Fuzzy assisted event driven data collection fromsensor nodes in Sensor-Cloud infrastructure

Suman Sankar Bhunia∗, Jayita Pal∗, Nandini Mukherjee†∗School of Mobile Computing & Communication†Department of Computer Science & Engineering

Jadavpur University, Kolkata, India

Email: {bhunia.suman, jayitapal.it}@gmail.com, nmukherjee@cse.jdvu.ac.in

Abstract—Wireless Sensor Network(WSN) consists of sensornodes which are deployed densely in an area of interest. Thearea is intended to be sensed or monitored. Each sensor nodeis a tiny and power constrained device which is assigned thetask of monitoring. One of the most important requirements forwireless sensor networks is systematic collection of data, wheresensed data are collected at sensor nodes and forwarded to a basestation through number of hops for further processing. This paperpresents an event-driven data gathering scheme in a Sensor-Cloudinfrastructure. The scheme uses Fuzzy logic to ensure efficientdata collection and report a select set of data which is required.Aim of this work is to minimal use of communication resource andreduce overhead while collecting sensor data. Also, an attempt ismade to implement the scheme on TinyOS using TelosB motesfor real-time testing.

Keywords—Sensor-Cloud, Wireless Sensor Network, Fuzzy Set,Data Collection, TinyOS.

I. INTRODUCTION

Wireless Sensor Network (WSN) is a widely used technol-ogy that has potential applications in almost every aspect ofhuman life and may influence it largely [1]. They allow thephysical environment to be measured at high resolutions andgreatly increase the quantity of real-world data and informationfor applications.

The huge amount of data gathered using sensors arerequired to be stored and be made available for anytime,anywhere access. Such services are offered by Grid or Cloudenvironment. A Sensor-Cloud can be used for this purpose.Two state-of-the-art technologies will be combined : CloudComputing and Wireless Sensor Networks. Cloud offers flex-ible way of accessing data at low cost. These provide highcomputational power, high capacity and highly available stor-age and scalability. Further, these ensure location independenceand optimized resource utilization. Thus, these integrated in-frastructure is well suited for adaptive and pervasive computingapplications enabling Internet of Things (IoT) and aimed to beused in real-world applications like environmental monitoring,vehicular pollution management and health-care monitoring.

However, the cloud environments rely highly on Internet.Therefore, in order to realize the full potential of WSN throughthe Internet, integration of the two technologies is needed,complying with the current and existing standards. In the eraof Internet of Things (IoT) and Machine-to-machine (M2M)communications, IPv6 is widely used. So, a good solution isIPv6 based WSN. 6LoWPAN standard [2] is suitable for this

purpose. In [3], an IPv6 based WSN is tested and deployedsuccessfully. Now, advantages of both Cloud Computing andIPv6 based WSN may be tapped.

Usually, sensor networks require periodic data update fromthe nodes. But it may incur in greater communication overheadand higher energy consumption. Also, large payload may leadto packet fragmentations which play a crucial role in overallperformance of data forwarding scheme. In [4], it is shown that“the amount of energy spent for sending a single bit of datais approximately same with that of executing 800 instructionsin a sensor node”. So, the amount of data exchanged shouldbe minimized in order to decrease energy consumption andincrease network lifetime.

The above mentioned problems in data collection may beresolved if an event driven scheme is put in place. Here, oneof the most critical tasks for sensor nodes is to detect varioususeful occurrence of events and collect data in a reliable andtimely manner. An event can be defined as simple (atomic) orcomposite [6]. An atomic event may be detected depending onthe observation of one parameter. If the value of the parameteris higher or lower than a specified threshold, an atomic eventis detected. A composite event is nothing but combination ofmultiple atomic events.

In this paper, an event-driven data gathering scheme isproposed where some rules are set in collecting or sensingthe data. For this, we use fuzzy logic. Event uncertainty andimpreciseness are described by fuzziness. WSN is typicallyused to monitor some parameters or events which are complex,ambiguous and vagueness embedded in their nature. Therefore,a fuzzy based data collection approach is a suitable option.This data gathering scheme aims at less frequent data trans-missions, less power consumption and limited payload. Thiswork may be helpful for the said infrastructure as it will begathered with selected set of data as per requirement. Properanalysis of the data may be done as undesired data will notbe picked up from the sensor nodes.

