Using the Wireless Sensor Networks WSNs in a wide variety of applications is currently considered one of the most challenging solutions. For instance, this technology has evolved the agriculture field, with the precision agriculture challenge. In fact, the cost of sensors and communication infrastructure continuously trend down as long as the technological advances. So, more growers dare to implement WSN for their crops. This technology has drawn substantial interests by improving agriculture productivity. The idea consists of deploying a number of sensors in a given agricultural parcel in order to monitor the land and crop conditions. These readings help the farmer to make the right inputs at the right moment. In this paper, we propose a complete solution for gathering different type of data from variable fields of a large agricultural parcel. In fact, with the in-field variability, adopting a unique data gathering solution for all kinds of fields reveals an inconvenient approach. Besides, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, our approach deals with a context aware data gathering strategy. In other words, depending on a defined context for the monitored field, the data collector will decide the data gathering strategy to follow. We prove that this approach improves considerably the lifetime of the application.
Water consumption in agriculture is the most part of water resources consumption in domestic, industrial and agricultural purposes. So, valuation of agricultural water productivity is important for improving the agricultural water resources utilization, especially in the arid areas [
1) Identification of the sensing agricultural parameters.
2) Identification of the sensing location and deployment of the sensor network.
3) Transferring data from crop field to control station for decision making.
4) Actuation and Control decision based on sensed data.
In the second kind of applications, the actuation part is not considered. These parts are detailed in the following paragraphs.
This task is performed by the agricultural engineer. It consists of choosing the agricultural parameters to sense in order to achieve the target. Some of the common and most critical measured factors in agricultural WSN are air temperature (T) and relative humidity (RH), soil moisture, salinity, etc. [
Every deployment has its own needs imposed by the type of the monitored crop or plant or by other special application. The coverage of the sensor nodes in agricultural WSN must be dense, i.e. one sensor node every one square meter (1 m2) [
Many agricultural deployment strategies assume the sensor nodes sensing and sending data to be time-driven, such as in [
Actuators like sprinklers, foggers, and valve controlled irrigation system are available and are controlled by the base station.
As a delay tolerant application, precision agriculture may rely on a data gathering strategy better than a tree- routing technique. In particular, we are interested in designing data gathering protocols in WSNs to increase the lifetime of the system, making it more energy efficient. Many data gathering schemes have been proposed for wireless sensor networks. Our data gathering strategy inspires from the strategies presented in the following paragraphs.
The goal of this strategy [
The key idea of ADA-DG [
Ci: BPj or (BPk and BPl)
this constraint means that:
“BPi is Green”→ “BPj is red” or “BPk and BPl are red”.
Our goal consists of satisfying all the clauses Ci. By resolving this problem, the different truth statement defines the different possibilities to construct red-green network. The goal of ADA-DG strategy is to find the trajectory visiting the minimum number of red break points; this means that the required solutions is that having the minimum number of truth values in their truth statement. We may have many solutions. So, the second criterion for selecting the RBP consists of minimizing the cost of the selected trajectory, in terms of number of relay hops. Therefore, [
At the second step, the DC computes the shortest possible route that visits each RBP exactly once and turns back to the RBP containing the sink. This route is deducted using an heuristic for the Traveling Salesman Problem TSP.
Our contribution in this paper consists of including context awareness in data gathering schemes for considering the in-field variability agricultural parcel. In fact, context awareness would incorporate a degree of intelligence in the sensor nodes so that they can decide the action to do, depending on a local context. Since we are dealing with agriculture, two types of contexts may be considered: the first one consists of the sensed data value such as temperature or humidity (current context) and the second one is an implicit input defined for different land conditions such as crop types, location or time of the day.
In this paper, we deal about the topology illustrated in
We now explain how we incorporate context awareness in choosing the data gathering strategy. Our approach is based on the ADA-DG strategy. Besides, we suppose that a Field Master FM is selected in each field. His role consists of saving the last context of the field and sending it to the Parcel Master PM using WIFI technology. The field master saves the worst value of captured data, adds the date of capture and sends them to the PM. We also note that the data gathering may be done by a data collector robot or any other instrument that visits the field to make insecticide, to harvest crops or any other reason. The decision of the strategy is made only by the PM after collecting the n last contexts. The PM will visit k fields (k ≤ n) depending on the time of the last data gathering of each field, the last context and the required delay. Concerning the context model, we inspire from the pyramid model proposed in [
Algorithm 1. DC moving path.
In the first approach, we suppose that all the sensors deployed in a field send their data separately to the data collector. However, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, in order to improve the lifetime of the application, we propose to group the sensor nodes into islands with near sensed values. For instance, in a given land, we may have some permanent variabilities, for example, a hill or plain space, a shadow or light area for a given time-of the day... So, having the same permanent condition of a given part of the field may imply finding very near values of sensed data. Then, sensing data from only one node of a part of the field, having near conditions, may satisfy the precision agriculture application. For this purpose, we propose in this section to group the nodes into islands having near sensed values. In the following paragraph, we define the islands definition. Once the islands are defined, alternatively each node is visited in the same time-of the day. The other nodes sleep in order to preserve their energy. We notice that a trigger of new island creation is defined. In our case, a high difference between the actual and the last sensed data from the same island triggers new islands creation. Other triggers may be defined depending on the type of crops. In the following paragraphs, we explain the island creation algorithm and the corresponding data gathering approach.
