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3School of Computer Science, University of Guelph, Canada. 4Department of ... color sensors and provides a low cost and real-time monitoring system to grow ...

2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

CPWS: An Efficient Routing Protocol for RGB Sensor-based Fish Pond Monitoring System Nidal Nasser1, 2, ANK Zaman3, Lutful Karim3 and Nargis Khan4 1

Electrical & Computer Engineering Dept, College of Engineering, Alfaisal University, Saudi Arabia 2 Adjunct Associate Professor, School of Computing, Queen’s University, Canada 3 School of Computer Science, University of Guelph, Canada 4 Department of Computer Science, Ryerson University, Canada Email: [email protected], 3{azaman, lkarim}, [email protected]

Abstract – This paper proposes a simple Wireless Sensor Network (WSN)-based water level and water quality monitoring system for fish ponds. The proposed architecture uses RGB color sensors and provides a low cost and real-time monitoring system to grow healthy fish and avoid anomalies such as overflow or low water level and the death or disease of fishes for unhealthy water (e.g., rise of acid level due to the change of pH and lack of oxygen in water) in a pond. In this simple monitoring system, sensors monitor the water level, dissolved oxygen, temperature and pH level of the water of the fish ponds at some predefined sensing interval. We also introduce a simple but efficient Clustering Protocol for Water Sensor network (CPWS) for the proposed fish pond monitoring framework in terms of network energy consumptions, network lifetime and number of data communications.

Moreover, we introduce a simple Clustering Protocol of Water Sensor network (CPWS) that is effective for WSN-based fish pond monitoring systems as well as energy efficient. Simulation results show that the CPWS protocol has longer network lifetime and thus, works several years without replacing the batteries. The rest of the paper is organized as follows. Section II presents the preliminary ideas about pH level, and oxygen level for fish pond monitoring. Section III presents some existing work on fish pond monitoring systems and routing protocols. Section IV presents the architecture of proposed WSN-based fish pond monitoring system and CPWS routing protocol. We evaluate the performance of the proposed architecture and routing protocol in Section V. Section IV concludes the paper with some ideas for the future research in this area.

Keywords – Wireless Sensor Network; fish pond monitoring; RGB sensors; LEACH; water level; disolve oxygen; pH.




Wireless Sensor Networks (WSNs) have achieved widespread applicability in several environmental monitoring applications due to the availability, ease of installation as well as maintenance, and the affordable price of sensors. However, a limited WSN-based water quality monitoring applications are being used in industry. Water quality of a fish pond is mainly monitored by lab-based testing. It is time consuming as pond owners send sample to the laboratory and have the result after testing. Though a few WSN-based water quality monitoring applications [5, 6, 11, 12] have been introduced recently, they are expensive and use complex architecture. Moreover, they are not suitable for fish pond monitoring since they are mostly designed for deep ocean water monitoring having different types of nodes including ordinary sensor nodes, surface buoys, and autonomous underwater vehicles (AUVs). Several nodes dive and rise in the ocean water and work as anchor nodes by receiving their positions from surface buoys or GPS. These nodes dive into the deep water to transmit data to ordinary nodes. Ordinary nodes also require extra hardware to transmit data using acoustic signals because Radio Frequency (RF) is absorbed by salinity of ocean water and cannot propagate well underwater. Routing protocols for these existing WSNs are also not energy efficient and suitable for WSNs that are used for small scale fish pond monitoring.

