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Satoshi Nagata - One of the best experts on this subject based on the ideXlab platform.
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physical cell id Detection Probability using synchronization signals of nb iot radio interface in 28 ghz band
Vehicular Technology Conference, 2020Co-Authors: Daisuke Inoue, Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using the narrowband primary synchronization signal (NPSS) and narrowband secondary synchronization signal (NSSS) based on the narrowband Internet-of-Things (NB-IoT) radio interface considering frequency offset and the maximum Doppler frequency in the 28-GHz band. Simulation results show that autocorrelation based NPSS Detection achieves almost the same PCID Detection Probability as that for cross-correlation based NPSS Detection using frequency offset estimation and compensation before the NPSS received timing Detection, while the former achieves a lower level of computational complexity than the latter. We also show that when using autocorrelation based NPSS Detection, the loss in the PCID Detection Probability at the carrier frequency of $f_{c}=28$ GHz compared to that for $f_{c}=3.5$ GHz is only approximately 5% at the average received SNR of 0dB when the frequency error of a temperature compensated crystal oscillator of a set of user equipment (UE) is 20 ppm. Therefore, we conclude that the multiplexing schemes and sequences of NPSS and NSSS based on the NB-IoT radio interface associated with autocorrelation based NPSS Detection will be applicable to the 28-GHz frequency spectra.
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physical cell id Detection Probability using synchronization signals for nr radio interface in 28 ghz band
Asia-Pacific Conference on Communications, 2019Co-Authors: Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using the primary synchronization signal (PSS) and secondary synchronization signal (SSS) for the New Radio (NR) radio interface considering the large frequency offset in the 28-GHz band. Simulation results show that crosscorrelation based PSS Detection achieves a slightly higher PCID Detection Probability compared to autocorrelation based PSS Detection in the lower average received signal-to-noise power ratio (SNR) below approximately 0 dB for the subcarrier spacings (SCSs) of 120 and 240 kHz. Meanwhile, the required computational complexity for the PCID Detection of the latter method is much lower than the former method. We also show that when the frequency error of a standard oscillator of a set of user equipment is e = 20 ppm, the PCID Detection Probability is decreased by only approximately 28 (22)% and 3 (6)% compared to that without frequency offset at the average received SNR of −10 and 0 dB, respectively, for the SCS of 240 (120) kHz. We conclude that the multiplexing scheme for the PSS and SSS and their sequences are effective in achieving a high PCID Detection Probability considering the large frequency offset even with e = 30 ppm in the 28-GHz band.
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physical cell id Detection Probability using synchronization signals for nr radio interface below 6 ghz
Asia-Pacific Conference on Wearable Computing Systems, 2019Co-Authors: Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical cell identity (PCID) Detection Probability using synchronization signals based on the New Radio radio interface considering the frequency offset, maximum Doppler frequency, and co-channel interference (CCI) for frequency spectra below 6 GHz based on link-level simulations. We assume cross-correlation based primary synchronization signal Detection, followed by frequency offset estimation and compensation, and then secondary synchronization signal Detection. Simulation results show that the PCID Detection Probability is degraded according to the increase in the frequency offset for the subcarrier spacings (SCSs) of 15 and 30 kHz. However, we show that the 30-kHz SCS is more effective in mitigating the degradation in the PCID Detection Probability than the 15-kHz SCS for a frequency offset greater than approximately 20 kHz. We also show that a high PCID Detection Probability is achieved for the maximum Doppler frequency of up to 888.8 Hz, which corresponds to the moving speed of 480 km/h at the carrier frequency of 2 GHz. Finally, we show that the PCID Detection Probability with the received signal-to-interference power ratios of -3 dB and 0 dB is degraded by approximately 7% and 52%, respectively, compared to that without the CCI at the average received signal-to-noise ratio of 0 dB for the maximum frequency offset of 20 kHz.
