50,441 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
Synchronous wearable wireless body sensor network composed of autonomous textile nodes
A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system
Analysis of a distributed fiber-optic temperature sensor using single-photon detectors
We demonstrate a high-accuracy distributed fiber-optic temperature sensor using superconducting nanowire single-photon detectors and single-photon counting techniques. Our demonstration uses inexpensive single-mode fiber at standard telecommunications wavelengths as the sensing fiber, which enables extremely low-loss experiments and compatibility with existing fiber networks. We show that the uncertainty of the temperature measurement decreases with longer integration periods, but is ultimately limited by the calibration uncertainty. Temperature uncertainty on the order of 3 K is possible with spatial resolution of the order of 1 cm and integration period as small as 60 seconds. Also, we show that the measurement is subject to systematic uncertainties, such as polarization fading, which can be reduced with a polarization diversity receiver
Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks
In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods
Estimation Diversity and Energy Efficiency in Distributed Sensing
Distributed estimation based on measurements from multiple wireless sensors
is investigated. It is assumed that a group of sensors observe the same
quantity in independent additive observation noises with possibly different
variances. The observations are transmitted using amplify-and-forward (analog)
transmissions over non-ideal fading wireless channels from the sensors to a
fusion center, where they are combined to generate an estimate of the observed
quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by
the fusion center, the equal-power transmission strategy is first discussed,
where the system performance is analyzed by introducing the concept of
estimation outage and estimation diversity, and it is shown that there is an
achievable diversity gain on the order of the number of sensors. The optimal
power allocation strategies are then considered for two cases: minimum
distortion under power constraints; and minimum power under distortion
constraints. In the first case, it is shown that by turning off bad sensors,
i.e., sensors with bad channels and bad observation quality, adaptive power
gain can be achieved without sacrificing diversity gain. Here, the adaptive
power gain is similar to the array gain achieved in Multiple-Input
Single-Output (MISO) multi-antenna systems when channel conditions are known to
the transmitter. In the second case, the sum power is minimized under
zero-outage estimation distortion constraint, and some related energy
efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs
In this letter, we propose space-time spreading (STS) of local sensor
decisions before reporting them over a wireless multiple access channel (MAC),
in order to achieve flexible balance between diversity and multiplexing gain as
well as eliminate any chance of intrinsic interference inherent in MAC
scenarios. Spreading of the sensor decisions using dispersion vectors exploits
the benefits of multi-slot decision to improve low-complexity diversity gain
and opportunistic throughput. On the other hand, at the receive side of the
reporting channel, we formulate and compare optimum and sub-optimum fusion
rules for arriving at a reliable conclusion.Simulation results demonstrate gain
in performance with STS aided transmission from a minimum of 3 times to a
maximum of 6 times over performance without STS.Comment: 5 pages, 5 figure
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