Matching Items (14)

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Resource allocation in communication and social networks

Description

As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to

As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to enhance performance of networks, it is essential that scarce networking resources be allocated smartly to match the continuously changing network environment. This dissertation focuses on two different kinds of networks - communication and social, and studies resource allocation problems in these networks. The study on communication networks is further divided into different networking technologies - wired and wireless, optical and mobile, airborne and terrestrial. Since nodes in an airborne network (AN) are heterogeneous and mobile, the design of a reliable and robust AN is highly complex. The dissertation studies connectivity and fault-tolerance issues in ANs and proposes algorithms to compute the critical transmission range in fault free, faulty and delay tolerant scenarios. Just as in the case of ANs, power optimization and fault tolerance are important issues in wireless sensor networks (WSN). In a WSN, a tree structure is often used to deliver sensor data to a sink node. In a tree, failure of a node may disconnect the tree. The dissertation investigates the problem of enhancing the fault tolerance capability of data gathering trees in WSN. The advent of OFDM technology provides an opportunity for efficient resource utilization in optical networks and also introduces a set of novel problems, such as routing and spectrum allocation (RSA) problem. This dissertation proves that RSA problem is NP-complete even when the network topology is a chain, and proposes approximation algorithms. In the domain of social networks, the focus of this dissertation is study of influence propagation in presence of active adversaries. In a social network multiple vendors may attempt to influence the nodes in a competitive fashion. This dissertation investigates the scenario where the first vendor has already chosen a set of nodes and the second vendor, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes so that after the influence propagation, the second vendor's market share is larger than the first.

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Date Created
  • 2014

Bluetooth low energy for use with MEM sensors

Description

ABSTRACT

Designers creating the next generation remote sensing enabled smart devices need to overcome the challenges of prevailing ventures including time to market and expense.

To reduce the time and effort involved

ABSTRACT

Designers creating the next generation remote sensing enabled smart devices need to overcome the challenges of prevailing ventures including time to market and expense.

To reduce the time and effort involved in initial prototyping, a good reference design is often desired and warranted. This paper provides the necessary reference materials for Designers to implement a wireless solution efficiently and effectively.

This document is intended for users with limited Bluetooth technology experience.

Many sensing-enabled devices require a ‘hard-wire’ or cable link to a host monitoring system. This can limit the potential for product advancements by anchoring the system to a single location preventing portability and the convenience of a remote system. By removing the “wired” or cabled portion from a design, a broader scope of devices becomes feasible.

One common problematic area for these types of sensors is within the internal medicine field. Proximity sensing is far more practical and less invasive to implement than surgical implantation. Bluetooth Low Energy (BLE) systems solve the hard wired problem by decoupling the physical sensor from the host system through a BLE transceiver that can send information to an external monitoring system. This wireless link enables new sensor technology to be leveraged into previously unobtainable markets; such as, internal medicine, wearable devices, and Infotainment to name a few. Wireless technology for sensor systems are a potentially disruptive technology changing the way environmental monitoring is implemented and considered.

With this BLE design reference, products can be created with new capabilities to advance current technologies for military, commercial, industrial and medical sectors in rapid succession.

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Date Created
  • 2015

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Localization in wireless sensor networks

Description

In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and

In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered.

In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.

In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.

A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.

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  • 2016

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Robust distributed parameter estimation in wireless sensor networks

Description

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.

Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.

Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.

Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.

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Date Created
  • 2017

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Distributed inference over multiple-access channels with wireless sensor networks

Description

Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels

Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using the asymptotic variance of the estimator. The loss in performance due to varying assumptions on the limited amounts of channel information at the sensors is quantified. With multiple antennas at the FC, a distributed detection problem is also considered, where the error exponent is used to evaluate performance. It is shown that for zero-mean channels between the sensors and the FC when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the FC. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the FC is shown to be no more than a factor of 8/π for Rayleigh fading channels between the sensors and the FC, independent of the number of antennas at the FC, or correlation among noise samples across sensors. In a second class of problems, sensor observations are transmitted to the FC using constant-modulus phase modulation over Gaussian multiple-access-channels. The phase modulation scheme allows for constant transmit power and estimation of moments other than the mean with a single transmission from the sensors. Estimators are developed for the mean, variance and signal-to-noise ratio (SNR) of the sensor observations. The performance of these estimators is studied for different distributions of the observations. It is proved that the estimator of the mean is asymptotically efficient if and only if the distribution of the sensor observations is Gaussian.

