Redundancy Aware Data Aggregation for Pest Control ... - IEEE Xplore

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

Redundancy Aware Data Aggregation for Pest Control in Coffee Plantation using Wireless Sensor Networks Roshan Zameer Ahmed

Rajashekhar C Biradar

Electronics and Communication Reva Institute of Technology and Management,Bangalore [email protected]

Information Science Reva Institute of Technology and Management,Bangalore [email protected]

Abstract—Wireless Sensor Networks (WSNs), are selfconfigured and infrastructure less networks which are made of small devices with dedicated sensors and wireless transceivers. The objectives of a WSN are to collect data from the environment and transmit to a coverage site where the data can be observed and analyzed. The advantage of using the sensor devices to monitor the environment is that it does not require infrastructure such as electric mains for power supply and wired lines for Internet connections to collect data and do not require human communication while deploying. Major threat to Coffee plantation is a pest known as Coffee White Stem Borer (CWSB) which is the most serious pest of Coffea Arabica. Coffee production can be enhanced if we devise a mechanism to detect the pest at its inception stage using automated detection system designed with WSN. Data aggregation in WSN for pest identification significantly reduces the redundancy of data and helps to trace the pest accurately. In this paper, we propose Redundancy Aware Data Aggregation (RADA) for CWSB pest identification in the Coffea Arabica plants. The presence of pests is identified by using the Ultrasonic mechanism in sensor nodes that helps us to eliminate redundant information from multiple nodes at the Cluster Head (CH). This scheme successively conglomerates the characteristics designed for CWSB identification and initiates the rescue mechanism from the user end. Simulation analysis is done based on the aggregation ratio and control overhead at the CH.

Keywords: Wireless Sensor Networks, Data Aggregation, Coffea Arabica, CWSB. I. I NTRODUCTION Coffee is a tropical plant which grows in a humid environment with precipitation needs of 1,500 mm. The typical weather condition should amid 19 to 25◦ C with elevation up to 2,000 meters. A coffee tree can remain productive up to 20 years with the harvest period occurring after the showery period while the tree produces berries. Coffee production is the backbone of the economy of several countries and it grades among the most valuable crop cultivation products in the world[1]. The two main variety of coffee are Arabica (Coffea Arabica) and Robusta (Coffea Robusta) that are cultured on a vendeur scale. India is the sixth major (after Brazil, Colombia, Vietnam, Indonesia and Ethiopia) maker of

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coffee, manufacturing about an approximate 3,18,000 tones throughout 2012-13. India grows together Arabica (1/3 of production) and Robusta (2/3 of production) with the total planted area of coffee covering around 3,80,000 hectares. The CWSB is the most serious to Coffea Arabica cultivation in some Asian Countries. CWSB was first reported in India in 1838[2]. Robusta coffee plants though broadly cultivated, the CWSB does not attack, as its stem and primary branches are a lot thicker and harder, due to which larvae do not subsist well, and the females do not have a preference to lay eggs on it[3]. These beetles are active during bright and hot weather. The Coffea Arabica is placed in a form of square or triangular covering in the field area. The height of Coffea Arabica stem is 3.0-3.5ft and distance between the coffee stem is 6ft (1.823 mts). Each female lays about 100 eggs which are elongate, oval, white and turn pale yellow later. Eggs hatch in 9-15 days. The hatched out grubs bore into the bark and feed on the periphery of the stem for two months. Later, they enter the hard wood and make tunnels in all the directions which may extend into the roots. The grub stage lasts for about 10 months after which the pupa stage lasts for 3-4 weeks which then transforms into an adult beetle. The adult remains in the tunnel for 3-7 days, and comes out by cutting an exit hole[4][6]. The cavity size during the boring activity caused by grub in the stem is 4 mm and 1 inch. This cavity causes the stem to become porous internally. According to literature mentioned the cavity is caused only by the CWSB and no other pest activity have been found inside the coffee stem. By identifying the pores within the stem along with its location would be beneficial in arresting the pest and blocking its exit. By this the population growth can be controlled. The affected plants show outwardly visible ridges around the stem. They also exhibit signs like wilting and yellowing. If the infested plant is 7-8 years old, it dies in a year. The older plants withstand the attack for a few seasons. Infested plants are less productive, yielding more floats[5][7]. WSN contains a large quantity of sensor nodes, which are closely organized either inside the object of perception or very close to it. The arrangement of these nodes need not be engineered or pre-determined.

