Fuzzy based energy efficient sensor network protocol for precision agriculture

Computers and Electronics in Agriculture 130 (2016) 20–37

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Original papers

Fuzzy based energy efficient sensor network protocol for precision agriculture Sonam Maurya, Vinod Kumar Jain ⇑ PDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Dumna Airport Road, P.O. Khamaria, Jabalpur, M.P., India

a r t i c l e

i n f o

Article history: Received 15 February 2016 Received in revised form 24 September 2016 Accepted 24 September 2016

Keywords: WSN Fuzzy logic Energy efficient Periodic Threshold sensitive Hybrid routing

a b s t r a c t Today’s, wireless sensor network have become a more emerging technology in precision agriculture. This paper proposes a novel approach based on sensor network technology to control the irrigation in agricultural field automatically. All sensor nodes deployed in the field, continuously sense soil temperature, soil moisture and air humidity of the agricultural field and transmit this information to base station only when the user defined periodic timer or sensed attributes values exceed desired threshold. In the proposed routing protocol, region-based static clustering approach is used to provide efficient coverage over entire agricultural area and threshold sensitive hybrid routing is used for transmitting sensed data to base station. The proposed protocol uses fuzzy logic technique to select the best cluster head among other sensor nodes in a particular round which minimizes the energy consumption of nodes in every data transmission period. The proposed energy-efficient protocol is compared with existing benchmark EEHC, DEEC, DDEEC and RBHR routing protocols. The analysis and experimental results show a significant decrement in data transmission rate due to user-defined transmission thresholds. The balanced use of fuzzy logic technique, static clustering and hybrid routing approach efficiently reduce energy consumption of sensor nodes in every data transmission round and prolongs the overall network lifetime by 173.16%, 149.22%, 99.49% and 47.39% as compared to EEHC, DEEC, DDEEC and RBHR protocol respectively. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Over the recent years, wireless sensor network (WSN) technologies have a wide range of application domains such as military applications, precision agriculture, environmental monitoring, health-care applications, industry applications and time critical applications discussed in Zheng and Jamalipour (2009). For these applications, designing a reliable and energy-efficient communication protocol are the most important challenge. The energy consumption in transmitting data from source node to base station (BS) mainly affects the sensor network lifetime. A wireless sensor networks consists of hundred or thousands of battery-powered sensor nodes that are randomly deployed in a large geographical area. The deployed sensor nodes are able to collect large amounts of data/information and then communicate it to a central authority. Fig. 1 shows the architecture of WSN deployed for precision agriculture. The sensor nodes in the network communicate among themselves by using the RF (radio-frequency) links of ISM (Industrial, scientific, and medical) radio bands (such as 902–928 MHz ⇑ Corresponding author. E-mail address: [email protected] (V.K. Jain). http://dx.doi.org/10.1016/j.compag.2016.09.016 0168-1699/Ó 2016 Elsevier B.V. All rights reserved.

and 2.4–2.5 GHz). Generally, a BS is also deployed in the network with the sensor nodes to make a connection between the WSN and outside world. Thus, the BS is equipped with both RF and GSM (Global System for Mobile communications) or GPRS (General Packet Radio Service). A remote user can monitor the field conditions, and control the actuator devices and sensor nodes. For example, a user can switch on/off a valve/pump when the water level applied to the field reaches to some predefined threshold value. The WSNs in agriculture domain may bring out the important contribution to precision agriculture. In precision agriculture, various parameters like environment temperature, soil type, moisture, humidity, air pressure, etc. vary rapidly from one region to another region and these parameters affect the quantity, cost and quality of production. Therefore, we need to design some decision based automatic tools or techniques for applying the right amount of input (water, fertilizers, pesticides, etc.) at the right time and at a right location to improve the production quality of crops, discussed in Awasthi and Reddy (2013). In the modern automatic irrigation system, the farmers do not need to continuously monitor the agriculture field to acquire information about the field. This monitoring can be done automatically by collecting the real-time data of soil, weather and air quality in a more accurate manner by using

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21

Internet

Database and Server

Remote User

Base Station Sensor node

Agricultural field Fig. 1. Wireless sensor network deployed for precision agriculture.

sensors. A predictive analysis is required to make smarter decision so that the irrigation can take place automatically only when there will be intense requirement of water. For this purpose, periodic threshold sensitive region-based hybrid routing protocol is proposed in this paper. The proposed protocol is an energy-efficient protocol in which monitoring of the field can be done automatically in a more efficient way by using sensors and the user defined thresholds help to save energy and time of farmers from continuous monitoring of field to find out a particular area where irrigation is required. The main objective of this paper is to provide an energy-efficient routing protocol for automated irrigation system. Therefore, we propose a novel fuzzy-based energy-efficient routing protocol for precision agriculture in this paper. Some key features of the proposed routing protocol are highlighted as follows:
The proposed protocol uses a region-based static clustering approach, for providing efficient coverage over the large geographical area where the entire network field is divided into fixed regions in such a way that each region contains different types of heterogeneous sensor nodes.
The periodic threshold sensitive hybrid routing is used for transmitting data to BS when the information about the field is required. For this purpose, user defined data transmission thresholds (i.e. rigid threshold, mild threshold and periodic timer) are used and once the environmental parameters value crosses the threshold limit, sensor nodes send data packets to BS. Therefore, these thresholds help to reduce the number of transmission to BS.
In the network, heterogeneous types of sensor nodes are considered where some nodes have less energy as compared to other nodes, send their data directly to the BS while other high energy nodes, use fuzzy based cluster head technique for transmitting their data to BS. This helps to minimize the energy consumption of sensor nodes in every data transmission period and improves the network lifetime.

The rest of the paper is organized as follows: design issues and literature review have been discussed in Section 2, and Section 3 introduces proposed protocol in detail, a comparative study of simulation based results are presented in Section 4 and finally we provide conclusion and future work related to our proposed protocol in Section 5.

2. Design issues and literature review The major challenges of WSNs are to develop low-cost energyefficient routing protocols with simple operation and high reliability. In recent years, wireless technologies have a growing interest in precision agriculture. Rawat et al. (2014) provides an exhaustive survey on recent developments and major challenges to facilitate the deployment of sensor network technology in real world scenario. Ojha et al. (2015) studied the current state of art in WSNs and their applicability in farming and precision agriculture applications. The authors analyzed the problems of the existing precision agriculture applications with detailed case studies for global as well as for the Indian scenarios. Sudha et al. (2011) provided various different data transmission methods based on TDMA (Time Division Multiple Access) scheduling for an automatic irrigation system. Different data aggregation methods were used in order to increase energy efficiency of the network. Zhou et al. (2009) designed a star-based intelligent system using ZigBee technology for the irrigation purpose. The micro-controller used in that system was supposed to be powered by the external power supply and sensor nodes were mostly in sleep mode. Kalaivani et al. (2011) provided an outline of distinct ZigBee-based WSNs used for the precision agriculture. They have identified various issues regarding ZigBee in agriculture domain; i.e. how the factors such as base station antenna height, node spacing, and density of leaves affect the signal strength. Nikolidakis et al. (2015) proposed a scheme based on the collaboration of an integrated system for automated irrigation management with an advanced novel routing protocol for WSNs.

