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Abstract— In this paper, a novel strategy for eliminating redundancy in the data dissemination process is proposed. A cluster based infrastructure is considered where the entire geographical area of interest is divided into grid based clusters having a representative member in each of the cluster. The output from the sensors is made to pass through a context aware system to ensure the validity of the sensor data. Redundancy in the above valid data can be of two types – Intra cluster and Inter Cluster. Based on the type of redundancy, redundancy elimination is performed at header nodes or at intermediate nodes thus facilitating efficient transmission of data from source node to access node. 1. INTRODUCTION

In wireless sensor networks, sensors are employed in the area of interest to gather data or to monitor the environment. Sensors can be deployed in a random fashion or can be placed in strategic locations. The data collected by each node is forwarded to the sink(destination) through a process known as “Data Dissemination”. However the sensors in wireless sensor networks are battery powered due to which it has limited lifetime. So the data dissemination policy has to be framed in such a way that the energy consumption is optimal.

2. RELATED WORK

The entire geographical area is divided into grid based clusters[1]. Each cluster has a header node which acts as a representative node of that cluster (Fig 1).

Fig 1. Cluster based virtual infrastructure The header node is responsible for the location update of the mobile sink groups. The header node in each cluster is elected

using backward timer. The access node (sink) queries the source node for data. The data dissemination process from source node to access node (sink) is exploited using header to header forwarding[2]. Whenever an event occurs in the monitoring environment, the sensor node detects it and forwards the relevant data to the sink node for processing and to take the necessary actions[3][4]. Eq.,1 below shows the relationship between sensor lifetime and battery power as, lifetime is directly proportional to the battery power remaining. Battery power is consumed when data is transmitted. Hence by eq.,2, more the amount of data transmitted, more the power consumed.

Lifetime of a Battery power of the sensor node sensor node

Eq.,1 More the power consumed, less the life time of the sensor nodes.

Amount of data Amount of transmitted power consumed

Eq.,2 So the data dissemination process has to be efficient in

order to reduce the amount of data transmitted and to increase the lifetime of the sensor nodes which results in improved reliability of the network.

3. PROPOSED WORK In the above virtual infrastructure when the access node queries two or more source node for data there is a high probability of data redundancy in data dissemination process[8]. Hence if redundant data are to be transmitted again and again, the amount of data transmitted increases which ultimately results in high consumption of power and bandwidth. This reduces the lifetime of the network and leads to the failure of the application. 3.1 Types of redundancy in the above infrastructure: Intra-Cluster redundancy: When the source nodes lie in the same cluster. Inter-Cluster redundancy: When the source nodes lie in adjacent or neighboring clusters. Due to the above condition the lifetime of the sensor node decreases reducing the reliability of the network.

REDD: Redundancy Eliminated Data Dissemination in Cluster Based Mobile Sinks

Sumalatha Ramachandran*, Aswin Kumar Gopi#, Giridara Varma Elumalai#, Murugesan Chellapa#

*-Assistant Professor, #-Research Student, Department Of Information Technology, Anna University

MIT, Anna University, Chennai. June 3-5, 2011

978-1-4577-0590-8/11/$26.00 ©2011 IEEE

IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011

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3.2. Redundancy elimination The main objective of the project is to increase the lifetime of the sensor by framing an efficient data dissemination policy which involves identifying redundant data and eliminating the same before the data reaches the sink node. 3.2.1 Eliminating Intra-Cluster redundancy:

When the source nodes lie in the same cluster the data to be transmitted is compressed at the source nodes. The compressed data is then forwarded to the header node where redundancy elimination takes place (Fig 2). Then the data dissemination is brought about by header to header forwarding until the data reaches the sink. Fig 2. Intra- Cluster Redundancy Elimination 3.2.1 Eliminating Inter-Cluster Redundancy

When the source nodes lie in adjacent clusters the shortest path from header node to access node for each source node is found. These paths tend to converge at a header node present in some other cluster. Elimination of redundant data is performed at this header node and compression at their respective source header (Fig 3).

Fig 3. Inter-Cluster Redundancy Elimination

3.3. Module description The proposed architecture of Redundancy Eliminated Data

Dissemination consists of the following modules,

1. Cluster Formation 2. Dynamic Topology Management 3. Header Node Election 4. Data Compression 5. Context Aware System 6. Finding Shortest Node Converging Path 7. Redundancy Elimination

3.4. System Architecture of REDD

Fig 4. System Architecture

Each node is deployed in the area of interest to observe the

environment around and must belong to a cluster defined by its geographic axes. The nodes that fall within a particular cluster form a group under a representative node called Header Node. This is done by Cluster Formation and Header Election modules. Since the sensor nodes are mobile, the position of each sensor node keeps on changing. So the sensor node associates itself with a particular cluster from time to time with the help of Dynamic Topology Management module.