A fish farm consists of a number of ponds to cultivate fish e.g., shrimp and prawns. For instance, commercial shrimp farming began in 1970s mostly in Asia (Bangladesh, China, and Thailand), and Latin America (Brazil). To grow up healthy fish it is mandatory to have right level of water and good water quality in fish ponds. Wireless Sensor Network (WSN) can be considered as the most potential solution to monitor, and maintain the proper water level and quality in fish ponds. The water level of a fish pond depends on the actual depth, and the density of fish in that pond. Water quality of a fish pond depends on the pH level, dissolved oxygen, and temperature and few other criteria. However we are interested to monitor the pH level, temperature and the dissolved oxygen level for this project for our proposed fishpond monitoring application. The proposed monitoring system helps the pond owner/manager to know about the water level as well as water quality in a real-time manner. Thus, appropriate measures can be taken to add water in dry season or drain water usually in rainy season and improve the water quality of the ponds by controlling pH level and dissolved oxygen. The temperature of a pond that helps to understand the nature of fish and fish culture mainly depends on the geographical location of the pond. On the other hand, acidic or basic characteristics of water are determined by the pH levels. In fish blood the acceptable range of pH is 6.5 to 9.0 (it is 7.4 on an average). Fishes are not comfortable with the water that has below or upper pH ranges 4.0 to 5.0 or 9.0-11.0 respectively. pH level less than 6.5 is not enough for proper

Thus, we introduce a simple WSN-based fish ponds monitoring application as a part of agricultural production chain, which uses RGB (RGB: Red, Green and Blue colors) sensors. The proposed method helps the fish farmers to have real-time readings about the water quality of their pond as well as water level. The primary contribution of this work is the introduction of the RGB sensor to monitor water height.

978-1-4673-1430-5/12/$31.00 ©2012 IEEE



time and thus, can be used by related public offices and other users.

fish growth and fishes may even die at any pH level less than 5.0 [9]. The pH readings can help the fish farmers to take proper care of the fish ponds. Oxygen level is very important in fish pond for fish survival. Parts per million (ppm) is the scale to measure dissolve Oxygen in the water, and generally water contains 3-10 ppm of Oxygen. Warm water fishes (e.g., carp, catfish) can survive at 5ppm, on the other hand, cold water fishes (e.g., salmon, trout) need 7 ppm dissolve Oxygen to survive. Due to photosynthesis of plants (algae and aquatic plants) water gains oxygen, then due to respiration process of fishes and aquatic animals, the amount of oxygen is reduced from water [10]. Hence, fish farmers also need to know the oxygen level in the pond water to grow healthy fish. III.

A WSN based water quality monitoring system using Zigbee and the IEEE 802.15.4 compatible transceiver is presented in [12]. In the proposed system, the BS is placed at the center of a flat open field, and sensor nodes are positioned with specific intervals and angles (between 0º to 360º) around the BS to collect the reading data of pH, temperature, and turbidity at any time (real-time) and send data to BS or control/monitoring room. Similarly, PipeNet [14], a WSNbased system that collects data (hydraulic and acoustic/vibration) from water transmission pipelines and analyzes the collected data for detecting leaks.


Due to increasing number of underwater applications, designing efficient and robust routing protocols for underwater WSN is very important. Among several existing routing protocols of underwater WSN Yan and Cui [16] propose a Depth-Based Routing for Underwater Sensor Networks (DBR) in which nodes decide whether a packet to be forwarded or not based on the depth of a node and the previous node which is a greedy algorithm. However, every node must be equipped with a depth sensor in DBR that increases energy consumptions and deployment cost. DBR also increases the routing complexity. Ayaz et al. [2] propose Hop-by-Hop Dynamic Addressing Based (H2-DAB) under water routing protocol that uses greedy method to send sensor data to the surface. One of the potential problems of H2-DAB protocol is high end-to-end data transmission delay. Chen et al. [3] proposed Low Propagation Delay Multi-Path Routing (MPR) underwater routing algorithm that forms several subpaths during the routing path construction. To avoid collision, MPR uses many matrix operations that cause high energy consumptions. In general, most underwater routing protocols are complex because they are designed for different types of sensors including autonomous underwater vehicles (AUVs), ASVs. These protocols use acoustic signals for communication since RF signals do not propagate well underwater. Thus, these protocols are not suitable for WSN that are used in fish pond monitoring.