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Li and Shen (2013a,b) presented energy-efficient WSN system for black-pepper crop monitoring in tropical areas. To provide reliable communication among the nodes, author optimized the BS antenna height. In the developed protocol, symmetrical double chain topology was used to improve the sensor network lifetime. The main drawback of this approach was that sensor nodes sent their data continuously to BS which used to consume lots of energy in data transmission. Vellidis et al. (2008) developed smart sensor array for measuring soil and air temperature for irrigation in cotton field. The designed system was able to reliably monitor soil water status. The authors did not pay attention towards the development of energy-efficient protocol. Morais et al. (2008) designed a ZigBeebased multi-powered wireless power management subsystem where sensor nodes battery was recharged with energy harvested from the surrounding environment. So, most of the routing protocols developed for precision agriculture are mainly concerned with the high reliability, and they do not consider to save the energy of a sensor node. The most of the routing protocols developed for precision agriculture also do not provide an efficient solution for the farmers, which enables to take necessary action when irrigation needs to be started in the field. One of the most important issues in WSNs is to develop an energy-efficient communication protocol. There are so many energy-efficient cluster based routing protocols such as ‘LEACH’ developed by Heinzelman et al. (2000), ‘TEEN’ by Manjeshwar and Agrawal (2001), ‘APTEEN’ by Manjeshwar and Agrawal (2002), ‘HEED’ by Younis and Fahmy (2004), ‘JCOCR’ by Ge et al. (2008), ‘CDMMP’ by Li et al. (2008), ‘ELCH’ by Lotf et al. (2008), ‘EECFP’ by Allirani and Suganthi (2009), ‘DHAC’ by Lung and Zhou (2010), routing protocol by Wei and Zhang (2010), routing protocol by Wei et al. (2010), ‘DCD’ by Doddapaneni et al. (2014) and ‘ECHERP’ by Nikolidakis et al. (2015) exist in the literature but most of these routing protocols are not suitable for precision agriculture because it does not provide efficient coverage over the entire agricultural area. In these protocols, sensor nodes are deployed randomly over the entire network area, and if more sensor nodes are deployed far away from BS then there would be some areas left unattended in the field which creates a coverage hole in the network discussed in Zhu et al. (2012) and Ammari and Das (2012). Since for efficient irrigation in the field, users/farmers require information from each and every part of the field. Therefore, the routing protocol must provide efficient coverage over the entire agricultural field. For this, every part of the field must be covered (or sensed) by some sensor nodes. Most of these routing protocols do not ensure the proper utilization of node’s energy because the selection of a cluster head (CH) node was random or based on residual energy only. These protocols were developed to basically perform in homogeneous environment. But in real scenario, different applications demand heterogeneous sensor nodes for data acquisition. Some protocols such as ‘SEP’ developed by Smaragdakis et al. (2004), ‘DEEC’ by Qing et al. (2006), ‘E-SEP’ by Aderohunmu and Deng (2009), ‘EEHC’ by Kumar et al. (2009b), ‘EECHE’ by Kumar et al. (2009a), ‘DDEEC’ by Elbhiri et al. (2010), ‘EDFCM’ by Zhou et al. (2010), ‘EEERP’ by Khalil and Attea (2011) and ‘ERP’ by Bara and Khalil (2012) provide an efficient way to increase the reliability and network lifetime by using some heterogeneous sensor nodes in the network. But most of the energy-efficient heterogeneous routing protocols also suffer from the same problem. It does not guarantee efficient coverage over the entire network area and only residual energy is the primary concern for the selection of CH nodes. To provide efficient coverage over entire area, a Region-Based Hybrid Routing (RBHR) protocols have been proposed in Maurya and Daniel (2014a,b, 2015) for gathering and processing information from the whole network. In RBHR protocol, the outer regions are not fairly divided into equal area. The protocols are given with

two level of heterogeneity of sensor nodes that continuously transmit a huge amount of data packets to BS which consumes lots of energy in continuous data transmission.

3. Proposed protocol The detailed explanation and working principle of proposed protocol are discussed in this section. Energy efficiency is the most important issue when designing communication protocol for WSN where some applications (such as military applications, health-care applications and traffic management) demand continuous information from the network field; while some applications (such as precision agriculture and industrial applications) demand information from the network only when it is necessary. Therefore in this paper, an energy-efficient periodic threshold sensitive region-based hybrid routing protocol is proposed for the efficient irrigation in the agricultural field. The proposed protocol is the most suitable energy-efficient routing protocol for precision agriculture. 3.1. Assumptions Before going into the details of proposed protocol some assumptions about the network are made as:
Network architecture uses region-based static clustering throughout the network lifetime.
In the network, all the sensor nodes and BS are stationary after deployment.
BS is located at the center and has no energy constraints.
BS is within the range of each sensor node.
All the sensor nodes are aware of their own locations. The location of the nodes can be achieved by satellite based GPS system (Global Positioning System) or by using any energy-efficient localization techniques for WSNs proposed in the literature Niculescu (2004), Gopakumar and Jacob (2008), etc.
Three types of heterogeneous sensor nodes are considered for sensing and transmitting data to BS. 3.2. System composition In this paper, a well-suited network model is considered for precision agriculture. The proposed model uses threshold sensitive hybrid routing approach to measure three environment parameters essential for irrigation; first is the soil temperature, second one is the soil moisture content of the field and third is the air humidity which are important for the efficient irrigation. According to the temperature, moisture and humidity value of the agricultural field, an automatic decision can be taken as when irrigation needs to be started which enables the farmers to increase yields and the quality of crops by maintaining proper water in the field during critical plant growth stages. The basic scenario of the sensor network consists of a sensor node and base station.
Sensor node: Three types of heterogeneous sensor nodes as Type-1, Type-2 and Type-3 nodes are considered in the network for sensing and transmitting information to BS. In a heterogeneous sensor network, two or more different types of nodes with different functionality in terms of sensing, storage, computation, communication and energy capacity are used as discussed in Mhatre and Rosenberg (2004) and Kumar et al. (2009b). In the proposed protocol, each different types of sensor nodes in the network (i.e. Type-1, Type-2 and Type-3 nodes) consists of a soil temperature sensor, a soil moisture sensor and air humidity sensor. Fig. 2 shows the block diagram of a sensor node. The sensor

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Fig. 2. Sensor node.

3.3. Region-based node deployment

nodes are deployed for sensing and transmitting the soil temperature, soil moisture and air humidity information of the agricultural field to a BS.
Base station: The base station (BS) consists of a transceiver, processor, and LCD display. BS collects the information about the temperature, moisture and humidity value of the field so that an end user can take a necessary action by analyzing these informations.

The benefits of using region based static clustering are discussed in Maurya and Daniel (2014b). We have considered 2

(A  A) unit agricultural field where BS is located at the center position of the field. In the network, three types of heterogeneous sensor nodes are deployed for sensing and transmitting the information about soil temperature, soil moisture and humidity of the agricultural field. To overcome the coverage hole problem and for providing efficient coverage to the entire agricultural field, region-based static clustering is used where the total area is divided into nine fixed regions as shown in Fig. 3. Here we are considering the specific land area, so the division of all the regions can be easily specified through field measurement. The region division can be done on the basis of the available number of different types of sensor nodes in the network and their range of sensing. Since we have assumed that BS is located at the center then corner’s area should be fairly divided. So, the outermost and middle region’s area is equally divided among four-four regions (i.e. REG-1, REG-2, REG-3 and REG-4 regions have equal area for outermost region and REG-5, REG-6, REG-7 and REG-8 regions have equal area for middle region). Thus, available number of Type-2 and Type-3 nodes are also equally divided. In case of non-uniform distribution of Type-2 and Type-3 nodes, overhead on a CH node of a particular outer/middle region which

Since the high energy sensor node costs higher, so in the network we have considered three types of sensor nodes in different-different numbers (as used in EEHC protocol). In the proposed protocol, the energy level, sensing range and transmitting range for each Type-1, Type-2 and Type-3 sensor nodes are different. We have considered that the higher energy sensor nodes have higher sensing and transmitting range. Generally, the radio transmission range is at least twice the sensing range as discussed in Zhang and Hou (2005). Type-1 node’s energy < Type-2 node’s energy < Type-3 node’s energy. Type-1 sensing range < Type-2 sensing range < Type-3 sensing range. Type-1 node’s transmission range < Type-2 node’s transmission range < Type-3 node’s transmission range.

(20, 100)

(0, 100)

(100, 100)

(40, 80)

REG-1 Type-2 Nodes

(80, 80)

(20, 80)

(100, 80) REG-5 Type-3 Nodes

(60, 60) (80, 60)

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REG-3 Type-2 Nodes

REG-9 Type-1 Nodes

REG-7 Type-3 Nodes

BS (50, 50)

(20, 40)

REG-8 Type-3 Nodes

REG-4 Type-2 Nodes

(60, 40) (40, 40)

REG-6 Type-3 Nodes

(80, 20)

(0, 20) (20, 20)

REG-2 Type-2 Nodes

(60, 20)

(80, 0)

(0, 0) Fig. 3. Region-based node deployment.

(100, 0)

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has more number of Type-2/Type-3 nodes may increase as compared to other outer/middle region’s CH node. The more a node is far from BS, the more it will consume energy in data transmission. It means the outermost region’s (REG-1, REG-2, REG-3 and REG-4) nodes that are very far from BS need more energy to transmit data to BS as compared to middle region’s (REG-5, REG-6, REG-7 and REG-8) nodes and innermost region’s (REG-9) nodes. Therefore, Type-3 nodes having more energy than Type-1 and Type-2 nodes should be placed in outermost regions, Type-2 nodes having energy less than Type-3 nodes but greater than Type-1 nodes should be placed in middle regions and finally the Type-1 nodes having very less energy should be deployed in innermost region. But, in the proposed network model, we have deployed Type-3 nodes in the middle regions, Type-2 nodes in the outermost regions and Type-1 nodes in the innermost region. The reason why we have deployed in this manner is clearly explained in next Section 3.4. The regionbased node deployment algorithm for proposed protocol is given in Algorithm 1.