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Performance of Physical Cell ID Detection Probability Considering Frequency Offset for NR Radio Interface
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Co-Authors: Kyogo Ota, Aya Shimura, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability considering frequency offset for 5th generation (5G) new radio (NR). We compare the primary synchronization signal (PSS) and PCID Detection probabilities of three PSS Detection methods: cross-correlation based Detection before frequency offset (FO) estimation and compensation, that after FO estimation and compensation, and autocorrelation based Detection at a set of user equipment. Link- level simulation results show that the cross- correlation based PSS Detection before the FO estimation and compensation achieves the highest PSS and PCID Detection probabilities in the carrier frequency fc region lower than approximately 14 GHz at the average received signal-to-noise power ratio of 0 dB for the standard oscillator frequency error of 1 ppm. The cross-correlation based PSS Detection after the FO estimation and compensation achieves much higher PSS and PCID Detection probabilities than that before the FO estimation and compensation for the fc region higher than approximately 14 GHz. The PCID Detection Probability of the cross-correlation based PSS Detection after the FO estimation and compensation is close to that of the autocorrelation based PSS Detection according to the increase in the fc value. Therefore, we conclude that by switching the cross- correlation based PSS Detection methods before and after the FO estimation and compensation, the PSS and secondary synchronization signal structures based on the NR radio interface achieve a high PCID Detection Probability for the fc value up to 60 GHz.
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physical cell id Detection Probability using synchronization signals for nr radio interface
Wireless Personal Multimedia Communications, 2018Co-Authors: Kyogo Ota, Mamoru Sawahashi, Aya Shimura, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using precoding vector switching (PVS) transmit diversity and receive diversity for 5G new radio (NR). We use the sequences and multiplexing schemes of the primary synchronization signal (PSS) and secondary synchronization signal (SSS) based on the 3rd Generation Partnership Project (3GPP) release 15 specifications to detect the best PCID among the 1008 candidates. Link-level simulation results show that receive diversity up to four antennas is very effective in achieving highly correct Detection probabilities of the PSS and SSS sequences, the combination of which provides the PCID, regardless of the propagation channel model. We also show that PVS transmit diversity with two antennas improves the PCID Detection Probability by approximately 5% - 10% in the high average received signal-to-noise power ratio (SNR) region when the PCID Detection Probability is greater than approximately 50% in the case of up to four receive antennas. Because further improvement in the PCID Detection Probability by increasing the number of transmit antennas to 4 antennas is small, it is shown that the application of PVS transmit diversity with two antennas is suitable for achieving high PCID Detection Probability in the NR radio interface. These conclusions are applicable to beamforming transmission for the PSS and SSS.
Kyogo Ota - One of the best experts on this subject based on the ideXlab platform.
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physical cell id Detection Probability using synchronization signals of nb iot radio interface in 28 ghz band
Vehicular Technology Conference, 2020Co-Authors: Daisuke Inoue, Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using the narrowband primary synchronization signal (NPSS) and narrowband secondary synchronization signal (NSSS) based on the narrowband Internet-of-Things (NB-IoT) radio interface considering frequency offset and the maximum Doppler frequency in the 28-GHz band. Simulation results show that autocorrelation based NPSS Detection achieves almost the same PCID Detection Probability as that for cross-correlation based NPSS Detection using frequency offset estimation and compensation before the NPSS received timing Detection, while the former achieves a lower level of computational complexity than the latter. We also show that when using autocorrelation based NPSS Detection, the loss in the PCID Detection Probability at the carrier frequency of $f_{c}=28$ GHz compared to that for $f_{c}=3.5$ GHz is only approximately 5% at the average received SNR of 0dB when the frequency error of a temperature compensated crystal oscillator of a set of user equipment (UE) is 20 ppm. Therefore, we conclude that the multiplexing schemes and sequences of NPSS and NSSS based on the NB-IoT radio interface associated with autocorrelation based NPSS Detection will be applicable to the 28-GHz frequency spectra.
