Algorithms For Data Gathering In Wireless Sensor Networks

Wireless sensor networks consist of a large number of small battery powered sensor nodes with limited energy resources which are responsible for sensing, processing, and transmitting the monitored data. Once deployed, the sensor nodes are normally inaccessible to the user, and thus replacement of the battery is generally not feasible. A major concern in designing and operating dense Wireless Sensor Networks (WSNs) is the energy-efficiency. Hierarchical clustering and cross-layer optimization are widely accepted as effective techniques to ameliorate this concern. We propose two different novel energy efficient algorithms to gather data from sensor nodes. Energy-Efficient Media Access Control (EE-MAC) protocol is the first algorithm, which has excellent scalability and performs well for both small and large sensor networks. We will also provide a theoretical analysis of the protocol and give guidelines on how to find the optimal protocol parameters such as the number of clusters. In addition, we develop and analyze a novel and scalable Spiraled Algorithm for Data-gathering (SAD) that periodically selects cluster heads according to their geographic locations and residual energy by sorting nodes on virtual spirals. Theoretical analysis and simulation results show that SAD can achieve as much as a factor of three prolonging network lifetime compared with other conventional protocols like LEACH especially when the network is large. Moreover, SAD is also able to distribute energy dissipation evenly throughout the sensors such that 80% of the nodes run out of batteries in the last 20% of the network lifetime.

This book presents high-quality, original contributions (both theoretical and experimental) on Information Security, Machine Learning, Data Mining and Internet of Things (IoT). It gathers papers presented at ICETIT 2019, the 1st International Conference on Emerging Trends in Information Technology, which was held in Delhi, India, in June 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, data analytics and cloud computing, security and privacy, and next generation computing technologies.

Wireless sensor networks are characterized by severe energy constraints, one to many flows and low rate redundant data. Most of the routing algorithms for traditional networks are address centric, and the ad hoc nature of wireless sensor network makes them unsuitable for practical applications. Also the algorithms designed for mobile ad hoc networks are unsuitable for wireless sensor networks due to severe energy constraints that require nodes to perform for months with limited resources, as well as the low data rate which the constraint implies. This thesis examines a model driven data gathering algorithm framework for wireless sensor networks. It was designed with a goal to decrease the overall cost in transmission by lowering the number of messages transmitted in the network. A combination of data- centric and address-centric approaches was used as guidelines during the design process. A shortest path heuristic where intermediate nodes forward interest messages whenever it is of lower cost is one of the heuristics used. Another heuristic used is the greedy incremental approach to build a lower cost tree from a graph with various producers and consumers. A cost division heuristic is used to divide cost of shared path into distinct paths as the path forks in a tree. This thesis analyzes the effects of these heuristics on the performance of the algorithm and how it lowers the overall cost with the addition of each heuristic.

A one-stop resource for the use of algorithms and protocols in wireless sensor networks From an established international researcher in the field, this edited volume provides readers with comprehensive coverage of the fundamental algorithms and protocols for wireless sensor networks. It identifies the research that needs to be conducted on a number of levels to design and assess the deployment of wireless sensor networks, and provides an in-depth analysis of the development of the next generation of heterogeneous wireless sensor networks. Divided into nineteen succinct chapters, the book covers: mobility management and resource allocation algorithms; communication models; energy and power consumption algorithms; performance modeling and simulation; authentication and reputation mechanisms; algorithms for wireless sensor and mesh networks; and algorithm methods for pervasive and ubiquitous computing; among other topics. Complete with a set of challenging exercises, this book is a valuable resource for electrical engineers, computer engineers, network engineers, and computer science specialists. Useful for instructors and students alike, Algorithms and Protocols for Wireless Sensor Networks is an ideal textbook for advanced undergraduate and graduate courses in computer science, electrical engineering,and network engineering.

Collecting and processing data is a necessary aspect of living in a technologically advanced society. Whether it’s monitoring events, controlling different variables, or using decision-making applications, it is important to have a system that is both inexpensive and capable of coping with high amounts of data. As the application of these networks becomes more common, it becomes imperative to evaluate their effectiveness as well as other opportunities for possible implementation in the future. Sensor Technology: Concepts, Methodologies, Tools, and Applications is a vital reference source that brings together new ways to process and monitor data and to put it to work in everything from intelligent transportation systems to healthcare to multimedia applications. It also provides inclusive coverage on the processing and applications of wireless communication, sensor networks, and mobile computing. Highlighting a range of topics such as internet of things, signal processing hardware, and wireless sensor technologies, this multi-volume book is ideally designed for research and development engineers, IT specialists, developers, graduate students, academics, and researchers.

This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Algorithms for mobile data collection in wireless sensor networks (WSNs) are designed to optimize the process of gathering data from multiple sensors distributed in a particular area. WSNs are composed of a large number of small, battery-powered devices called sensors that collect and transmit data wirelessly to a central node or base station. The main challenge in mobile data collection is to efficiently utilize the limited resources of these sensors, such as battery power, processing capabilities, and communication bandwidth, while achieving high data collection rates and minimizing the energy consumption. Several algorithms have been developed to address this challenge, and they can be broadly classified into three categories: Static-based algorithms: In this approach, a fixed path is predetermined for mobile nodes to follow, and data is collected from the sensors located along that path. This method is simple and easy to implement, but it may not be optimal in terms of energy efficiency and data collection rate. Dynamic-based algorithms: In this approach, the path of mobile nodes is dynamically determined based on the current status of the sensors, such as their energy level and data availability. This method can achieve better energy efficiency and data collection rate, but it requires more computational resources and communication overhead. Hybrid algorithms: These algorithms combine the advantages of both static and dynamic approaches to achieve better performance. For example, a hybrid algorithm may use a static path for initial data collection and then switch to a dynamic path based on the real-time status of the sensors. The performance of these algorithms can be evaluated based on various metrics such as energy consumption, data collection rate, communication overhead, and network lifetime. The choice of algorithm depends on the specific requirements and constraints of the application, such as the size and density of the sensor network, the data collection frequency, and the available resources.

This timely book offers a mixture of theory, experiments, and simulations that provides qualitative and quantitative insights in the field of sensor and actuator networking. The chapters are selected in a way that makes the book comprehensive and self-contained. It covers a wide range of recognized problems in sensor networks, striking a balance between theoretical and practical coverage. The book is appropriate for graduate students and practitioners working as engineers, programmers, and technologists.