In Algorithm 1, n is the total number of sensor nodes (all types) in the entire network, mo is the total number of Type-2 nodes in the network and m1 is the total number of Type-3 nodes in the network. 2

All these sensor nodes are deployed over the A  A unit agricultural area. The Type-1, Type-2 and Type-3 nodes are uniformly deployed in their respective regions. According to the deployment of the sensor nodes, a WSN may be classified into the deterministic or non-deterministic sensor network. In a deterministic network, the locations/positions of the sensor nodes are preplanned and static after the deployment. This type of sensor network may be used where the preplanned deployment of the nodes are possible. But, in most of the situations, such as in harsh or hostile environments the deployment of nodes in a preplanned manner is difficult. Therefore, the nodes are randomly deployed over the field without pre-planning and engineering. Obviously, the non-deterministic sensor networks are more flexible and scalable, but it requires higher control complexity discussed in Zheng and Jamalipour (2009). For very large agricultural field where preplanned deployment is difficult, the nodes can be randomly deployed over the field.

Algorithm 1. Region-based node deployment algorithm Input: A; n; m0 ; m1 Output: NodeType , deployed sensor node in A  A area Initialization: Field area = A  A, BS position = (A=2)  (A=2), NodeREG1 ¼ NodeREG2 ¼ NodeREG3 ¼ NodeREG4 ¼ m0 =4, NodeREG5 ¼ NodeREG6 ¼ NodeREG7 ¼ NodeREG8 ¼ m1 =4, NodeREG9 ¼ n  ðm0 þ m1 Þ 1:// For Type-2 node deployment in Region-1 (i.e. REG-1) 2: for i = 1 to NodeREG1 do 3: NodeType (i) = ‘Type-2’ 4: Type-2 sensor nodes are uniformly deployed in REG-1 which has an area (A=5)  (4  ðA=5Þ) 5: end for 6:// For Type-2 node deployment in Region-2 (i.e. REG-2) 7: for i = NodeREG1 þ 1 to NodeREG2 do 8: NodeType (i) = ‘Type-2’ 9: Type-2 sensor nodes are uniformly deployed in REG-2 which has an area (A=5)  (4  ðA=5Þ) 10: end for 11:// For Type-2 node deployment in Region-3 (i.e. REG-3) 12: for i = NodeREG1 þ NodeREG2 þ 1 to NodeREG3 do 13: NodeType (i) = ‘Type-2’ 14: Type-2 sensor nodes are uniformly deployed in REG-3 which has an area (A=5)  (4  ðA=5Þ) 15: end for 16:// For Type-2 node deployment in Region-4 (i.e. REG-4) 17: for i = NodeREG1 þ NodeREG2 þ NodeREG3 þ 1 to m0 do 18: NodeType (i) = ‘Type-2’ 19: Type-2 sensor nodes are uniformly deployed in REG-4 which has an area (A=5)  (4  ðA=5Þ) 20: end for 21:// For Type-3 node deployment in Region-5 (i.e. REG-5) 22: for i = m0 þ 1 to NodeREG5 do 23: NodeType (i) = ‘Type-3’ 24: Type-3 sensor nodes are uniformly deployed in REG-5 which has an area (A=5)  (2  ðA=5Þ) 25: end for 26:// For Type-3 node deployment in Region-6 (i.e. REG-6) 27: for i = m0 þ NodeREG5 þ 1 to NodeREG6 do 28: NodeType (i) = ‘Type-3’ 29: Type-3 sensor nodes are uniformly deployed in REG-6 which has an area (A=5)  (2  ðA=5Þ) 30: end for 31:// For Type-3 node deployment in Region-7 (i.e. REG-7) 32: for i = m0 þ NodeREG5 þ NodeREG6 þ 1 to NodeREG7 do

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33: NodeType (i) = ‘Type-3’ 34: Type-3 sensor nodes are uniformly deployed in REG-7 which has an area (A=5)  (2  ðA=5Þ) 35: end for 36:// For Type-3 node deployment in Region-8 (i.e. REG-8) 37: for i = m0 þ NodeREG5 þ NodeREG6 þ NodeREG7 þ 1 to m1 do 38: NodeType (i) = ‘Type-3’ 39: Type-3 sensor nodes are uniformly deployed in REG-8 which has an area (A=5)  (2  ðA=5Þ) 40: end for 41:// For Type-1 node deployment in Region-9 (i.e. REG-9) 42: for i = m1 þ 1 to n do 43: NodeType (i) = ‘Type-1’ 44: Type-1 sensor nodes are uniformly deployed in REG-9 which has an area (A=5)  (A=5) 45: end for

3.4. Periodic threshold-sensitive hybrid routing The proposed protocol is threshold sensitive protocol because nodes sense the environment parameters continuously but data transmission occurs only when the sensed value is in the range of interest. This helps the farmers/end users to take decision about irrigation. But, there is a limitation of threshold sensitivity, if the sensed value does not reach to set thresholds, nodes will never send any information to BS and the farmer will not come to know about the field conditions. So, we have proposed a periodic data transmission scheme in which data must be transmitted after a fixed time interval. In this case, the BS will receive information about field condition after every fixed time interval either the sensed value crosses threshold or not. Hence, the data transmission will not depend only upon the threshold limits, but it will also depend on

the periodic timer. The periodic threshold sensitive hybrid routing algorithm is given in Algorithm 2. The benefits of using hybrid routing approach for data transmission is discussed in Maurya and Daniel (2015). In the proposed protocol, all the Type-1 nodes deployed in REG-9 (i.e. Region-9) transmit their data directly to BS whereas Type-3 and Type-2 nodes deployed in middle and outermost regions respectively transmit their data via the CH node as shown in Fig. 4. If we allow Type-1 nodes to become CH, they will die soon because of energy dissipation in data aggregation is more as compared to direct data transmission. If Type-2 or Type-3 nodes which are within the range of the BS but very far from BS and send its data directly to BS, then node will consume more energy in direct data transmission because the node will have to transmit a data packet with high transmission power given in Heinzelman et al. (2000). So, to save the energy of

REG-3 REG-1 REG-4

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REG-7

Base Staon

REG-6

REG-8

REG-2

Type-2 Node

Type-3 Node

CH Node Fig. 4. Hybrid routing.

Type-1 Node

Base Station

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Type-2 and Type-3 nodes, clustering technique is used for Type-2 and Type-3 nodes only. This leads to the overall improvement in network lifetime. In the proposed protocol, Type-3 nodes having highest energy are deployed in middle regions instead of outermost regions because they have to perform additional data aggregation task from outermost region’s CH node as well as have to perform data aggregation from its own members. If we deploy Type-2 nodes in middle regions instead of Type-3 nodes then definitely all the Type-2 nodes will die soon as compared to Type-3 nodes because Type-2 nodes have less amount of energy as well as they have to perform additional data aggregation task. After the death of all Type-2 nodes, a coverage hole in the middle regions will get created. And due to coverage hole in middle regions the sensor nodes in outermost regions will not be able to communicate with BS in spite of being alive. Therefore, coverage hole in outermost regions also get created. So, to get rid of coverage hole problem Type-3 nodes are deployed in middle regions. The proposed protocol considers four modes of single-hop communication: (i) Communication between CH and CM (cluster member). (ii) Communication between outermost region’s CH node and middle region’s CH node. (iii) Communication between middle region’s CH node and BS. (iv) Communication between Type-1 nodes and BS. Algorithm 2. Threshold-sensitive hybrid routing algorithm Input: NodeType ; NodeEnergy , CTV; CMV; CHV; T 1 ðTÞ; T 1 ðMÞ; T 1 ðHÞ; T 2 ðTÞ; T 2 ðMÞ; T 2 ðHÞ; PT Output: Packet BS ; dead; dead1 ; dead2 ; dead3 Initialization: Packet BS ¼ 0, Packet CH ¼ 0, dead = 0, dead1 ¼ 0, dead2 ¼ 0, dead3 ¼ 0 1: for i = 1 to n do 2: // For Type-1 node 3: if NodeType (i) = ‘Type-1’ && NodeEnergy (i) > 0 then 4: if CTV P T 1 ðTÞ k CMV 6 T 1 ðMÞ k CHV 6 T 1 ðHÞ k (jCTVSTVj) P T 2 ðTÞ k (jCMV-SMVj) P T 2 ðMÞ k (jCHV-SHVj) P T 2 ðHÞ k PT expires then 5: Innermost region’s node sends a data packets to BS directly via single-hop communication 6: PacketBS ¼ Packet BS þ 1 7: NodeEnergy (i) = update node’s energy as per Eq. (2) 8: end if 9: end if 10: if NodeType (i) = ‘Type-1’ && NodeEnergy (i) 6 0 then 11: dead1 ¼ dead1 þ 1 12: end if 13: // For Type-2 node 14: if NodeType (i) = ‘Type-2’ && NodeEnergy (i) > 0 then 15: Region-wise CH selection using fuzzy logic technique 16: if NodeType (i) ! = ‘CH’ then 17: if CTV P T 1 ðTÞ k CMV 6 T 1 ðMÞ k CHV 6 T 1 ðHÞ k (jCTVSTVj) P T 2 ðTÞ k (jCMV-SMVj) P T 2 ðMÞ k (jCHV-SHVj) P T 2 ðHÞ k PT expires then 18: CM node sends a data packet to its own CH node 19: NodeEnergy (i) = update node’s energy as per Eq. (2) 20: end if 21: end if 22: if NodeType (i) = ‘CH’ then 23: if CTV PT 1 ðTÞ k CMV 6 T 1 ðMÞ k CHV 6 T 1 ðHÞ k (jCTVSTVj) P T 2 ðTÞ k (jCMV-SMVj) P T 2 ðMÞ k (jCHV-SHVj) P T 2 ðHÞ k PT expires then

