Energy Management In Wireless Sensor Networks
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Energy Harvesting in Wireless Sensor Networks and Internet of Things
The energy efficiency paradigm is a major bottleneck for the development of wireless sensor networks (WSNs) and Internet of Things (IoT) architectures and technologies. This edited book presents comprehensive coverage of energy harvesting sources and techniques that can be used for WSN and IoT systems.
Energy Management in Wireless Sensor Network Operations
In this dissertation, we develop and analyze effective energy management policies for wireless sensor networks in emerging applications. Existing methods in this area have primarily focused on energy conservation through the use of various communication techniques. However, in most applications of wireless sensor networks, savings in energy come at the expense of several performance parameters. Therefore it is necessary to manage energy consumption while being conscious of its effects on performance. In most cases, such energy-performance issues are specific to the nature of the application. Our research has been motivated by new techniques and applications where efficient energy-performance trade-off decisions are required. We primarily study the following trade-off cases: energy and node replacement costs (Case I), energy and delay (Case II), and energy and availability (Case III). We consider these trade-off situations separately in three distinct problem scenarios. In the first problem (Case I), we consider minimizing energy and node replacement costs in underwater wireless sensor networks for seismic monitoring application. In this case, we introduce mixed-integer programming (MIP) formulations based on a combined routing and node replacement policy approach and develop effective policies for large problem instances where our MIP models are intractable. In the second problem (Case II), we develop a Markov decision process (MDP) model to manage energy-delay trade-off in network coding which is a new energy-saving technique for wireless networks. Here we derive properties of the optimal policy and develop insights into other simple policies that are later shown to be efficient in particular situations. In the third problem (Case III), we consider an autonomous energy harvesting sensor network where nodes are turned off from time to time to operate in an "energy-neutral" manner. In this case, we use stochastic fluid-flow analysis to evaluate and analyze the availability of the sensor nodes under effective energy management policies. In each of the above problem cases, we develop analytical formulations, and derive and/or analyze policies that effectively manage the considered energy-performance trade-off. Overall, our analyses and solution methods make new contributions to both operations research and communication networking literature. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151380
Energy Management in Wireless Sensor Networks
Energy Management in Wireless Sensor Networks discusses this unavoidable issue in the application of Wireless Sensor Networks (WSN). To guarantee efficiency and durability in a network, the science must go beyond hardware solutions and seek alternative software solutions that allow for better data control from the source to delivery. Data transfer must obey different routing protocols, depending on the application type and network architecture. The correct protocol should allow for fluid information flow, as well as optimizing power consumption and resources – a challenge faced by dense networks. The topics covered in this book provide answers to these needs by introducing and exploring computer-based tools and protocol strategies for low power consumption and the implementation of routing mechanisms which include several levels of intervention, ranging from deployment to network operation. - Explores ways to manage energy consumption during the design and implementation of WSN - Helps users implement an increase in network longevity - Presents intrinsic characteristics of wireless sensor networks