15,846 research outputs found
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
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks
We consider a small extent sensor network for event detection, in which nodes
take samples periodically and then contend over a {\em random access network}
to transmit their measurement packets to the fusion center. We consider two
procedures at the fusion center to process the measurements. The Bayesian
setting is assumed; i.e., the fusion center has a prior distribution on the
change time. In the first procedure, the decision algorithm at the fusion
center is \emph{network-oblivious} and makes a decision only when a complete
vector of measurements taken at a sampling instant is available. In the second
procedure, the decision algorithm at the fusion center is \emph{network-aware}
and processes measurements as they arrive, but in a time causal order. In this
case, the decision statistic depends on the network delays as well, whereas in
the network-oblivious case, the decision statistic does not depend on the
network delays. This yields a Bayesian change detection problem with a tradeoff
between the random network delay and the decision delay; a higher sampling rate
reduces the decision delay but increases the random access delay. Under
periodic sampling, in the network--oblivious case, the structure of the optimal
stopping rule is the same as that without the network, and the optimal change
detection delay decouples into the network delay and the optimal decision delay
without the network. In the network--aware case, the optimal stopping problem
is analysed as a partially observable Markov decision process, in which the
states of the queues and delays in the network need to be maintained. A
sufficient statistic for decision is found to be the network-state and the
posterior probability of change having occurred given the measurements received
and the state of the network. The optimal regimes are studied using simulation.Comment: To appear in ACM Transactions on Sensor Networks. A part of this work
was presented in IEEE SECON 2006, and Allerton 201
Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks
Sensors are present in various forms all around the world such as mobile
phones, surveillance cameras, smart televisions, intelligent refrigerators and
blood pressure monitors. Usually, most of the sensors are a part of some other
system with similar sensors that compose a network. One of such networks is
composed of millions of sensors connect to the Internet which is called
Internet of things (IoT). With the advances in wireless communication
technologies, multimedia sensors and their networks are expected to be major
components in IoT. Many studies have already been done on wireless multimedia
sensor networks in diverse domains like fire detection, city surveillance,
early warning systems, etc. All those applications position sensor nodes and
collect their data for a long time period with real-time data flow, which is
considered as big data. Big data may be structured or unstructured and needs to
be stored for further processing and analyzing. Analyzing multimedia big data
is a challenging task requiring a high-level modeling to efficiently extract
valuable information/knowledge from data. In this study, we propose a big
database model based on graph database model for handling data generated by
wireless multimedia sensor networks. We introduce a simulator to generate
synthetic data and store and query big data using graph model as a big
database. For this purpose, we evaluate the well-known graph-based NoSQL
databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a
number of query experiments on our implemented simulator to show that which
database system(s) for surveillance in wireless multimedia sensor networks is
efficient and scalable
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
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