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Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey

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TLDR
This article defines WSNs with MEs and provides a comprehensive taxonomy of their architectures, based on the role of the MEs, and provides an extensive survey of the related literature.
Abstract
Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

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Distributed Mobile Sink Routing for Wireless Sensor Networks: A Survey

TL;DR: A survey of the existing distributed mobile sink routing protocols is presented and a definitive and detailed categorization is made and the protocols' advantages and drawbacks are determined with respect to their target applications.
References
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Journal ArticleDOI

Wireless sensor networks: a survey

TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Book

Pattern Recognition and Machine Learning (Information Science and Statistics)

TL;DR: Looking for competent reading resources?
Journal ArticleDOI

Reinforcement learning: a survey

TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content

Reinforcement Learning: A Survey

TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.

Optimized Link State Routing Protocol (OLSR)

TL;DR: The Optimized Link State Routing protocol is an optimization of the classical link state algorithm tailored to the requirements of a mobile wireless LAN and provides optimal routes (in terms of number of hops).
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