The job of detecting events nearby is done by the sensors at the physical level. These events are then combined together and stored locally. The sink node queries for data of interest from the source nodes through the Querying module. The query is then forwarded to the access nodes by Header to Header forwarding [9]. For this forwarding, the Shortest Path and the Shortest Converging Path are found by the sink. Based on the destination of the sinks, the corresponding path is used. While replying, data is compressed at the cluster head. Huffman encoding is used for compression of sensor data. The compressed output from the sensor is made to pass through a Context Aware System to ensure reliability of the sensor data. Redundancy in the valid data is eliminated either at the header node or at some intermediate junction node which is mentioned in the query by the sink node. The redundancy eliminated reply from the access nodes is then sent to the sink by Header to Header forwarding in the same way as the query.

On receiving the data, the Data Processing module of the sink node processes the data and performs the necessary action.

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3.5 Context Aware System In wireless sensor networks, the reliability is ensured by

making use of redundant data. But this is facilitated at the cost of excessive power consumption. Since the redundancy is going to be removed, it has to be ensured that the unique data has to be a reliable one [11][13]. To ensure that a context aware system is used as shown in Fig 5. The context aware architecture consists of the following modules

Sensor Output - Contains raw data about temperature and pressure in byte stream. Context Interpreter – Extracts the context (temperature | Pressure ) from the raw sensor data. Context Retriever – Retrieves the context that has to be compared with a set of rules. Rule Engine – Provides the set of rules as shown in Table 1. Fig 5. Context Aware Architecture

Java context awareness framework (JCAF) [10] is used to implement the context aware system. The compressed output from the sensor data is given as input to this module. The temperature and pressure context are extracted from the compressed output and then it is checked for validity against the rules defined in the rule engine. If the context is valid the data is forwarded to redundancy elimination module else it is suppressed.

TABLE I

INITIAL SET OF RULES DEFINED IN RULE ENGINE

UTmin & UTmax – Minimum and Maximum allowable temperatures UPmin & UPmax - Minimum and Maximum allowable Pressure

3.5 Redundancy Elimination One technique used to decrease the number of redundant

messages transmitted and thus pro-long the network lifetime is data aggregation. REDD protocol’s redundancy elimination algorithm is based on a single value called the correlation coefficient to represent the whole set of readings recorded by all the nodes in the sensor field.

The value of the correlation coefficient (H) ranges from 1 to 10. H = 1 is for strong correlation of data and as the correlation coefficient increases, the degree of correlation between data decreases. The following flowchart(Fig 6) describes the above procedure

Fig 6. Redundancy Elimination The correlation coefficient is calculated for each transmission. If the correlation co-efficient value is high (CC=1) the transmission is suppressed from further forwarding. In the other cases data is forwarded to the base station. 4. IMPLEMENTATION ISSUES 4.1 Algorithm: Cluster Formation The following algorithm gets the position of the node from the GPS device, calculates the grid center and joins the grid header if any. If there is no header yet in the grid, it becomes the header node.

Form_Cluster(Node temp) 1. xp getxPosition(); yp getyPosition(); 2. RFIDnode getRFID(xp , yp); 3. xc ( xp / Gsize ) * Gsize + Gsize /2 ; 4. yc ( yp / Gsize ) * Gsize + Gsize /2 ; 5. RFIDheader getRFID(xc , yc); 6. if (RFIDheader == NULL) setHeader(RFIDnode); 7. else 8. sendMsg(RFIDheader); 9. wait(); 10. if(receiveMsg()) setHeader(RFIDheader); 11. joinMember(RFIDnode); 12. endif 13. endif 14. end

S.No Condition Action 1 Temperature < UTmin Discard 2 Temperature > UTmax Discard 3 UTmin < Temperature < UTmax Allow 4 Pressure < UPmin Discard 5 Pressure > UPmax Discard 6 UPmin < Pressure < UPmax Allow

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Note: 1. When a node is created it contains the following information

a) RFID – Unique identity of the node b) Location information (X,Y co-ordinates)

2. Finding the grid to which the node belongs the following formula is used

xc ( xp / Gsize ) * Gsize + Gsize /2 ; yc ( yp / Gsize ) * Gsize + Gsize /2 ;

where xc, yc are the co-ordinates of header and Gsize is the grid size

4.2 Algorithm: Location Update (Dynamic Topology Management)

Whenever a node moves, it calculates its new grid center and checks with the old center. If they are not the same, it joins under the new header node and unregisters from the old header. If it is a header itself it initiates election process in the old grid.

