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Owing to the advancements of wireless communications and the cheaper sensor technology, Wireless Sensor Network (WSN) has gained its limelight. WSN can be deployed even in human inaccessible areas. Thus, the WSN must be able to work without human intervention. The performance of the WSN depends on the battery of the sensor nodes, as the sensor node will be shut down when its battery is drained completely.
We also address a variant of the traveling salesman problem known as the close-enough traveling salesman problem that is useful in devising energy-aware trajectories for the sink in a WSN. We formulate a mixed-integer programming model based on a discretization scheme for the problem. Both lower and upper bounds on the optimal tour length can be derived from the solution of this model, and the quality of the bounds obtained depends on the fidelity of the discretization scheme. Finally, we propose an extension to the lifetime maximization problem by examining the case.
Wireless Sensor Networks came into prominence around the start of this millennium motivated by the omnipresent scenario of small-sized sensors with limited power deployed in large numbers over an area to monitor different phenomenon. The sole motivation of a large portion of research efforts has been to maximize the lifetime of the network, where network lifetime is typically measured from the instant of deployment to the point when one of the nodes has expended its limited power source and becomes in-operational - commonly referred as first node failure. Over the years, research has increasingly adopted ideas from wireless communications as well as embedded systems development in order to move this technology closer to realistic deployment scenarios. In such a rich research area as wireless sensor networks, it is difficult if not impossible to provide a comprehensive coverage of all relevant aspects. In this book, we hope to give the reader with a snapshot of some aspects of wireless sensor networks research that provides both a high level overview as well as detailed discussion on specific areas.
Finally a book on Wireless Sensor Networks that covers real world applications and contains practical advice! Kuorilehto et al. have written the first practical guide to wireless sensor networks. The authors draw on their experience in the development and field-testing of autonomous wireless sensor networks (WSNs) to offer a comprehensive reference on fundamentals, practical matters, limitations and solutions of this fast moving research area. Ultra Low Energy Wireless Sensor Networks in Practice: Explains the essential problems and issues in real wireless sensor networks, and analyzes the most promising solutions. Provides a comprehensive guide to applications, functionality, protocols, and algorithms for WSNs. Offers practical experiences from new applications and their field-testing, including several deployed networks. Includes simulations and physical measurements for energy consumption, bit rate, latency, memory, and lifetime. Covers embedded resource-limited operating systems, middleware and application software. Ultra Low Energy Wireless Sensor Networks in Practice will prove essential reading for Research Scientists, advanced students in Networking, Electrical Engineering and Computer Science as well as Product Managers and Design Engineers.
A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle – distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.
We study lifetime of linear and two-dimensional Wireless Sensor Networks (WSNs) for the case where wireless sensors are not able to change their transmission power. In this case, unlike majority of existing works, power control cannot be used to increase network's lifetime. Instead, other approaches such as adding extra nodes and careful node placement and routing can be employed to improve network's lifetime. For linear WSNs, we consider two scenarios. In the first scenario, every node is required to periodically send packets to the base station while, in the second scenario, only a subset of nodes (e.g., a subset that covers the whole network) are required to do so. For the first scenario, we derive bounds on the maximum lifetime achievable. For the second scenario, we establish a lower bound on the minimum number of nodes required to achieve any specific lifetime, as in this scenario, lifetime can be arbitrarily increased by adding enough number of nodes in the network. Next, we extend our work to two-dimensional WSNs. As before, we do not use any power control mechanism. Similar to the first application in linear networks, we assume that all nodes send reports to the base station, periodically. However, rather than node placement, we focus on routing. Also, we assume that each node can aggregate all the received packets into a single packet. Our primary objective here is to improve network's lifetime. As a secondary objective, we aim at reducing network's delay while preserving network's lifetime.
The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. Provides insights into the theory, algorithms, implementation, and application of computational intelligence techniques Covers a wide range of applications of deep learning across various domains which are researching the applications of computational intelligence Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques
The nodes in a wireless sensor network (WSN) are generally energy constrained as the battery of a node may not be recharged or replaced. The lifetime of such a network is limited by the energy dissipated by individual nodes during signal processing and communication with other nodes. The issues of modeling a sensor network and assessment of its lifetime have received considerable attention in recent years. Another important performance metric of WSN is network coverage. It depends on several factors including sensing model that has been used to design the network model. The issues of lifetime and coverage have been addressed in the book. It has been observed that non-uniform energy drainage pattern affects network lifetime. Thus, equal energy consumption condition has been imposed. It has been observed that our proposed scheme provides 2.4 and 1.6 times lifetime improvement with respect to other reported node placement schemes under comparable conditions. Equal energy consumption condition has been extended to a wireless image sensor network.
Wireless sensor networks are made up of a large number of sensors deployed randomly in an ad-hoc manner in the area/target to be monitored. Due to their weight and size limitations, the energy conservation is the most critical issue. Energy saving in a wireless sensor network can be achieved by scheduling a subset of sensor nodes to activate and allowing others to go into low power sleep mode, or adjusting the transmission or sensing range of wireless sensor nodes. In this thesis, we focus on improving the lifetime of wireless sensor networks using both smart scheduling and adjusting sensing ranges. Firstly, we conduct a survey on existing works in literature and then we define the sensor network lifetime problem with range assignment. We then propose two completely localized and distributed scheduling algorithms with adjustable sensing range. These algorithms are the enhancement of distributed algorithms for fixed sensing range proposed in the literature. The simulation results show that there is almost 20 percent improvement of network lifetime when compare with the previous approaches.