Min Loading Max Reusability Fusion Classifiers for Sensor Data Model

International Journal on Smart Sensing and Intelligent Systems

Murthy

Srinivas

2008

Self Cite

With the availability of low-cost sensor nodes there have been many standards developed to integrate and network these nodes to form a reliable network allowing many different types of hardware vendors to coexist. Most of these solutions however have aimed at industry-specific interoperability but not the size of the sensor network and the large amount of data which is collected in course of its lifetime. In this paper we use well studied data compression algorithms which optimize on bringing down the data redundancy which is related to correlated sensor readings and using a probability model to efficiently compress data at the cluster heads. As in the case of sensor networks the data reliability goes down as the network resource depletes and these types of networks lacks any central synchronization making it even more a global problem to compare different reading at the central coordinator. The complexity of calibrating each sensor and using an adaptable measured threshold to correct the reading from sensors is a severe drain in terms of network resources and energy consumption. In this paper we separate the task of comparative global analysis to a central coordinator and use a reference PMax which is a normalized probability of individual source which reflects the current lifetime reliability of the sensors calculated at the cluster heads which then is compared with the current global reliability index based on all the PMax of cluster heads. As this implementation does not need any synchronization at the local nodes it uses compress once and stamp locally without any threshold such as application specific calibration values (30 o C) and the summarization can be application independent making it more a sensor network reliability index and using it independent of the actual measured values.

Section: A Scale Invariance Property In Clustering For Energy Dissipation In Rf Based Applicationssupporting

confidence: 66%

“…One of the alternate methods [7] used to have a better scale is using fuzzy logic. But here we like to define an event.…”

Section: Sorting Out Faulty Eventsmentioning

confidence: 99%

Training Data Compression Algorithms and Reliability in Large Wireless Sensor Networks

International Journal on Smart Sensing and Intelligent Systems

Murthy

Srinivas

2008

Self Cite

“…Lower-level sensor measurements task are prone to errors due to noise and faulty (Iyer et al, 2008) sensors. Incorrect results can be obtained as the upper level Gram Matrix relies on tuning few parameters.…”

Section: Methodsmentioning

confidence: 99%

“…This seminal work balances precision and accuracy to estimate the non-faulty (Iyer et al, 2008) sensor's confidence intervals. Real-time algorithm has been extended using Byzantine fault-tolerance principle to mitigate faulty sensors (Brooks and Iyengar., 1996), which are likely to trigger false alarms.…”

Section: Related Workmentioning

confidence: 99%

Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Shetty

Iyengar

2015

ABSTRACT:Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N 2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

“…The error bounds are shown in figure 4 in terms of Baysian probability. The data capacity as defined in Lemma 1 and is dealt in detail in a related work in [3], [4], [1].…”

Section: Comparison To Bayes Probabilitymentioning

confidence: 99%

Multi-hop scheduling and local data link aggregation dependant Qos in modeling and simulation of power-aware wireless sensor networks

Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wireless

Iyengar²,

Murthy

et al. 2009

Self Cite

In this study of wireless sensor networks (WSN) protocols, the application Qos, system, and protocol performance metrics are measured for a large scalable wireless deployment using a typical wireless radio and an energy model. As there are many different types of WSN algorithms, we have categorized it into pro-active, re-active, and query driven information processing. A typical Qos is based on the useful lifetime of sensor nodes, after which reliability of the sensor data cannot be guaranteed and typically, a threshold such as a percentage of the sensor drains out of energy or a minimum through-put of real-time data from the sensor network is expected, which is used to compare the Qos of the routing algorithm. The results from lifetime based Qos, measured in simulation seconds, for the implemented protocols show that with varying sampled data sources for a BE Qos multi-hop deployment and varying percentage of cluster heads in a time-synchronized deployment, the lifetime is based on network size and protocol invariant. However, low sensing ranges result in dense networks, and therefore, it becomes necessary to achieve an efficient medium-access protocol subjected to power constraints. Scalability of sensor network applications are based on energy energy-harvesting techniques in which the various layers of the network interoperate and extend the system network lifetime, the battery residual power per node, and the application reliability in terms of cross-layer energy savings. In this study, we have extended the lifetime metrics from a constant metrics into a break down of how much percentage of time is spent for Tx, Rx, and Idle tasks, respectively. This helps one to highlight the cross-layer energy dissipation per node and how the performance of an algorithm differs in terms of duty-cycling.Furthermore, we have shown that the energy savings due owing to the distributed algorithms in a large sensor network will not be practical without a complimentary lowerlayer MAC. We show have demonstrated that the Qos is very much related to the ambient conditions, namely, are the Rx and Idle modes. From these preliminary results, we have added a new category of WSN protocols which are based ion the renewable energy resources, namely, the Fusion Ambient Renewable Measuring Sensors (FARMS). The study of sensor FARMS -harvesting applications allows one to measure the impact on Idle, Sleep, and renewable energy cycles as well as their unique deployment (density) needs, as all the sensor are not active(Rx) at all times. We have also shown that the efficiency of cross-layer Qos performance of routing algorithms with MAC losses has a long tail which is similarly observed in Power Law. In this sensor network model we like to show the complexity of clustering, messaging and data rate in terms of ( (N ) log N ), (N ) and (log 2 N ) where N is the number of nodes.