24: 25:

Data aggregation by outermost region’s CH node CH node from outermost region sends a packet to middle region’s CH node via single-hop communication 26: NodeEnergy (i) = update node’s energy as per Eq. (1) 27: end if 28: end if 29: end if 30: if NodeType (i) = ‘Type-2’ && NodeEnergy (i) 6 0 then 31: dead2 ¼ dead2 þ 1 32: end if 33: // For Type-3 node 34: if NodeType (i) = ‘Type-3’ && NodeEnergy (i) > 0 then 35: Region-wise CH selection using fuzzy logic technique 36: if NodeType (i) ! = ‘CH’ then 37: if CTV P T 1 ðTÞ k CMV 6 T 1 ðMÞ k CHV 6 T 1 ðHÞ k (jCTVSTVj) P T 2 ðTÞ k (jCMV-SMVj) P T 2 ðMÞ k (jCHV-SHVj) P T 2 ðHÞ k P T expires then 38: CM node sends a data packet to its own CH node 39: NodeEnergy (i) = update node’s energy as per Eq. (2) 40: end if 41: end if 42: if NodeType (i) = ‘CH’ then 43: if CTV P T 1 ðTÞ k CMV 6 T 1 ðMÞ k CHV 6 T 1 ðHÞ k (jCTVSTVj) P T 2 ðTÞ k (jCMV-SMVj) P T 2 ðMÞ k (jCHV-SHVj) P T 2 ðHÞ k P T expires then 44: Data aggregation by middle region’s CH node 45: Middle region’s CH node sends a data packet to BS via single-hop communication 46: Packet BS ¼ PacketBS þ 1 47: NodeEnergy (i) = update node’s energy as per Eq. (1) 48: end if 49: end if 50: end if 51: if NodeType (i) = ‘Type-3’ && NodeEnergy (i) 6 0 then 52: dead3 ¼ dead3 þ 1 53: end if 54: end for 55: dead = dead1 þ dead2 þ dead3

In the proposed protocol, only one CH node is selected from each outermost and middle region. Therefore, for data transmission total eight CHs are selected in every round as shown in Fig. 4. The number of CH nodes within a region can be decided according to the availability of Type-2 and Type-3 nodes. In the network, if there are more number of CHs then it consumes more energy in data aggregation because a node requires more energy in data aggregation as compared to sensing and transmitting a data packet to BS (see Table 3). Therefore, the selection of number of CH nodes in each cluster should ensure proper load balancing in each outer/middle region. In the proposed protocol, since the number of Type-2 and Type-3 nodes in a particular outer and middle region respectively are very less as compared to innermost region’s nodes, so to save the data aggregation energy of a CH node, we have chosen only one CH from each outer and middle region. Once the CH is decided in a particular round, other CM nodes send their data to its own CH node (as shown in Fig. 4) and then after aggregation, a CH node forward the data to lower layer CH/BS. Three data transmission thresholds used in this paper are defined as follows:

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Rigid threshold (T 1 ): This is the threshold value for sensed attributes beyond which the nodes sensing this value must transmit data to BS.
Mild threshold (T 2 ): This is the threshold value for small change in the sensed attributes which triggers the sensor nodes to transmit data to BS.
Periodic timer (PT ): Inter-arrival time between two successive data transmission to BS. The rigid threshold (T 1 ), mild threshold (T 2 ) and periodic timer (P T ) can be modified by end user at any time according to crop water needs during the sensing process. These thresholds are mainly used for transmitting information to BS and it can be chosen based on the weather conditions and the requirement of water for a particular crop which is different for different crops. When there is a need for changing these thresholds value as per the requirement of water for a particular crop then the BS broadcasts these thresholds in the network. Therefore, the user can set these thresholds according to the need of irrigation in the agriculture field. The crop water needs discussed in FAO (1986) mainly depends on:
Crop type: crops like sugarcane or maize need more water than the crops like sorghum or millet.
Climate: in a hot and sunny climate, crops need more water per day than in a cool and cloudy climate.
Growth stage of the crop: fully grown crops need more water than the crops that have just been planted. The sensed values of temperature, moisture and humidity are stored in an internal variable called sensed temperature value (STV), sensed moisture value (SMV) and sensed humidity value (SHV). All the sensor nodes will transmit data in current round, only when either of the following conditions are satisfied: (i) The current temperature value (CTV) is greater than or equal to Rigid Threshold T 1 ðTÞ or current moisture value (CMV)/ current humidity value (CHV) is less than or equal to the Rigid Threshold T 1 ðMÞ/T 1 ðHÞ respectively. (ii) The absolute difference value (CTV-STV) or (CMV-SMV) or (CHV-SHV) are greater than or equal to Mild Threshold T 2 ðTÞ or T 2 ðMÞ or T 2 ðHÞ respectively. (iii) Periodic Timer (P T ) expires. In every round, CTV, CHV and CMV will sense by each sensor nodes. Whenever a node transmits data, CTV, CMV and CHV are set to sensed temperature value (STV), sensed moisture value (SMV) and sensed humidity value (SHV) respectively. 3.5. Efficient cluster head selection using fuzzy logic Initially, the BS broadcasts a TDMA schedule and requests all the sensor nodes to advertise themselves. Then all the nodes transmit their IDs, initial energy and location information to BS. After that the BS selects the best CH node from each middle and outermost regions using fuzzy logic then BS broadcasts the IDs and location information of the new CHs and the user defined data transmission thresholds (i.e. rigid threshold, mild threshold, periodic timer). All the sensor nodes in the network store this information in their internal memory. In this paper, we have assumed that BS is within the range of each sensor node, so that nodes can transmit information about the field conditions and its own initial information (i.e. IDs, initial energy and location information) to BS via single-hop communication (as shown in Fig. 4). But, in the case of very large field, where BS is very far from the CH node then the CH node can communicate to BS via multi-hop communication by selecting multiple optimum CHs in each region. In the network, each node

will try to find nearest neighbor CH node towards the BS to send their sensed data. If the CH node is within the range of a source node, then the source node will transmit data to the CH node directly otherwise the source node will transmit data by selecting highest energy sensor node towards the BS. The same approach will be applied for communication between outer region’s CH node and middle region’s CH node. This multi-hop communication approach will also minimize the energy consumption of the network as single-hop communication approach. But as per the assumptions, this work consider only four modes of single-hop communication as discussed in Section 3.4. The fuzzy logic technique is used to improve proper balancing of energy dissipation among the Type-2 nodes and Type-3 nodes. In every round, BS considers two parameters for CH selection such as; node’s distance from BS (for selecting CH node from middle regions)/node’s distance from CH (for selecting CH node from outermost regions) and residual energy of each node. In a particular round, as soon as the BS found better candidate for the CH node selection than the previously selected CH, then BS broadcasts the IDs and location information of newly selected CH node. The two input functions, distance and residual energy of a sensor node are transformed into fuzzy sets P and Q respectively defined as follows:


 P ¼ ðdist; lP ðdistÞ

n o Q ¼ ðene; lQ ðeneÞ where P and Q are defined as a universe of discourse for ‘Distance from BS/Distance from CH’ and for ‘Residual Energy’ of a sensor node respectively. lP ðdistÞ and lQ ðeneÞ are membership functions for distance and residual energy input variable respectively. (dist) and (ene) are the particular element of set P and Q respectively. The first-order radio energy model for 3-level heterogeneous wireless sensor network described in Kumar et al. (2009b) is used for calculating energy dissipation of each CH and CM node. The energy dissipated in a CH node during a particular round is calculated as:

(

enCH ¼

2

N  ðEelec þ EDA Þ þ Efs  N  ðdist BS Þ ;

if dist BS 6 d0 4

N  ðEelec þ EDA Þ þ Eamp  N  ðdist BS Þ ; if dist BS > d0 ð1Þ

where N is the packet size and Eelec is the per bit energy dissipation in running the transmitter or receiver circuit of a sensor device. Efs is the energy consumed by the amplifier to transmit over a shorter distance, Eamp is the energy consumed by the amplifier to transmit at a pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi longer distance and d0 ¼ Efs =Eamp . EDA is the data aggregation energy and distBS is the average distance between a CH node and BS. The energy dissipated by CM node during a particular round is equal to:

( enCM ¼

2

N  Eelec þ Efs  N  ðdist CH Þ ;

if dist CH 6 d0 4

N  Eelec þ Eamp  N  ðdist CH Þ ; if dist CH > d0

ð2Þ

where distCH is the average distance between a CM node and its CH node. The total initial energy of entire heterogeneous network (E) is defined in Eq. (3).

E ¼ n  Eo  ð1 þ mo  ða þ m1  bÞÞ

ð3Þ

where Eo is the initial energy of Type-1 nodes. In the network, a and b are the energy factor i.e. Type-3 nodes have b times more energy than Type-1 nodes, and Type-2 nodes have a times more energy than Type-1 node. The value for all the other variables defined in Eq. (3) are given in Table 3. Now, the degree of membership based on the ‘‘Node’s distance from BS” in case of Type-3 node or ‘‘Node’s distance from CH” node in case of Type-2 node is calculated as:

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Fig. 5. Membership function for ‘‘Distance-from-BS/distance-from-CH”.

Fig. 6. Membership function for ‘‘Residual energy”.

8 if dist 6 TH1 > < 1; lP ðdistÞ ¼ ðdist  TH1 Þ=ðTH2  TH1 Þ; if TH1 < dist < TH2 > : 0; if dist P TH2 The degree of membership based on the ‘‘Residual Energy” of each Type-2 and Type-3 node is calculated as:

8 if ene 6 TH3 > < 0; lQ ðeneÞ ¼ ðene  TH3 Þ=ðTH4  TH3 Þ; if TH3 < ene < TH4 > : 1; if ene P TH4 where TH1 = Minimum threshold for distance-from-BS/distance-fromCH parameter. TH2 = Maximum threshold for distance-from-BS/distance-fromCH parameter. TH3 = Minimum threshold for residual energy variable. TH4 = Maximum threshold for residual energy variable. These TH1 ; TH2 ; TH3 , and TH4 thresholds are used for CHs selection among Type-2 nodes and among Type-3 nodes. After calculating degree of membership, AND (^) fuzzy operator is used to find the fuzzy relation between membership parameters as,

n

o lP ðdistÞ ^ lQ ðeneÞ ¼ min lP ðdistÞ; lQ ðeneÞ (

lP ðdistÞ; if lP ðdistÞ 6 lQ ðeneÞ lP ðdistÞ ^ lQ ðeneÞ ¼ lQ ðeneÞ; if lQ ðeneÞ < lP ðdistÞ Graphical representation of a membership function based on the node distance from BS/distance from CH is shown in Fig. 5 and graphical representation of a membership function based on residual energy is shown in Fig. 6.

3.5.1. Rule evaluation for efficient cluster head selection The process of CHs selection is according to the precedence order of distance and residual energy of each Type-2 and Type-3 node. Table 1 shows three fuzzy membership functions for each input parameters (i.e. ‘Near’, ‘Considerable’ and ‘Far’ for distance parameter and ‘Low’, ‘Medium’ and ‘High’ for energy parameter). Table 2 defines all the possible combinations of different membership functions for the three input variables that result in nine logical rules for the fuzzy inference system. For example, if a node is ‘Near’ to BS and has ‘High’ energy then for CH selection, the node will have highest priority (i.e. P1 ) as well as if a node is ‘Far’ from BS and also has ‘Low’ energy then for CH selection, the node will

Table 1 Input function. Input parameters Distance-from-BS/CH Residual energy

Input membership function Near Low

Considerable Medium

Far High

Table 2 Logical rule sets. Distance-from-BS/CH

Residual energy

Priority

Near Near Near Considerable Considerable Considerable Far Far Far

Low Medium High Low Medium High Low Medium High

P6 P2 P1 P7 P4 P3 P9 P8 P5

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Start

Region Division No

Node Deployment

Alive node?

Yes

T1 or T2 or PT Reached?

No

Node Sends Data to BS

End

Yes Node Senses Data

Type-1 node? No

Yes

No T1 or T2 or PT Reached?

CH Selection

CH node? Yes

Yes

CM Nodes Sends Data to CH

No Data Aggregation by CH node Yes T1 or T2 or PT Reached? No

CH Node Sends Data to BS

Fig. 7. Operation flowchart for proposed protocol.

have lowest priority (i.e. P 9 ). Therefore, a Type-2 and Type-3 node having highest priority (defined in Table 2) among other nodes will be selected as a CH node from each outer and middle region. The priority order for CH selection is defined as follows:

P1 > P2 > P3 > P4 > P5 > P6 > P7 > P8 > P9 In case of priority tie, the precedence order for selecting a CH node is: Distance-from-BS/distance-from-CH > Residual energy. 3.6. Operation flowchart for proposed protocol The operation flowchart for the proposed routing protocol is given in Fig. 7. After the region-wise sensor node deployment, each node senses the environmental parameter continuously if a node is alive. Alive Type-1 nodes send the data packets directly to BS if, sensed parameter values cross the user defined data transmission thresholds and alive Type-2/Type-3 nodes send the data packets via CH node if, sensed parameter values cross the data transmission thresholds. This process repeats until the death of last alive sensor node.

4. Performance analysis We have simulated a heterogeneous clustered WSN model with area dimensions (100  100) m2 using MATLAB. The total 100 (n) sensor nodes (all types) are deployed in 100  100 m2 network area. The entire network area is divided into nine fixed regions and three types of heterogeneous sensor nodes are deployed in these nine regions according to Algorithm 1. The placement/ deployment of sensor nodes in the agricultural applications such as for irrigation depends on the sensing range of the sensors such that each and every part of the agriculture field must be covered (or sensed) by some sensor nodes. The optimum number of the sensor nodes for maintaining coverage and connectivity in the network can be calculated on the basis of the sensing range of the sensor node which is discussed in Zhang and Hou (2005). Therefore, by providing efficient coverage to entire agricultural field, user can decide in which area, irrigation is required. Since, the three types of sensor nodes have different energy level and sensing range, so we have deployed Type-1, Type-2 and Type-3 sensor nodes in different-different numbers. For example, in region-9, since we are deploying Type-1 sensor nodes which have

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S. Maurya, V.K. Jain / Computers and Electronics in Agriculture 130 (2016) 20–37

(0, 100)

(100, 100)

(20, 100)

REGION-1

(80, 80) (100, 80)

(20, 80) REGION-5

REGION-7 REGION-3

dist = 10

dist = 42.43

REGION-4

dist =72.1 REGION-6

dist = 20

(50, 50) REGION-8

(80, 20)

(0, 20) (20 , 20) REGION-2

(0, 0)

(80, 0)

(100, 0)

Fig. 8. Range for distance-from-BS/distance-from-CH variable in case of Type-3 and Type-2 nodes respectively.