Update_Location(Node node) 1. xold getxPosition(); 2. yold getyPosition(); 3. RFIDnode getRFID(xold , yold); 4. xoc ( xold / Gsize ) * Gsize + Gsize /2 ; 5. yoc ( yold / Gsize ) * Gsize + Gsize /2 ; 6. RFIDcurrentheader getRFID(xoc , yoc); 7. xnew setxPosition(); 8.ynew setyPosition(); 9.xnc ( xnew / Gsize ) * Gsize + Gsize /2 ; 10. ync ( ynew / Gsize ) * Gsize + Gsize /2 ; 11. RFIDnewheader getRFID(xnc , ync); 12. if (RFIDnewheader == RFIDcurrentheader ) 13. return; 14. else if (RFIDnewheader != RFIDcurrentheader) 15. sendMsg(RFIDnewheader) 16. wait(); 15. if(receiveMsg()) 16. setHeader(RFIDnewheader); 17. joinMember(RFIDnode); 20. endif 21. endif 18. End Note: 1.If a node moves within in its grid there is no change in topology. 2.If a member node of one grid moves to another it registers itself with the header of the new grid. 4.3 Algorithm: Header Node Election

If the current header has moved out of the grid or if its battery power has reduced below the threshold, it starts the election process and notifies the first node among its members. That node, if it has enough power, becomes the new header and advertises its election as the new header to other member nodes.

Elect_Header() Initialize node[i]battery 100 for all I; if (i%5==0) GRID((i/5)/ Gsize , (i/5)% Gsize )header node[i/5*5]; end if if (onBatteryChange()) if(Cur_HeaderBattery < 100&&Cur_HeaderBattery >50) continue; else Old_Header Cur_Header; Cur_Header (Cur_Header.Members[] next); Cur_Header.Members[].Join Old_Header; end if end if end

4.4 Algorithm: Sensor Data Forwarding

When a node wants to send data to another node, it first calculates the shortest path to the destination and then forwards the data to the next header node in the path along with information regarding the path by Header to Header forwarding [9]. The next header node on receiving the data forwards it to the next down lower in the order and so on.

Here the distance is not based on link weight but on number of hops between source and destination. An on-demand shortest path routing protocol is chosen for this. Forward(Graph, Source) 1. for each vertex v in Graph: 2. dist[v] := infinity ; 3. previous[v] := undefined ; 4. end for ; 5. dist[source] := 0 ; 6. Q := the set of all nodes in Graph ; 7. while Q is not empty: 8. u := vertex in Q with smallest dist[] ; 9. if dist[u] = infinity: 10. break ; 11. endif ; 12. remove u from Q ; 13. for each neighbor v of u: 14. alt := dist[u] + dist_between(u, v) ; 15. if alt < dist[v]: dist[v] := alt ; 16. previous[v] := u ; 17. endif ; 18. end for ; 19. end while ; 20. return dist[] ; 21. end 4.5 Algorithm: Context Aware Detection The following algorithm is used to check the validity of the sensor data originating from the sensor nodes. The temperature and pressure context is extracted from the sensor data and checked against the rules present in the rule engine. If the context satisfies the rules then the data is transmitted else it is suppressed.

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Check_Detect(Sensor_data, Rules) 1. Temperature extract_temp(Sensor_data); 2. Pressure extract_pressure(Sensor_data); 3. If check_context(Temperature) 4. If check_context(Pressure) 5. Transmit(Sensor_data) 6. endif 7. endif 8. end 4.6 Algorithm: Redundancy Elimination The valid transmissions may contain redundant data and they have to be eliminated before they reach the base station. The following algorithm is used to eliminate the redundancy present in the sensor data which calculates correlation co-fficient and based on its value the data is transmitted.

