Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science

Effective and Efficient Communication and Collaboration in Participatory Environments

Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments. There are three main contributions in this dissertation: 1. A novel scheme for predicting contact volume during an opportunistic contact (PCV); 2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and 3. An algorithm for mobility and orientation estimation in mobile environments (MOEME). The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes. The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost. We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets

Robust, Resilient Networked Communication in Challenged Environments

In challenged environments, digital communication infrastructure may be difficult or even impossible to access. This is especially true in rural and developing regions, as well as in any region during a time of political or environmental crisis. We advance the state of the art in wireless networking and security to design networks and applications that rapidly assess changing networking conditions to restore communication and provide local situational awareness. This dissertation examines new systems for responding to current and emerging needs for wireless networks. This work looks across the wireless ecosystem of widely deployed standards. We develop new tools to improve network assessment and to provide robust and reliable network communication. By incorporating new technological breakthroughs, such as the wide commercial success of Unmanned Aircraft Systems (UAS), we introduce novel methods and systems for existing wireless standards for these challenged networks. We assess how existing technologies and standards function in difficult environments: lacking end-end Internet connectivity, experiencing overload or other resource constraints, and operating in three dimensional space. Through this lens, we demonstrate how to optimize networks to serve marginalized communities outside of first world urban cities and make our networks resilient to natural and political crisis that threaten communication

