24,044 research outputs found
RESOURCE AND ENVIRONMENT AWARE SENSOR COMMUNICATIONS: FRAMEWORK, OPTIMIZATION, AND APPLICATIONS
Recent advances in low power integrated circuit devices,
micro-electro-mechanical system (MEMS) technologies, and
communications technologies have made possible the deployment of
low-cost, low power sensors that can be integrated to form wireless
sensor networks (WSN). These wireless sensor networks have vast
important applications, i.e.: from battlefield surveillance system
to modern highway and industry monitoring system; from the emergency
rescue system to early forest fire detection and the very
sophisticated earthquake early detection system. Having the broad
range of applications, the sensor network is becoming an integral
part of human lives. However, the success of sensor networks
deployment depends on the reliability of the network itself. There
are many challenging problems to make the deployed network more
reliable. These problems include but not limited to extending
network lifetime, increasing each sensor node throughput, efficient
collection of information, enforcing nodes to collaboratively
accomplish certain network tasks, etc. One important aspect in
designing the algorithm is that the algorithm should be completely
distributed and scalable. This aspect has posed a tremendous
challenge in designing optimal algorithm in sensor networks.
This thesis addresses various challenging issues encountered in
wireless sensor networks. The most important characteristic in
sensor networks is to prolong the network lifetime. However, due to
the stringent energy requirement, the network requires highly energy
efficient resource allocation. This highly energy-efficient resource
allocation requires the application of an energy awareness system.
In fact, we envision a broader resource and environment aware
optimization in the sensor networks. This framework reconfigures the
parameters from different communication layers according to its
environment and resource. We first investigate the application of
online reinforcement learning in solving the modulation and transmit
power selection. We analyze the effectiveness of the learning
algorithm by comparing the effective good throughput that is
successfully delivered per unit energy as a metric. This metric
shows how efficient the energy usage in sensor communication is. In
many practical sensor scenarios, maximizing the energy efficient in
a single sensor node may not be sufficient. Therefore, we continue
to work on the routing problem to maximize the number of delivered
packet before the network becomes useless. The useless network is
characterized by the disintegrated remaining network. We design a
class of energy efficient routing algorithms that explicitly takes
the connectivity condition of the remaining network in to account.
We also present the distributed asynchronous routing implementation
based on reinforcement learning algorithm. This work can be viewed
as distributed connectivity-aware energy efficient routing. We then
explore the advantages obtained by doing cooperative routing for
network lifetime maximization. We propose a power allocation in the
cooperative routing called the maximum lifetime power allocation.
The proposed allocation takes into account the residual energy in
the nodes when doing the cooperation. In fact, our criterion lets
the nodes with more energy to help more compared to the nodes with
less energy. We continue to look at the problem of cooperation
enforcement in ad-hoc network. We show that by combining the
repeated game and self learning algorithm, a better cooperation
point can be obtained. Finally, we demonstrate an example of
channel-aware application for multimedia communication. In all case
studies, we employ optimization scheme that is equipped with the
resource and environment awareness. We hope that the proposed
resource and environment aware optimization framework will serve as
the first step towards the realization of intelligent sensor
communications
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Performance and Detection of M-ary Frequency Shift Keying in Triple Layer Wireless Sensor Network
This paper proposes an innovative triple layer Wireless Sensor Network (WSN)
system, which monitors M-ary events like temperature, pressure, humidity, etc.
with the help of geographically distributed sensors. The sensors convey signals
to the fusion centre using M-ary Frequency Shift Keying (MFSK)modulation scheme
over independent Rayleigh fading channels. At the fusion centre, detection
takes place with the help of Selection Combining (SC) diversity scheme, which
assures a simple and economical receiver circuitry. With the aid of various
simulations, the performance and efficacy of the system has been analyzed by
varying modulation levels, number of local sensors and probability of correct
detection by the sensors. The study endeavors to prove that triple layer WSN
system is an economical and dependable system capable of correct detection of
M-ary events by integrating frequency diversity together with antenna
diversity.Comment: 13 pages; International Journal of Computer Networks & Communications
(IJCNC) Vol.4, No.4, July 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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