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

This allows random positioning in unreachable grounds or disaster reinforcement operations. WSN have been expended for many applications together with environmental assistance, skill monitoring and armed surveillance. Characteristically, WSNs have enormous quantity of nodes with the competency to communicate among themselves and as well to an peripheral sink or a base station (BTS)[8]. The sensors can be spread arbitrarily in harsh environments for instance battleground or deterministically located at specific site. The sensors organize themselves to form a communication network for instance a single multi-hop network otherwise a hierarchical organization with numerous clusters and cluster head (CH). The sensors periodically sense the information process it and broadcast it to the BTS. The regularity of information treatment and the number of sensors which report the information usually depends on the specific application[9][10]. A. Related Works Some of the related works on data aggregation are as follows. Data aggregation is carried out by creating a chain among the sensor nodes in order that every node will receive and broadcast merged information to the adjoining neighbors, information gathered are sent from a node to an another node, and all nodes get the opportunity to be head for broadcast to the BTS[11]. Data aggregation in[12] is described in terms of sleep scheduling algorithms with the identification of contiguous link scheduling problem by assigning consecutive time slot for data collection. The authors in[13] describe fault tolerance data aggregation process that remove the deceptive data sent by the compromised nodes by making use of locality sensitive hashing scheme. In [14] data aggregated are collected at the nodes by the coverage frequency and aggregation ratio at the CH. In [15] a scheme named support vector machine (SVM) is developed that reduces the redundancy on the aggregation tree for a given sensor network. B. Our Contributions Our contributions in this research are as follows. (1) Data gathering in WSN using Ultrasonic Active Sensor (UAS) to detect the existence of CWSB, (2) Designing suitable packet format to store the data gathered from various trees at a sensor node, (3) Aggregating the data at a sensor node by eliminating the redundancy by matching process, (4) Transporting the aggregated data to a CH. At a CH, repeat step 2 and 3 for further aggregation to reduce the redundancy of data received from various nodes.

between the frequencies at which the sound or light waves leave a source and that at which they reach an observer caused by relative motion of the observer and the wave source. The data gathering, data aggregation and fusion of the data are the primary inset in the pest identification process. The UAS sensor nodes are placed in the coffee field area according to their transmission range in such a way that 10-15 coffee plants are covered for CWSB pest detection. Each sensor node called as module is pre-orderly assigned the coffee stems. We divide the coffee field into small areas with every area having a set of sensor nodes covering the coffee stem. Each cluster of sensor nodes have CH to communicate the CWSB pest detection to the end user. Sensor node sends the CWSB pest detection status to its CH. Each CHs in the coffee field periodically sends the CWSB pest detection status of all sensor nodes in its cluster to the sink node which is easily accessed by the end user as shown in Figure 1.

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Coffee Stem Sensor Node

Fig. 1.

Affected Stem CH

Deployment of Sensors in Coffee Plantation

At the sink node, the user has to take control over the situation based on the information provided by the CHs. In our proposed method, we divide this gathering of data regarding CWSB pest detection into three parts. The first part involves gathering the data from sensor nodes on pest detection at CH which is described in Section II-A. In the second part, the CH aggregates the gathered data by fusing into described format and sends to the sink node as explained in Section IIB. Finally, the third part involves data aggregation from the CH at sink node so that the status of each area in the coffee field can be given to the user for further control measures is described in Section II-C. A. Data Gathering from Sensor Nodes

II. DATA AGGREGATION FOR P EST I DENTIFICATION The proposed method uses UAS for identification of the grub existence around its cavity area by spreading across the sound waves of frequency about 40 Khz to a distance of 10-15 meters. The UAS receiver picks up the reflected sound waves which are further sent to the appropriate circuit for analysis. The change observed in the sound wave is considered as a shift in frequency. This shift in frequency triggers an alarm. The UAS uses the phenomenon of Doppler Effect. The difference