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physical cell id Detection Probability using synchronization signals for nr radio interface in 28 ghz band
Asia-Pacific Conference on Communications, 2019Co-Authors: Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using the primary synchronization signal (PSS) and secondary synchronization signal (SSS) for the New Radio (NR) radio interface considering the large frequency offset in the 28-GHz band. Simulation results show that crosscorrelation based PSS Detection achieves a slightly higher PCID Detection Probability compared to autocorrelation based PSS Detection in the lower average received signal-to-noise power ratio (SNR) below approximately 0 dB for the subcarrier spacings (SCSs) of 120 and 240 kHz. Meanwhile, the required computational complexity for the PCID Detection of the latter method is much lower than the former method. We also show that when the frequency error of a standard oscillator of a set of user equipment is e = 20 ppm, the PCID Detection Probability is decreased by only approximately 28 (22)% and 3 (6)% compared to that without frequency offset at the average received SNR of −10 and 0 dB, respectively, for the SCS of 240 (120) kHz. We conclude that the multiplexing scheme for the PSS and SSS and their sequences are effective in achieving a high PCID Detection Probability considering the large frequency offset even with e = 30 ppm in the 28-GHz band.
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physical cell id Detection Probability using synchronization signals for nr radio interface below 6 ghz
Asia-Pacific Conference on Wearable Computing Systems, 2019Co-Authors: Kyogo Ota, Satoshi NagataAbstract:This paper presents the physical cell identity (PCID) Detection Probability using synchronization signals based on the New Radio radio interface considering the frequency offset, maximum Doppler frequency, and co-channel interference (CCI) for frequency spectra below 6 GHz based on link-level simulations. We assume cross-correlation based primary synchronization signal Detection, followed by frequency offset estimation and compensation, and then secondary synchronization signal Detection. Simulation results show that the PCID Detection Probability is degraded according to the increase in the frequency offset for the subcarrier spacings (SCSs) of 15 and 30 kHz. However, we show that the 30-kHz SCS is more effective in mitigating the degradation in the PCID Detection Probability than the 15-kHz SCS for a frequency offset greater than approximately 20 kHz. We also show that a high PCID Detection Probability is achieved for the maximum Doppler frequency of up to 888.8 Hz, which corresponds to the moving speed of 480 km/h at the carrier frequency of 2 GHz. Finally, we show that the PCID Detection Probability with the received signal-to-interference power ratios of -3 dB and 0 dB is degraded by approximately 7% and 52%, respectively, compared to that without the CCI at the average received signal-to-noise ratio of 0 dB for the maximum frequency offset of 20 kHz.
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Performance of Physical Cell ID Detection Probability Considering Frequency Offset for NR Radio Interface
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Co-Authors: Kyogo Ota, Aya Shimura, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability considering frequency offset for 5th generation (5G) new radio (NR). We compare the primary synchronization signal (PSS) and PCID Detection probabilities of three PSS Detection methods: cross-correlation based Detection before frequency offset (FO) estimation and compensation, that after FO estimation and compensation, and autocorrelation based Detection at a set of user equipment. Link- level simulation results show that the cross- correlation based PSS Detection before the FO estimation and compensation achieves the highest PSS and PCID Detection probabilities in the carrier frequency fc region lower than approximately 14 GHz at the average received signal-to-noise power ratio of 0 dB for the standard oscillator frequency error of 1 ppm. The cross-correlation based PSS Detection after the FO estimation and compensation achieves much higher PSS and PCID Detection probabilities than that before the FO estimation and compensation for the fc region higher than approximately 14 GHz. The PCID Detection Probability of the cross-correlation based PSS Detection after the FO estimation and compensation is close to that of the autocorrelation based PSS Detection according to the increase in the fc value. Therefore, we conclude that by switching the cross- correlation based PSS Detection methods before and after the FO estimation and compensation, the PSS and secondary synchronization signal structures based on the NR radio interface achieve a high PCID Detection Probability for the fc value up to 60 GHz.
