Published on

IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. Ramanpreet Kaur, Jaspal Singh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1843-1847 Neural Based Sensor Signal Change Detection By Using Radial Bias Function Ramanpreet Kaur1, Jaspal Singh2 1 M-Tech (ECE), R.I.E.I.T Ropar , Punjab, India 2 Professor, Electronics and Communication R.I.E.I.T, Ropar , Punjab, IndiaABSTRACT The paper describes the basic techniques The goal of the research presented in this paper is toinvolved on the detection of sensor signal develop a detection system, which warns about achanges, and describes their possible change of sensor signals in complex networkimplementation on the lower level in sensor systems by comparison of currently acquirednetworks. Embedded into the communication measurement results against an association modelprotocols, the signal change detection will allow derived during execution. The detector operationdata compression for improving network should minimize probabilities of missing the changeefficiency. It might enhance reliability and (false negative) and/or false positive alarmssecurity also. The algorithms utilizes a neural (recognizing a ―normal‖ deviation, which is due tonetwork function prediction methodology in the inaccuracy of measurement results, as a change).order to determine if the sensor signals have Normally measurement results fluctuate with somechanged. The literature survey is done and the degree, which does not constitute any significantdifferent algorithms are studied based on their change. The detection should be performed byperformance and parameter choice and the comparison of imprecise data against uncertainrelationship between the threshold values and models.false positive and negative rates are studied the Change detection is as an identification ofbasis algorithms involved in this work are MLP unforeseen change in general characteristics andand RBF Algorithms. parameters of the signal observed. The case involving time series is one of the most interestingKeywords:- Multi layer Perceptron , Radial applications of the problem, attracting muchBasis Function , Sensor networks, Artificial attention, while being more complicated than anNeural networks outlier detection .A number of different approaches and algorithm which did not produce a genericINTRODUCTION solution, have been proposed for change detection. A wireless sensor network is a collection of New results in the development of a neural networknodes organized into a cooperative network. Each based change detector. This describes thenode consists of processing capability (one or more architecture of the change detection system based onmicrocontrollers, CPUs or DSP chips), may contain the neural network function predictor. The neuralmultiple types of memory (program, data and flash network inputs a few different sensor signals thatmemories), have a RF transceiver (usually with a allows detecting changes not in one signal only butsingle omni-directional antenna), have a power also in the relationships between signals. Thissource (e.g., batteries and solar cells), and feature improves detection reliability.accommodate various sensors and actuators. Thenodes communicate wirelessly and often self- NEURAL NETWORK SIGNAL CHANGEorganize after being deployed in an ad hoc fashion. DETECTIONSystems of 1000s or even 10,000 nodes are Neural networks are computational modelsanticipated. Such systems can revolutionize the way that share some of the properties of the brain. Thesewe live and work. networks consist of many simple ―units‖ working in SENSOR networks (SN) technology has parallel with no central control, and learning takesthe potential of becoming the backbone of a new place by modifying the weights betweengeneration of real intelligent systems, capable of connections. The basic components of an ANN areachieving symbiosis with the environment and ―neurons‖, weights, and learning rules.embracing the methods and mechanisms that exist in In general, neural networks are utilized tonatural intelligent systems. In order to accomplish establish a relationship between a set of inputs and athis goal, sensor networks have to adopt some set of outputs. ANNs are made up of three differentelements of cognition in addition to collecting data types of ―neurons‖: (1) input neurons, (2) hiddenfrom the environment and communicating them neurons, and (3) output neurons. Inputs are provided,such as continuous self-development and self- to the input neurons, such as machine parameters,modification with the goal to significantly improve and outputs are provided to the output neurons.self-reliability, efficiency, and security. These outputs may be a measurement of the 1843 | P a g e