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Data gathering at each CH from sensor nodes is described in this section. Sensor node has unique identification number stored as SNID . The sensor node which acts as CH also has additional unique identification number as CHID . It is assumed that the SNID and CHID are numeric and allocated sequentially starting from 0. For every area, CH is elected CHID among the set of sensor nodes in that area based on the remaining available energy. Every sensor node in that area use the CHID for sending the information of the CWSB pest

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

detection. The sensor nodes reactively monitor for the pest activity. On the pest detection, the information is sent by the sensor node to its respective CH in the form of packet as shown in Figure 2, which includes CHID , SNID and the status of the sensor. The packet is identified as SNDAT A . The sensor node status can be either “Y” if CWSB pest detected or “N” if CWSB pest not yet detected. One bit is sufficient to maintain this status as the memory is one of the critical resource in WSN. CH

Fig. 2.

ID

SN

ID

STATUS (Y/N)

Sensor Node Packet format

On detection of CWSB pest, the corresponding sensor nodes sends SNDAT A which is received in its coverage area. The sensor node acting as CH reads the CHID in that packet. If it is same as its CHID then it maintains the database regarding the status of sensor node for CWSB pest detection near the coffee field area as shown in Figure 3. Otherwise, it discards the packet. The sensor nodes those are not CH simply discards all the received packets. Initially, all sensor nodes status is maintained as “N” at the CH database. SN ID

Status

SN 1

Y

SN 2

N

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Algorithm 1 Data Gathering at Cluster Head 1: Input: Broadcasted SNDAT A packet from sensor nodes, CHidOwn this CH ID ; 2: Output: Aggregated SN data in DBCH ; 3: Nomenclature: CHidR ← Received CH id Status Received status SNID Received sensor node id 4: Begin 5: Schedule event at CH to collect SNDAT A packet; 6: Extract CHidR ← CHID from SNDAT A ; 7: if (CHidR == CHidOwn ) then 8: Extract Sid ← SNID from SNDAT A ; 9: Extract SC ← Status in SNDAT A ; 10: Search for Sid in DBCH ; 11: if Sid in DBCH then 12: SDB ← Status in DB for Sid ; 13: if (SC = Y  ) then 14: if (SDB = N  ) then 15: SDB = Y  ; 16: end if 17: end if 18: end if 19: else 20: Discard received SNDAT A ; 21: end if 22: End

CH ID

SNSTART

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SN N

Fig. 3.

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Cluster Head DataBase

SN0

The information from SNDAT A received at the SNDAT A is extracted and maintained in the database as given in the Algorithm 1. The CHID extracts the SNID from the SNDAT A and search for entry which includes extracted SNID in the CH database. If the status of that SNID is “Y” in SNDAT A and status of that SNID is “N” in the extracted entry from the CH database, the CH database is updated for that SNID by changing the status to “Y”. B. Data Aggregation at CH The information gathered at the CH database from the sensor nodes in its area is send at every hour to the sink node so that the end user can get updated information of CWSB pest detections every hour. To transfer the status of CWSB pest detection, we propose the data aggregation technique in this section. We assume that the transmitted packet has a payload size of one byte and the packet header size is two bytes. The total size of the transmitted packet called as CHDAT A is three byte at the CH as shown in Figure 4. As only one bit is used to maintain the status of CWSB pest detection, the payload can send only eight sensor node CWSB

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SN 2

Fig. 4.

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SN 4

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CH Packet format

pest detection status. However, the CH has more than eight sensor node CWSB pest detection status. The CH maintains payload id starting from 0 for each payload sent to the sink. To identify the sensor node CWSB pest detection status along with its SNID , we add starting SNID as part of the packet header. The lowest significant bit 0 in the payload indicates the status of the sensor node with the SNID as SNIDstart . The bit 1 indicates the status of the sensor node with the SNID as SNIDstart + 1 and so on. The most significant bit 7 indicates the status of the sensor node with the SNID as SNIDstart + 7. In the Figure 4, the CH packet is formed by attaching its CHID with payload and SNIDstart . The payload is aggregation of eight status of the sensor node from CH database. The first eight status of the sensor node is aggregated in this manner by giving payload id as 0 and transferred to the sink node. For the first CH packet, the SNIDstart is equal to 0 as it is sending the status of sensor nodes with SNID from 0 to 7. Then the next eight status of the sensor node with