The goal of this algorithm consists of grouping the sensor nodes having usually near values of sensed data into the same island. For this purpose, deployed sensors exchange the sensed data and compare them with the received values of their neighbors. Neighbor nodes having sensed data, that values are in a given precision interval [Vmin, Vmax], are considered in the same island. Otherwise, a new island is defined when a neighbor node senses data that values are out of the precision interval. We suppose that network is connected, which means that each node can be reached. The neighboring nodes exchange the following messages:
・ CMsg (Check Message): containing the dynamic sensed data (such as temperature T), the neighbor nodes check the similarity of T with their sensed data (belonging to a given [Vmin, Vmax] interval).
・ NIMsg (New Island Message): Announce a new island definition.
・ EMsg (End Message): allow the Island Master IM to know the node’s identifiers of its island.
We denote:
- Di: the data sensed by node i.
- Ti: the instant when node i receives a CMsg.
- LTi: the last time when node i received a CMsg.
- Delay: the maximum delay to construct an island.
- Dbt: the minimum delay between two triggers to reconstruct islands.
- i is an end node if it has not descendant neighbors.
- CMsg (n, E, D): a CMsg sent by node n, passing by nodes in the set E. E is initially an empty set. D is the data captured by node n.
The flowchart of the proposed algorithm is shown in
rithm, is declared IM. It sends a CMsg (IM, TIM) containing its ID and the sensed data to its neighbors. Each node j, receiving a CMsg, compares the received data with that sensed. In case of belonging to the precision interval, then if node j is neither an end node nor a node in a loop situation, than it sends the data to its neighbors after adding its ID. Otherwise, it sends an EMsg to the parent node (source node). In the other case, a NIMsg is sent to parent node.
An alternative circumstance could occur when more than one IM is declared in the process. This situation is marked by receiving more than one CMsg from different source nodes within a Dbt delay. In that case, the node waits until receiving all CMsgs, and then it send an EMsg to the IM, having the smallest ID, after including all nodes known from other received CMsgs.
We notice that a node i concludes that it has received a CMsg (E1, T) in a loop situation when the last received CMsg (E2, T) is such that
For the sake of clarity, an example of islands creation with 3 distinct temperature intervals: (27) (29) (30) (32) and (33) (35) is illustrated in
Initially, the node 1 sends a CMsg including its identifier and the sensed temperature (30) to its neighbors. Each node receiving CMsg checks if its sensed data is in the corresponding temperature interval. In that case, it adds its identifier and forwards the CMsg to its neighbors. Otherwise, it sends a NIMsg to its parent nodes. Nodes receiving a NIMsg forward it to the IM. In this case, node 9 senses a data that value is out of the interval (30) (33). So, it is declared IM and sends a NIMsg to the source node (node 3). This one forwards the message until the IM (node 1). Node 9 does not have descendant node. So, it forms an island containing only itself (node 9). Node 6 receives a CMsg in a loop situation, and then it will send an EMsg to IM (node 4).
In this paper, we are interested to precision agriculture applications. In that case, network is not necessarily connected. However, knowing that the DC maintains the positions of all deployed nodes, we apply the island creation algorithm to each connected part of the network. Then, the DC will visit the different nodes that are declared IM. The first tour of the DC is a discovery tour. In the issue, the DC will maintain the real IM’s list in an ascendant order of their identifiers and the identifiers of nodes composing each island.
The DC will visit only one sensor of each defined island IM. In order to balance the energy consumption between all nodes, IM is alternatively selected from island nodes and with the condition that at the same time of the day different IM are selected. Knowing the IM in each tour, a node can select its tour to be an IM. During the
other tours, it remains in sleeping mode in order to conserve its energy. The reconstruction of islands is triggered by the DC.
In this section, we present simulation results to evaluate the efficiency of the proposed data gathering strategies. We carried out several simulations using NS2 simulator.
We analyze the strategies efficiency in the two following case:
・ case 1: using the first approach for which we suppose that readings are done every 1 hour when the temperature goes on or above the threshold level, here it is 30 degree Celsius. Otherwise, readings are done every 2 hours.
・ case 2: using the first approach, readings are done every 1 hour whatever the previous sensed temperature.
In
Network configuration | Number of fields Fields area Rc Bandwidth Mac protocol DC speed | 3 (200 m × 200 m), (300, 300), (400, 400) 50 m 2 Mbps 802.11 1 m/s |
---|---|---|
Energy model | Type Initial energy Transmit power Receive power Sensing power | Battery 100 Joules 0.36 watt 0.24 watt 0.015 watt |
case. So, when introducing context awareness based on the dynamic parameter temperature, we can gain in terms of network lifetime. In the first case, the DC can do more than 10 tours before sensors failure; however in the second case only 6 tours are done.
Similarly, in
In this section, we evaluate the effect of the node islands creation on the efficiency of the solution.
presents the energy consumed rate after 4, 8 and 12 tours. Besides, we present the energy consumed for island creation. Obviously, we found that the energy increase with the number of tours. Moreover, when the number of groups increases, the corresponding number of island masters increase. So, we observe that the energy consumed will increase with the number of active nodes representing the island master nodes.
The aforementioned approaches have incorporated context awareness for gathering data from an agricultural parcel. Uniform and dynamic parameters are defined. Depending on these parameters, data gathering strategy is defined with optimum energy preservation. Our approaches have been evaluated based on some simulations scenarios but the feasibility of the system and their productivity cannot be proved without real system tests. In order to prove their feasibility, our future work consists of modeling the system with an adequate analytical approach.
NourBrinis,Leila AzouzSaidane, (2016) Context Aware Wireless Sensor Network Suitable for Precision Agriculture. Wireless Sensor Network,08,1-12. doi: 10.4236/wsn.2016.81001