  2. 2. Ramanpreet Kaur, Jaspal Singh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1843-1847performance of the process, such as part output. An error is determined for the output andmeasurements. The network is trained by propagated backwards through the layers. Finally,establishing the weighted connections between the these errors are used to adjust the connectionsinput neurons and output neurons via the hidden between nodes. The backpropagation algorithmneurons. Weights are continuously modified until allows for movement to a minimal error over thethe neural network is able to predict the outputs course of the training process. Each time the weightsfrom the given set of inputs within an acceptable are changed, the direction and magnitude of theiruser-defined error level. change is determined so as to make a move towards Although change detection in signals had the minimal error.been investigated for a considerable time, over the The main idea of the methodology islast two decades there have been new important 1. To perform change detection on a set ofdevelopments. The literature on change novelty networked sensor signals based on the differencesdetection is rapidly growing due to applications in between current signal values and predicted, financial mathematics and economics. 2. A neural network is employed to produceThese detection techniques have developed into these signal predictions from a recent history ofvarious models. From a generic point of view, ANN sensor measurements.models seem to be appropriate as a base for 3. The neural networks can be utilized withdeveloping fundamental algorithms utilizing no priori information on the data distribution ofopportunities, which arise from networking of the domain and without specifying othersensor systems. parameters related to the data.—Neural networks are distributed nonlinear devices 4. This allows the network to detect changesTheir ability to deal with nonlinear, non stationary, and after that ―renormalize‖ itself by learning newand non-Gaussian processes makes them attractive signal values to be used for further changefor WSN practical applications. Accordingly, ANN detection.have the inherent ability to model underlying From a generic point of view, ANN modelsnonlinearities contained in the physical mechanism seem to be appropriate as a base for developingresponsible for generating input data. fundamental algorithms utilizing opportunities,—Neural networks, operating in a supervised which arise from networking of sensor systems. Themanner, are universal approximators basic techniques involved in signal change detection They can approximate any continuous input-output is explained .mapping to any desired degree of approximation, LEON REZNIK et al [1] (2005): developsgiven a sufficient number of hidden units. intelligent protocols, based on the detection of—A neural network consists of a massively parallel sensor signal changes. The signal change detectionprocessor that has the potential to be fault tolerant. will allow data compression for improving networkAs MLP consists of a large number of neurons efficiency. The protocol utilizes a neural networkarranged in the form of layers with each neuron in a function prediction methodology to determine if theparticular layer connected to a large number of sensor signals have changed. The parameter choicesource nodes in the previous layer. This form of and relationship between the threshold values andglobal interconnectivity has the potential to be fault false positive and negative rates are studied. Changetolerant, in the sense that the performance degrades detection is based on the difference between currentgracefully under adverse operating conditions. If a signal values and the predicted values. Noveltyneuron or its synaptic links are damaged, the recall detection is utilized to detect changes and after thatquality of a stored pattern is impaired, but owing to to relearn new signal values to be used for furtherthe highly distributed nature of the network, the changes detection. The results of the experimentsdamage has to be extensive before the performance conducted demonstrate that the change detectionis seriously affected. This property open an system is both accurate and reliable. These resultsopportunity to design an ANN topology distributed confirm the benefits anticipated from the modifiedover a SN infrastructure. neural network and demonstrate its usefulness in—Neural networks have a natural ability to adapt sensor prediction and change detection.their free parameters according to changes in the Tayeb Al Karim et al (2) (2011) The paperenvironment in which they operate. describes the results of an empirical study aiming to In this tuning of free parameters in a neural demonstrate that a cognition ability may be treatednetwork is a more straightforward task (and as a generic sensor network feature. The newtherefore easily accomplished by a non expert user) architecture with neural networks distributed overthan would be the case with other nonparametric the sensor network platforms was developed formethods. sensor network engineering applications. The Multi-Layer Perceptrons (MLPs) are neural detection system learns to detect the change of notnetworks that have nodes arranged in multiple only the signal levels but also sensor signal shapeslayers, with connections from one layer to the next. and parameters that represent a more complicatedData is feed forward through the network to produce task. The architecture allows for a significant 1844 | P a g e