In this section, we present a number of Wireless Sensor Network (WSN)-based water monitoring applications. A flood monitoring system using WSN [11] was designed that trigger an alarm if flood situation comes. The proposed system used varying number of instances to monitor flood based on the characteristics of the location to be monitored. According to the architecture in Figure 1, the box with the sensor can move up and down to read the stationary magnets. The sensor is also responsible to recognize the direction of the movement for water height readings. To ensure the communication with the base station (BS) the sensor box is always kept above the water level using a floater.

Figure 1: Flood monitoring system [1] Mustafa et al. [8] created a WSN based system that collects data from water sources e.g., temperature, pH level, amount of dissolved oxygen, and wind speed to measure water quality. The authors used Crossbow Technology's mica2 motes, MDA300 data acquisition boards and external sensors to collect and store the data for analysis. They compared this proposed system with a wired system (DATALOGGER CR3000), and proved that WSN worked perfectly.

IV. PROPOSED FRAMEWORK In this section, we present the architecture of our proposed fish pond monitoring system along with the Clustering Protocol of Water Sensor network (CPWS). A. Architecture of Fish Pond MonitoringSystem The architecture of the proposed fish pond system that monitors the water level and water quality of fish ponds is presented in Figure 2. The fish pond monitoring system is designed as simple, low cost, energy efficient, and sustainable. The main design challenges of the proposed water monitoring system are to ensure that the sensors are always on the surface of the water and able to work in unusual circumstances like rain.

Dunbabin et al. implemented an autonomous surface vehicle (ASV) [4] that can navigate in inland water (for Lake Wivenhoe, located west of Brisbane, Australia) to collect data of water depth, temperature and dissolved oxygen. The proposed ASV equipped with mobile sensor network for online communication to send various reading data. WSNs were also deployed in two volcanic islands in the East Sea of Korean Peninsula for real time flood monitoring. Sensor nodes for monitoring water level, and flow velocity were deployed in both upstream and downstream region of 14 rivers. These sensors transmit data to the base station (BS) through multihop. Surveillance cameras were integrated with a Web application interface to receive image data and guarantee reliability of the system. Information collected from the WSNs are disseminated through Web applications and SMS in real-

To monitor the water level, we assemble a rectangular fixed pillar in the pond that contains the color mark (RGB: Red, Green and Blue) regions as shown in Figure 2. A water proof transparent floater contains a RGB sensor that can sense the color marks of the pillar. For normal circumstances, it is expected that the RGB sensors will be at green color zone. As we choose a rectangular pillar, the color codes and sensors are


in four surfaces to achieve the reliability of the reading. Figure 2 illustrates a RGB sensor. The height monitoring of the framework works as follows.

not mandatory but can be used to monitor the WSN remotely. In this paper, we consider a Web server to implement an information portal to see the collected data to build awareness in fish industries. As mentioned earlier a database is used to log data sent from sensing nodes, and BS. These data are used to build a water expert system through a long term monitoring and analysis. Figure 3 explains the data flow from WSN to the end user(s).

Desired/Healthy Water Level The RGB sensors sense green color when the height of water is at the desired level (e.g., enough water is in the pond). Low Water Level The RGB sensors sense the blue color when the height of water is at low level. These data will be transmitted to the base station (BS) to activate an actuator (e.g., water pump or suppler) to add water to the pond. High Water Level The RGB sensors sense red color when the height of water is at higher than the expected level. In such case, the actuator needs to drain water to avoid overflow and loss of fish.

Figure 3: Data flow and storage of the proposed WSN The development of WSN application for fish industry increases efficiencies, productivity and profitability by avoiding unwanted situations using real-time monitoring. The real-time data from the fish ponds allow fish farmers to adjust strategies as is desired.

The sensors that monitor water quality (e.g., pH level, oxygen level) are deployed on the water surface and floating (Figure 2). These sensors collect the surface water temperature, pH level, and the level of the dissolved oxygen, and direct the data to the BS through gateway nodes. In the proposed system, a database is maintained to store the collected data for future use to build a water expert system.