Table 3 Simulation parameters. Parameters

Values

Total number of sensor nodes in network (n) Total number of Type-1 nodes in the network area ðn  ðmo þ m1 ÞÞ Total number of Type-2 nodes in network (mo ) Total number of Type-3 nodes in network (m1 ) Total number of Type-2 nodes in Region-1 (REG-1) Total number of Type-2 nodes in Region-2 (REG-2) Total number of Type-2 nodes in Region-3 (REG-3) Total number of Type-2 nodes in Region-4 (REG-4) Total number of Type-3 nodes in Region-5 (REG-5) Total number of Type-3 nodes in Region-6 (REG-6) Total number of Type-3 nodes in Region-7 (REG-7) Total number of Type-3 nodes in Region-8 (REG-8) Initial Energy of a Type-1 node (E0 ) Initial Energy of a Type-2 node (E0  ð1 þ aÞ) Initial Energy of a Type-3 node (E0  ð1 þ bÞ) Energy factor (a) and (b) Total initial energy of the entire network according to Eq. (3) Energy consumed by amplifier to transmit over a longer distance (Eamp ) Energy consumed by amplifier to transmit over a shorter distance (Efs ) Energy consumed in the electronics circuit to transmit or receive the signal (Eelec ) Data aggregation energy (EDA ) Packet size (N) Rigid threshold for soil temperature (T 1 ðTÞ) Mild threshold for soil temperature (T 2 ðTÞ) Rigid threshold for soil moisture (T 1 ðMÞ) Mild threshold for soil moisture (T 2 ðMÞ) Rigid threshold for air humidity (T 1 ðHÞ) Mild threshold for air humidity (T 2 ðHÞ) Periodic timer (P T ) Minimum threshold for ‘Distance-from-CH’ variable in case of Type-2 node (TH1 ) Maximum threshold for ‘Distance-from-CH’ variable in case of Type-2 node (TH2 ) Minimum threshold for ‘Residual energy’ variable in case of Type-2 node (TH3 ) Maximum threshold for ‘Residual energy’ variable in case of Type-2 node (TH4 ) Minimum threshold for ‘Distance-from-BS’ variable in case of Type-3 node (TH1 ) Maximum threshold for ‘Distance-from-BS’ variable in case of Type-3 node (TH2 ) Minimum threshold for ‘Residual energy’ variable in case of Type-3 node (TH3 ) Maximum threshold for ‘Residual energy’ variable in case of Type-3 node (TH4 )

100 36 40 24 10 10 10 10 6 6 6 6 0:3 J 0.6 J 0.9 J a ¼ 1 and b ¼ 2 56.4 J 0.0013 pJ/bit/m4 10 pJ/bit/m2 50 nJ/bit 5 nJ/bit/report 500 byte 35  C 4 C 30% 10% 30% 10% 5s 40 m 55 m 0.24 J 0.42 J 22.5 m 32.5 m 0.36 J 0.63 J

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lowest energy level and sensing range, so to cover entire region-9, we have to deploy more numbers of Type-1 sensor nodes as compared to other region’s sensor nodes. For simplicity and simulation purpose, we have deployed 40% of n as Type-2 nodes (mo ), 60% of mo as Type-3 nodes (m1 ) and rest as Type-1 nodes. These Type-2 and Type-3 nodes are equally and uniformly distributed in each outer and middle regions respectively. The Type-2 nodes distance (dist) from lower layer CH node (i.e. CH node selected from middle regions) must lie between 20 m and 72.1 m and Type-3 nodes distance (dist) from BS must lie between 10 m and 42.43 m as shown in Fig. 8. The residual energy (ene) of Type-1, Type-2 and Type-3

nodes must lie between 0–0.3 J, 0–0.6 J and 0–0.9 J respectively. For the experiment purpose, soil temperature of the field is varying from 0  C to 50  C, soil moisture content of the field is ranging between 0% and 100% and air humidity of the field is varying from 0% to 100%. Table 3 discusses other simulation parameters used for data transmission and CHs selection. The value of rigid and mild threshold for soil temperature (i.e. T 1 ðTÞ ¼ 35  C and T 2 ðTÞ ¼ 4  C respectively) are considered for a maize crop. Maize requires abundant sunlight for optimum yields. The optimum temperature for the growth and development of maize crop is 25–30  C; tem-

Table 4 Protocols comparison in case of Type-2 nodes deployed in middle regions for RBHR and proposed protocol. Performance metrics

EEHC

DEEC

DDEEC

RBHR

Proposed

Increments over EEHC (%)

Increments over DEEC (%)

Increments over DDEEC (%)

Increments over RBHR (%)

Number of rounds after first sensor node dead (Stability Period) Number of rounds after 50% sensor nodes dead Number of rounds after all sensor nodes dead (Network Lifetime) Number of rounds after first Type-1 sensor node dead Number of rounds after 50% Type-1 sensor nodes dead Number of rounds after all Type-1 sensor nodes dead Number of rounds after first Type-2 sensor node dead Number of rounds after 50% Type-2 sensor nodes dead Number of rounds after all Type-2 sensor nodes dead Number of rounds after first Type-3 sensor node dead Number of rounds after 50% Type-3 sensor nodes dead Number of rounds after all Type-3 sensor nodes dead Total number of packets transmitted to BS (Throughput)

859

880

843

1251

1660

93.25

88.64

96.92

32.69

1164

1767

1792

2452

3362

188.83

90.27

87.61

37.11

2771

3135

3808

4940

7094

156.01

126.28

86.29

43.60

859

880

843

2429

3009

250.29

241.93

256.94

23.88

960

1047

928

2479

3399

254.06

224.64

266.27

37.11

1044

1225

1118

2499

4037

286.69

229.55

261.09

61.54

928

1613

1554

1251

1660

78.88

2.91

6.82

32.69

1230

1818

1837

1761

2566

108.62

41.14

39.68

45.71

1695

2050

2251

2631

3707

118.70

80.83

64.68

40.90

1665

2387

2523

1827

2519

51.29

5.53

0.2

37.88

2114

2590

3265

2836

4437

109.89

71.31

35.90

56.45

2771

3135

3808

4940

7094

156.01

126.28

86.29

43.60

6047

119,958

139,274

99,011

57,907

857.62

51.73

58.42

41.51

Table 5 Protocols comparison in case of Type-3 nodes deployed in middle regions for RBHR and proposed protocol. Performance metrics

EEHC

DEEC

DDEEC

RBHR

Proposed

Increments over EEHC (%)

Increments over DEEC (%)

Increments over DDEEC (%)

Increments over RBHR (%)

Number of rounds after first sensor node dead (Stability Period) Number of rounds after 50% sensor nodes dead Number of rounds after all sensor nodes dead (Network Lifetime) Number of rounds after first Type-1 node dead Number of rounds after 50% Type-1 sensor nodes dead Number of rounds after all Type-1 sensor nodes dead Number of rounds after first Type-2 sensor node dead Number of rounds after 50% Type-2 sensor nodes dead Number of rounds after all Type-2 sensor nodes dead Number of rounds after first Type-3 sensor node dead Number of rounds after 50% Type-3 sensor nodes dead Number of rounds after all Type-3 sensor nodes dead Total number of packets transmitted to BS (Throughput)

840

851

777

1064

1408

67.62

65.45

81.21

32.33

1157

1819

1740

2420

3211

177.53

76.53

84.54

32.69

2884

3161

3949

5345

7878

173.16

149.22

99.49

47.39

840

851

777

2405

3112

270.48

265.69

300.51

29.40

986

1032

950

2454

3252

229.82

215.12

242.32

32.52

1077

1217

1065

2948

4063

277.25

233.85

281.50

37.82

955

1643

1593

1064

1408

47.43

14.30

11.61

32.33

1221

1856

1801

1924

2924

139.48

57.54

62.35

51.98

1579

2170

2343

3047

4889

209.63

125.30

108.66

60.45

1750

2362

2462

1450

1780

1.71

24.64

27.70

22.76

2157

2642

3133

1939

2598

20.45

1.67

17.08

33.99

2884

3161

3949

5345

7878

173.16

149.22

99.49

47.39

6108

120,872

137,625

102,130

63,090

932.91

47.80

54.16

38.23

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(a) Overall dead sensor nodes till current round

(c) Overall packets transmitted to BS till current round

(e) Overall residual energy of the network after 2000, 2500 & 3000 rounds

(b) Overall dead Type-1 nodes till current round

(d) Overall dead Type-2 nodes till current round

(f) Overall dead Type-3 nodes till current round

(g) Number of dead nodes after 1000, 2000 & 3000 rounds Fig. 9. Simulation results in case of Type-2 nodes deployed in middle regions for RBHR and proposed protocol.

