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Fundamentals of Wireless Sensor Networks: Theory and Practice
Abstract
In this book, the authors describe the fundamental concepts and practical aspects of wireless sensor networks. The book provides a comprehensive view to this rapidly evolving field, including its many novel applications, ranging from protecting civil infrastructure to pervasive health monitoring. Using detailed examples and illustrations, this book provides an inside track on the current state of the technology. The book is divided into three parts. In Part I, several node architectures, applications and operating systems are discussed. In Part II, the basic architectural frameworks, including the key building blocks required for constructing large-scale, energy-efficient sensor networks are presented. In Part III, the challenges and approaches pertaining to local and global management strategies are presented - this includes topics on power management, sensor node localization, time synchronization, and security. At the end of each chapter, the authors provide practical exercises to help students strengthen their grip on the subject. There are more than 200 exercises altogether. Key Features: Offers a comprehensive introduction to the theoretical and practical concepts pertaining to wireless sensor networks Explains the constraints and challenges of wireless sensor network design; and discusses the most promising solutions Provides an in-depth treatment of the most critical technologies for sensor network communications, power management, security, and programming Reviews the latest research results in sensor network design, and demonstrates how the individual components fit together to build complex sensing systems for a variety of application scenarios Includes an accompanying website containing solutions to exercises (http://www.wiley.com/go/dargie_fundamentals) This book serves as an introductory text to the field of wireless sensor networks at both graduate and advanced undergraduate level, but it will also appeal to researchers and practitioners wishing to learn about sensor network technologies and their application areas, including environmental monitoring, protection of civil infrastructure, health care, precision agriculture, traffic control, and homeland security.
... Figure 2 shows how AoA can be used to estimate the user location (as the angles at which the signals are received by the antenna array can help locate the user device.). Time of Flight (ToF) or Time of Arrival (ToA) exploits the signal propagation time to calculate the distance between the transmitter Tx and the receiver Rx [40]. The ToF value multiplied by the speed of light c = 3 × 10 8 m/sec provides the physical distance between Tx and Rx. ...
... This is because the obstacles deflect the emitted signals, which then traverse through a longer path causing an increase in the time taken for the signal to propagate from Tx to Rx. Let t 1 be the time when Tx i sends a message to the Rx j that receives it at t 2 where t 2 = t 1 + t p (t p is the time taken by the signal to traverse from Tx to Rx) [40]. So the distance between the i and j can be calculated using Equation (5) ...
... RToF measures the round-trip (i.e., transmitter-receivertransmitter) signal propagation time to estimate the distance between Tx and Rx [40]. The ranging mechanisms for both ToF and RToF are similar; upon receiving a signal from the transmitter, the receiver responds back to the transmitter, which then calculates the total round-trip ToF. ...
Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
... These networks frequently consist of inex-35 pensive, tiny sensors that can be widely used to gather large datasets. Every sensor in 36 the network is usually made to identify particular biological or environmental factors, 37 allowing for focused reactions and real-time insights [6]. Depending on the architecture 38 of the network and the speed needed for data analysis, the acquired data is ...
... Increased sampling frequencies 99 offer more precise information but also result in a larger amount of data that must be sent, analyzed, and After data are collected in sensor networks it gets processed to produce insights. For real-time systems pro-108 cessing has to happen as data is being collected to support fast decision making -a critical need in industries 109 like industrial automation, autonomous vehicles and healthcare [6]. Key data processing techniques in these [31]. ...
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT. In particular, this article explores how data from different sensors in real-time can improve AI models' comprehension of surroundings, user context, and physical conditions. Lastly, the article delves into the scientific principles supporting sensor technologies, data processing methods, and their fusion with gener-ative models such as ChatGPT to develop adaptable, dynamic systems that engage with humans intelligently in real-time. Some of the specific topics that are investigated include the science be-hind sensor networks and acquiring real-time data; how ChatGPT can analyze sensor data to generate dialogue that is sensitive to context; Instances in healthcare (such as using wearable sen-sors along with AI chatbots for patient treatment) and smart homes (interaction with AI assis-tants driven by sensors). These subjects will prove advantageous for researchers in sensor tech-nology as well as AI development, showcasing interdisciplinary progress in smart systems.
... After data are collected in sensor networks, the data are processed to produce insights. For real-time systems, processing has to happen as data are being collected to support fast decision-making-a critical need in industries like industrial automation, autonomous vehicles and healthcare [6]. The key data processing techniques in these networks are edge computing, data fusion and machine learning. ...