Eliminate_Redundant(Valid_Transmission_Set) 1. foreach (Transmission in Valid_Transmission_Set) 2. CCi= Calc_Correlation_Co-efficient(Transmission) 3. if(CCi!=1) 4. Transmiti(Data); 5. endif 6. endfor 7. end 5. PERFORMANCE ANALYSIS The initial network deployment consists of 3x3 grids with 5 nodes in each grid respectively. The battery level of each sensor is initially set to maximum (100%). The following Table 2 shows the specifications of simulation scenario

TABLE 2 SMULATION SCENARIO

Number of Transmissions

Number of Valid Transmissions

Number of Invalid Transmissions

Number of Redundant Transmissions

Number of Unique Transmissions

100 95 5 2 93 200 193 7 6 187 300 287 13 14 273 400 375 25 27 348 500 468 32 36 432

The following graph (Fig 7) represents the validity ratio

which is defined as the ratio between number of valid transmissions and number of total transmissions. It can be inferred that as the number of transmission increases the validity ratio decreases. As the total number of transmissions increases, the number of suppressed invalid transmissions also increases. Hence resulting in more power saving.

Fig 7. Validity Ratio The redundancy ratio which is defined as the ratio between the number of redundant transmissions and total number of transmissions shows that as the number of transmissions increase the redundancy also increases. This is depicted in the following graph (Fig 8). In the graph below it can be seen that if the number of transmission packets is 500 the redundancy ratio is almost 0.07 which is relatively high and increases the amount of power wasted due to redundant transmissions. In order to reduce the amount of power wasted there is a necessity for redundancy elimination process.

Fig 8. Redundancy Ratio Power retention ratio is the ratio between number of suppressed transmissions and total number of transmissions. The power retention ratio graph below (Fig 9) depicts that when considering only unique transmissions after the redundancy elimination process, the amount of power saved is relatively high. Fig 9. Power retention Ratio

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The power consumption graph shown in (Fig.10) implies that the amount of power consumed with redundant transmissions is higher when compared to the power consumed after eliminating redundant transmissions, thereby proving the effectiveness of the REDD architecture.

Fig 10. Power Consumption 6. CONCLUSION AND FUTURE WORK Thus, the REDD helps in optimizing the data dissemination policy by compressing and eliminating the redundant sensor data there by reducing the amount of data transmitted and amount of power consumed. This results in increased lifetime of the sensor nodes and improves the reliability of sensor network. REFERENCES [1] S.Park, E.Lee, M.S.Jin and S.H.Kim, “Novel Strategy for data dissemination to mobile sinks in wireless sensor networks,” IEEE Communication Letters., vol. 14, no.3, pp. 202-204, Mar. 2010. [2] A.C. Viana, A.Ziviani and R.Friedman, “Decoupling Data Dissemination from Mobile Sink’s Trajectory in Wireless Sensor Networks,” IEEE Communication Letters., vol. 13, no. 3, Mar. 2009. [3] E. Hamida and G. Chelius, “Strategies for data dissemination to mobile sinks in wireless sensor networks,” IEEE Wireless Commun., vol. 15, no.6, pp. 31-37, Dec. 2008. [4] Machado, Goussevskaia, Mini, Rezende, Loureiro, Mateus, and Nogueira, “Data Dissemination in Autonomic Wireless Sensor Networks,” IEEE Wireless Commun, vol. 23, no. 12, Dec 2005. [5] L.Song and D.Hatzinakos, “Architecture of Wireless Sensor Networks With Mobile Sinks: Sparsely Deployed Sensors,” IEEE Transactions on Vehicular Tech, vol. 56, no. 4, Jul 2007 [6] F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, “A two-tier data dissemination model for large-scale wireless sensor networks,” in Proc. ACM International Conference on Mobile Computing and Networking (MobiCom), Sep. 2002. [7] S. Park, D. Lee, E. Lee, F. Yu, Y. Choi, and S. Kim, “A communication architecture to reflect user mobility issue in wireless sensor fields,in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2007. [8] E. B. Hamida and G. Chelius, “Line-based data dissemination protocol forwireless sensor networks with mobile sink,” in Proc. IEEE InternationalConference on Communications (ICC), May 2008. [9] Y. Yu, R. Govindan, and D. Estrin. Geographical and Energy Aware Routing: A Recursive Data Dissemination Protocol for Wireless Sensor Networks. Technical Report UCLA/CSD-TR-01-0023, UCLA Computer Science Dept., May 2001. [10] Jakob E. Bardram, “The Java Context Awareness Framework (JCAF) – A Service Infrastructure and Programming Framework for Context-Aware Applications” [11] M. Baldauf, S. Dustdar, F. Rosenberg, "A Survey On Context-Aware Systems", International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 63-277, Inderscience Publishers, 2007. [12] K.E. Kjaer, "A Survey of Context-Aware Middleware", Proceedings of the IASTED software engineering conference, 2007.

[13] A. Singh, M. Conway, "Survey of Context aware Frameworks – Analysis and Criticism", UNC-Chapel Hill ITS Version: 1, 2006.

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