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Author: Adam Barker
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 0
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Book Description
Wireless sensor networks (WSN) are typically formed ad hoc and utilize mesh topologies which enable the individual nodes to form the infrastructure allowing senders and receivers outside of RF range to pass messages through intermediate nodes. The individual nodes themselves are typically smaller devices, which run on a battery and utilize a microcontroller for processing. The primary function of a WSN is to sense various attributes of the environment and relay that data back to an end point for further exploitation. In most WSN, that end point is fixed, hardwired to a power source, and connected directly to pre-existing network infrastructure. A subset of WSN, which we call peer-to-peer WSN, perform all the functions of a typical WSN, but the end points are not fixed. The individual nodes in these scenarios must rely on their onboard capacity for computation to transform the raw sensor data into usable information while simultaneously optimizing the flow of information and the longevity of the network.This peer-to-peer WSN is the focus of our use case for this thesis in which we develop, with the help of a specific form of machine learning known as reinforcement learning, routing algorithms that can utilize the peer-to-peer WSN structure to efficiently forward and transform data into usable information for utilization at the end point embedded within the network. We will utilize deep reinforcement learning and graph neural networks to develop algorithms that will allow peer-to-peer WSN to learn functions for determining ideal policies within a given state of the network. We will demonstrate, using both simulation and testing on live wireless networks, improvement over the currently deployed WSN routing algorithms that rely on flooding and shortest path algorithms to determine their actions.
Author: Adam Barker
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 0
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Book Description
Wireless sensor networks (WSN) are typically formed ad hoc and utilize mesh topologies which enable the individual nodes to form the infrastructure allowing senders and receivers outside of RF range to pass messages through intermediate nodes. The individual nodes themselves are typically smaller devices, which run on a battery and utilize a microcontroller for processing. The primary function of a WSN is to sense various attributes of the environment and relay that data back to an end point for further exploitation. In most WSN, that end point is fixed, hardwired to a power source, and connected directly to pre-existing network infrastructure. A subset of WSN, which we call peer-to-peer WSN, perform all the functions of a typical WSN, but the end points are not fixed. The individual nodes in these scenarios must rely on their onboard capacity for computation to transform the raw sensor data into usable information while simultaneously optimizing the flow of information and the longevity of the network.This peer-to-peer WSN is the focus of our use case for this thesis in which we develop, with the help of a specific form of machine learning known as reinforcement learning, routing algorithms that can utilize the peer-to-peer WSN structure to efficiently forward and transform data into usable information for utilization at the end point embedded within the network. We will utilize deep reinforcement learning and graph neural networks to develop algorithms that will allow peer-to-peer WSN to learn functions for determining ideal policies within a given state of the network. We will demonstrate, using both simulation and testing on live wireless networks, improvement over the currently deployed WSN routing algorithms that rely on flooding and shortest path algorithms to determine their actions.
Author: Bhanu Chander
Publisher: CRC Press
ISBN: 1040027121
Category : Technology & Engineering
Languages : en
Pages : 368
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Book Description
The edited book Intelligent Wireless Sensor Networks and Internet of Things: Algorithms, Methodologies and Applications is intended to discuss the progression of recent as well as future generation technologies for WSNs and IoTs applications through Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). In general, computing time is obviously increased when the massive data is required from sensor nodes in WSN’s. the novel technologies such as 5G and 6G provides enough bandwidth for large data transmissions, however, unbalanced links faces the novel constraints on the geographical topology of the sensor networks. Above and beyond, data transmission congestion and data queue still happen in the WSNs. This book: Addresses the complete functional framework workflow in WSN and IoT domains using AI, ML, and DL models Explores basic and high-level concepts of WSN security, and routing protocols, thus serving as a manual for those in the research field as the beginners to understand both basic and advanced aspects sensors, IoT with ML & DL applications in real-world related technology Based on the latest technologies such as 5G, 6G and covering the major challenges, issues, and advances of protocols, and applications in wireless system Explores intelligent route discovering, identification of research problems and its implications to the real world Explains concepts of IoT communication protocols, intelligent sensors, statistics and exploratory data analytics, computational intelligence, machine learning, and Deep learning algorithms for betterment of the smarter humanity Explores intelligent data processing, deep learning frameworks, and multi-agent systems in IoT-enabled WSN system This book demonstrates and discovers the objectives, goals, challenges, and related solutions in advanced AI, ML, and DL approaches This book is for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
Author: Kunalbhai Shah
Publisher:
ISBN:
Category : Reinforcement learning
Languages : en
Pages :
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Book Description
