Park, Ho MinSon, Sang Hyuk
Son, Sang Hyuk
Ko, Jeong Gil
Issued Date
Awarded Date
2016. 8
HHuman Activity Inference SystemsSmartphonesWireless Sensor NetworksSmart EnvironmentsIoT architectures행동 추론 시스템스마트폰무선센서네트워크스마트 환경사물인터넷구조
After the advent of Internet of Things (IoT) in late 1990s, everyday objects are envisioned to be tightly interconnected by equipping them with wireless sensing and actuation capabilities to monitor and manipulate target environments. Consequently, such an extensive interconnection of physical objects generates massive amount of sensory data representing the physical phenomenon. While such functionalities are the basis of IoT, the raw data itself cannot be used to make smart and autonomous actuations. Considering such a lack of intelligence, many scholars stressed the need of context-aware computings to attain comprehensive knowledge regarding environmental contexts, including human activity contexts.
Considering the fact that most of IoT applications and services are designed to be human centric, we emphasized the need of Human Activity Inference Systems (HAIS) while addressing key research problems raised when HAIS is used by different IoT services. Throughout this work, we selected four interesting services, which we elaborated in the main chapters, within three IoT application domains. After going through each technical chapters, we noted that accurate context inferencing results yielded from HAIS can benefit one or more IoT architecture layer(s) by carefully controlling various system parameters depending on the human activity contexts.
Contributions of each technical chapters can be briefly summarized as follows.
The very first chapter started from questioning the possibilities of identifying a human activity context, namely driving. According previous studies, texting while driving significantly increases the chance of getting into an accidents, thus appropriate protection services must be applied when the user is found to be driving a vehicle. In order to extract such a human activity context, we proposed a HAIS which effectively extract, analyze, and fuse the heterogeneous sensory information on commodity smartphones. In accordance with the IoT architecture layers, this chapter contributed to improve the performance of top two layers, namely application service and information integration layers.
Next chapter was initiated with an objective to compress the capabilities of a microphone sensor to fit in resource-limited IoT platforms. In fact, we proposed a HAIS, which extracts most frequently found office meeting activities using heterogeneous IoT-enabled objects installed throughout a smart office environment. we believe that our considerations can open the possibilities to quickly commercialize human activity context extraction systems to everyday users by providing them with an easy-to-install system that is applicable to various application scenarios. Under such a system, various smart office applications are be integrated to increase the human comfort levels and maximize the operational efficiency. The contributions of this chapter dealt with information integration layer and application service layer.
Throughout the third chapter, we present an activity-aware sensor cycling solution tailored to smart home environments that significantly increases the accuracy and reliability of activity detection by exploiting the inherent correlations residing in the residents' behavioral patterns. The proposed solution predicts the activity patterns that are most likely to occur and, based on the prediction results, determines the role of each sensor to monitor the environment. The contributing factor with this work was that human activity context extracted by HAIS can also be used to enhance the performance of object sensing layer by carefully controlling the sensory data acquisition techniques. We note that this chapter dealt with the object sensing layer from the IoT architecture.