  3. 3. Ramanpreet Kaur, Jaspal Singh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1843-1847reduction in resource consumption without sensor networks. The power savings are evaluatedcompromising the change detection performance. from the perspective of the WSN nodes. PowerImplemented as an agent controlling the sensor mode switching is determined via change detectionnetwork self-adjustment to the objects under in measured signals. The change detection is basedmeasurement in the sensor network composed from on the observation of number of signals which aretypical sensor motes, the novel neural network measured in the environment and the measurementstructures may achieve a significant saving in power results delivered to the base station for processing.consumption and an increase in a possible network To perform this change detection ANN baseddeployment time from a few days to a few years. intelligent agent has been developed andThe experiments prove that a neural-network-based implemented and a novel neural network topologychange detection system is feasible for sensor has was designed for wireless sensor networks. Thenetworks application designs and could be architecture is built upon a multilayer perceptronsuccessfully implemented on the technological neural network to reduce the number of connectionsplatforms currently available on the market. The between the layers. The experiment conductedproposed MTBMLP architecture has been tested and confirms that this network decreases the resourcescompared against a conventional MLP on changes such as time and power consumption required for itsof both frequencies and amplitudes signal implementation without causing any increase inparameters and with different types of signals (sine false alarm rates.and square waves, constant values). LEON REZNIK et al 2008 puts the concept LEON REZNIK et al [3] (2005) describes of cognitive sensor networks. This research paperthe design and implementation of a novel intelligent describes the results of an experimental study andsensor network protocol that enhances reliability development of novel neural network intelligentand security by detecting a change in sensor signals. technique for signal change detection in sensorNeural network function prediction methodology is networks. The detection system learns to detect theused to predict sensor outputs to determine if the change of not only the signal levels but also sensorsensor outputs have changed. The experiments signal shapes and parameters. The ANN is capableperformed to the different training times and to handle detection with both problems as well as tothreshold values and these experiments adapt to the new pattern. The ANN architecturedemonstrates that the system performs better when maps with the ANN structure and reducespresented with correlated data rather than randomly connectivity. Signal propagation through the ANNgenerated data the difference between the false faster in both forward and backward directions thatalarm rates and detection for the correlated and allows increasing speed of the application. At theuncorrelated sensor and changes have been seen same time novel structure reduces the consumptionand these results confirm the benefits anticipated of major resources network bandwidth processorfrom the modified neural network and shows its power and memory usage.usefulness in sensor prediction and novelty TAYEB AL KARIM et al (2005) In thisdetection. Detection rates and false alarm rates research paper detection system is developed andbecome better when data and changes presented to change of sensor signals in complex networkthe system are correlated in positive. The limitation systems is studied by comparing currently acquiredis the system’s inability to learn properly long clock measurement results against an association modelsignals. The second problem is the predetermined derived during execution. The detector operationerror threshold. The threshold can be set very high should minimize the probabilities of false negativeor very low causing a notable increase in the false or false positive alarms. Measurement results arealarm rate and too high causing a drop in the not always accurate, they always fluctuate withdetection rate. some degree. This paper describes the architecture GREGORY VON PLESS [4] et al of the change detection system based on the neuralintroduces Modified time based multilayer network function predictor. Two function predictionperceptron (MTBMLP), complex structure designs are compared based on the standard multi-composed by a few time-based multilayer layer perceptron (MLP) and Radial basis functionperceptrons. This modification reduces connections, network (RBF).isolates information for each function and produces It studies the dependence of the detectionknowledge about the system of functions as a whole. and false alarm rates on the threshold values forIt is powerful function predictor that converges different signals and changes. With respect to thequickly and accurately predict cyclic sinusoid training time, the detection rates and false positivefunctions. This neural network is applied for novelty rates remained unchanged with training times at anyand change detection in signals delivered by sensor given threshold. With respect to the thresholdnetworks and for edge detection in image detection rates went down with higher thresholds.processing. MTBMLP performs much quicker than the MLP. JODY PODPORA et al [5] (2008) develop Different experiments were conducted in this paperan intelligent and power conservation scheme for which includes turning lights on/off, flickering 1845 | P a g e