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physical cell id Detection Probability using synchronization signals for nr radio interface
Wireless Personal Multimedia Communications, 2018Co-Authors: Kyogo Ota, Mamoru Sawahashi, Aya Shimura, Satoshi NagataAbstract:This paper presents the physical-layer cell identity (PCID) Detection Probability using precoding vector switching (PVS) transmit diversity and receive diversity for 5G new radio (NR). We use the sequences and multiplexing schemes of the primary synchronization signal (PSS) and secondary synchronization signal (SSS) based on the 3rd Generation Partnership Project (3GPP) release 15 specifications to detect the best PCID among the 1008 candidates. Link-level simulation results show that receive diversity up to four antennas is very effective in achieving highly correct Detection probabilities of the PSS and SSS sequences, the combination of which provides the PCID, regardless of the propagation channel model. We also show that PVS transmit diversity with two antennas improves the PCID Detection Probability by approximately 5% - 10% in the high average received signal-to-noise power ratio (SNR) region when the PCID Detection Probability is greater than approximately 50% in the case of up to four receive antennas. Because further improvement in the PCID Detection Probability by increasing the number of transmit antennas to 4 antennas is small, it is shown that the application of PVS transmit diversity with two antennas is suitable for achieving high PCID Detection Probability in the NR radio interface. These conclusions are applicable to beamforming transmission for the PSS and SSS.
Michael Richlen - One of the best experts on this subject based on the ideXlab platform.
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estimation of the Detection Probability for yangtze finless porpoises neophocaena phocaenoides asiaeorientalis with a passive acoustic method
Journal of the Acoustical Society of America, 2008Co-Authors: Tomonari Akamatsu, Jay Barlow, Ding Wang, Kexiong Wang, Songhai Li, Shouyue Dong, Xiujiang Zhao, Brent S Stewart, Michael RichlenAbstract:Yangtze finless porpoises were surveyed by using simultaneous visual and acoustical methods from 6 November to 13 December 2006. Two research vessels towed stereo acoustic data loggers, which were used to store the intensity and sound source direction of the high frequency sonar signals produced by finless porpoises at Detection ranges up to 300m on each side of the vessel. Simple stereo beam forming allowed the separation of distinct biosonar sound source, which enabled us to count the number of vocalizing porpoises. Acoustically, 204 porpoises were detected from one vessel and 199 from the other vessel in the same section of the Yangtze River. Visually, 163 and 162 porpoises were detected from two vessels within 300m of the vessel track. The calculated Detection Probability using acoustic method was approximately twice that for visual Detection for each vessel. The difference in Detection probabilities between the two methods was caused by the large number of single individuals that were missed by visua...
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estimation of the Detection Probability for yangtze finless porpoises neophocaena phocaenoides asiaeorientalis with a passive acoustic method
Journal of the Acoustical Society of America, 2008Co-Authors: Tomonari Akamatsu, Jay Barlow, Ding Wang, Kexiong Wang, Shouyue Dong, Xiujiang Zhao, Brent S Stewart, Michael RichlenAbstract:Yangtze finless porpoises were surveyed by using simultaneous visual and acoustical methods from 6 November to 13 December 2006. Two research vessels towed stereo acoustic data loggers, which were used to store the intensity and sound source direction of the high frequency sonar signals produced by finless porpoises at Detection ranges up to 300 m on each side of the vessel. Simple stereo beam forming allowed the separation of distinct biosonar sound source, which enabled us to count the number of vocalizing porpoises. Acoustically, 204 porpoises were detected from one vessel and 199 from the other vessel in the same section of the Yangtze River. Visually, 163 and 162 porpoises were detected from two vessels within 300 m of the vessel track. The calculated Detection Probability using acoustic method was approximately twice that for visual Detection for each vessel. The difference in Detection probabilities between the two methods was caused by the large number of single individuals that were missed by visual observers. However, the sizes of large groups were underestimated by using the acoustic methods. Acoustic and visual observations complemented each other in the accurate Detection of porpoises. The use of simple, relatively inexpensive acoustic monitoring systems should enhance population surveys of free-ranging, echolocating odontocetes.
