Energy Efficient Downstream Communication in Wireless Sensor Networks

This dissertation studies the problem of energy efficient downstream communication in Wireless Sensor Networks (WSNs). First, we present the Opportunistic Source Routing (OSR), a scalable, reliable, and energy-efficient downward routing protocol for individual node actuation in data collection WSNs. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node’s upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in the packet header. OSR is scalable to very large-size WSN deployments, since each resource-constrained node in the network stores only the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. The evaluations in both simulations and real-world testbed experiments demonstrate that OSR significantly outperforms the existing approaches in scalability, reliability, and energy efficiency. Secondly, we propose a mobile code dissemination tool for heterogeneous WSN deployments operating on low power links. The evaluation in lab experiment and a real world WSN testbed shows how our tool reduces the laborious work to reprogram nodes for updating the application. Finally, we present an empirical study of the network dynamics of an out-door heterogeneous WSN deployment and devise a benchmark data suite. The network dynamics analysis includes link level characteristics, topological characteristics, and temporal characteristics. The unique features of the benchmark data suite include the full path information and our approach to fill the missing paths based on the principle of the routing protocol

Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation

We present the opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things IoT. We devise a novel adaptive Bloom filter mechanism to efficiently encode the downward source route in OSR, which significantly reduces the length of the source route field in the packet header. Moreover, each node in the network stores only the set of its direct children. Thus, OSR is scalable to very large-size WSN/IoT deployments. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node's upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy networks (LLNs). Our evaluation of OSR via both simulations and real-world testbed experiments demonstrates its merits in comparison with the state-of-the-art protocols

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students’ learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT

Scalable Downward Routing for Wireless Sensor Networks Actuation

In this paper, we study the downward routing for network control/actuation in large-scale and heterogeneous wireless sensor networks (WSNs). We propose the opportunistic source routing (OSR), a scalable and reliable downward routing protocol for WSNs. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node's upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode the downward source-route in OSR, which enables a significant reduction of the length of source-route field in the packet header. OSR is scalable to very large-size WSN deployments, since each resource-constrained node in the network stores only the set of its direct children. We present an analytical scalability model and evaluate the performance of OSR via both the simulations and real-world testbed experiments, in comparison with the standard RPL (both storing mode and non-storing mode), ORPL, and the representative dissemination protocol Drip. Our results show that the OSR significantly outperforms RPL and ORPL in scalability and reliability. OSR also achieves significantly better energy efficiency compared with TinyRPL and Drip which are based on the same TinyOS platform as OSR implementation

Understanding Compressed Sensing Inspired Approaches for Path Reconstruction in Wireless Sensor Networks

Abstract: Understanding per-packet routing dynamics in deployed and complex wireless sensor networks (WSNs) has become increasingly important for many essential tasks such as network performance analysis, operation optimization, system maintenance, and network diagnosis. In this paper, we study routing path recovery for data collection in multi-hop WSNs at the sink using a very small and fixed path measurement carried in each packet. We analyze the two recent compressed sensing (CS) inspired approaches called RTR and CSPR. We evaluate RTR versus CSPR as well as other state-of-the-art approaches including MNT and Pathfinder via simulations. Our work provides insights into the better understanding of the profound impacts of different CS-inspired approaches on their respective path reconstruction performance and the resource requirement on sensor nodes. The evaluation results show that the RTR significantly outperforms CSPR, MNT and Pathfinder

Smart Phone Based Mobile Code Dissemination for Heterogeneous Wireless Sensor Networks

Low-power Wireless Sensor Networks (WSNs) are being widely used in various outdoor applications including environmental monitoring, precision agriculture, and smart cities. WSN is a distributed network of sensor devices where the software running on the sensor devices defines how the devices should operate. In real-world WSN deployments, sensor node's software update is required to fix bugs and maintain optimal operation. In this paper, we present a novel mobile code dissemination tool based on smart phone running on Android Operating System for heterogeneous WSN reprogramming. Our implementation builds upon Mobile Deluge with new enhancements and more convenient mobile code dissemination tool in practice. We have evaluated our application performance on Android platform, and validated our mobile tool with a real-world outdoor low-power heterogeneous WSN deployment, demonstrating its practical merit

Gs-Coupled Adenosine Receptors Differentially Limit Antigen-Induced Mast Cell Activation

