Distributed Detection in Neural Network basedMultihop Wireless Sensor Network

Jabal Raval, Bhushan JagyasiTCS Innovation Labs Mumbai

Tata Consultancy Services, IndiaEmail: {jabal.raval, bhushan.jagyasi}@tcs.com

Abstract—In this paper, a Neural Network based data aggrega-tion approach to detect the binary events in a multi-hop WirelessSensor Network has been proposed. We envision every node ina network as a unit of neuron which gets trained by using theneural network based back propagation algorithm. As comparedto the LMS based Adaptive Weighted Aggregation scheme fortree network, the proposed Neural Network based wireless sensornetwork approach leads to a significant improvement in detectionaccuracy without much energy losses due to communication andcomputation overhead. We also compare the detection accuracyof the proposed Neural Network based scheme with that ofthe non-adaptive Bayesian approach which requires aprioriknowledge of the sensor’s performance indices.

I. INTRODUCTION

Multi-hop wireless sensor networks (WSN) [1] are theadhoc networks of the sensor node in which information, pro-cessed or raw, flows from various nodes to the sink node in amultihop manner. A problem of distributed detection, as in ourearlier work [2]–[5], for binary event detection in a tree typeof multihop wireless sensor network has been addressed in thispaper. The applications pertaining to the detection of event cancater to different domains like agriculture [6], environmental,transport and surveillance. In agriculture applications, keyevents like high disease risk, presence of pathogen [6], highhumidity, nutrient deficiency and gaseous emission by a certaincrop can be detected collaboratively by a multihop wirelesssensor network. Similarly events like Volcano, Landslides, andCyclones [7]–[10] can also be detected by using distributeddetection in wireless sensor networks.

For several applications it is not feasible to revisit thedeployment site hence it is very crucial for WSNs to remainfunctional for long time after being deployed. There arehardware specific solutions proposed to improve the life-time of sensor nodes like: the use the batteries with highercapacity and using solar panel with rechargeable batteries.However, these solution are not cost effective and also posethe requirement of sensor nodes with higher form factor.Further in literature, there has also been focus on proposingenergy efficient distributed detection algorithms which resultsin higher WSN lifetime [11]–[17]. These approaches resultin energy efficient algorithms by restricting the transmissionmade by the nodes in order to save the communication cost.

In [11], [12], Varshney et al. presented a Bayesian basedone-bit CV rule for data fusion in star topology (single hop

network) for the binary event detection. The CV rule pre-requisites the knowledge of performance indices, that is in thiscase probability of detection and probability of false alarm, foreach sensor node in order to make an optimum one bit decisionat the fusion center. In [4], we presented a multi-bit extensionto the CV rule for a multihop WSN with tree topology. Thismultibit CV rule also requires apriori knowledge of the perfor-mance indices of all sensor nodes forming a network. In [5],we proposed an LMS based Adaptive Weighted AggregationScheme (AdWAS) for same setup of WSN, which requiredtrue event knowledge for weight update in the training phase.

In this paper, we re-visit the problem of binary event detec-tion in WSN with the limitation of one bit of transmission fromany node to its parent node and propose a 1− bit aggregationscheme by modeling the WSN as a Neural Network (referFig. 2). In 1−bit aggregation scheme, each intermediate nodequantizes its sensors observation using 1−bit and fuses it withthe one bit information received from its each child node toresult in a one-bit decision. Each node further communicatesits one bit decision to its parent node until it reaches sinknode where a final decision is made indicating occurrence ornon-occurrence of the event.

In Section II, a neural network based algorithm has beenproposed to perform a distributed detection in a multihopwireless sensor network with tree topology. Simulation setupand computational results are presented in Section III. Wefinally conclude the paper in Section IV.

II. PROPOSED MULTIHOP WIRELESS SENSOR NEURALNETWORK (MWSNN) FRAMEWORK

In this section, we present a proposed neural network basedwireless sensor network for binary event detection wherebinary event hypothesis H ∈ {0, 1}. In a tree topology, sensornodes make a distributed binary decision about the eventhypothesis H and propagate the same to their parent until itreaches the sink node. The final decision about the occurrenceor non-occurrence of the event is made by the sink node.