Remainder of the paper is organized as follows. In SectionII, the Sensor-Cloud framework is presented. Subsequently,data collection and proposed scheme are discussed in sectionIII and IV repectively. The implementation effort on TinyOSis briefed in section V. Finally, we conclude the paper with adirection for future work in section VI.

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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DOI 10.1109/CCGrid.2014.77

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Fig. 1. Sensor-Cloud framework

II. SENSOR-CLOUD FRAMEWORK

We have proposed a generalized sensor-cloud frameworkin Figure 1 that can be used for different applications, such asenvironment monitoring, habitat monitoring, and health-caremanagement. In [5], some of the challenges related to sensor-cloud framework are discussed with particular focus on health-care application.

A layered architecture of Sensor-Cloud is presented inFigure 2. In this diagram, total seven layers are proposed. Thelayers are numbered from lowest one in ascending order.

• Layer 1 is the lowest layer which consists of vari-ous hardware resources of Sensor-Cloud infrastruc-ture. Sensors, mobile devices, storage, servers arethe resources lies on this layer. These resources areconnected with network.

• Layer 2 handles routing of data among the sensordevices, as well as other resources. When the hardwareresources become mobile, mobility should be managed

Fig. 2. Layered architecture of Sensor-Cloud

so that connectivity and security of the resources arenot compromised anyhow. Here, Information providerwill help to gather updated information about theresources.

• Aggregation and conversion of sensor data are donewith the help of sensor data handling component inlayer 3. In this layer, scheduling decisions of Sensor-Cloud resources are taken in order to handle the issueslike energy conservation and load balancing withouthampering flow of data.

• Naming and addressing issues are taken care of inlayer 4. Name of a particular resource may be resolvedhere to ensure that right resource is addressed. Hugedata may be managed efficiently in the Sensor-Cloudinfrastructure. It is also planned to incorporate aresource broker for maintaining Quality of Service(QoS).

• Layer 5 facilitates application-level tasks like regis-tration of intended users, data visualization and queryhandling.

• Layer 6 is the service provider layer and layer 7provides access to these services through a web portal.Though, currently we are developing only healthcareservices, the proposed infrastructure in generic tosupport other applications as shown in Figure 2.

As shown in the layered architecture [Figure 2], sensor datahandling is a major component of the integrated infrastructure.Collection of sensor data is discussed in the following section.

III. DATA COLLECTION

In recent times, there are lot of enthuaism on WSNresearch. But data collection in WSN remains in early stage

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of research. In various applications, it is required to acquiredifferent types of sensor data precisely. The data either maybe collected by the sink node or the sensor nodes may gatherand forward themselves.The sink node may request data bysending a query throughout the deployed network. This queryis received at sensor nodes. Once the data matches the query,the sensor node routes back the data to the sink. In most sensormonitoring applications, uninterrupted flow of data is required.The data may be sent through multiple hops to the sink node.During my research work on routing schemes in IP-WSN [7],it is observed that packet fragmentations play a crucial rolein overall performance of a routing scheme. More sensor datamay lead to burdened payload which results in fragmentationsof the packet concerned. In turn, more fragmentations makethe latency longer for data collection.

Moreover, when the data is being transmitted toward thesink node, more sensing data may be accumulated alongthe route. Thus, a huge traffic may be occurred during datacollection. This should be handled carefully. Otherwise, itmay result in unbalanced and inefficient energy dissipation,congested network. In turn, performance will degrade in theWSN concerned.

To tackle these issues, a data collection scheme is proposedin the next section.

A. Problem Statement

With the advancement of sensor technologies, sensing de-vices are basically multi-sensor systems which include varioussensing options and configurations. Data gathering from awireless sensor network with various sensor resources andmultiple sensor parameters, is an uphill task. Large scaledeployment of such wireless sensor network results in hugedata. So, data may be collected on occurrence of an event.Also, it will be beneficial if the node may decide which sensor-data is required to be sent at that particular event. Usuallyin event detection framework, sensor nodes collect local datafrom environment and send it to fusion center. Then the fusioncenter makes the decision and propagate back to the sensornodes. In this scheme, we do away with this fusion centersand the event detection and decision will be taken by sensornode itself.

