2,717 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
Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks
Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment
In the last decade, integrated logistics has become an important challenge in
the development of wireless communication, identification and sensing
technology, due to the growing complexity of logistics processes and the
increasing demand for adapting systems to new requirements. The advancement of
wireless technology provides a wide range of options for the maritime container
terminals. Electronic devices employed in container terminals reduce the manual
effort, facilitating timely information flow and enhancing control and quality
of service and decision made. In this paper, we examine the technology that can
be used to support integration in harbor's logistics. In the literature, most
systems have been developed to address specific needs of particular harbors,
but a systematic study is missing. The purpose is to provide an overview to the
reader about which technology of integrated logistics can be implemented and
what remains to be addressed in the future
Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices
In recent years, ZigBee has been proven to be an excellent solution to create scalable and flexible home automation networks. In a home automation network, consumer devices typically collect data from a home monitoring environment and then transmit the data to an end user through multi-hop communication without the need for any human intervention. However, due to the presence of typical obstacles in a home environment, error-free reception may not be possible, particularly for power constrained devices. A mobile sink based data transmission scheme can be one solution but obstacles create significant complexities for the sink movement path determination process. Therefore, an obstacle avoidance data routing scheme is of vital importance to the design of an efficient home automation system.
This paper presents a mobile sink based obstacle avoidance routing scheme for a home monitoring system. The mobile sink collects data by traversing through the obstacle avoidance path. Through ZigBee based hardware implementation and verification, the proposed scheme successfully transmits data through the obstacle avoidance path to improve network performance in terms of life span, energy consumption and reliability. The application of this work can be applied to a wide range of intelligent pervasive consumer products and services including robotic vacuum cleaners and personal security robots1
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