A. Overview of Neural Networks

The Neural Networks are widely used machine learningalgorithms which can cater to the detection and classifica-tion problems. In Neural Network, the neurons are the keycomputational elements which process the information comingfrom various input links called dendrites to result in an output

978-1-4799-2179-9/14/$31.00 ©2014 IEEE

called as activation (refer Fig. 1). There are various weightsassociated with each neuron and neural network tries to updatethese weights until the convergence is achieved. The genericneural network model contains an input layer and an outputlayer; the layers between these two layers are called the hiddenlayers.

Fig. 1. A Typical Neural Network

We receive inputs from the input layer which are usuallyfeature vectors of the corresponding input data and the sizeof the input layer is decided by the size of the feature vector.The hidden layers and the neurons in the hidden layer initiallyforward propagate the input to the next layer. In a classificationproblem, the size of the output layer is decided by the numberof classes into which the input data is to be classified. Theactivation of the neuron in the output layer is compared withthe actual desired output and subsequently the deviation ofthe result obtained from neural network with respect to outputis calculated and denoted as the error. This error is backpropagated and is used to calculate the gradients to updatethe weights of the neurons.

B. Proposed Neural Network based approach for MultihopWireless Sensor Network

We model the Wireless Sensor Network with tree topologyas a Neural Network and follow the back propagation approachto update the weights from sink node to the leaf nodes (referFig. 2). In an unbalanced tree type of multihop wirelesssensor network, we assume that every node can detect aparticular binary event with some precision p. In the proposedframework, the sensor nodes in the wireless sensor network areconsidered to be neurons which are also sensors themselvesto make the observation of the environment. Unlike in atypical neural network Fig. 1, where the nodes transmits theiractivation (output) to the all nodes in the next level, we proposea framework where every neuron transmits the data to only oneneuron in the next level. This is to protect the tree structurewhich is formed by taking into consideration the practicalconstraints, like deployment and energy, of wireless sensornetworks. Hence we assume the weights of the links that arenot connected to be zero.

In the forward propagation, the information flows fromleaf nodes to the sink node during which each intermediatenode processes the data received in the following manner. Let

Fig. 2. Neural Network based WSN

yj(n) be the observation made by node Sj and wj(n) be itscorresponding weight, for a particular instance n. We computeXj(n) of node Sj as

Xj(n) = yj(n)wj(n) +∑K=k

Yk(n)wk(n) (1)

where, K is set of indices of all child nodes of node Sj , Yj(n)is output after applying the sigmoid function to Xj(n).

As in neural network forward propagation, this sigmoidfunction defined as Y = 1/(1 + e−X) is used at eachintermediate node to make a binary decicion. After applyingsigmoid,the decision Yj(n) of node Sj is computed using

Yj(n) = 1/(1 + e−Xj(n)) (2)

This binary decision Yj(n) made by each node gets propa-gated to the sink node. The binary decision made by the sinknode would be estimate of the event hypothesis Hest(n) =Ysink(n). The error at the sink node is defined as the errorbetween the actual desired output H(n) and the estimatedoutput Hest(n) as e(n) = H(n)−Hest(n).

As in neural network back propagation, the error form thesink node to the leaf nodes gets propagated in a multihopmanner. In order to obtain the error at any given node we firstfind out its parent nodes error and multiply that error with thecorresponding weights. Hence the error ej(n) at any node Sj

for an instance n is given by

ej(n) = eParentj (n)wj(n)Yj(n)(1− Yj(n)) (3)

where Parentj stands for the index of the parent node of Sj .The term Y(1-Y) is included because the error term in (4)

depicts the value of the differential derivative of the sigmoidfunction. The error can now be used along with Xj(n) at everynode Sj to derive the value of gradient gradj using (4) whichis in turn used to update the weights.

gradj(n) = αej(n)Xj(n) + µgradj(n− 1) (4)

The value of step size α and acceleration factor µ are carefullyselected so that the algorithms does not diverge.

The weights are finally updated as,

wj(n+ 1) = wj(n) + gradj(n) (5)

The new weights are used in the next training sequence totrain the algorithm which uses forward and back propagationalternatively till the convergence is obtained.

