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Doctoral Thesis Energy Efficient Data Collection in Body Area Doctoral Thesis Energy Efficient Data Collection in Body Area Nanonetworks by Liu Bo Graduate School of Systems Information Science Future University Hakodate March 2019 Abstract Body Area Nanonetworks (BANNs) are an important class of emerging nanonet- works, where tiny nano-nodes move in human body and can communicate with each other over peer-to-peer wireless links. Since they can provide inexpensive and con- tinuous health monitoring services, BANNs hold great promises for many important biomedical applications in immune and drug delivery systems. In these systems, it is critical for nano-nodes to collect real-time data information. However, the con- straint of extremely limited energy stored in nano-nodes poses a great obstacle to the future applications of BANNS. Thus, a comprehensive study on the energy effi- cient data collection schemes in BANNs is of great importance for supporting their applications. We first propose a lightweight data collection scheme under a simple yet effi- cient scenario with multiple nano-nodes and only one nano-router in BANNs. In the scheme, we employ a sleep/wake-up mechanism to avoid unnecessary energy con- sumption when no eternal request comes. With a careful consideration of both node available energy and transmission energy consumption, we then design a new node selection strategy to further reduce the energy consumption in the data collection process. We further conduct extensive simulations for both the proposed data col- lection scheme and the benchmark greedy scheme to validate energy efficiency of our scheme as well as to illustrate the impacts of network parameters on data collection performance. We further propose a new energy efficient data collection scheme under a complex scenario with multiple nano-nodes and nano-routers in BANNs. In the scheme, we first categorize data as emergent data and normal data with a careful consideration of the energy constraints. Based on such classification, we then generate distinct packets and assign different priorities to them. In BANNs, the normal data is usually sent regularly, while the emergent data is sent immediately. Extensive simulations are also provided to validate energy efficiency of our scheme in comparison with other benchmark greedy scheme, and to illustrate the impacts of network parameters on data collection. Finally, we propose a relay-based energy-efficient data collection scheme with the minimum energy coding. Under the scheme, we derive the maximum nano-node density for reliable communication in BANNs, and also conduct density-dependent reliability analysis. Both rate-energy and delay-energy tradeoffs are further investi- gated with constant codebook size and constant Hamming distance, respectively. We also provide extensive simulations to validate our scheme. i ii Acknowledgments I would like to express my deepest gratitude to all those who gave me the opportunity to complete this thesis. First and foremost, I am deeply indebted, grateful to my supervisor, Professor Xiaohong Jiang for giving me copious amounts of insightful guidance, constant support, and expertise on every subject that arose throughout all these years. He is more than a supervisor and a teacher but a role model and a friend. Working with him is proven to be an enjoyable and rewarding experience. I would also like to give my special thanks to Professor Zhenqiang Wu for giving me a lot of help in my academic research. This thesis would not have been possible without their guidance. I would like to acknowledge my thesis committee members, Professor Yuichi Fu- jino, Professor Hiroshi Inamura and Professor Masaaki Wada, for their interests and for their constructive comments that help to improve this thesis. I am also very grateful to all the people. I have interacted with at Future Univer- sity Hakodate, specifically everyone affiliated with Jiang’s Laboratory. My graduate study at the Future University Hakodate is a really rewarding experience. This work is dedicated to my family, whose love and unconditional support provide a constant inspiration in my life. In particular, I would like to thank my wife for her understanding, encouragement, and support in these years. iii THIS PAGE INTENTIONALLY LEFT BLANK iv Contents Abstract i Acknowledgments iii List of Figures x 1 Introduction 1 1.1 Background ................................ 1 1.1.1 RelatedTerms........................... 3 1.1.2 BANNs Applications Fields . 11 1.2 Motivations ................................ 16 1.3 ThesisOutline............................... 19 2 Related Works 21 2.1 Wakeup-based Energy-efficient Data Collection . 21 2.2 Timing-based Energy-efficient Data Collection . 23 2.3 Relay-based Energy-efficient Data Collection . 25 3 Preliminaries 29 3.1 ArchitectureinBANNs.......................... 