Figure 4: CPWS routing protocol B. Routing Protocol We introduce a simple Clustering Protocol of Water Sensor network (CPWS) for fish pond monitoring. Generally, a fish farm consists of a number of ponds to cultivate fish, e.g., shrimp and prawns, which are close to each other. Sensors (RGB and pH sensors) that are deployed in a pond form a cluster. Thus, number of clusters in the Water Sensor Network (WSN) depends on the number of monitoring fish ponds. Figure 4 illustrates the architecture of CPWS which has four clusters. Initially, base station (BS) randomly selects a node as cluster head (CH) in each pond or cluster. BS also selects the data sensing and/or transmission interval and informs each node through CH. CHs select only a few pH and oxygen sensors as active based on the sensing range and the monitoring area (pond size). Other pH and oxygen sensors remain in sleep mode by turning their radio off. However, CHs select both of the RGB sensors as active that are deployed in each pond. Water quality of fish pond does not vary greatly in different part of the pond. Thus, it is not important to consider the full sensing coverage of the pond while selecting the active pH and oxygen sensors. CHs select

Figure 2: Proposed architecture of the water level monitoring system (primary level) The proposed system architecture includes several components: a number of sensing nodes, gateway, base station (BS) and a database. Sensor nodes performs the specific task, sensing and transmitting data to the BS via gateway according to the specific rule set by users or network implementers. The gateway is a device that collects data from sensors, performs data aggregation and sends the aggregated data to BS. Data aggregation is used to ignore the unwanted part of the data and forwards the necessary data to BS and also to reduce the number of redundant data. Thus, energy efficiency of the WSN is achieved by reducing number of data transmissions. BS sends or broadcasts its interest to sensing nodes and collects data related to the interest message. Web Servers are


the most residual energy nodes as active which are reselected after a certain number of rounds (i.e., data sensing) to balance the energy dissipations of individual sensors and network.

NetLife rain

that NetLiferain in rainy season. From this analysis we can assume that the average network lifetime, avgNetLife will be ( NetLife rain  Netlife dry ) avgNetLife 2 (7) NetLife rain  (k u NetLife rain ) (k  1) NetLife rain 2 2 C. Simulation Setup and Results We use randomly connected Unit Disk Graphs (UDGs) on an area of 400 meters x 400 meters as a network simulation model. Communications among sensors that are floating on the surface of water and also RGB sensors are done through Radio Frequency (RF) signals. Since the monitoring parameters (e.g., water height, pH level) do not frequently change in pond water the sensing interval will be adjusted accordingly. For instance, during heavy rainfall, RGB sensors are adjusted to transmit water height data frequently, e.g., every 5 minutes. However, the sensing interval can be 24 hours or more during dry season. Simulation parameters and their respective values are presented in Table I.

V. PERFORMANCE EVALUATION In this section, we analyze the network lifetime of the proposed Clustering Protocol of Water Sensor network (CPWS), present simulation setup and results A. Energy Model Let ETX and E RX represent energy consumptions for transmitting and receiving data of size ndata to/from another node at distance d and are denoted as


n data u H data  n data u d 2 u H air n data u H data


Table II. Simulation Parameters and Their Values Parameter Value Network (pond) area 400 m X 400 m Number of ponds (cluster) of almost equal size 4 Number of RGB sensors 8 Number of water quality sensors 32 Data packet size 24 bytes Energy consumptions for sending data packets 50 nJoule/bit Energy consumptions in free space/air 0.01 nJoule/bit/m2 Initial node energy 2000 Joules


In Equations (1) and (2), H data and H air represent the energy spent in transmitter electronics circuitry and RF amplifiers for propagation loss, respectively. Equation 1 presents that the transmitting energy consumptions of a sensor, P are proportional to d2, where P transmits a sensed data packet to another node or base station (BS) at distance d.

We measure the performance of the CPWS protocol for pond water monitoring system and compare with the standard low energy adaptive clustering hierarchy (LEACH) routing protocol [15] in terms of total network energy consumptions, number of communications and remaining network energy (i.e., network lifetime) for varying the data sensing interval.

