UC Berkeley UC Berkeley Electronic Theses and Dissertations Title A Low-Power Mobile Sensing Architecture Permalink https://escholarship.org/uc/item/2jf6n2sw Author Dutta, Prabal Publication Date 2009 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California A Low-Power Mobile Sensing Architecture by Prabal Kumar Dutta A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor David E. Culler, Chair Professor Scott Shenker Professor Ion Stoica Professor Paul K. Wright Fall 2009 The dissertation of Prabal Kumar Dutta is approved: Chair Date Date Date Date University of California, Berkeley Fall 2009 A Low-Power Mobile Sensing Architecture Copyright 2009 by Prabal Kumar Dutta 1 Abstract A Low-Power Mobile Sensing Architecture by Prabal Kumar Dutta Doctor of Philosophy in Computer Science University of California, Berkeley Professor David E. Culler, Chair The system and network architecture for stationary sensornets is largely solved today with many commer- cial solutions now available and standardization efforts underway at the IEEE, IETF, ISA, and within many industry groups. However, the existing techniques for reliable, low-power communications in stationary sensornets fail on both counts when confronted with mobility. In this dissertation, we argue that awareness of real or potential mobility enables a solution that handles the mobile case well, and supports stationary networks as a special case. This dissertation addresses micropower mobiscopes, a nascent class of mobile sensornets – small, embedded, and battery-powered systems – that experience unpredictable but structured mobility and are severely energy-constrained. We show how awareness of mobility can simplify their com- munication challenges, enable low-power operation, and enhance the reliability of data delivery. We introduce the MOV metric, a measure of mobility, and present techniques to gather it on a near nano-power budget. We also present iCount, a regulator-integrated energy meter design that allows nodes to introspect their own energy usage, and adapt their behavior to the actual energy availability and con- sumption. Integrating the pieces, we present three concrete hardware platforms that support our mobile sensing architecture. We develop a novel asynchronous neighbor discovery algorithm called Disco that allows nodes to operate their radios at very low duty cycles and yet still discover neighbors without any external synchronization information. Recognizing the necessity of beaconing in mobile networks, and the need for mobile-stationary node interactions, we design a link layer synchronization primitive, Backcast, and a receiver-initiated link layer, HotMac, that are suitable for mobile sensing, but also work for stationary networks across a range of conventional data collection workloads and a broad range of duty cycles. We evaluate our thesis with three mobile sensing applications that embody our proposed architecture. The three applications – AutoWitness, SleepTrack, and CommonSense – are representative of asset tracking, health and fitness, and participatory urban sensing, and they each stress different aspects of the architecture, including motion detection, neighbor discovery, communications, interaction patterns, energy management, and data transport. These design points illustrate that our architecture is general enough to enable a range of applications but specific enough to support them well. Professor David E. Culler Dissertation Committee Chair i To Wendy, whose boundless patience is exceeded only by her love and generosity. ii Contents List of Figures vi List of Tables xii 1 Introduction 1 1.1 Mobility Changes Everything . .2 1.2 Problem Statement . .3 1.3 A Low-Power Mobile Sensing Architecture . .3 1.4 Contributions . .4 1.5 Roadmap . .5 2 Background 7 2.1 Traditional Mobiscopes . .8 2.1.1 Vehicular . .8 2.1.2 Handheld . .8 2.2 Micropower Mobiscopes . .9 2.2.1 Typical Application Classes . .9 2.2.2 Application Requirements . 10 2.3 Technical Problem Statement . 11 3 3P’s Hardware Platform Architecture 12 3.1 Overview . 12 3.2 Related Work . 14 3.3 Building Block Approach . 15 3.4 Core Module . 16 3.4.1 Component Choices Revisited . 17 3.4.2 Implementation Decisions . 23 3.4.3 Mechanical Design . 24 3.4.4 Performance Microbenchmarks . 25 3.4.5 Future Directions . 26 3.5 Expert Peripheral Modules . 26 3.5.1 USB/Power Module . 27 3.5.2 Bulk Storage Module . 27 3.6 Expert Circuit Subsystems: Implementing the MOV Metric . 27 iii 3.6.1 Detecting Shock and Vibration . 28 3.6.2 Detecting Acceleration, Gestures, and Displacement . 28 3.6.3 Detecting General Activity Recognition . 29 3.7 Expert Circuit Subsystems: Measuring Nodal Energy Consumption . 30 3.8 Prototyping . 32 3.8.1 Try It And See with Agile Platform Prototyping . 32 3.8.2 Debugging . 33 3.9 Carrier Board Case Studies . 33 3.10 Discussion . 34 3.11 Summary . 35 4 Asynchronous Neighbor Discovery 36 4.1 Overview . 36 4.2 Related Work . 37 4.3 Disco Design . 39 4.3.1 Simplified Algorithm . 39 4.3.2 Coprimes are not Enough . 41 4.3.3 Choosing Primes . 41 4.3.4 Slot Non-Alignment . 42 4.3.5 Duty Cycle from Discovery Latency . 43 4.3.6 Duty Cycle Granularity . 44 4.3.7 Robustness to Clock Skew . 44 4.4 Disco Implementation . 45 4.5 Simulation Study . 47 4.5.1 Simulation Models . 47 4.5.2 Discovery Latency Comparison . 47 4.5.3 Discovery Latency: A Deeper Look . 49 4.5.4 Impact of Duty Cycle Asymmetry . 52 4.5.5 Latency-Driven Discovery with Small Encounter Windows . 52 4.6 Empirical Performance Evaluation . 54 4.6.1 Discovery Rate . 54 4.6.2 Discovery Latency in Clusters . 54 4.7 Discussion . 56 4.7.1 Beacon Rate Adaptation . 56 4.7.2 Accelerating Discovery with Gossip . 57 4.7.3 Secure Discovery . 57 4.8 Summary . 57 5 A Link Layer Synchronization Primitive 58 5.1 Overview . 58 5.2 Related Work . 60 5.3 Backcast Design . 62 5.3.1 Simplified Model . 63 5.3.2 Phase Offset . 64 5.3.3 Power Differences . 67 iv 5.3.4 Frequency Skews . 67 5.3.5 Capture . 67 5.3.6 Analytical Performance Model . 67 5.4 Backcast Implementation . 69 5.4.1 Application Programming Interface . 70 5.4.2 CC2420 Radio . 70 5.4.3 CC2520 Radio . 70 5.5 Evaluation . 71 5.5.1 Performance in a Controlled Two-Node Case . 71 5.5.2 Performance in a Controlled Multinode Setting . 72 5.5.3 Performance in a More Realistic Setting . 74 5.5.4 Large Scale Performance in a More Realistic Setting . 76 5.5.5 Energy Microbenchmarks . ..