Mast cell activation results in the immediate release of proinflammatory mediators prestored in cytoplasmic granules, as well as initiation of lipid mediator production and cytokine synthesis by these resident tissue leukocytes. Allergen-induced mast cell activation is central to the pathogenesis of asthma and other allergic diseases. Presently, most pharmacological agents for the treatment of allergic disease target receptors for inflammatory mediators. Many of these mediators, such as histamine, are released by mast cells. Targeting pathways that limit antigen-induced mast cell activation may have greater therapeutic efficacy by inhibiting the synthesis and release of many proinflammatory mediators produced in the mast cell. In vitro studies using cultured human and mouse mast cells, and studies of mice lacking A2B receptors, suggest that adenosine receptors, specifically the Gs-coupled A2A and A2B receptors, might provide such a target. Here, using a panel of mice lacking various combinations of adenosine receptors, and mast cells derived from these animals, we show that adenosine receptor agonists provide an effective means of inhibition of mast cell degranulation and induction of cytokine production both in vitro and in vivo. We identify A2B as the primary receptor limiting mast cell degranulation, whereas the combined activity of A2A and A2B is required for the inhibition of cytokine synthesis

IL-4 Amplifies the Pro-Inflammatory Effect of Adenosine in Human Mast Cells by Changing Expression Levels of Adenosine Receptors

Adenosine inhalation produces immediate bronchoconstriction in asthmatics but not in normal subjects. The bronchospastic effect of adenosine is largely mediated through adenosine-induced mast cell activation, the mechanism of which is poorly understood due to limitations in culturing human primary mast cells. Here, we show that human umbilical cord blood -derived mast cells incubated with the Th2 cytokine IL-4 develop increased sensitivity to adenosine. Potentiation of anti-IgE- induced and calcium ionophore/PMA-induced degranulation was augmented in mast cells cultured with IL-4, and this effect was reduced or abolished by pre-treatment with A2BsiRNA and selective A2B receptor antagonists, respectively. IL-4 incubation resulted in the increased expression of A2B and reduced expression of A2A adenosine receptors on human mast cells. These results suggest that Th2 cytokines in the asthmatic lung may alter adenosine receptor expression on airway mast cells to promote increased responsiveness to adenosine

Mechanistic Studies into Interfacial Interactions via Chemical Vapor Deposition Polymerization

Chemical vapor deposition (CVD) polymerization is a widely used fabrication method for preparing substrate-independent thin film polymer coatings for a broad range of applications. Functional poly(p-xylylene) (PPX) coatings are specific examples of CVD polymer coatings, and they can be applied for surface functionalization and bio-conjugation. The first portion of this dissertation serves to explore the fundamental mechanism of area-selective CVD polymerization, which has a high potential to be utilized as one of the bottom-up processes. In this dissertation, we report a systematic study into the impact of thermodynamic processes on the area-selectivity of chemical vapor deposition (CVD) polymerization of functional [2.2]paracyclophanes (PCP). Adhesion mapping of pre-closure CVD films by atomic force microscopy (AFM) provided a detailed understanding of the geometric features of the polymer islands that form under different deposition conditions. Our results suggest a correlation between interfacial transport processes, including adsorption, diffusion, and desorption, and thermodynamic control parameters, such as substrate temperature and working pressure. This work culminated in a kinetic model that predicted both area-selective and non-selective CVD parameters for the same polymer/substrate ensemble (PPX-C + Cu). These findings are corroborated by STEM results indicating extensive reorientation of continuous CVD thin films on deposition-prohibited substrates at temperatures above 120 oC. Moreover, deposition on patterned substrates (Ru patterns on Si substrates) suggests that the area-selectivity is not affected by the surface geometry of hybrid substrates, such as the structure of patterns and feature/spacing sizes of patterns, supporting the application of area-selective CVD polymerization on 3-D materials. While limited to a focused subset of CVD polymers and substrates, this work provides an improved mechanistic understanding of area-selective CVD polymerization and highlights the potential for thermodynamic control in tuning area-selectivity. The second portion of this dissertation serves to extend the use of CVD-based reactive PPX coatings as a surface modification strategy to enhance biomolecule and biomaterial interaction. In this dissertation, we report a precise cell attachment method using CVD-initiated atom transfer radical polymerizations (ATRP), which provides a convenient access route to controlled radical polymerization on a wide range of different materials, to grow polyethylene glycol methacrylate (PEGMA) polymer brushes. This antifouling material shows the resistance of both protein and cell, promoting a high yield of cell attachment to the targeted sites. Moreover, this dissertation also demonstrates the use of CVD-based co-polymer coatings as intermediate layers to immobilize multiple biomolecules on substrates. CVD copolymer coating with designed functional groups was deposited on the biomaterial surface to selectively conjugate both viral vectors and peptides through chemical reactions. The ability to tether lentiviral vectors together with a mesenchymal stem cell (MSC)-binding peptide enhances cell communication among MSCs and increases cell binding and differentiation, providing a safe and efficient gene therapy delivery strategy.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/176431/1/xyzhong_1.pd