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IOT Based Energy Efficient Routing Strategy in Wireless Sensor Networks for Precision Agriculture
K. Muruganandam, Usha Chauhan SEECE, Galgotias University ABSTRACT: Wireless sensor networks (WSNs) can be
used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data. There is an increasing focus on IoT based precision agriculture to increase the productivity and yield in the farm fields through real- time monitoring of agriculture field parameters. The data in the farm field is collected using sensors such soil sensor, temperature and humidity sensor, air quality sensor, and video camera mounted on drones. The data from each sensor is then aggregated at the base station and forwarded to a gateway. Recent research work conducted by Microsoft on IoT based precision agriculture has reported that designing the energy efficient data aggregation method for such IoT based networks is one of the classical research challenges. A smart irrigation system can be built with smart sensor networks for collecting field values and can be analysed using rules for effectively watering the plants. Hence, a new sensor network assisted irrigation system and rule based analysis model have been developed in this research work to enhance the efficiency and energy consumption using different models.
I. INTRODUCTION
Agriculture plays a key role in the development of human civilization, and also plays a tactical role in the process of economic development of a nation. In addition, agricultural sector has a great importance in the different aspects of life such as it is the source of livelihood of many people around the world, roughly 2.5 billion of the rural people directly rely on agriculture as a mean of living it contributes to national income for most developing countries, and it provides the food for population and fodder for animals. It an important role in the process of economic prosperity of a nation. It has already made an amazing contribution in the economic
development of developed nations and its huge role in the economic prosperity of the developing countries and the poor countries is of vital importance. If the agricultural sector fails it adversely affects both the economy as well as an individual. In many parts of the world, farmers still use traditional methods to cultivate and harvest crops. Latest techniques have been introduced to tackle these problems with aim of increasing the productivity and the quality of crops. New techniques are less labour intensive than the traditional ones as there is a greater reliance on machinery.
Nowadays sophisticated technologies such as temperature, moisture, soil, pressure and wind sensors, robots, GPS technology etc. are being used.
II. WIRELESS SENSOR NETWORK IN SMART AGRICULTURE
The different wireless protocols and standards that are used in agriculture. These wireless technologies are also compared to identify the most convenient technology in terms of power consumption and the efficiency of the network. IoT is a revolution for the future reality where everything that can utilize a connection will be connected. IoT system constructed from current Long Term Evolution (LTE) functionalities. This permits all network facilities such as security, tracking, policy, charging, and authentication to be totally supported.
2.1 GSM BASED SMART AGRICULTURE The main purpose of using sensors in smart agriculture is to increase the overall crop productivity. Sensors help to sense various physical conditions like temperature, humidity, soil moisture, pH level of the soil, water level, air pressure, intrusion detection, water level etc.During this a user can increase crop productivity using minimum resources hence decreasing the overall budget of the system as well. The sensors help the user in accurate use of irrigation, fertilizers, pesticides etc.
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Actually the base station(control section)and the user end consisting of Internet or GSM enabled devices which can receive all the data gathered by the sensors with the help of which a user can analyse the data and hence can act accordingly. The sensors are deployed in the agricultural field.
2.2 Radio Optimization Scheme for Smart Agriculture.
Previous studies show that the power is mostly dissipated in the RF components of WSNs than in data processing units, such as microprocessors and power controllers, the various radio optimization techniques as follows as, (i) transmission power control (TPC), (ii) modulation scheme, and (iii) cognitive radio.
In the TPC scheme, the sensor nodes modify the transmitted power to save energy, stimulate avoiding interference, and establish a communication link. TPC can be used in the agricultural field, where the RF transmitted power of the sensor nodes can be modified to reduce their power consumption based on the measured distance between the sink node and the sensor nodes.
The modulation scheme parameters can be adjusted to their optimal values to preserve the minimum power consumption of radio modules. Anane et al. [88]
investigated the Frequency-shift keying (FSK) and Minimum-shift keying (MSK) modulation strategies to minimize the total power consumption of the sensor node required to transmit given data packets.