The last chapter introduced a light-weight, energy-efficient, low-latency privacy protection scheme for smart home environments against side channel attacks. According to the studies, data encryptions cannot provide acceptable level of privacy protections against smart adversaries. In fact, we introduced the concept of cloaking activities to hide the actual human activity context while saving as much energy as possible. Contributing factors of this work dealt with privacy protection and data exchange layer to support the intelligent IoT applications and services provided through smart home environments. ⓒ 2016 DGIST
Table Of Contents
1 Introduction 1--
1.1 Internet of Things Paradigm and Need for Context-aware Computing 1--
1.1.1 Internet of Things 1--
1.1.2 Context-aware Computing 3--
1.2 Importance of Human Activity Inference Systems 3--
1.3 System-level Considerations when Designing HAIS 4--
1.4 Research Problems and Contributions 5--
1.4.1 Driver’s Smartphone Identification for Distraction-free Driving 6--
1.4.2 Office Activity Context Inferencing under Resource-limited Platforms 7--
1.4.3 Reliable and Energy-efficient Sensor Cycling for Smart Home Services 7--
1.4.4 Energy-efficient Smart Home Privacy Protection using Activity Contexts 7--
1.5 Dissertation Organization 8--
2 Automatic Identification of Driver’s Smartphone 9--
2.1 Introduction 9--
2.2 System Design 11--
2.2.1 Design Overview 12--
2.2.2 Walking and Standing Detector (WSD) 12--
2.2.3 Smartphone Position Classifier (SPC) 16--
2.2.4 Entrance Detector (ETD) 17--
2.2.5 Entrance Direction Classifier (EDC) 23--
2.2.6 Seated Row Classifier (SRC) 25--
2.2.7 Activating Distracted Driving Prevention Services 28--
2.3 Performance Evaluation 29--
2.3.1 Experimental Setup 29--
2.3.2 Evaluation Criteria 29--
2.3.3 Evaluation Results 30--
2.4 Related Work 41--
2.5 Conclusion 43--
3 Utilizing Resource-limited Sensors for Office Activity Context Extraction 44--
3.1 Introduction 44--
3.2 Activity Context Extraction: Issues with utilizing resource limited hardware 46--
3.3 ReLiSCE: Utilizing Resource-limited Sensors for Activity Context Extraction 48--
3.3.1 Defining the Target Detection Activity States 48--
3.3.2 Hardware Components in ReLiSCE 49--
3.3.3 Microphone Array Sensor Processing Scheme 52--
3.3.4 PIR Sensor Processing Scheme 57--
3.3.5 Illumination Sensor Processing Scheme 58--
3.3.6 Combining Heterogeneous Sensors In ReLiSCE 58--
3.3.7 Minimizing Sensor Usage 60--
3.4 Performance Evaluation 60--
3.4.1 Microphone Array Sensor Performance 61--
3.4.2 PIR Sensing Platform Evaluation 64--
3.4.3 Illumination Sensing Platform Evaluation 68--
3.4.4 Discussions on Connecting ReLiSCE with Applications 71--
3.5 Related Work 72--
3.6 Conclusion 74--
4 Activity-aware Sensor Cycling for Human Activity Monitoring 75--
4.1 Introduction 75--
4.2 Algorithm Description 77--
4.2.1 Selecting Active Sensors via Activity Prediction 78--
4.2.2 Appointing Sentry Sensors Based on Semantic Similarity 78--
4.3 Performance Evaluation 80--
4.3.1 Simulation Setup 80--
4.3.2 Activity Detection Accuracy 81--
4.3.3 Fairness in Energy Consumption 81--
4.4 Conclusion 84--
5 Energy-Efficient Privacy Protection Using Behavioral Semantics 85--
5.1 Introduction 85--
5.2 A Side Channel Attack for ADL Detection 87--
5.3 Semantic Privacy Preservation via Energy-Efficient Traffic Generation 90--
5.3.1 Semantic Distance between Sensors 94--
5.3.2 Semantic Similarity Graph (SSG) 95--
5.3.3 Cloaking Activities and Privacy 96--
5.3.4 Lower Layer Considerations 97--
5.4 Performance Evaluation 97--
5.4.1 Experimental Setup 97--
5.4.2 Evaluation Results 99--
5.4.3 Discussions 106--
5.5 Related Work 107--
5.6 Conclusion 109--
6 Conclusion 110--
References 112
Information and Communication Engineering
Related Researcher
  • 손상혁 Son, Sang Hyuk 정보통신융합전공
  • Research Interests Real-time system; Wireless sensor network; Cyber-physical system; Data and event service; Information security; 실시간 임베디드 시스템
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Department of Electrical Engineering and Computer Science Theses Master


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