Let us assume, a sensing device D has three sensors andthe sensor parameters namely S1,S2 and S3.

D = {S1, S2, S3} (1)

Instead of collecting all these three parameters continuously,we may collect when an event occurs. The event may be pre-defined in the form of some threshold values of the parameters.S1th, S2th and S3th are the threshold values of the parametersS1, S2 and S3 respectively. If these parameters are not disjointrather related to each other such that value of S2 depends onthat of S1 and value of S3 depends on that of S2, the datagathering function may be defined as:

f(Dg) =

{f(S1) if S1 < S1th

f(S1, S2) if S1 ≥ S1th & S2 < S2th

f(S1, S2, S3) otherwise(2)

Objective of this event-driven data gathering is minimum usageof communication resource and to reduce overhead. Now, it

is required to define and detect events. Also, data collectionhas to be done based on these events. For this purpose, fuzzylogic is used. But in order to detect events, Fuzzy rulesuse membership categories of different parameters instead ofthreshold values of various parameters.

IV. PROPOSED SCHEME USING FUZZY LOGIC

Few properties of Fuzzy logic, make it suitable for address-ing aforementioned issues in WSN. These properties are:

1) Fuzzy logic can tolerate sensor readings which areimprecise and unreliable to some extent.

2) It is much closer to the way of human thoughtprocess.

3) Fuzzy logic is much more intuitive and easier to useas compared to other classification algorithms basedon probability theory.

In this section, a Fuzzy Logic assisted data collectionsystem is conceptualized as shown in Figure 3. This systeminvolves three steps.

Fig. 3. Fuzzy Logic assisted data collection system

• Firstly, Data from multiple sensors form crisp set. Thecrisp input variables x ∈ X (X is the set of possibleinput variables) are converted into fuzzy linguisticvariables by Fuzzifier with the help of membershipfunctions which are defined in knowledge base. Lin-guistic variables is defined by Zadeh as “variableswhose values are not numbers but words or sentencesin a natural or artificial language”[9].

• Then, the fuzzified values in the fuzzy sets are pro-cessed by fuzzy inference engine using the pre-set rulesin the form of if-then statements. These predefinedrules may be derived from domain knowledge pro-vided by experts. In this stage the inference schememaps input fuzzy sets to output fuzzy sets.

• Finally, the defuzzifier computes a crisp result fromthe fuzzy sets output. The crisp output value containsthe control actions (ie. sensor data that should betransmitted, alert) that should be taken.

The above mentioned 3 steps are named as fuzzification,decision making and defuzzification respectively. Details de-scription are included in the following subsections.

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A. Fuzzification

The concept of fuzzy set and fuzzy logic was introduced byZadeh in 1965. A crisp set is a collection of elements whichhas no ambiguity for the belonging of each element to theset concerned. But in a fuzzy set, each element has gradedmembership in the real interval [0, 1]. Thus, boundary of theset contained is ambiguous. The fuzzification is the processof transforming crisp values into fuzzy linguistic variables [8].The fuzzifier converts a crisp value into degrees of membershipby applying the corresponding membership functions. A mem-bership function determines the certainty with which a crispvalue is associated with a specific linguistic value. Some ofthe most frequently used shapes include triangular, trapezoidal,and Gaussian- shaped.

B. Fuzzy Inference System

Decision making is done at the Fuzzy Inference System.A rule-base helps to make the decision. A rule-base composedof a set of linguistic statements, called rules. These rulesare in the form of “IF premise, THEN consequent”where thepremise consists of fuzzy input variables which are connectedby logical functions (ie. AND, OR, NOT) and the consequentconsists of fuzzy output variable.

C. De-fuzzification

The transformation from a fuzzy set to a crisp number iscalled defuzzification. In practice, defuzzification is done usingcentroid method.

z∗ =

∫μA(z) ∗ zdz∫μA(z)dz

(3)

where, μA(z) is the membership function of set A.