살아있는 뉴런과 동물에서 mRNA 관찰에 대한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 물리·천문학부(물리학전공), 2021.8. 이병훈.mRNA는 유전자 발현의 첫번째 산물이면서, 리보솜과 함께 단백질을 합성한다. 특히 뉴런에서, 몇몇 RNA들은 자극에 의해 만들어지고, 뉴런의 특정 부분으로 수송되어 국소적으로 단백질 양을 조절할 수 있게 한다. 최근 mRNA 표지 기술의 발전으로 살아있는 세포에서 단일 mRNA를 관찰하는 것이 가능해졌다. 이 연구에서, 우리는 RNA 이미징 기술을 이용해, 기억 형성과 상기할 때 활성화된 뉴런의 집합을 찾는 것 뿐 아니라, 뉴런의 축삭돌기에서 mRNA가 어떻게 수송되는지를 관찰했다. 이 논문의 첫 부분에서 우리는 신경 자극에 반응해서 만들어지는 것으로 알려진, Arc 유전자의 전사를 관찰하였다. 기억은 engram 혹은 기억 흔적 (memory trace)라고 불리는 뉴런들의 집합에 저장되어 있다고 생각된다. 그러나, 시간에 따라서 이런 기억 흔적세포들의 집합이 어떻게 변하고, 변화하면서도 어떻게 정보를 유지할 수 있는지 잘 알려져 있지 않다. 또한, 살아있는 동물에서, 기억 흔적세포를 긴 시간 동안 여러 번 찾아내는 것은 어려운 일이었다. 이 연구에서는 genetically-encoded RNA indicator (GERI) 기술을 사용해, 기억 흔적세포의 표식으로 널리 사용되는 Arc mRNA의 전사과정을 살아있는 쥐에서 관찰하였다. GERI를 이용함으로써, 기존 방법들의 한계점이었던 시간 제약 없이, 실시간으로 Arc를 발현하는 뉴런들을 찾아낼 수 있었다. 쥐에게 공간 공포 기억을 주고 나서 여러 번 기억을 상기시키는 행동실험 후에 Arc를 발현하는 세포를 식별했을 때, CA1에서는 Arc를 발현하는 세포가 이틀 후에는 더 이상 활성화되지 않았으나, RSC의 경우 4퍼센트의 뉴런들이 계속해서 활성화하는 것을 관찰했다. 신경활동과 유전자 발현을 같이 조사하기 위해, 쥐가 가상 환경을 탐험하고 있을 때 GERI와 칼슘 이미징을 동시에 진행하였다. 그 결과, 기억을 형성할 때와 상기시킬 때 Arc를 발현했던 뉴런들이 기억을 표상하는 것을 알아낼 수 있었다. 이처럼 GERI 기술을 이용해 살아있는 동물에서 유전자 발현된 세포를 찾아내는 방식은 다양한 학습 및 기억 과정에서 기억 흔적세포의 dynamics에 대해 알아낼 수 있을 것으로 기대된다. 이 논문의 두번째 부분에서, 우리는 세포 골격의 기본 구성 단위가 되는 β-actin의 mRNA를 축삭돌기에서 관찰하였다. mRNA의 국소화 (localization)를 통한 국소 단백질 합성은 축삭돌기 (axon)의 성장과 재생에 중요한 역할이 있다고 알려져 있다. 하지만, 아직 mRNA의 국소화가 축삭돌기에서 어떻게 조절되고 있는지 잘 알려져 있지 않다. 이 문제를 해결하기 위해서, 우리는 모든 β-actin mRNA가 형광으로 표지된 유전자 변형 쥐를 이용해, 살아있는 축삭돌기에서 β-actin mRNA를 관찰하였다. 이 쥐의 뉴런을 축삭을 구분해 줄 수 있는 미세유체 장치 (microfluidic device)에 배양한 뒤에, β-actin mRNA를 관찰하고 추적을 진행했다. 축삭은 세포 몸통으로부터 길게 자라기 때문에 mRNA가 먼 거리를 수송되어야 함에도 불구하고, 대부분의 mRNA가 수상돌기에 비해 덜 움직이고 작은 영역에서 움직이는 것을 보았다. 우리는 β-actin mRNA가 주로 축삭돌기의 가지가 될 수 있는 filopodia 근처와, 시냅스가 만들어지는 bouton 근처에 국소화되는 것을 관찰했다. Filopodia와 bouton이 actin이 풍부한 부분으로 알려져 있기 때문에, 우리는 액틴 필라멘트와 β-actin mRNA의 움직임간에 연관성을 조사했다. 흥미롭게도, 우리는 β-actin mRNA가 액틴 필라멘트와 같이 국소화 되고, β-actin mRNA가 액틴 필라멘트 안에서 sub-diffusive한 움직임을 보였으며, 먼 거리를 움직이던 mRNA도 액틴 필라멘트에 고정되는 모습도 확인할 수 있었다. 축삭에서 β-actin mRNA 움직임을 본 이번 관찰은 mRNA 수송 및 국소화에 대한 생물물리학 적 메커니즘의 기반이 될 수 있을 것이다.mRNA is the first product of the gene expression and facilitates the protein synthesis. Especially in neurons, some RNAs are transcribed in response to stimuli and transported to the specific region, altering local proteome for neurons to function normally. Recent advances of mRNA labeling techniques allowed us to observe the single mRNAs in live cells. In this thesis, we applied RNA imaging technique not only to identify the neuronal ensemble that activated during memory formation and retrieval, but also to traffic mRNAs transported to the axon. In the first part of the thesis, we observed the transcription site of Arc gene, one of the immediate-early gene, which is rapidly transcribed upon the neural stimuli. Because of the characteristic of expressing in response to stimuli, Arc is widely used as a marker for memory trace cells thought to store memories. However, little is known about the ensemble dynamics of these cells because it has been challenging to observe them repeatedly over long periods of time in vivo. To overcome this limitation, we present a genetically-encoded RNA indicator (GERI) technique for intravital chronic imaging of endogenous Arc mRNA. We used our GERI to identify Arc-positive neurons in real time without the time lag associated with reporter protein expression in conventional approaches. We found that Arc-positive neuronal populations rapidly turned over within two days in CA1, whereas ~4% of neurons in the retrosplenial cortex consistently expressed Arc upon contextual fear conditioning and repeated memory retrievals. Dual imaging of GERI and calcium indicator in CA1 of mice navigating a virtual reality environment revealed that only the overlapping population of neurons expressing Arc during encoding and retrieval exhibited relatively high calcium activity in a context-specific manner. This in vivo RNA imaging approach has potential to unravel the dynamics of engram cells underlying various learning and memory processes. In the second part of this thesis, we imaged β-actin mRNAs, which can generate a cytoskeletal protein, β-actin, through translation. Local protein synthesis has a critical role in axonal guidance and regeneration. Yet it is not clearly understood how the mRNA localization is regulated in axons. To address these questions, we investigated mRNA motion in live axons using a transgenic mouse that expresses fluorescently labeled endogenous β-actin mRNA. By culturing hippocampal neurons in a microfluidic device that allows separation of axons from dendrites, we performed single particle tracking of β-actin mRNA selectively in axons. Although axonal mRNAs need to travel a long distance, we observed that most axonal mRNAs show much less directed motion than dendritic mRNAs. We found that β-actin mRNAs frequently localize at the neck of filopodia which can grow as axon collateral branches and at varicosities where synapses typically occur. Since both filopodia and varicosities are known as actin-rich areas, we investigated the dynamics of actin filaments and β-actin mRNAs simultaneously by using high-speed dual-color imaging. We found that axonal mRNAs colocalize with actin filaments and show sub-diffusive motion within the actin-rich regions. The novel findings on the dynamics of β-actin mRNA will shed important light on the biophysical mechanisms of mRNA transport and localization in axons.1. INTRODUCTION, 1 1.1. Neuronal ensemble, 1 1.2. Immediate-early Gene (IEG), 3 1.3. Methods for IEG-positive neurons, 3 1.4. Two-photon microscope, 5 1.5. References, 7 2. IMAGING ARC mRNA TRANSCRIPTION SITES IN LIVE MICE, 9 2.1. Introduction, 9 2.2. Materials and Methods, 10 2.3. Results and Discussion, 18 2.4. References, 26 3. NEURONS EXPRESSING ARC mRNA DURING REPEATED MEMORY RETRIEVALS, 28 3.1. Introduction, 28 3.2. Results and Discussion, 28 3.3. References, 35 4. NEURAL ACTIVITIES OF ARC+ NEURONS, 36 4.1. Introduction, 36 4.2. Materials and Methods, 37 4.3. Results and Discussion, 38 4.4. References, 52 5. AXONAL mRNA DYNAMICS IN LIVE NEURONS, 54 5.1. Introduction, 54 5.2. Materials and Methods, 55 5.3. Results and Discussion, 59 5.4. References, 70 6. CONCLUSION AND OUTLOOK, 72 ABSTRACT IN KOREAN (국문초록), 76박