  4. 4. Ramanpreet Kaur, Jaspal Singh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1843-1847lights, gradual change of temperature with opening adoor.1. PROBLEM FORMULATION The analytical formulation of the researchis based on function prediction using neural network.There is rapid enhancement in this field ofapplication and their comparative factor have to beexplored to generate recurrent priority. We havedifferent algorithms in neural networks for sensorsignal change detection. Multilayer perceptron(MLP) and Radial basis network (RBF) are used todetermine change in sensor outputs. RBF performswell than MLP for all parameters. Now days anumber of structures have been proposed for signalchange detection. Structure based on RBF functionis expected to perform better in predicting accuracy Fig 2: Relationship between threshold and detectionwith low false alarm rates. Thus in this paper we rates for MLP, RBF and improved RBFpurpose a structure based on RBF algorithm whichwill improve the parameters (threshold and traininglengths) in signal change detection of the sensor 3. CONCLUSION It is necessary to develop new ANNnetworks then the existing.To improve the topologies to produce the most efficient ANN basedperformance of RBF by varying intrinsic parameters applications on network platforms. Ideally the(threshold & training length) is the major objective topologies should be scalable over large networkswhich we realise from the research gaps of the basic and have made apparent an advantage inliterature survey. performance and efficiency when using Radial Basis activation for sensor network change detection2. RESULTS AND DISCUSSIONS purposes. The greatest advantages are the greater The RBF are able to predict the sensor signal prediction accuracy. The learning ratefunctions with greater accuracy. With the threshold advantage could be used most effectively for changeset to 0.1 these changes were detected with a rate of detection in sensor signals with changes that are98%.This allows the use of a lower threshold value densely distributed over time. False alarms werein the novelty detector. The improved RBF has its virtually nonexistent in the RBF network. A Radialoptimal performance at a lower threshold. At lower Basis activation function is clearly the better choicethreshold it is able to detect 100% of sensor signal for sensor signal change detection systems.changes. The MLP operates optimally at the 0.5threshold value, however the results are significantlyworse than that of the RBF. With 100% detections, REFERENCES 1. Leon Reznik , Gregory Von Pless , Tayeb Althe MLP has an average false alarm rate of 7.19%. Karim, ‖ Embedding intelligent sensorThe Figure shows the relationship between signal change detection into sensor networkthreshold and detection rates for MLP, RBF and protocols, ‖ IEEE computer, Aug 2005IMPROVED RBF. 2. Tayeb Al Karim ,‖ Distributed Neural networks for signal change detection on the way to cognition in sensor networks‖ , IEEE sensor journals vol. 11 . no. 3 March 2011. 3. Leon Reznik , Gregory Von Pless,‖intelligent protocols based on sensor signal change detection‖ , IEEE proceedings system communications 2005. 4. GREGORY VON PLESS ,‖Modified time based multilayer perceptron (MTBMLP), complex structure composed by a few time- based multilayer perceptrons for image processing applications‖, Proceedings of International Joint Conference on neural networks Montreal, Canada July 2006. 5. J. Podpora, L. Reznik, and G. Von Pless, ―Intelligent real-time adaptation for powerFig 1: Basic Performance of temperature with efficiency in sensor networks,‖ IEEEsensors deployed 1846 | P a g e
  5. 5. Ramanpreet Kaur, Jaspal Singh / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1843-1847 Sensors J., vol. 8, no. 12, pp. 2066–2073, accuracy of measurement‖, Measurement, Dec. 2008. 1985, Vol. 3, No. 3, pp. 98 – 1066. Ma J, Perkins S., ―Time-series novelty detection using one class support vector machines‖, Proceedings of the International Joint Conference on Neural Networks, 20-24 July 2003, pp. 1741 – 1745.7. Dasgupta D., Forrest S., ―Novelty detection in time series data using ideas from immunology‖, In Proceedings of the 5th International Conference on Intelligent systems, Reno, Nevada, June 19-21, 1996.8. R. Kozma, H. Aghazarian, T. Huntsherger, E. Tunstel, and W. J. Freeman, ―Computational aspects of cognition and consciousness in intelligent devices,‖ IEEE Comput. Intell. Mag.,9. S. Rajasegarar, C. Leckie, and M. Palaniswami, ―Anomaly detection in wireless sensor networks,‖ IEEE Wireless Commun., vol. 15, no. 4, pp. 34–40, Aug. 2008.10. R. Sutharshan, L. Christopher, P. Marimuthu, and C. B. James, ―Distributed anomaly detection in wireless sensor networks,‖ in Proc. 10th IEEE Singapore Int. Conf. Communication Systems (ICCS 2006). 2006, pp. 1–5.11. A. P. R. da Silva, M. H. T. Martins, B. P. S. Rocha, A. A. F. Loureiro, L. B. Ruiz, and H. C.Wong, ―Decentralized intrusion detection in wireless sensor networks,‖ in Proc. 1st ACM Int. Workshop Quality of Service Security inWireless and Mobile Networks, Montreal, QC, Canada, 2005, pp. 16–23.12. M. Markou and S. Singh, ―Novelty detection: A review—Part 1: Statistical approaches,‖ Signal Process., vol. 83, pp. 2481–2497, 2003.13. L. Reznik, M. Negnevitsky, and C. B. Hoffman, ―Contents based security enhancement in sensor networks protocols,‖ in Proc. 2004 IEEE Int. Conf. Computational Intelligence for Homeland Security and Personal Safety (CIHSPS 2004) , 2004, pp. 87–91.14. Wood A.D. and J.A. Stankovich ―Denial of service in sensor networks‖, IEEE Computer, October 2002, pp. 54-62.15. Boulis A., Ganeriwal S., Srivastava M.B. ―Aggregation in sensor networks: an energy- accuracy trade-off ― In: Proceedings of the First IEEE. 2003 IEEE International Workshop on Sensor Network Protocols and Applications, 2003, 11 May 2003, pp.128 – 13816. Reznik L. and Solopchenko G.N. ―Use of a priori information on functional relations between measured quantities for improving 1847 | P a g e