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models’ comprehension of surroundings, user contexts, and physical conditions. Lastly, this article delves into the scientific principles supporting sensor technologies, data processing methods, and their fusion with generative models such as ChatGPT to develop adaptable, dynamic systems that engage with humans intelligently in real time. Some of the specific topics that are investigated include the science behind sensor networks and acquiring real-time data, how ChatGPT can analyze sensor data to generate dialogue that is sensitive to context, instances in healthcare (such as using wearable sensors along with AI chatbots for patient treatment), and smart homes (interaction with AI assistants driven by sensors). These subjects will prove advantageous for researchers in sensor technology as well as AI development, showcasing interdisciplinary progress in smart systems.
... In these systems, the output cannot be accurately measured; only whether they belong to certain known sets can be determined. For instance, due to measurement limitations, sensor network data often result in set-valued observations with a finite number of bits, or even just 1 bit [2][3][4]. This means that each sensor only indicates whether the measured value exceeds a threshold. ...
This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations rely on projection operators, which leads to a high computational complexity of much higher than O(n^2). In response, this paper introduces a recursive projection-free identification algorithm that incorporates a specialized cut-off coefficient to fully utilize prior information, thereby eliminating the need for projection operators. The algorithm is proved to be mean square and almost surely convergent. Furthermore, to better leverage prior information, an adaptive accelerated coefficient is introduced, resulting in a mean square convergence rate of O(1/k) , which matches the convergence rate with accurate observations. Inspired by the structure of the Cramer-Rao lower bound, the algorithm can be extended to an information-matrix projection-free algorithm by designing adaptive weight coefficients. This extension is proved to be asymptotically efficient for first-order FIR systems, with simulations indicating similar results for high order FIR systems. Finally, numerical examples are provided to demonstrate the main results.
... As is known [19], the trilateration method is used when there are a number of "anchor" nodes with known coordinates and it is necessary to measure the coordinates of a node with unknown coordinates. In this case, the RSS method is used to measure the distances from the "anchor" nodes to the desired node. ...
p>The methods of estimating the coordinates of sensor nodes based on the measurements made at the “anchor” nodes are widely used in WSNs. In particular, such methods include the RSS method, which is based on measuring the power of signals coming from sensors. The article shows that a similar method can be used for estimating the coordinates of an observation object in the WSN. The efficiency of measuring the coordinates of such an object in the presence of power measurement errors is analyzed. The conditions for increasing this efficiency have been identified. It is shown that the estimation is biased, but the magnitude of the bias is practically independent of the observational conditions and, therefore, can be easily compensated. </p
... Blok diagram sistem menggambarkan rancangan sistem yang terdiri atas bagian sensor [11] yang terhubung dengan unit pemancar data dalam hal ini adalah ESP8266 [12] seperti pada gambar 1 dan bagian unit pemroses data yang terdiri ada server web dan server basisdata yang terhubung dengan bagian penerima data dalam ini adalah wifi seperti pada gambar 2. Gambar 1. Blok sensor dan pemancar Data yang diambil dari sensor seperti gambar 2, selanjutnya dikirimkan melalui unit ESP8266 yang terhubung dengan protokol internet jaringan wifi, selanjutnya jaringan wifi pada bagian penerima seperti gambar 2 akan mengirimkan data langsung ke server basisdata, ...
Pertanian berbasis teknologi merupakan pendekatan modern yang memanfaatkan Internet of Things (IoT) untuk mendukung proses produksi secara efisien. Di Kampung Durian Tarung, Pasar Ambacang, Kuranji, Padang, Sumatera Barat, kelompok petani hortikultura menghadapi tantangan dalam menentukan jenis tanaman yang sesuai dengan kualitas tanah di lahan mereka. Penelitian ini mengembangkan sistem berbasis IoT untuk analisis lahan yang dapat merekomendasikan jenis tanaman optimal sesuai kondisi tanah. Teknologi IoT diimplementasikan untuk mengumpulkan data tanah, seperti pH, kelembaban, dan suhu tanah, melalui sensor-sensor nirkabel. Data tersebut dianalisis dengan metode fuzzy, yang memungkinkan pengambilan keputusan berdasarkan sejumlah parameter kualitas tanah untuk memberikan rekomendasi yang lebih tepat dan real-time. Hasil uji coba menunjukkan bahwa sistem mampu mengukur kondisi tanah secara akurat dan memberikan rekomendasi tanaman yang sesuai berdasarkan data kualitas tanah yang diperoleh. Penggunaan metode fuzzy terbukti efektif dalam menyaring data dari sensor dan menyesuaikan rekomendasi tanaman berdasarkan nilai ambang pada pH, kelembaban, dan suhu tanah. Penelitian ini berkontribusi pada bidang pertanian cerdas dengan menghadirkan solusi berbasis IoT yang spesifik untuk kebutuhan petani hortikultura lokal. Menggabungkan teknologi sensor nirkabel dan metode fuzzy memungkinkan analisis yang lebih adaptif dan relevan dengan kondisi tanah di lahan pertanian. Keberhasilan penelitian ini memberikan arah bagi pengembangan sistem serupa yang dapat diterapkan di wilayah lain, serta menggarisbawahi peran teknologi IoT dalam mendukung keberlanjutan pertanian melalui pemanfaatan data secara optimal.