In wireless sensor networks (WSN), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. WSN applications need to cope with such dynamicity and uncertainty intrinsic in sensor networks, while simultaneously trying to achieve efficient resource utilization. A middleware framework with support for autonomous, adaptive and distributed sensor management, can simplify development of such WSN applications. We present a reinforcement learning based WSN middleware framework to enable autonomous and adaptive applications with support for efficient resource management. The uniqueness of our framework lies in using a bottom-up approach where each sensor node is responsible for its resource allocation/task selection while ensuring optimization of system-wide parameters like total energy usage, network lifetime etc. The framework allows creation of a distributed and scalable system while meeting applications' goals. In this dissertation, a Q-learning based scheme called DIRL (Distributed Independent Reinforcement Learning) is presented first. DIRL learns the utility of performing various tasks over time with mostly local information at nodes. DIRL uses these utility values along with application constraints for task management subject to optimal energy usage. DIRL scheme is extended to create a two-tier reinforcement learning based framework consisting of micro-learning and macro-learning. Microlearning enables individual sensor nodes to self-schedule their tasks using local information allowing for a real-time adaptation as in DIRL. Macro-learning governs the micro-learners by setting their utility functions in order to steer the system towards applications' optimization goal (e.g. maximize network lifetime etc). The effectiveness of our framework is exemplified by designing a tracking/surveillance application on top of it. Finally, results of simulation studies are presented that compare performance of our scheme against other existing approaches. In general for applications requiring autonomous adaptation, our two-tier reinforcement learning based scheme on average is about 50% more efficient than micro-learning alone and many-fold more efficient than traditional resource management schemes like static scheduling, while maintaining necessary accuracy/performance. Efficient data collection in sparse WSNs by special nodes called Mobile Data Collectors (MDCs) that visit sensor nodes is investigated. As contact times are not known a priori and in order to minimize energy consumption, the discovery of an incoming MDC by the static sensor node is a critical task. Discovery is challenging as MDCs participating in various applications exhibit different mobility patterns and hence require unique design of a discovery strategy for each application. In this context, an adaptive discovery strategy is proposed that exploits the DIRL framework and can be effectively applied to various applications while minimizing energy consumption. The principal idea is to learn the MDC's arrival pattern and tune the sensor node's duty cycle accordingly. Through extensive simulation analysis, the energy efficiency and effectiveness of the proposed framework is demonstrated. Finally, design and evaluation of a complete and generalized middleware framework called DReL is presented with focus on distributed sensor management on top of our multi-layer reinforcement learning scheme. DReL incorporates mechanisms and communication paradigms for task, data and reward distributions. DReL provides an easy-to-use interface to application developers for creating customized applications with specific QoS and optimization requirements. Adequacy and efficiency of DReL is shown by developing few sample applications on top of it and evaluating those applications' performance.
Author: Bhanu Chander
Publisher:
ISBN: 9781032764979
Category : Artificial intelligence
Languages : en
Pages : 0
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Book Description
"The edited book Intelligent Wireless Sensor Networks and Internet of Things: Algorithms, Methodologies and Applications is intended to discuss the progression of recent as well as future generation technologies for WSNs and IoTs applications through Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). In general, computing time is obviously increased when the massive data is required from sensor nodes in WSN's. The novel technologies such as 5G and 6G provides enough bandwidth for large data transmissions, however, unbalanced links face the novel constraints on the geographical topology of the sensor networks. Above and beyond, data transmission congestion and data queue still happen in the WSNs. This text: Addresses the complete functional framework workflow in WSN and IoT domains using AI, ML, and DL models. Explores basic and high-level concepts of WSN security, and routing protocols, thus serving as a manual for those in the research field as the beginners to understand both basic and advanced aspects sensors, IoT with ML & DL applications in real-world related technology. Based on the latest technologies such as 5G, 6G and covering the major challenges, issues, and advances of protocols, and applications in wireless system. Explores intelligent route discovering, identification of research problems and its implications to the real world. Explains concepts of IoT communication protocols, intelligent sensors, statistics and exploratory data analytics, computational intelligence, machine learning, and Deep learning algorithms for betterment of the smarter humanity. Explores intelligent data processing, deep learning frameworks, and multi-agent systems in IoT-enabled WSN system. This book demonstrates and discovers the objectives, goals, challenges, and related solutions in advanced AI, ML, and DL approaches. This book is for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology"--
Author: Farhan Khan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
One of the most fundamental aspects of wireless sensor networking based applications is that they are either designed to monitor physical quantities, observe various phenomena, disseminate useful information to autonomous or semi-autonomous agents, or simply gather information in their surrounding environment. The collected information may be used by a cyber-physical system or transmitted via a data network to a remote location for subsequent data processing. In both cases, the information can become meaningless if the current location of the sending sensor node is not known or the reported information or observation is not accurately location stamped. In addition to this, there are certain tracking applications, monitoring applications, and geographical routing protocols that put a stringent demand that the location of sensor nodes should be known a priori. This work proposes a distributed localization algorithm that describes how a small sub-region in a sensing eld can construct a spatial map of the locations of all the neighbouring nodes based on inter-node distances and how each sub-region can then stitch its own map with those of all other sub-regions in its close proximity with the outcome that the collection of stitched maps forms a consistent coordinate system. The proposed localization algorithm employs concepts of range lookup, multidimensional scaling, and least-squares tting to compute locations of static sensor nodes. The proposed algorithm can compute relative coordinates without the use of any anchor nodes and is also capable of converting the relative coordinates into absolute coordinates if a certain minimum number of anchor nodes become available at a later stage. The proposed localization scheme is only one component of a proposed framework which aims to enhance road tra c safety by employing static roadside sensors. In addition to the localization service, three more components have been proposed for the road tra c safety framework namely a road segment surveillance scheme to detect vehicles on two-way roads, an adaptive data forwarding scheme to route data among roadside sensors using reinforcement learning, and a reverse forwarding scheme to deliver road condition information or warning messages from static roadside sensors to vehicles approaching a designated region-of-interest.
