29 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Mobile Ad Hoc Networks
Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms
Cooperative Authentication in Underwater Acoustic Sensor Networks
With the growing use of underwater acoustic communications (UWAC) for both
industrial and military operations, there is a need to ensure communication
security. A particular challenge is represented by underwater acoustic networks
(UWANs), which are often left unattended over long periods of time. Currently,
due to physical and performance limitations, UWAC packets rarely include
encryption, leaving the UWAN exposed to external attacks faking legitimate
messages. In this paper, we propose a new algorithm for message authentication
in a UWAN setting. We begin by observing that, due to the strong spatial
dependency of the underwater acoustic channel, an attacker can attempt to mimic
the channel associated with the legitimate transmitter only for a small set of
receivers, typically just for a single one. Taking this into account, our
scheme relies on trusted nodes that independently help a sink node in the
authentication process. For each incoming packet, the sink fuses beliefs
evaluated by the trusted nodes to reach an authentication decision. These
beliefs are based on estimated statistical channel parameters, chosen to be the
most sensitive to the transmitter-receiver displacement. Our simulation results
show accurate identification of an attacker's packet. We also report results
from a sea experiment demonstrating the effectiveness of our approach.Comment: Author version of paper accepted for publication in the IEEE
Transactions on Wireless Communication
Toward Collinearity-Avoidable Localization for Wireless Sensor Network
In accordance with the collinearity problem during computation caused by the beacon nodes used for location estimation which are close to be in the same line or same plane, two solutions are proposed in this paper: the geometric analytical localization algorithm based on positioning units and the localization algorithm based on the multivariate analysis method. The geometric analytical localization algorithm based on positioning units analyzes the topology quality of positioning units used to estimate location and provides quantitative criteria based on that; the localization algorithm based on the multivariate analysis method uses the multivariate analysis method to filter and integrate the beacon nodes coordinate matrixes during the process of location estimation. Both methods can avoid low estimation accuracy and instability caused by multicollinearity
Moving-baseline localization for mobile wireless sensor networks
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (leaves 93-98).The moving-baseline localization (MBL) problem arises when a group of nodes moves through an environment in which no external coordinate reference is available. When group members cannot see or hear one another directly, each node must employ local sensing and inter-device communication to infer the spatial relationship and motion of all other nodes with respect to itself. We consider a setting in which nodes move with piecewise-linear velocities in the plane, and any node can exchange noisy range estimates with certain sufficiently nearby nodes. We develop a distributed solution to the MBL problem in the plane, in which each node performs robust hyperbola fitting, trilateration with velocity constraints, and subgraph alignment to arrive at a globally consistent view of the network expressed in its own "rest frame." Changes in any node's motion cause deviations between observed and predicted ranges at nearby nodes, triggering revision of the trajectory estimates computed by all nodes. We implement and analyze our algorithm in a simulation informed by the characteristics of a commercially available ultra-wideband (UWB) radio, and show that recovering node trajectories, rather than just locations, requires substantially less computation at each node. Finally, we quantify the minimum ranging rate and local network density required for the method's successful operation.by Jun-geun Park.S.M