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

SNID from 8to15 is aggregated by giving payload id as 1 and transferred to the sink node. The aggregation by giving next payload id and transmission of eight status of the sensor node continues until all status of the sensor nodes in CH database is transmitted. The CH waits for the acknowledgement ACK for the transmitted packet for the specified time duration. While waiting for the acknowledgement of the transmitted packet, the next eight status of the sensor node is aggregated and transferred to the sink node until at least one of the acknowledgement for the transmitted packets is timeout. The format for the acknowledgement of the transmitted packet is as shown in Figure 5. CH ID

Fig. 5.

SN START

Acknowledgement Packet Format for Transmitted Packet

Upon receiving the ACK packet within specified waiting time, the CH check the SNIDstart of ACK packet. If it is −1, it resents status of sensor nodes from SNIDstart = 0. Otherwise, it calculates the ACK receiving id rcid as dividing SNIDstart of ACK packet by 8 as we are sending eight status. The CH maintains payload id as SUid = rcid . If the rcid is less than the current payload id, the CH updates status in its database for the corresponding sensor nodes to “N”. The status is updated to “N” as it is already informed to the end user through the sink node and no needs to maintain here. If the acknowledgement is not received within the time then the CH resents all the packets from last received acknowledgement id SUid . The algorithm for data aggregation and transmission at the CH is given 2. C. Data Aggregation at Sink Node Sink node has the status of all sensor nodes regarding the detection of CWSB pest in coffee field area. Each tree in the coffee field is identified with a unique tree id. Sink node can be accessed by the end user to control the identified CWSB pest manually. To maintain the status of all sensor nodes, sink node collects the CH packets CHDAT A every hour from each CH in the coffee field. It has the CH id, status of eight sensor nodes, and starting sensor node id which indicates the first status bit in the payload as shown in Figure 4. The sink node maintains the database for each CH with the corresponding sensor node status as shown in Figure 6. It has CH id, status of its sensor nodes along with its sensor node id, trees ids in its coffee field area and last sensor node for which the status received LastP acketstartSNID . Initially LastP acketstartSNID = −1. The tree ids in the CH area is stored in the sink database to inform the end user in terms of trees not in terms of sensor nodes as end use may not be technical person. We assume that the tree ids in an area are numeric and sequential. Sink node searches for the received CHID as part of CHDAT A in its database. If it is available at the sink database, it checks for the last sensor node for which the status is received LastP acketstartSNID . If it is −1 and SNIDstart in CHDAT A is 0, then status for sensor nodes 0 to 7 is updated

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Algorithm 2 Aggregated Data Transfer at CH 1: Input: DBCH information; 2: Output: Transfer of SN data from DBCH to Sink Node; Updated DBCH information; 3: Nomenclature: SNIDstart ← first SNID in payload; P ayload ← collected eight status stating from SNIDstart from DBCH ; P acket ← generated packet from P ayload, CHID and SNIDstart ; 4: Begin 5: α = 60 minutes and t = 0; 6: id = 0; SNIDstart = 0 7: P ayload ← First eight status from DB; 8: P ayloadid = 0 9: SUid = 0; 10: while (t%α == 0) do 11: while (DatabaseEnd) do 12: Generate P acket for sink node by attaching CHID and SNIDstart with P ayload; 13: Send P acket to sink node and wait for ACK; 14: P ayloadid + +; id + +; SNIDstart = SNIDstart + 8 15: Collect P ayload ← Next eight status from DB; 16: if (ACK received within time for payload with id less than P ayloadid ) then 17: if (SNIDstart inACK = −1) then 18: P ayloadid = 0; SNIDstart = 0; 19: else 20: Curid = P ayloadid − 1 and rcid = (SNIDstart inACK)/8; 21: if (Curid >= rcid ) then 22: Update status in DB for all packets from SUid till rcid to ’N’; 23: SUid = rcid ; 24: end if 25: end if 26: end if 27: if (ACK time out) then 28: Resend all packets from SUid + 1; 29: P ayloadid = SUid + 1 30: SNIDstart = P ayloadid ∗ 8 31: end if 32: end while 33: t + +; 34: end while 35: End