Kenneth H Pollock - One of the best experts on this subject based on the ideXlab platform.
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estimation of Detection Probability in manatee aerial surveys at a winter aggregation site
Journal of Wildlife Management, 2007Co-Authors: Holly H Edwards, John E. Reynolds, Bruce B Ackerman, Kenneth H Pollock, James A. PowellAbstract:Abstract Estimating components of Detection Probability is crucial to improving the design of aerial surveys for wildlife populations, and this is especially true for species of marine mammals that are threatened or endangered. To evaluate the Probability that Florida manatees (Trichechus manatus latirostris) will be detected by observers during aerial surveys, we conducted 6 series of survey flights, during mornings and afternoons on 14–16 consecutive days over the Tampa Electric Company's (TECO) Big Bend power plant discharge canal in Tampa Bay, Florida, USA (winter 2000 through 2003). Our objective was to understand how our ability to detect manatees at a winter aggregation site affects aerial survey counts, so that we may improve techniques for estimating manatee population size. We estimated the Probability that manatees would be present at the warm-water discharge of the plant during winter cold fronts and estimated the overall Detection Probability of manatees present at the plant and the 2 compone...
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separating components of Detection Probability in abundance estimation an overview with diverse examples
2004Co-Authors: Kenneth H Pollock, Larissa L Bailey, Theodore R Simons, Helene Marsh, George L Farnsworth, Mathew W AlldredgeAbstract:[Extract] Estimation of population abundance of rare and elusive species critically depends on the estimation of Detection Probability under a particular sampling method. If we ignore the issue of animals not being available, then we obtain an estimate of the size of the available component of the population rather than the total population size. The available component may be only a small portion of the total population. In addition, this component may vary with time and with important auxiliary variables in ways that are so complex that it is unsatisfactory for monitoring the population (see Chapter 4). Animals have to be "available" to a sampling method to be detected. In many animal populations not all animals are available to be captured using a particular sampling method. There may be many reasons for this. For example, in an aerial survey of dugongs (sea cows) off the coast of Australia, some dugongs may be underwater and invisible to the observers searching for them in the aircraft. Even if animals are available, they still have to be detected. This perception process is also uncertain, so, for example, if a dugong is on the surface of the water, observers in the aircraft may still miss it. In this chapter we consider in detail a model for Detection Probability that accounts for the processes of availability and perception. Methodology for estimating these two components of Detection Probability is illustrated with three diverse examples involving aerial surveys of marine mammals (dugongs), point counts of terrestrial birds, and capture-recapture studies of terrestrial salamanders. The statistical methodology used in the three examples is very different. We will use the dugong survey as a first example of a solution to a general problem of lack of availability (Marsh and Sinclair 1989; Pollock et al.in press). We then will show that very similar conceptual problems arise in many other settings and biometricians are now seeking solutions to them. Two other problems we consider are (1) estimation of density of birds based on point counts in which birds are detected by their calls but birds do not always call (i.e., they are unavailable for auditory Detection; Farnsworth et al. 2002); and (2) population estimation of terrestrial salamanders, which presents a similar conceptual and practical problem 'because sdamanders may be underground where they cannot be counted or captured (i.e., they are physically unavailable to capture because they are not present on the surface; Bailey et al. 2004a,b). Many formulations ignore the unavailable part of the population, but doing so may be unsatisfactory unless this component is a very small and constant part of the population.
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the relationship between species Detection Probability and local extinction Probability
Oecologia, 2004Co-Authors: Russell Alpizarjara, Kenneth H Pollock, James D Nichols, James E Hines, John R Sauer, Christopher S RosenberryAbstract:In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow Detection probabilities that are <1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction Probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of Detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the Detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that Detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species Detection Probability covaries negatively with local Probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of Detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction Probability performed fairly well and provided no basis for preferring one to the other.