A cognitive radio is an intelligent wireless communication network in which the wireless communication channel in the spectrum band can be selected efficiently. The transmission metrics can be adjusted accordingly. A cognitive radio requires more energy than conventional devices do because it includes sophisticated and complex functions. Therefore, an energy-efficient cognitive radio network poses challenges, especially with regard to the use of battery energy.
2.3 The irrigation and decision making system In agriculture, the efficient management of water should be considered. Therefore, the water required for irrigation has to be minimized. The proposed system based on the information sent from the sensors estimate the quantity of water needed. The sensors are used to send to the base station the humidity and the temperature of the soil, the humidity, the temperature and the speed of the air, and the duration of sunshine per day. The proposed system based on these values calculates the water quantity for irrigation. Moreover, to develop a more accurate system the historical data regarding the quantity of irrigation used in previous period are considered to adjust the quantity of water that is needed for irrigation. A comparison between current and past states is necessary to arrive at optimum decisions. The main parameters that affect the surface runoff are the following: the climate (rainfall intensity, moisture
content, wind, evaporation) and the type of vegetation.
An important factor for the implementation of an irrigation system is the knowledge of the soil moisture at the beginning of the growing season, which depends mainly on the weather conditions during the preceding winter. Additionally, the knowledge of the water quantity stored in underground aquifers, which is available for plants.
2.4 Routing Protocol for smart Agriculture
Routing protocol introduces another power reduction scheme for agriculture WSNs to minimize the path between sensor nodes and sink node, thereby the power consumption of the WSN is reduced. Routing protocol can be performed via (i) sink mobility, (ii) multi-path routing, (iii)cluster architecture, and (iv) routing metric.
Sink mobility is proposed to a novel mechanism that adopts unmanned aerial vehicles (UAV) to gather data from forest regions instead of classical sensing from nodes deployed in WSN. The proposed method enables the UAV to collect data from WSN in harsh terrain and transmit it to the base station situated far from the sensing area. Therefore, the multi-hop transmission between cluster heads can be completely avoided,and the communication range can be extended.
Multi-path routing has also been suggested by other
scholars for agriculture WSNs.
The proposed an agricultural field weather monitoring
system based on
the ZigBee wireless protocol. An advance energy- efficient routing protocol was implemented for WSNto automate irrigation management. The transmitter could modify its power based on the distance between the sensor node and base station to save energy. The results revealed that network lifetime is to be increased.
Cluster head method is considered by to extend the ZigBee WSN lifetime for agricultural application. In addition, the quality of the crop yields is improved and theircostis minimized. The research presented WSN coverage measurements in a mixed crop farmland. An adaptive energy consumption model for each sensor node was proposed and used to compute the energy consumption in the network.
Routing metric. It is used two routing metrics, namely, remained energy and expected transmission count, to compute the optimal routing path to the sink node that ensures low power consumption. “A scalable context- aware objective function (SCAOF)” is proposed by combining robustness-aware, energy-aware, resource- aware, and reliability-aware contexts that render to the compound routing metrics scheme.
Energy-Efficient Data Routing Method:
Energy Efficient Routing is a
hierarchicalclusterbasedprotocol which achieves a good performance in termsoflifetime by minimizing energy consumption for network communication and balancingenergyloadamong all nodes. After gathering the data from differentclusters, the CHs need to forward
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the datatotheBS.Hence, the forwarding nodes are selected based onthehighest residual energy among the
nodes. Residual
energy is defined as the remaining power of a sensornode whenever topology changes, which canbeanindicator of the stability of a link and thesurvivaltimeof a node. The nodes which are havingthehighestenergy are selected to forward the data to the BS. It helps to improve the packet delivery ratio from the packet loss.
Agriculture Requirements for IoT :
The IoT represents the visibility of a group of systems, technologies, platforms, anddesignprinciples for joining things, depending on the physical surroundings, through the use of the Internet.PA is an application that can employ the benefits of IoT to increase production efficiency, improvethe quality of yields, reduce the negative ecological impact, prevent the prevalence of plant-eatingpests or plant diseases, alert farmers about farm fires and increase the profitability of severalagricultural production schemes. Agriculture involves farming, plantingandanimal rearing,andithas grown under the scope of IoT recently. Tracking of animals, monitoring of farms,andirrigation processes are the main domains of IOT for cultivation.