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Date Created
  • 2010

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Self-configuring and self-adaptive environment control systems for buildings

Description

Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights

Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.

For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.

For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.

The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.

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Date Created
  • 2015

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Wireless Sensor Data Transport, Aggregation and Security

Description

Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things

Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters.

The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.

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Date Created
  • 2017

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Flexi-WVSNP-DASH: a wireless video sensor network platform for the Internet of Things

Description

Video capture, storage, and distribution in wireless video sensor networks

(WVSNs) critically depends on the resources of the nodes forming the sensor

networks. In the era of big data, Internet

Video capture, storage, and distribution in wireless video sensor networks

(WVSNs) critically depends on the resources of the nodes forming the sensor

networks. In the era of big data, Internet of Things (IoT), and distributed

demand and solutions, there is a need for multi-dimensional data to be part of

the Sensor Network data that is easily accessible and consumable by humanity as

well as machinery. Images and video are expected to become as ubiquitous as is

the scalar data in traditional sensor networks. The inception of video-streaming

over the Internet, heralded a relentless research for effective ways of

distributing video in a scalable and cost effective way. There has been novel

implementation attempts across several network layers. Due to the inherent

complications of backward compatibility and need for standardization across

network layers, there has been a refocused attention to address most of the

video distribution over the application layer. As a result, a few video

streaming solutions over the Hypertext Transfer Protocol (HTTP) have been

proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion

Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These

frameworks, do not address the typical and future WVSN use cases. A highly

flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)

are introduced. The platform's goal is to usher video as a data element that

can be integrated into traditional and non-Internet networks. A low cost,

scalable node is built from the ground up to be fully compatible with the

Internet of Things Machine to Machine (M2M) concept, as well as the ability to

be easily re-targeted to new applications in a short time. Flexi-WVSNP design

includes a multi-radio node, a middle-ware for sensor operation and

communication, a cross platform client facing data retriever/player framework,

scalable security as well as a cohesive but decoupled hardware and software

design.

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Date Created
  • 2017

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Consensus algorithms and distributed structure estimation in wireless sensor networks

Description

Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion

Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where

all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.

Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.

Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.

Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.

Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.

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Date Created
  • 2017

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Passive thermochemical energy storage system for low power sensor modules for space applications

Description

Surface exploration of the Moon and Asteroids can provide important information to scientists regarding the origins of the solar-system and life . Small robots and sensor modules can enable low-cost

Surface exploration of the Moon and Asteroids can provide important information to scientists regarding the origins of the solar-system and life . Small robots and sensor modules can enable low-cost surface exploration. In the near future, they are the main machines providing these answers. Advanced in electronics, sensors and actuators enable ever smaller platforms, with compromising functionality. However similar advances haven’t taken place for power supplies and thermal control system. The lunar south pole has temperatures in the range of -100 to -150 oC. Similarly, asteroid surfaces can encounter temperatures of -150 oC. Most electronics and batteries do not work below -40 oC. An effective thermal control system is critical towards making small robots and sensors module for extreme environments feasible.

In this work, the feasibility of using thermochemical storage materials as a possible thermal control solution is analyzed for small robots and sensor modules for lunar and asteroid surface environments. The presented technology will focus on using resources that is readily generated as waste product aboard a spacecraft or is available off-world through In-Situ Resource Utilization (ISRU).

In this work, a sensor module for extreme environment has been designed and prototyped. Our intention is to have a network of tens or hundreds of sensor modules that can communicate and interact with each other while also gathering science data. The design contains environmental sensors like temperature sensors and IMU (containing accelerometer, gyro and magnetometer) to gather data. The sensor module would nominally contain an electrical heater and insulation. The thermal heating effect provided by this active heater is compared with the proposed technology that utilizes thermochemical storage chemicals.

Our results show that a thermochemical storage-based thermal control system is feasible for use in extreme temperatures. A performance increase of 80% is predicted for the sensor modules on the asteroid Eros using thermochemical based storage system. At laboratory level, a performance increase of 8 to 9 % is observed at ambient temperatures of -32oC and -40 oC.

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Created

Date Created
  • 2016