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Mobility increases localizability: A survey on wireless indoor localization using inertial sensors

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p

Towards Secure, Power-Efficient and Location-Aware Mobile Computing

In the post-PC era, mobile devices will replace desktops and become the main personal computer for many people. People rely on mobile devices such as smartphones and tablets for everything in their daily lives. A common requirement for mobile computing is wireless communication. It allows mobile devices to fetch remote resources easily. Unfortunately, the increasing demand of the mobility brings many new wireless management challenges such as security, energy-saving and location-awareness. These challenges have already impeded the advancement of mobile systems. In this dissertation we attempt to discover the guidelines of how to mitigate these problems through three general communication patterns in 802.11 wireless networks. We propose a cross-section of a few interesting and important enhancements to manage wireless connectivity. These enhancements provide useful primitives for the design of next-generation mobile systems in the future.;Specifically, we improve the association mechanism for wireless clients to defend against rogue wireless Access Points (APs) in Wireless LANs (WLANs) and vehicular networks. Real-world prototype systems confirm that our scheme can achieve high accuracy to detect even sophisticated rogue APs under various network conditions. We also develop a power-efficient system to reduce the energy consumption for mobile devices working as software-defined APs. Experimental results show that our system allows the Wi-Fi interface to sleep for up to 88% of the total time in several different applications and reduce the system energy by up to 33%. We achieve this while retaining comparable user experiences. Finally, we design a fine-grained scalable group localization algorithm to enable location-aware wireless communication. Our prototype implemented on commercial smartphones proves that our algorithm can quickly locate a group of mobile devices with centimeter-level accuracy