29 3.2 Nano-nodeEnergyConsumption. 31 3.3 EnergyHarvestingSystems . 35 3.3.1 EnergyHarvestingfromtheBloodstream . 36 3.3.2 EnergyHarvestingfromanExternalSource . 36 v 4 Wakeup-based Energy-efficient Data Collection Scheme in BANNs 39 4.1 SystemModel............................... 39 4.2 Wakeup-based Energy-efficient Data Collection Scheme . 43 4.2.1 Basic Definitions . 43 4.2.2 Wake-up/SleepMechanism. 44 4.2.3 Nano-nodeSelectionStrategy . 45 4.2.4 Operations of Data Collection Scheme . 45 4.3 PerformanceEvaluation ......................... 46 4.3.1 Average Available Energy . 47 4.3.2 AveragePathLoss ........................ 50 4.4 Simulation Results . 51 4.4.1 Simulation Settings . 51 4.4.2 Simulation Results . 51 4.5 Summary ................................. 53 5 Timing-based Energy-efficient Data Collection Scheme in BANNs 57 5.1 SystemModel............................... 57 5.2 Timing-based Energy Efficient Data Collection Scheme . 59 5.2.1 Timing/priority Mechanism . 60 5.2.2 NeighborDiscovery........................ 61 5.2.3 Participating Nodes . 61 5.3 PerformanceEvaluation ......................... 63 5.3.1 EnergyAnalysis.......................... 63 5.3.2 Delay Analysis . 64 5.4 Simulation Results . 66 5.4.1 Simulation Settings . 66 5.4.2 Simulation Results . 67 5.5 Summary ................................. 71 6 Relay-based Energy-efficient Data Collection Scheme in BANNs 73 6.1 SystemModel............................... 73 vi 6.2 MinimumEnergyCodes ......................... 75 6.2.1 Minimum Expected Weight . 75 6.2.2 Codebookgeneration . .. .. 76 6.3 Relay-based Energy-efficient Data Collection Scheme . 77 6.4 PerformanceEvaluation ......................... 79 6.4.1 MaximumNodeDensity ..................... 79 6.4.2 Rate-DelayandEnergy-Delay . 82 6.5 Simulation Results . 83 6.6 Summary ................................. 87 7 Conclusion 89 7.1 SummaryoftheThesis.......................... 89 7.2 FutureWorks ............................... 90 Bibliography 93 Pulications 101 vii viii List of Figures 1-1 Approaches for the development of nano-machines . ... 4 1-2 Functional architecture mapping between nano-machines of a micro or nano-robot,andnano-machinesfoundincells. 7 1-3 Immunesystemsupport ......................... 12 1-4 Nanorobot................................. 13 1-5 Drugdeliverysystems .......................... 14 1-6 Healthmonitoring ............................ 15 1-7 Geneticengineering............................ 16 1-8 Themaincontentsofthisthesis . 19 3-1 BANNarchitecture ............................ 30 3-2 Architectureofanano-sensordevice. 32 3-3 Biotransferrable graphene wireless nano-sensor . .... 35 4-1 The system model consists of one nano-interface, one nano-routers and multiplenano-nodes. ........................... 40 4-2 Energy harvested in the ultra-nano-capacitor as a function of the num- berofcycles. ............................... 49 4-3 Simulation results for the performance of average available energy. 55 4-4 Simulation results for the performance average path loss. .... 56 5-1 Illustrationofsystemmodel . 58 5-2 Average available energy comparison for TBEES, Greedy and Flooding schemes .................................. 67 ix 5-3 Average available energy comparison with respect to decreasing num- berofparticipatingnodes ........................ 68 5-4 Average delay comparison for TBEES, Greedy and Flooding schemes 69 5-5 Average delay comparison with respect to decreasing number of par- ticipatingnodes.............................. 70 6-1 BANNs with potential destinations and potential interfering nano-nodes 74 6-2 Example of a network topology with four nano-nodes. .. 78 6-3 Maximum allowed node density vs. source set cardinality for p=0.05. 83 6-4 Rate-delayandenergy-delayforM=16,p=0.01 . 84 6-5 Rate-delayandenergy-delayford=3,p=0.01 . 86 x Chapter 1 Introduction In this chapter, we first introduce the background of our study and some related terms (like nanonetworks, nano-machine, body area nanonetworks, etc). Then we describe the motivations of this thesis, and the outline of this thesis. 1.1 Background Recent advancements in nano-technology have boosted the growth in small-scale com- munication. Specially, the wireless nanonetworks are a class of new small-scale com- munication paradigm. The emergence of nano-technology paves the path of devel- oping nano-machines to perform very simple and specific tasks at nano-level such as computing, data storing, sensing and actuation [1] for diversified applications in biomedical, environment science, industrial development, food science, military, etc [2, 3]. The nano-machines can be developed for performing highly-sophisticated tasks such as recognizing and destroying tumors cells via penetration of sensitive body sites including the spinal cord, gastrointestinal,
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