III. RESULTS

We simulated the proposed Neural Network algorithm tocheck the performance and compared it with other existingaggregation schemes such as Counting rule, LMS basedAdaptive Weighted Aggregation Scheme (AdWAS) [5] and theBayesian based CV rule [4]. The Counting rule, aslso knownas the Majority rule, is the simplest aggregation scheme usedin tree topology in which each intermediate node makes themajority decision among the information received from itschild nodes and its own observation. Hence the Countingrule does not assumes availablity of any apriori infromation.The AdWAS [5] requires the training sequence consisting oftrue event hypothesis to adapt the weights associated with thedecisions made by each node. The CV rule [4], [11], requiresapriori knowledge of performance indices of each node in thetree topology. As in AdWAS, the proposed Multihop WirelessSensor Neural network framework also requires knowledge oftrue event hypothesis for the training phase.

In a simulation set up, the 100 nodes were deployedrandomly with uniform distribution in a square area of size 100square unit. The connectivity had been estabilished betweennodes to form a tree network, as shown in Fig 3, usingthe Bellman Ford Routing algorithm. The nodes send theirdata to the parent node which aggregates the data and sendsa one bit aggregated summary to it’s parent node till theinformation reaches sink node. In this setup, we assume allnodes to be equally precise with precision p. We experimentedwith various values of precisions and number of iterations ofrandomly generated equiprobable binary event. While trainingthe Neural Network as well as LMS based AdWAS updatethe weights, we assume the precision value of p = 0.8 andtraining was carried out for 2000 iterations.

The Fig. 3 shows the error values which was in-turn usedto update the weights. As depicted, the LMS based algorithmwill update the weights using the constant error values for allthe sensor nodes in a tree topology. However, in the NeuralNetwork based approach, the update the weights for eachnode happens according to the back-propagated error whichis observed to be different for different nodes in the network.

The error value calculated at the sink node has beenaveraged over 100 iterations to obtain the mean square errorwhich is plotted with respect to the number of iterations in Fig.4 for both Neural Network and LMS based approaches. Boththe approaches presents a similar trend and time to converge.

The performance of the proposed Neural Network basedscheme has been evaluated by computing the detection accu-racy as presented in our earlier work [4], [5]. The definitions ofthe Pd and Pf are assumed to be same where Pd denotes the

Fig. 3. Tree Network formed by Bellman Ford Routing Algorithm

Fig. 4. Learning Curves for LMS based AdWAS and Back propagation basedNeural Network

probability of true event detection and the Pf stands for theprobability of false detection. The accuracy is again definedin the same manner as in [4] by giving equal weights to bothPd and Pf . Hence accuracy = 0.5 ∗ Pd + 0.5 ∗ (1 − Pf).We considered 20 different deployments for computing theaccuracies.The accuracies in Fig. 5 have been calculated bytaking average of the performance of all 20 deployments. Fig.5 compares the performance of the proposed back propagationbased Neural network approach with the existing LMS basedAdaptive Weighted Aggregation Scheme, Bayesian Based CVrule and the Counting Rule. As depicted, when we compareboth the adaptive schemes, the proposed Neural Networkbased scheme performs significantly better than Adaptive

Fig. 5. Accuracy Plot

Weighted Aggregation Scheme while both having almost samecomputational complexity and requiring similar communica-tion cost. In the training phase, the communication overheadin the proposed Neural Network approach is due to therequirement of the back propagation of the error in a multihopmanner from the sink node to the leaf nodes where somecomputations happen at the intermediate nodes also. While inthe LMS based AdWAS the error at the sink node is required tobe broadcasted to all nodes. In comparison to the non adaptivebayesian based CV rule which requires apriori knowledgeof probabilities, the proposed Neural Network based schemeresults in a comparable performance for higher precisionvalues.

Although the training was performed assuming a particularprecision value of p = 0.8, the accuracy results obtainedare consistent even for different operating precisions. Thisindicates the proposed Neural Network based approach canpractically be adopted in real deployments even when preci-sion of sensors is unknown.

IV. CONCLUSION

The problem of binary event detection for an unbalancedtree topology based multi hop wireless sensor network wasapproached using the widely used Neural Network Back-propagation algorithm. The proposed scheme update theweights using the error localised to a node as compared tothe existing schemes where global errors are used to updatethe weights. The proposed scheme gives better results in termsof accuracy of detection as compared to the AdWAS schemewith relatively similar computation and communication cost.