Sensors in WSN can be employed to gather information about environmental and physicalfeatures, whereas actuators are used to respond to the feedback to control or to perform an action overthe conditions (Figure 3, Part B). The use of sensors in agricultural applications
poses a numberof
requirements, including gathering of soil, weather, and crop information; surveillance of agriculturalareas, water and fertilizer requirements of diverse pieces of rough land, severalcropsonasinglepiece of acreage, different requirements of crops for unlike soil and weather circumstances, andeliminating of interactive solutions and relying on proactive solutions.
All these requirements areapplied and processed in parallel. Therefore, different sensors and actuators must be used to handlethis information and respond to different conditions. IoT-based agriculture applications changed classical agricultural surveillance approaches byspeedily providing quantitative data with significant temporal and spatial resolution. Several IoTplatforms have recently been available in the market. For instance, in agricultural applications,Smart Farm Net platforms can be used to provide more than 30 packages for goodcommercialproducts, such as and experimental, such as Motes and Arduino sensor platforms. Other examples of agricultural applications based on IoT.
1. Path Selection Algorithm.
2. Dijsktra’s Algorithm.
3. Improved Duty cycling algorithm.
PATH SELECTION ALGORITHM :
In the proposed methodology, we employ certain advancement in path selection where we select the clusters through which data can be transmitted. This will improve the overall efficiency of the system. Data loss and computational time can be improved if the optimal path is selected for transmission. This technique can be employed in various IoT applications like field monitoring, quality prediction etc.The main purpose of the WSN platform is to provide the users of the IoT application an updated view of the events of interest in the field using path selection algorithm. The tiered structure of the used platform was introduced by one of the first long-termoutdoor WSN experiments for environmental monitoring and allows:
A good functional separation of platform components for optimization according to application requirements.
A cloud-based field data access to bridge the latency- energy trade-offs of the low power communication segments and the ubiquitous and fast access to field data for endusers.The sensor nodes are optimized for field data acquisition using on-board transducers and processing. Then these sensor nodes are clustering and select the best path to reach gateways using short-range RF communications, either directly or through other nodes.
2 .Dijsktra’s Algorithm.
An energy saving routing algorithm based on Dijkstra (ESRAD) is introduced for WSN.It selects the path with least energy consumption by considering energy consumed by nodes electronics and the energy consumption during data transmission phase. Then, it employs Dijkstra to search for the shortest path from the source node to the sink node. This technique results in the increase of energy consumption of the individual nodes due to increased computational overhead. Further in, balanced and energy efficient multi-hop techniques for routing in WSN are proposed. It is a centralized routing protocol in which BS assigns weight matrix to the network and then uses Dijkstra algorithm to calculate optimal data path from source to sink node.
This approach makes it applicable to the scenarios where periodic or query-based data reporting is required.
3. Improved Duty Cycling Algorithm
The proposed algorithm enhances the performance of the data aggregator node in terms of energyefficiencyandQoSefficiency.Toevaluatetheperfor mancemeritofthealgorithm,weconductedsimulations in Network Simulator (ns2). The performance of proposed algorithm is compared with two existing algorithms and thesimulationresultsshowthattheproposedimproveddutyc yclingalgorithmperformsbetterwithregardstovarious performance metrics such as energy consumption, residual energy, throughout, and the processingtime.This paper presents the related work on energy efficient duty cycling algorithms. In this section, we present a systematic review identifying gaps of various research
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studies conducted on duty cycling algorithms including adaptive control duty-cycling algorithms, adaptive duty- cycling congestioncontrolalgorithm,adaptiveharvesting- awareduty-
cyclingalgorithm.Inaddition,arecentdevelopmentin this direction using data-driven approach is also described in this section
This algorithm helps in predicting the future energy based on the previous data. But, this algorithm does not work well under uncontrollable or unpredictable harvesting situations. To overcome this limitation, Christopher M. Vigorito (2008) [5] proposed the adaptive control of DC algorithm for harvesting circuit.
This modified algorithm aims to balance the power supply, reduce the variance at various sensor nodes under different environmental conditions. It also ensures the maximum performance while maintaining the tenable stability of DC. As the number of sensors nodes increases and the data traffic generated by such nodes grows exponentially as nodes grows, the network become congested which results in decrease in throughput and increase in energy consumption.
