There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging (DMC) problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work, we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks

To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakage-prone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settings - indoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.

/pdf/leakage-aware-energy-synchronization-for-wireless-sensor-uurhbqe0rm.pdf

Leakage-aware energy synchronization for wireless sensor networks

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than the systems that harvested dynamic energy from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present an example on determining the optimal remaining energy level for individual sensor nodes to send out their recharging requests. Through extensive simulation with real-world system settings, we verify our analysis matches the simulation results well and the system designs based on our analysis are effective.

/pdf/on-demand-charging-in-wireless-sensor-networks-theories-and-1kyb5x0vdx.pdf

On-demand Charging in Wireless Sensor Networks: Theories and Applications

Existing flooding algorithms have demonstrated their effectiveness in achieving communication efficiency and reliability in wireless sensor networks. However, further performance improvement has been hampered by the assumption of link independence, a design premise imposing the need for costly acknowledgements (ACKs) from every receiver. In this paper, we present Collective Flooding (CF), which exploits the link correlation to achieve flooding reliability using the concept of collective ACKs. CF requires only 1-hop information from a sender, making the design highly distributed and scalable with low complexity. We evaluate CF extensively in real-world settings, using three different types of testbeds: a single hop network with 20 MICAz nodes, a multihop network with 37 nodes, and a linear outdoor network with 48 nodes along a 326-meter-long bridge. System evaluation and extensive simulation show that CF achieves the same reliability as the state-of-the art solutions, while reducing the total number of packet transmission and dissemination delay by 30 - 50% and 35 - 50%, respectively.

/pdf/exploring-link-correlation-for-efficient-flooding-in-dwlzb5q4h6.pdf

Exploring link correlation for efficient flooding in wireless sensor networks

Market-based electricity pricing provides consumers an opportunity to lower their electric bill by shifting consumption to low price periods. In this paper, we explore how to lower electric bills without requiring consumer involvement using an intelligent charging system, called SmartCharge, and an on-site battery array to store low-cost energy for use during high-cost periods. SmartCharge's algorithm reduces electricity costs by determining when to switch the home's power supply between the grid and the battery array. The algorithm leverages a prediction model we develop, which forecasts future demand using statistical machine learning techniques. We evaluate SmartCharge in simulation using data from real homes to quantify its potential to lower bills in a range of scenarios. We show that typical savings today are 10-15%, but increase linearly with rising electricity prices. We also find that SmartCharge deployed at only 22% of 435 homes reduces the aggregate demand peak by 20%. Finally, we analyze SmartCharge's installation and maintenance costs. Our analysis shows that battery advancements, combined with an expected rise in electricity prices, have the potential to make the return on investment positive for the average home within the next few years.

/pdf/smartcharge-cutting-the-electricity-bill-in-smart-homes-with-3eivmnhngw.pdf

Ting Zhu

Papers

Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks

Leakage-aware energy synchronization for wireless sensor networks

On-demand Charging in Wireless Sensor Networks: Theories and Applications

Exploring link correlation for efficient flooding in wireless sensor networks

SmartCharge: cutting the electricity bill in smart homes with energy storage