REFERENCES

[1] L. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A surveyon sensor networks,” IEEE Communication Magazine, August 2002.

[2] B. G. Jagyasi, B. K. Dey, S. N. Merchant, and U. B. Desai, “Anmmse based weighted aggregation scheme for event detection usingwireless sensor network,” 14th European Signal Processing Conference,EUSIPCO 2006, Sep 2006.

[3] ——, “Weighted aggregation scheme with lifetime-accuracy tradeoffin wireless sensor network,” in Proc. 4th International Conference onIntelligent Sensing and Information Processing 2006, ICISIP 2006, Dec2006.

[4] ——, “An efficient multibit aggregation scheme for multi-hop wirelesssensor network,” EURASIP Journal on Wireless Communications andNetworking, vol. 2008.

[5] B. G. Jagyasi, S. N. Merchant, D. Chander, U. B. Desai, and B. K.Dey, “Adwas: Adaptive weighted aggregation scheme for single-hopand multi-hop wireless sensor network,” in Proc. Second InternationalConference on Communication and Networking in China, ChinaCom2007, Aug 2007.

[6] A. Pande, B. G. Jagyasi, and R. Choudhuri, “Late blight forecast usingmobile phone based agro advisory system,” in Proc. third InternationConference on Pattern Recognition and Machine Intelligence, PReMI2009, Dec 2009.

[7] G. Werner-Allen, J. Johnson, M. Ruiz, J. Lees, and M. Welsh, “Moni-toring volcanic eruptions with a wireless sensor network,” in Proc. 2ndEuropean Workshop on Wireless Sensor Networks (EWSN 05), January-Febuary 2005.

[8] G. Werner-Allen, K. Lorincz, M. Welsh, O. Marcillo, J. Johnson,M. Ruiz, and J. Lees, “Deploying a wireless sensor network on an activevolcano,” IEEE Internet Computing Online, vol. 10, March-April 2006.

[9] C. Hartung, R. Han, C. Seielstad, and S. Holbrook, “Firewxnet: Amultitiered portable wireless system for monitoring weather conditionsin wildland fire environments,” in Proc. 4th international conference onMobile systems, applications and services MobiSys 2006, vol. 10, June2006.

[10] A. Sheth, K. Tejaswi, P. Mehta, C. Parekh, R. Bansal, S. N. Merchant,U. B. Desai, C. Thekkhath, K. Toyama, and T. Singh, “Poster abstract-senslide: A sensor network based landslide prediction system,” in Proc.3rd ACM Conference on Embedded Networked Sensor Systems, SenSys,Nov 2005.

[11] Z. Chair and P. K. Varshney, “Optimum data fusion in multiple sensordetection systems,” IEEE Trans. Aerospace Electro. Sys., vol. 27, pp.98–101, Jan 1986.

[12] ——, “Distributed bayesian hypothesis testing with distributed datafusion,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 18, no. 5,pp. 695–699, Oct 1988.

[13] R. Viswanathan and P. K. Varshney., “Distributed detection with multiplesensors: Part i - fundamentals,” In Proceedings of the IEEE, vol. 85,no. 1, pp. 54–63, January 1997.

[14] R. Blum, S. Kassam, and H. V. Poor, “Distributed detection with multiplesensors: Part ii - advanced topics,” In Proceedings of the IEEE, vol. 85,no. 1, pp. 64–79, January 1997.

[15] B. Chen, R. Jiang, T. Kasetkasam, and P. K. Varshney, “Channel awaredecision fusion in wireless sensor networks,” IEEE Transactions onSignal Processing, vol. 52, no. 12, Dec 2004.

[16] R. Niu and P. K. Varshney, “Distributed detection and fusion in a largewireless sensor network of random size,” EURASIP Journal on WirelessCommunication and Networking, vol. 4, pp. 462–472, 2005.

[17] R. Niu, P. K. Varshney, M. H. Moore, and D. Klamer, “Decision fusionin a wireless sensor network with a large number of sensors,” in Proc.7th IEEE International Conference on Information Fusion (ICIF’04),Stockholm, Sweden, June-July 2004.