S. Maurya, V.K. Jain / Computers and Electronics in Agriculture 130 (2016) 20–37

(a) Overall dead sensor nodes till current round

(b) Overall dead Type-1 nodes till current round

(c) Overall packets transmitted to BS till current round

(d) Overall dead Type-2 nodes till current round

(e) Overall residual energy of the network after 2000, 2500 & 3000 rounds

(f) Overall dead Type-3 nodes till current round

(g) Number of dead nodes after 1000, 2000 & 3000 rounds Fig. 10. Simulation results in case of Type-3 nodes deployed in middle regions for RBHR and proposed protocol.

33

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S. Maurya, V.K. Jain / Computers and Electronics in Agriculture 130 (2016) 20–37

Fig. 11. Network lifetime with different P T .

Fig. 12. Network lifetime with different T 2 ðTÞ.

Fig. 13. Network lifetime with different T 1 ðTÞ.

perature above 35  C reduce yields which is discussed in Brink et al. (2006). Therefore, we have chosen rigid threshold (T 1 ðTÞ) and mild threshold (T 2 ðTÞ) for soil temperature as 35  C and 4  C respectively which means as soon as the node senses greater than or equal to 35  C value a data packet containing this information will be sent to BS. Also, if there is a 4  C change as compared to previously sensed temperature value a data packet containing this information will be sent to BS. Similarly, for simulation purpose we have chosen the rigid and mild threshold for soil moisture content (i.e. T 1 ðMÞ ¼ 30% and T 2 ðMÞ ¼ 10% respectively) and rigid and mild threshold for air humidity (i.e. T 1 ðHÞ ¼ 30% and T 2 ðHÞ ¼ 10% respectively). These thresholds value can be set by the users according to the need of irrigation in the agriculture

field for a particular crop. The periodic timer (PT ) parameter value of 5 s specifies here that data transmission will occur in every 5th round if sensed parameter values do not cross the desired thresholds. To evaluate the performance of our proposed protocol, following metrics are used: (i) Stability period: is the time duration from start of network operation to the death of first sensor node in the network. (ii) Network lifetime: is the time duration from start of network operation to the death of last alive node in the network. (iii) Throughput: is the total number of data packets sent to the BS during entire network lifetime.

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(iv) Residual energy: is the overall remaining energy that a node has after a particular round. 4.1. Simulation results In this subsection, comparative study of the behavior of proposed protocol with EEHC, DEEC, DDEEC and RBHR protocol are discussed. In the Section 3.4, we have clearly explained that why we should deploy Type-3 nodes in middle regions instead of Type-2 nodes. In addition to explanation, the fidelity of the deployment is shown by simulation results. In Tables 4 and 5, proposed protocol is compared with EEHC, DEEC, DDEEC and RBHR protocol in terms of energy consumption, network lifetime and number of packets transmitted to BS. For the comparison, we have simulated the DEEC, DDEEC and RBHR protocol with 3-levels of heterogeneity. Table 4 and Fig. 9 show comparison results with other protocols when Type-2 nodes deployed in middle regions and Table 5 and Fig. 10 show comparison results with other protocols when Type-3 nodes deployed in middle regions for RBHR and proposed protocol. Figs. 11–13 show the network lifetime in case of different periodic timer (P T ), different mild threshold for soil temperature (T 2 ðTÞ) and different rigid threshold for soil temperature (T 1 ðTÞ) value respectively. Instead of these three simulation parameters, all the other parameters are same as defined in Table 3. 4.2. Results analysis This subsection, comparatively analyze our proposed protocol in comparison to other existing routing protocols such as EEHC, DEEC, DDEEC and RBHR protocol. Figs. 9a and 10a show the network lifetime of EEHC, DEEC, DDEEC, RBHR and proposed protocol as per the simulation parameter defined in Table 3. In EEHC protocol, all the three types of sensor nodes are deployed randomly over the entire network area and also the CH nodes are selected randomly in every round. Therefore, it is possible that a node which is far away from BS and also having less energy may be selected as a CH node. In such a situation, nodes will die very quickly and the network lifetime of EEHC protocol will be very short. Due to the random deployment of nodes, it does not provide efficient coverage to entire agricultural field. The DEEC and DDEEC protocols are developed to perform in a heterogeneous environment. In both DEEC and DDEEC protocol, the nodes are deployed randomly over the entire network area. Therefore, they also do not guarantee efficient coverage which is highly desirable for efficient irrigation in the field. In DEEC protocol, CHs are selected in every round by calculating a probability, based on the ratio between the residual energy and average energy of the network. Due to the consideration of residual energy for CH selection, the lifetime of the network in case of DEEC protocol is increased as compared to EEHC protocol. The DDEEC protocol is based on DEEC protocol, where all the sensor nodes use the residual and initial energy level to select the CH nodes. In DEEC protocol, the higher energy sensor nodes penalize, specially when their residual energy deplete and become in the range of the lower energy sensor nodes. In this situation, the higher energy sensor nodes die quickly than the others nodes in the network. But in DDEEC protocol, the higher energy nodes are preferred to be selected as a CH node for the initial transmission rounds, and when their energy decreases sensibly, these nodes will have the equal CH selection probability like the lower energy nodes. This prolongs the overall network lifetime of DDEEC protocol as compared to both the EEHC and DEEC protocol. The RBHR protocol provides efficient coverage over large geographical area. Due to the deployment of nodes in respective regions according to their energy level (i.e. as per the literature, higher energy nodes are deployed far away from BS such as Type-

1 in innermost region, Type-2 in middle regions and Type-3 in outermost regions) and hybrid routing scheme, RBHR enhances the network lifetime as compared to other EEHC, DEEC, and DDEEC routing protocols. Although, the RBHR is an energy-efficient routing protocol and provides efficient coverage but it is not suitable for the precision irrigation because the protocol is applicable where continuous data transmission to BS is required (e.g. in military applications, health-care applications, etc.). Therefore, for efficient irrigation in the field where continuous information about the field is not required, a novel periodic threshold sensitive hybrid routing protocol is proposed in this paper. The three data transmission thresholds (i.e. rigid, mild and periodic timer) are used for transmitting field information to the BS. The data packets are transmitted to BS only when the rigid threshold or mild threshold or periodic timer crosses the set value. For providing efficient coverage over entire network area modified region-based static clustering approach is proposed where nodes are deployed in respective regions according to their performing task & energy level both (i.e. Type-1 in innermost regions, Type-2 in outermost regions and Type-3 in middle regions). Due to balanced deployment of nodes, enhanced hybrid routing scheme (discussed in 3.4) and the best CH node selection by considering both the residual energy as well as distance parameter using fuzzy logic technique, lifetime of the network is increased as compared to RBHR protocol. The simulation results also verify that the proposed protocol prolongs the network lifetime over the EEHC, DEEC, DDEEC and RBHR protocol. All the sensor nodes are dead after 2884, 3161, 3949, 5345 and 7878 rounds in case of EEHC, DEEC, DDEEC, RBHR and proposed protocol respectively as shown in Fig. 10a. Figs. 9c and 10c show the total number of data packets received at the base station till current round. As per our simulation result shown in Fig. 10c, in case of EEHC protocol, total number of data packets received at BS (i.e. total 6108 data packets) is comparatively very less as compared to all the other protocols due to its shorter network lifetime. As the network lifetime of both the DEEC and DDEEC protocol is increases in comparison to EEHC, the total number of data packets received at BS also increases (i.e. total 120,872 and 137,625 data packets, in case of DEEC and DDEEC respectively). The total number of data packets received at BS in case of RBHR is decreased (i.e. total 102,130 data packets) due to only four CH node selection from outer regions. This restricts the transmission rate towards the BS as compared to EEHC, DEEC, and DDEEC. The packet transmission rate towards BS in case of our proposed protocol is decreased (i.e. total 63,090 data packets), as compared to DEEC, DDEEC and RBHR protocol due to user defined data transmission thresholds because data packets are received at BS only when nodes cross the data transmission thresholds (either rigid or mild or periodic timer), but it increased as compared to EEHC protocol due to overall improvement in sensor network lifetime. Figs. 9b and 10b show the number of dead Type-1 nodes till current round. The number of dead Type-1 nodes in case of proposed protocol are less as compared to other routing protocols over the Table 6 Network lifetime in case of different P T ; T 2 ðTÞ and T 1 ðTÞ. Thresholds

