10,551 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
Data Transmission with Reduced Delay for Distributed Acoustic Sensors
This paper proposes a channel access control scheme fit to dense acoustic
sensor nodes in a sensor network. In the considered scenario, multiple acoustic
sensor nodes within communication range of a cluster head are grouped into
clusters. Acoustic sensor nodes in a cluster detect acoustic signals and
convert them into electric signals (packets). Detection by acoustic sensors can
be executed periodically or randomly and random detection by acoustic sensors
is event driven. As a result, each acoustic sensor generates their packets
(50bytes each) periodically or randomly over short time intervals
(400ms~4seconds) and transmits directly to a cluster head (coordinator node).
Our approach proposes to use a slotted carrier sense multiple access. All
acoustic sensor nodes in a cluster are allocated to time slots and the number
of allocated sensor nodes to each time slot is uniform. All sensor nodes
allocated to a time slot listen for packet transmission from the beginning of
the time slot for a duration proportional to their priority. The first node
that detect the channel to be free for its whole window is allowed to transmit.
The order of packet transmissions with the acoustic sensor nodes in the time
slot is autonomously adjusted according to the history of packet transmissions
in the time slot. In simulations, performances of the proposed scheme are
demonstrated by the comparisons with other low rate wireless channel access
schemes.Comment: Accepted to IJDSN, final preprinted versio
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
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