in sink node database and ACK is send to the corresponding CH with SNIDstart = −1. After updating the database, last sensor node for which the status received is set to 1 i.e., LastP acketstartSNID = 1. If it is −1 and SNIDstart in CHDAT A is not 0, sink node has loss of packets from SNID = 0 and SNstart − 1. If SNIDstart in CHDAT A is not equals to last sensor node for which the status received plus seven, sink node has loss of packets from SNID = (LastP acketstartSNID + 7) to SNstart − 1. Otherwise, the

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

CHID

SN ID SN 1 SN 2

CH1

SN 3

Status

Trees Covered

Y

Tree No. 1, 2, 3, 4, 5, 6, 7, 8

N

Tree No. 9, 10, 11, 12, 13, 14, 15, 16

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Tree No.

17, 18, 19, 20, 21, 22, 23, 24

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CH2

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Fig. 6.

Sink DataBase System

status for sensor nodes SNstart to SNstart + 7 is updated in sink node database and ACK is send to the corresponding CH with SNIDstart . After updating the database, the last sensor node for which the status received is set to SNstart i.e. LastP acketstartSNID = SNstart . The algorithm for data aggregation at sink node is given 3. III. S IMULATION Redundancy Aware Data Aggregation (RADA) is simulated on QualNet 5.2 Network Simulator to assess the performance and effectiveness of the approach. Simulation environment for the proposed work consists of three models: Network model, Propagation model and Traffic model. In the network model, sensor nodes are placed in an area of ’l x b’ square meters. It consists of ’N’ number of nodes that are assumed to be connected to a base station at the boundary of a network. Propagation model uses free space model with propagation constant β. Transmission range of a node is ’r’ for one-hop distance. The constant bit rate model is used in traffic model to transmit fixed size packets (p). Coverage area around each node has a bandwidth, BW singlehop, which includes the noise factor, channel frequency shared among its neighbors. The proposed scheme is simulated using the following simulation inputs. l = 500 mtrs., b = 500 mtrs., N = [5 to 50]., β = 2.5, p = [50 to 70 bytes], BW singlehop = 20 Mbps, r = 50 mtrs., A. Result Analysis In this paper, we have analyzed two results: Aggregation Ratio (AR) and Control Overhead (CO). 1) Aggregation Ratio (AR): The AR is defined as the ratio of the number of packets after eliminating redundant packets/redundant parameters in a packet to the total number of packets received at the CH by all the nodes. 2) Control Overhead (CO): The CO is defined as the number of additional control packets required to aggregate the data at the CH. 3) Analysis of AR: AR is plotted in Figure 7 with varying number of nodes for packet lengths of 50 bytes and 70 bytes. It is observed that the proposed RADA effectively aggregates the data by eliminating redundancy form various nodes and it reduces to almost 10% when the number nodes reaches 50 in a cluster. This is because multiple nodes might have sensed the same event which is communicated to CH and

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Algorithm 3 Data Aggregation at Sink Node 1: Input: CHDAT A packet from CH; 2: Output: Aggregated CH data in DBsink ; 3: Nomenclature: SNIDstart ← first SNID in payload; P ayloadid ← collected status for sensor nodes at CH; LastP acketstartSNID = −1 last sensor node for which the status received; 4: Begin 5: Schedule event to collect CHDAT A ; 6: while (CHDAT A ) do 7: Extract SNstart ← SNIDstart in CHDAT A ; 8: Extract CHID ← CHID in CHDAT A ; 9: Search for CHID in the DBsink ; 10: if CHID in the DBsink then 11: if (LastP acketstartSNID == −1) then 12: if (SNstart = 0) then 13: Loss of CHDAT A from SNID = 0 to SNstart − 1; 14: else 15: S ← (SN status from P ayloadid of CHDAT A ); 16: i = 1; 17: while (i