Thresholds

Round after all sensor nodes are dead

T 1 ðTÞ ¼ 35  C and T 2 ðTÞ ¼ 4  C

PT = 8 s PT = 5 s PT = 1 s

9148 7878 5532

T 1 ðTÞ ¼ 35  C and P T = 5 s

T 2 ðTÞ ¼ 6  C T 2 ðTÞ ¼ 4  C T 2 ðTÞ ¼ 2  C

8213 7878 4820

T 2 ðTÞ ¼ 4  C and P T = 5 s

T 1 ðTÞ ¼ 40  C T 1 ðTÞ ¼ 35  C T 1 ðTÞ ¼ 30  C

8025 7878 4768

36

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same number of rounds because the Type-1 nodes having less energy as compared to Type-2 and Type-3 nodes are deployed near the BS region (i.e. in REG-9), while in case of other (i.e. in EEHC, DEEC and DDEEC) routing protocols, Type-1 nodes are deployed over the entire network area which consumes more energy in long-distance communications. Figs. 9d and 10d show the number of dead Type-2 nodes till current round. The number of dead Type-2 nodes in case of proposed protocol are less as compared to EEHC, DEEC and DDEEC protocols due to same reason as discussed for the Type-1 nodes. In Figs. 9d and 10d, we can see that when Type-2 nodes are deployed in middle region then all the Type-2 nodes have died earlier (i.e. all Type-2 nodes dead after 3707 rounds) as compared to Type-2 nodes deployed in outermost regions (i.e. all Type-2 nodes dead after 4889 rounds) because Type-2 nodes have less amount of energy as compared to Type-3 nodes as well as they have to perform additional data aggregation task from outermost region’s CH node as well as data aggregation task from its own CMs. Figs. 9f and 10f show the number of dead Type-3 nodes till current round. In Figs. 9f and 10f, we can see that the when Type-3 nodes are deployed in outermost region then 50% Type-3 nodes are dead after 4437 rounds as compared to Type-3 nodes deployed in middle regions (i.e. 50% Type-3 node dead after 2598 rounds) because the Type-3 nodes are the highest energy sensor nodes in the network as well as they have to perform data aggregation task from its own CM nodes only. Figs. 9g and 10g show the number of dead sensor nodes after 1000, 2000 & 3000 rounds. The total initial energy of the network is given in Eq. (3). Figs. 9e and 10e show overall residual energy of the network after 2000, 2500 & 3000 rounds and these figures show that in case of our proposed protocol, the average energy consumption of each sensor nodes are reduced in every round as compared to other routing protocols. In case of Type-2 nodes deployed in middle region, after the 3000 round, overall 69 sensor nodes are alive as compared to Type-3 nodes deployed in middle regions (only 65 sensor nodes are alive) because alive Type-3 nodes are only sensing the environment parameter but not communicating with BS as most of the Type-2 nodes are dead. Therefore, more nodes are alive after 3000 round because node consumes less energy in sensing than that for communication. We have also simulated and analyzed the performance of our proposed protocol with the various combination of data transmission thresholds shown in Figs. 11–13 (in case of Type-3 nodes deployed in middle regions). Table 6 shows the corresponding round in which all sensor nodes are dead (i.e. Network lifetime) in case of our proposed protocol as per the simulation results provided in Figs. 11–13. In Table 6, we can see that as the value of periodic timer (PT ) decreases, the lifetime of the network also decreases. When P T ¼ 1, all the sensor nodes are dead after 5532 rounds as compared to when PT ¼ 5 (nodes dead after 7878 rounds) and PT ¼ 8 (nodes dead after 9148 rounds) because the nodes are transmitting data packets to BS after every 1 s if rigid or mild threshold do not reach to desired threshold. The nodes consume more energy when it transmits data packet to BS after every 1 s as compared to when P T ¼ 5 and PT ¼ 8. Also in such a situation, when nodes are transmitting data packets after every 1 s (that means in every round) it behaves similar to RBHR protocol. The proposed protocol fairly divides the outermost and middle regions, and equally distributes the nodes in these regions, in contrast to RBHR protocol. Therefore, as per Tables 6 and 5, the network lifetime (i.e. all sensor nodes dead after 5532 rounds at PT ¼ 1 s) of the proposed protocol increases in comparison to RBHR protocol where all nodes are dead after 5345 rounds. Similarly, as the value of mild threshold for soil temperature (T 2 ðTÞ) decreases, the lifetime of the network also decreases. When

T 2 ðTÞ ¼ 2  C, all the sensor nodes are dead after 4820 rounds as compared to when T 2 ðTÞ ¼ 4  C (nodes dead after 7878 rounds) and T 2 ðTÞ ¼ 6  C (nodes dead after 8213 rounds) because the nodes are transmitting data packets to BS more frequently. The nodes will transmit data packets more when it senses a temperature difference of 2  C than previously sensed temperature value. Similarly, as the value of rigid threshold for soil temperature (T 1 ðTÞ) decreases, the lifetime of the network also decreases. When T 1 ðTÞ ¼ 30  C, all the sensor nodes are dead after 4768 rounds as compared to when T 1 ðTÞ ¼ 35  C (nodes dead after 7878 rounds) and T 1 ðTÞ ¼ 40  C (nodes dead after 8025 rounds) because the nodes are transmitting data packets to BS more frequently. The simulation results show that the proposed protocol gives better result in most of the cases with various combinations of data transmission thresholds in comparison to other existing protocols. Therefore, we can say that the proposed protocol is an energyefficient routing protocol which is applicable for agriculture domain. Also, in this subsection, to test the performance of proposed node deployment concept of Type-3 nodes in middle regions, we have analyzed it in two different cases.
Case-1: in which Type-2 nodes are deployed in middle region and corresponding results are presented in Table 4.
Case-2: in which Type-3 nodes are deployed in middle region and corresponding results are presented in Table 5. In Table 4, we can see that in case of our proposed protocol, all the sensor nodes have died after 7094 rounds and Table 5 show that all the sensor nodes have died after 7878 rounds. Also, in Case-1, all Type-2 sensor nodes have died after 3707 rounds but in the Case-2, all Type-2 sensor nodes have died after 4889 rounds. In Case-1, after the death of all Type-2 nodes, Type-3 nodes will sense only but never communicate with the BS. In this situation, a coverage hole is created in both the middle and outermost regions because Type-2 nodes are deployed in middle regions. After the death of all Type-2 nodes, only Type-1 nodes will communicate with BS until the death of last alive Type-1 node, if any. Therefore, according to Table 4 after the death of all the Type-1 nodes in 4037 rounds, logically the BS does not receive any information about the field while Type-3 nodes are still alive. Therefore, in case-1, BS is receiving information about the field till 4037 rounds. But in Case-2, after the death of all Type-2 nodes in 4889 rounds, Type3 nodes will communicate with BS. In such a situation coverage hole is created in outermost regions only. Some Type-3 nodes will guaranteed be alive after the death of all Type-2 nodes because they are high energy nodes and deployed near the BS as compared to Type-2 nodes. According to Table 5, after the death of all Type-2 nodes some Type-1 and Type-3 nodes are still alive and communicating with BS. In this case, BS is receiving information about the field till 7878 rounds (all the Type-3 nodes are dead after 7878 rounds). Hence, the deployment of Type-3 nodes in middle regions is efficient and we achieve longer network lifetime because the BS is receiving information till 7878 rounds.

5. Conclusions and future work In this proposed threshold sensitive region-based hybrid routing protocol, the deployment of heterogeneous sensor nodes within fairly divided fixed regions, ensures proper coverage of entire network field. The Type-2 and Type-3 nodes die slower than Type-1 nodes due to balanced deployment of heterogeneous nodes in different regions. The dynamic clustering routing protocols such as LEACH, SEP, E-SEP, EEHC, DEEC and DDEEC use the weighted random probability for the selection of CHs node, which does not

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ensure the proper utilization of node’s energy in every data transmission period. Therefore, in the proposed protocol, to ensure proper utilization of node’s energy in every round, fuzzy logic technique is used for CHs selection, and to provide efficient coverage over entire area, region-based static clustering approach is used. The balanced use of fuzzy logic technique, region-wise node deployment and enhanced hybrid routing, saves much more amount of energy consumed by sensor nodes. When we compare the proposed protocol with EEHC, DEEC, DDEEC and RBHR, network lifetime of proposed protocol is increased as compared to the other protocols, because data is transmitted to BS only when it is required. Hence, proposed protocol minimizes the energy consumption of sensor nodes due to reduction in total number of data transmission per round. The data transmission thresholds (i.e. T 1 ; T 2 and P T ) can be set to receive information according to users need. Therefore, the information regarding the agricultural field received at BS helps the farmer to take a decision about irrigation. Further, this work may be extended to develop energy-efficient routing protocols for wireless sensor networks with mobile base station and sensor nodes. The work can be further extended to support multi-hop communication model in case of monitoring very large field.

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