D. Application of the scheme

A test-case is built for environment monitoring using datafrom three sensors ie. temperature, humidity, light intensity.These are taken as input fuzzy variables and comfortabilityof the environment is the output variable. Membership func-tions of these fuzzy variables are user defined. Number ofmembership functions depend on required resolution. Here,the membership functions low, medium and high are definedon input variables as shown in Figures 4, 5, 6. Output variableis defined as very-low, low, medium, high and very-high asshown in Figure 7.

Fig. 4. Membership Function for Humidity

Fig. 5. Membership Function for Temperature

Fig. 6. Membership Function for Light

Now, fuzzy inference engine helps to ascertain the com-fortability and detect various events. The rules are set in sucha way that fuzzy inference engine may decide which sensorparameters are required to be transmitted to the sink nodeat any given circumstances. Also, events are defined throughthe rules so that on occurrence of an event, required sensorparameters are forwarded.The rules are created using the Fuzzy Inference System (FIS)in the Matlab Fuzzy Toolbox[10]. Figure 8 shows a samplefuzzy calculation of comfortability of the weather in fourthcolumn based on the fuzzified values of light intensity, tem-perature, humidity in first three columns respectively. Also,the decision on which particular parameters are supposed tobe forwarded is shown in last column of the rule.

A rule looks like: IF Light Intensity is low AND Tem-perature is low and Humidity is low THEN Comfortability isvery high and the Parameter to be transmitted to sink is LightIntensity. It is rule no. 1 in Figure 8.

Thus, all the parameters are not required to be transmitted.Only selected set of data is sent on occurrence of a particularevent. Figures 9, 10, 11 show control surface of comfortabilitybased on the input parameters.

V. IMPLEMENTATION IN TINYOS

We have used the Crossbow’s TelosB [12] mote as ahardware platform for our experiments. It is an open sourceplatform designed to enable cutting-edge experimentation forthe research community.It bundles all the essentials into asingle platform, including USB programming capability, anIEEE 802.15.4 compliant radio chip (CC2420) with integratedantenna, a 8 MHz TI MSP430 microcontroller with 10kBRAM and 1MB external flash for data logging Programmingand data collection via USB. There is a sensor suite includingintegrated light, temperature and humidity sensor.

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Fig. 8. Fuzzy rules for event driven data collection

Fig. 7. Membership Function for comfortability

Fig. 9. Surface view of comfortability with respect to light and humidity

TinyOS 2.1.1 [11] is used for this work. TinyOS was started asa collaboration between the University of California, Berkeleyin co-operation with Intel Research and Crossbow Technology.

After installing the data gathering scheme onto few TelosBmotes, we deployed these motes for data gathering. Thisdeployment was meant for monitoring the environment. Whenintensity of the light is very high, it is prone to heat up theenvironment. So, temperature sensor is activated to fetch thecurrent data. If temperature is also on higher side, humidity

Fig. 10. Surface view of comfortability with respect to temperature andhumidity

Fig. 11. Surface view of comfortability with respect to light and temperature

measurement is required to ascertain discomfort in the par-ticular environment. Then only humidity sensor returns backthe current value. Thus, data may be collected based on pre-defined events. We have obtained the sensor data on a terminalas shown in Figure 12. When light is low, only intensity oflight (in lux) is shown. In case, light is not low, temperature(in centigrade) is displayed along with the intensity of light.

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Humidity is shown when temperature, light both are not low.

Fig. 12. Screenshot of event driven data gathering

VI. CONCLUSION

In this paper, an event driven data collection scheme inwireless sensor network is proposed. Fuzzy logic is used fordetection of an event and identifying which data is required tobe forwarded. The proposed scheme handles the uncertaintypresent in the data effectively. Also, the decision based onthis approach is accurate. Thus, the data collection schemehelps to gather required data only. As per requirement ofapplication, the membership functions and the parameters canbe changed and modified. Rules also could be altered andadjusted according to parameters for further extending thework in various applications. This is an early stage work. It isrequired to do performance study of the scheme as comparedto conventional data collection in wireless sensor network.

ACKNOWLEDGMENT

This work is partially supported by funding received fromDST-NRDMS for carrying out the research project entitled”Development of an Integrated Web portal for Health-caremanagement based on Sensor-Grid technologies”. Research offirst author is supported by Tata Consultancy Services throughtheir Research Scholarship Program.

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