Author: Ansgar Kellner
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In the near future, Wireless Sensor Networks (WSNs) are expected to play an important role for sensing applications, in the civilian as well as in the military sector. WSNs are autonomous, distributed, self-organised networks consisting of multiple sensor nodes. Usually, the limited radio range of the nodes, arising from energy constrains, is overcome by the cooperation of nodes. As the Combinatorial Optimisation Problem (COP) of routing is computationally hard, often approximation algorithms are preferred, which are capable of finding near optimal solutions within polynomial time. A simple ...
Author: Sagayam, K. Martin
Publisher: IGI Global
ISBN: 1799850692
Category : Computers
Languages : en
Pages : 405
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Book Description
Wireless sensor networks have gained significant attention industrially and academically due to their wide range of uses in various fields. Because of their vast amount of applications, wireless sensor networks are vulnerable to a variety of security attacks. The protection of wireless sensor networks remains a challenge due to their resource-constrained nature, which is why researchers have begun applying several branches of artificial intelligence to advance the security of these networks. Research is needed on the development of security practices in wireless sensor networks by using smart technologies. Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks provides emerging research exploring the theoretical and practical advancements of security protocols in wireless sensor networks using artificial intelligence-based techniques. Featuring coverage on a broad range of topics such as clustering protocols, intrusion detection, and energy harvesting, this book is ideally designed for researchers, developers, IT professionals, educators, policymakers, practitioners, scientists, theorists, engineers, academicians, and students seeking current research on integrating intelligent techniques into sensor networks for more reliable security practices.
Author: Anna Förster
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
ISBN: 9783838109367
Category :
Languages : de
Pages : 236
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Book Description
Routing and clustering for wireless sensor networks (WSN) play a significant role for reliable and energy efficient data dissemination. Although these research areas attract a lot of interest lately, there is still no holistic approach that is able to meet the requirements and challenges of many different applications and network scenarios, like various network sizes and topologies, multiple mobile data sinks, or node failures. The main goal of this work is to demonstrate that machine learning is a practical approach to a range of complex distributed problems in WSNs. Showing this will open up new paths for development at all levels of the communication stack. To achieve this goal we present a robust, energy-efficient, and flexible data dissemination framework consisting of the routing protocol FROMS and the clustering protocol Clique. Both are based on reinforcement learning, and exhibit vital properties such as robustness against mobility, node and link failures, fast recovery after failures, very low control overhead and a wide variety of supported network scenarios and applications. Both protocols are fully distributed and have minimal communication overhead.
Author: Maung Phyo
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838304373
Category :
Languages : en
Pages : 84
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Book Description
Recent advances in wireless sensor networks (WSNs) have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Much attention has been given to the routing protocols since they might differ depending on the applications and network architectures. This proposed work, LEACH with Maximum Lifetime Routing, is based on the objective of maximizing the system lifetime. In this algorithm, clusters are formed randomly in each time period. Cluster heads transmit data to base station via multi-hop routing. By comparison with LEACH, LEACH with Maximum Lifetime Routing has better performance in time till first node dies and total dead nodes. Furthermore, nodes die in approximately uniformly distributed locations in LEACH with Maximum Lifetime Routing; there is no one section of the environment that is not being sensed as nodes dies, as occurs in the other algorithms.
Author: Willem Dirk van Driel
Publisher: Springer
ISBN: 3030165779
Category : Technology & Engineering
Languages : en
Pages : 457
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Book Description
This book describes for readers various technical outcomes from the EU-project IoSense. The authors discuss sensor integration, including LEDs, dust sensors, LIDAR for automotive driving and 8 more, demonstrating their use in simulations for the design and fabrication of sensor systems. Readers will benefit from the coverage of topics such as sensor technologies for both discrete and integrated innovative sensor devices, suitable for high volume production, electrical, mechanical, security and software resources for integration of sensor system components into IoT systems and IoT-enabling systems, and IoT sensor system reliability. Describes from component to system level simulation, how to use the available simulation techniques for reaching a proper design with good performance; Explains how to use simulation techniques such as Finite Elements, Multi-body, Dynamic, stochastics and many more in the virtual design of sensor systems; Demonstrates the integration of several sensor solutions (thermal, dust, occupancy, distance, awareness and more) into large-scale system solutions in several industrial domains (Lighting, automotive, transport and more); Includes state-of-the-art simulation techniques, both multi-scale and multi-physics, for use in the electronic industry.