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estimating site occupancy and species Detection Probability parameters for terrestrial salamanders
Ecological Applications, 2004Co-Authors: Larissa L Bailey, Theodore R Simons, Kenneth H PollockAbstract:Recent, worldwide amphibian declines have highlighted a need for more extensive and rigorous monitoring programs to document species occurrence and detect population change. Abundance estimation methods, such as mark–recapture, are often expensive and impractical for large-scale or long-term amphibian monitoring. We apply a new method to estimate proportion of area occupied using Detection/nonDetection data from a terrestrial salamander system in Great Smoky Mountains National Park. Estimated species-specific Detection probabilities were all <1 and varied among seven species and four sampling methods. Time (i.e., sampling occasion) and four large-scale habitat characteristics (previous disturbance history, vegetation type, elevation, and stream presence) were important covariates in estimates of both proportion of area occupied and Detection Probability. All sampling methods were consistent in their ability to identify important covariates for each salamander species. We believe proportion of area occupie...
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estimating Detection Probability parameters for plethodon salamanders using the robust capture recapture design
Journal of Wildlife Management, 2004Co-Authors: Larissa L Bailey, Theodore R Simons, Kenneth H PollockAbstract:Abstract Recent concern over global amphibian population declines has highlighted a need for more extensive, rigorous monitoring programs. Two sources of variation, spatial variation and variation in Detection Probability, make the design and implementation of effective monitoring programs difficult. We used Pollock's robust design in a 3-year capture–recapture study to estimate Detection Probability and temporary emigration for Plethodon salamanders in Great Smoky Mountains National Park (Tennessee/North Carolina), USA. We used 12 competing models to determine the importance of temporary emigration, and we explored temporal and behavioral effects on conditional capture probabilities. The top 4 models all included random temporary emigration, and Akaike model weights indicated that this parameter was the most important. Models that contained behavioral effects in capture probabilities were selected more often than models with equal capture probabilities for marked and previously unmarked individuals. The ...
Dharma P Agrawal - One of the best experts on this subject based on the ideXlab platform.
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gaussian versus uniform distribution for intrusion Detection in wireless sensor networks
IEEE Transactions on Parallel and Distributed Systems, 2013Co-Authors: Yun Wang, Dharma P AgrawalAbstract:In a Wireless Sensor Network (WSN), intrusion Detection is of significant importance in many applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the Detection Probability is the same for any point in a WSN. However, some applications may require different degrees of Detection Probability at different locations. For example, an intrusion Detection application may need improved Detection Probability around important entities. Gaussian-distributed WSNs can provide differentiated Detection capabilities at different locations but related work is limited. This paper analyzes the problem of intrusion Detection in a Gaussian-distributed WSN by characterizing the Detection Probability with respect to the application requirements and the network parameters under both single-sensing Detection and multiple-sensing Detection scenarios. Effects of different network parameters on the Detection Probability are examined in detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically formulate Detection Probability in a random WSN and provides guidelines in selecting an appropriate deployment strategy and determining critical network parameters.
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intrusion Detection in homogeneous and heterogeneous wireless sensor networks
IEEE Transactions on Mobile Computing, 2008Co-Authors: Yun Wang, Xiaodong Wang, Bin Xie, Demin Wang, Dharma P AgrawalAbstract:Intrusion Detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a battlefield. The intrusion Detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to characterize the WSN parameters such as node density and sensing range in terms of a desirable Detection Probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, we derive the Detection Probability by considering two sensing models: single-sensing Detection and multiple-sensing Detection. In addition, we discuss the network connectivity and broadcast reachability, which are necessary conditions to ensure the corresponding Detection Probability in a WSN. Our simulation results validate the analytical values for both homogeneous and heterogeneous WSNs.