B. Network Lifetime Let us assume that hopi is the number of hops between nodei and base station (BS), where 1 d i d n node Thus, nodei

Network Energy Consumptions (Joule)

transmits data packet of size ndata hopi hops. Therefore, the number of transmitting and receiving nodes are hopi and (hopi -1), respectively except BS. If ETX and ERX are the average transmission and reception energy consumptions for a single hop data transmission of the network the total energy consumptions of transmitting a data packet from each node of the network to BS will be n node n node (3) E total ¦ ETX u hopi  ¦ E RX u (hopi  1) i 1 i 1 Let us assume nodes are homogeneous in terms of initial same energy, E. Thus, the total network energy is


(n node u E )


From Equation 5 we can infer that the network lifetime, NetLife dry in dry season will be at least k times higher than

After the specified sensing interval, active pH and oxygen sensors and RGB sensors wake-up and transmit data to CH, which will aggregate data and sends to BS if BS is within the communication range of CH. Otherwise, a CH forms a path to BS through other CHs and gateway nodes, where gateway nodes will also be selected by CHs for inter-cluster communications. During the initial network setup, a CH decides whether it can directly transmit data to BS or need to select gateway nodes and a data transmission path to transmit data to BS. Figure 4 illustrates the working principle of CPWS routing protocol.


NetEnergy E total u t h


If t h and t l are the data transmission interval of each nodei in rainy and dry seasons, respectively, where k t 2 and

50 45




35 30 25 20 15 10 5 0






Sensing Interval (Minutes)


k u tl


Figure 5: Comparison of network energy consumptions of the proposed CPWS protocol with LEACH

If the idle energy consumptions of all nodes between two consecutive data sensing interval is E idle n node u eidle then the network lifetime in rainy season is obtained using Equations 3 and 5 as follows.

Figure 5 shows that the network energy consumptions in the CPWS routing protocol is much lower than that in the


LEACH routing protocol over a number of data sensing interval in minutes. This is because the proposed CPWS routing protocol selects a few active nodes in each pond (or cluster) that transmit data to their cluster heads (CHs) whereas, all member nodes of a cluster send data to CH using TDMA scheme in LEACH.

colors in a rectangular fixed pillar that contains the color mark (RGB: Red, Green and Blue) and is assembled in the pond. This system is very simple as compared to the existing water quality monitoring systems. Moreover, we introduce an energy efficient routing protocol, namely Clustering Protocol of Water Sensor network (CPWS) for this WSN-based fish pond monitoring system which periodically transmits data over a short distant base station. The proposed framework can be used as a potential solution for monitoring water quality and depth in fish farms for growing healthy fishes e.g., prawns and shrimp in shallow water.


Network Lifetime (Joule

79990 79980 79970






79950 79940


79930 60






Sensing Interval (Minutes) [5]

Figure 6: Comparison of network lifetime of the proposed CPWS protocol with LEACH



Number of Communications

CPWS 1000000


800000 600000

[8] 400000



[10] [11]

0 60





Sensing Interval (Minutes) [12]

Figure 7: Comparison of number of communications of the proposed CPWS protocol with LEACH


Figures 6 illustrates that the network lifetime of CPWS routing protocol (in terms of remaining network energy) is much higher than that of LEACH protocol because sensors in the CPWS routing protocol do not require frequent data sensing for fish pond monitoring system whereas this is not the case for LEACH routing protocol. Figure 7 illustrates the same phenomenon of CPWS routing protocol over LEACH in terms of the number of data communications. We also validate experimental results through statistical analysis (student’s ttest). In addition, the CPWS routing protocol achieves much longer network lifetime in dry season (that spans all most half of a year) than the rainy season because the data sensing interval is larger in dry season due to the seldom change of the water quality and level in fish pond.




VI. CONCLUSION AND FUTURE WORK In this paper, we introduce a Wireless Sensor Network (WSN)-based fish pond monitoring system that can easily measure water level using RGB color sensors and water quality using pH and oxygen sensors. RGB sensors detect


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