... Positioning techniques involve various signal metrics used to localize users or devices in complex indoor environments. Key methods include Time of Arrival (TOA) [42], Angle of Arrival (AOA) [43], Received Signal Strength Indicator (RSSI) [44], Channel State Information (CSI) [45], Phase of Arrival (PoA) [46], Time Difference of Arrival (TDoA) [47], Time of Flight (ToF) [48], Return Time of Flight (RToF) [49], and Pedestrian Dead Reckoning (PDR) [50]. Hybrid techniques that combine these metrics have been developed to enhance positioning performance. ...
In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users’ location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.
The chapter delves into the innovations in machine learning techniques and sensor applications that are reshaping the study of human emotion perception and activity recognition. In the framework of Active and Assisted Living (AAL), the author discusses the urgent need to assist individuals, especially those with special needs and the elderly, in order to enhance their quality of life. It opens by emphasizing the value of AAL, which aims to meet the cognitive, affective, and somatic requirements of people with a wide range of disabilities. The complex spatial–temporal context of individuals in smart environments is characterized by using a strong knowledge representation technique, such as ontologies or conceptual models. This facilitates effective thinking processes for identifying the best assistance options for a given person. Human activity recognition and emotion perception using many sensors are also explored in this chapter, from more traditional machine learning models to novel deep learning approaches. These techniques use multimodal sensors, including those that can’t be seen, to decode complicated human actions. As an example of the breadth of recent developments in sensor technology, we examine in depth the use of invasive, noninvasive, and covert sensors for emotion recognition. Insights into the possibilities of novel ways to identify the human essence through the integration of machine learning and sensor applications are provided in this chapter. Researchers, practitioners, and technologists are inspired to work together and create to better AAL and the human experience as a whole into action for Quality of life (QoL).
We present design patterns used by software components in the TinyOS sensor network operating system. They differ significantly from traditional software design patterns because of the constraints of sensor networks and to TinyOS's focus on static allocation and whole-program composition. We describe how nesC has evolved to support these design patterns by including a few simple language primitives and optimizations.
THE ALOHA SYSTEM is composed of a related series of contracts and grants from a variety of funding agencies with principal support from ARPA, which deal with two main themes: computer communications (TASK 1), and computer structures (TASK 2).Under computer-communications there is work in (a) Studies on computer communications using radio and satellites, (b) The development of a prototype radio-linked time-sharing network, (c) System studies and planning for a Pacific area computer communications network linking major universities in the U.S., Japan, Australia and other Pacific countries.Under computer structures, we are engaged in research/development in multiprocessor computing structures, computer networks, and geographically distributed computing systems. This work is being undertaken in two phases: 1) the establishment of a research facility and 2) the research work itself. The research facility is centered around the BCC 500 computing system.
In this work, we present a self-management service for Wireless Sensor Networks (WSNs) that automatically controls the network redundancy. Based on a density control function, this service improves the monitoring potential of the sensor nodes. Our simulation experiments show that this self-management service provides good and lasting coverage, as desired by WSNs applications. Copyright © 2006 John Wiley & Sons, Ltd.
This article presents an overview of terrestrial time signal stations and broadcasted information that is available for the synchronisation of radio clocks. It excludes time synchronization using the Global Positioning System GPS, which is discussed in an article by P. Dana in this issue. A number of effects that influence achievable synchronization accuracies, relating to the operating conditions of broadcasting radio stations as well as the transmission of radio signals, are discussed. Techniques employed in receivers to improve safety and accuracy of provided timing information are also reviewed.