2,063 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
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
A Cross-Layer Design Based on Geographic Information for Cooperative Wireless Networks
Most of geographic routing approaches in wireless ad hoc and sensor networks
do not take into consideration the medium access control (MAC) and physical
layers when designing a routing protocol. In this paper, we focus on a
cross-layer framework design that exploits the synergies between network, MAC,
and physical layers. In the proposed CoopGeo, we use a beaconless forwarding
scheme where the next hop is selected through a contention process based on the
geographic position of nodes. We optimize this Network-MAC layer interaction
using a cooperative relaying technique with a relay selection scheme also based
on geographic information in order to improve the system performance in terms
of reliability.Comment: in 2010 IEEE 71st Vehicular Technology Conference, 201
A Hole Avoiding Routing Protocol with Relative Neighborhood Graph for Wireless Sensor Network
[[abstract]]In wireless sensor networks, ¿holes¿ are hardly to know its location and avoid either because of various actual geographical environments. A hole can be dynamically formed due to unbalanced deployment, failure or power exhaustion of sensors, animus interference, or physical barriers such as buildings or mountains. Hence, we hope to propose the RNG Hole Avoiding Routing protocol, RNGHAR which can model ¿holes¿ existed in wireless sensor network and event packets can avoid meeting a ¿hole¿ in advance instead of bypassing a hole when it meets the hole. This paper proposes a novel algorithm RNGHAR which uses RNG (relative neighborhood graph) modeling holes then we can collect hole information in order to construct in advance hole avoiding routing path. Hence event packets will be guided to overcome the hole and move along the shortest path from source node to the sink node. Simulation studies show that my proposed method achieves good performance in terms of average hop count, packet delivery success rate and power consumption in comparison with the existing protocols.[[sponsorship]]IEEE Taipei Section; National Science Council; Ministry of Education; Tamkang University; Asia University; Providence University; The University of Aizu; Lanzhou University[[conferencetype]]國際[[conferencetkucampus]]æ·¡æ°´æ ¡åœ’[[conferencedate]]20091203~20091205[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa
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