1.1 Improved Duty Cycling (IDC)algorithm IDC is based on energy efficient data aggregation at the base station using residual energy parameters. The proposed IDC uses two residual energy thresholds; one for the network and the other residual energy threshold for
thepath.Asshowninthealgorithm1,thedutycycledperform edonBSnodeaccordingto,networkconditionsprior to data transmission phase. Algorithm 2 is showing the proposed optimal path selection and data transmission technique using the remaining energy parameter. The all possible paths from source sensor node to the BS are discovered, according to algorithm 2, and then select the shorted path for the datatransmission.
Algorithm 1: Modified FARMBEATS (IDC) Input:
G = (N, X, Y) // G is Duty Cycled WSN graph with N Sensor Nodes with X * Y size network.
Output: Minimum energy consumption
σ1: remaining energy threshold value for the network DC = 0; // duty cycle time interval
1. Deploy network G with DutyCycled 2. Initialize DC =1;
3. Set BS in Inactive State at time intervalDC 4. IF (event) //Event such as bad weather or cloudy weatheretc.
5. Compute the path for data aggregation (Algorithm2)
6. Select and return the path with minimum hops to start data sending from S to T at timeintervalDC+1
7. Set Sensor Nodes in sleep modeDC+1 8. ENDIF
9. Wakeup the BS at time intervalDC+2 10. Transmit collected data tosink
11. Check the sensor nodes status at each time intervalDC++
12. IF (Φi<σ1)
13. select another node and sleep theBS 14. ELSE
15. continue and keep wake up theBS 16. ENDIF
17. IF data sendingcomplete 18. sleepBS
19. ENDIF
In algorithm 2, c represents the number of hops for the current distance between i and j node i.e. Dij. The variable I0 represents the set of neighboring or intermediate node for the source S to Destination T node in order to discoverthe path. In steps 14-18, check the available possible neighboring nodes or intermediate node at the current time for the source node to discover the path. It should satisfy the condition at step 14. Φi holds the number of initial neighboring nodes for node i.
and I0 is the current neighboring node. N is the total number of nodes.
4. Simulation Results
For performance evaluation, we designed an algorithm IDC and compare it against the state of art of other algorithms DC [15] and NDC (AODV) in NS2. The experiment conducted on the Ubuntu operating system andNS-2.34 version. We designed network to check the scalability of the proposed protocol. Figure 1, 2, 3, and 4 showing
theaveragethroughput,processingtime,energyconsumptio nandremainingenergyperformanceevaluationrespectively .
Fig.1 Average throughput performance;
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Fig.2 Processing time performance;
Fig.3 Average energy consumption
Fig.4. Remaining energy performance.
Figures 1-4 are representing the performance of proposed algorithm compared to the previous duty cycling (DC) and no duty cycling (NDC) algorithms. As shown in figure 1, the throughput of IDC algorithm is more as compared to the DC and NDC algorithms.
Similarly, the processing time is significantly reduced in the proposed algorithm as compared to recent DC [15]algorithmasdepictedinfigure-2.Asdepictedbyfigure- 3,theaverageenergyconsumption of the overall network nodes is less with IDC compared to DC and NDC. Also the average residual energy of a node in the network is more with IDC thereby increasing the network lifetime depicted byfigure-4.
4.
Conclusion and future work
Thepaperstudieddutycyclingproblemandproposedamodif ieddutycyclingefficientpathplanningalgorithmfor the IoT
based precision agriculture system. Performance of the proposed IDC is evaluated and analyzed with the previous DC and NDC algorithms. Simulation results show the superiority of IDC over other two algorithms.
We presented the simulation results for the evaluation of proposed protocol IDC. We vary the density of sensor nodes to check the scalability performance of IDC. The simulation results revealed that the proposed algorithm
is showing
improvementinenergyefficiencyaswellasthroughputperfo rmances.Asanongoingwork,weareaimingtotestthe improveddutyalgorithmonthehardwarebasestationinthefi eld.Forfuturework,itwillbeinterestingtoinvestigate the clustering based technique to optimize the network lifetime performance.
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