An Energy Efficient Wireless Sensor Network For Forest Conservation

Abstract:

Climate change is the biggest threat to nature and humanity in the 21st century. Global warming due to excess carbon emissions is one of the major causes of climate change. Though factors like industrial effluents, automobile exhaust contribute a little to this catastrophe the ‘lion’s share’ for global warming is due to deforestation. Governments of various countries have taken many serious measures both socially and technically to keep a vigil on the poachers. The methods adopted by them include, satellite imaging, fixing cameras over a particular area in the forest, awareness programs, DNA testing and automatic alarming system. But the major constrain one can find in all these methods are, huge cost of deployment , power consumption, life time of the gadgets and other ethical issues. This paper proposes a novel wireless sensor networking algorithm that can prolong the network’s life time using Heterogeneous – Hybrid Energy Efficient Distributed Protocol (H-HEED)

Keywords:

Wireless Sensor Network, Network Lifetime, Heterogeneity

1.Introduction

Global warming is a serious catastrophe and however, when forests are destroyed by forest fire and also by activities such as logging and land conversion for agriculture, they release large quantities of CO2 and other greenhouse gases into the atmosphere. Hence, reducing deforestation and forest degradation must be the part of the solution to the global climate change problem. From the statistics taken, 12 -15 million hectares of forests are lost every year that results in 15 % of greenhouse gas emission globally. 87% of global deforestation occurs mainly in tropical countries like Brazil, Indonesia, etc. causing 210 giga tons of carbon emission. A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. Wireless sensor networking research has been mainly focused on internal wireless sensor network issues such as MAC, routing protocols energy saving HW design, issues related to node’s life time and to some extent architecture of gateways that connect a wireless sensor network with the rest of the world. Increasing sensor node’s life time can be done using HEED protocol. Hybrid Energy-Efficient Distributed (HEED) protocol [4] is the clustering protocol. It uses residual energy as primary parameter and network topology features (e.g. node degree, distances to neighbors) are only used as secondary parameters to break tie between candidate cluster heads, as a metric for cluster selection to achieve load balancing. In this all nodes are assumed to be homogenous i.e. all sensor nodes are equipped with same initial energy. But, in this paper we study the impact of heterogeneity in terms of node energy. We assume that a percentage of the node population is equipped with more energy than the rest of the nodes in the same network – this is the case of heterogeneous sensor networks. As the lifetime of sensor networks is limited there is a need to re-energize the sensor network by adding more nodes. These nodes will be equipped with more energy than the nodes that are already in use, which creates heterogeneity in terms of node energy. [15]

2. Existing System For Forest Monitoring

2.1 . Remote Sensing Model

Remote sensing is an essential tool of land-change science because it facilitates observations across larger extents of Earth’s surface than is possible by ground-based observations. This is accomplished by use of cameras, multi-spectral scanners, RADAR mounted on air- and space-borne platforms, yielding aerial photographs, satellite imagery, RADAR and LiDAR datasets. Data available from remote sensing vary from the very high-resolution datasets produced irregularly over extents no larger than a single state or province (by aerial photography, imaging, LiDAR, and by high resolution satellite sensors such as IKONOS and Quickbird), to regional datasets produced at regular intervals from satellites (e.g., Landsat, SPOT), to the lower-resolution (> 250 m) datasets now produced across the entire Earth on a daily basis (e.g., MODIS). By comparing the data with respect to extent and texture of the forest area one can identify the illegally poached or extinguished forest area due to forest fire.

2.2 Automatic Alarm System

This method aims at providing alarm to the main monitoring system using sensors and MSP430 controller and chipcon CC2420 using zigbee protocol. The disadvantage in this method is that the sensor node’s life time is short since there is more power consumption

3. Issues in the Present Methodologies

In case of remote sensing, it covers over large area. This does not allow the forest officials to find out the exact place in which the poaching has taken place. This method demands huge sum of money and regular observation of the satellite. In case of alarming systems, sensor node’s power consumption is too high which prevents sensor nodes being long lasting. Hence clustering using HEED protocol can be used to monitor poaching by increasing the sensor nodes life time in a cost effective manner. This paper proposes a novel algorithm to increase the lifetime of the sensor nodes to detect poaching. The proposed algorithm requires two steps of simulation. The first step describes the cluster formation in the HEED protocol and explains about heterogeneous H-HEED protocol. The second sectionshows the performance of H-HEED by simulations and compares it with HEED

4. Proposed Methodology

4.1 Formation Of Heed Protocol

Assume that there are N sensor nodes, which are randomly dispersed within a 100m*100m square region (Figure 1). Following cluster assumptions are made regarding the network model is:

1. Nodes in the network are quasi-stationary.
2. Nodes locations are unaware i.e. it is not equipped by the GPS capable antenna.
3. Nodes have similar processing and communication capabilities and equal significance.
4. Nodes are left unattended after deployment.

Cluster head selection is primarily based on the residual energy of each node. Since the energy consumed per bit for sensing, processing, and communication is typically known, and hence residual energy can be estimated. Intra cluster communication cost is considered as the second parameter to break the ties. A tie means that a node might fall within the range of more than one cluster head. When there are multiple candidate cluster heads, the cluster head yielding lower intra-cluster communication cost are favored. The secondary clustering parameter, intra-cluster. Communication cost, is a function of (i) cluster properties, such as cluster size, and (ii) whether or not variable power levels are permissible for intra-cluster communication. If the power level used for intracluster communication is fixed for all nodes, then the cost can be proportional to (i) node degree if the requirement is to distribute load among cluster heads, or (ii) 1/node degree , if the requirement is to create dense clusters. This means that a node joins the cluster head with minimum degree to distribute cluster head load or joins the one with maximum degree to create dense clusters. Each node performs neighbor discovery, and broadcasts its cost to the detected neighbors. Each node sets its probability of becoming a cluster head, CHprob, as follows:

CH prob = max(cprob*(Eresidual/Emax),pmin)) (1)

Where, Cprob is the initial percentage of cluster heads among n nodes (it was set to 0.05), while Eresidual and Emax are the residual and the maximum energy of a node (corresponding to the fully charged battery), respectively. The value of CHprob is not allowed to fall below the threshold pmin (i.e. 10-4). The clusters formation by HEED protocol is shown in figure 1.

4.2 Heterogenous Network Model

In 2-level H-HEED protocol, two types of sensor nodes, i.e., the advanced nodes and normal nodes are used. Let us assume there are ‘N’ numbers of sensor nodes deployed in a field. E0 is the initial energy of the normal nodes, and m is the fraction of the advanced nodes, which own a times more energy than the normal ones. Thus there are m * N advanced nodes equipped with initial energy of Eₒ*(1+α) and (1-m)*N , and normal nodes equipped with an initial energy of E0. The total initial energy of the network [9] is given by:

[image: ](2)

So, this type of networks has am times more energy and virtually am more nodes. In 3-level H-HEED protocol, there are three types of sensor nodes, i.e. the supernodes, advancednodes and the normal nodes. Let m be the fraction of the total number of nodes N, and m0 is the percentage of the total number of nodes N * m which are equipped with β times more energy than the normal nodes, called as the supernodes, the number is N * m *m0. The rest N * m * (1-m0) nodes are having a times more energy than the normal nodes, being called as advanced nodes and the remaining N * (1- m) nodes are the normal nodes. E0 is the initial energy of the normal nodes. The energy of each supernode is Eₒ*(1+β) and the energy of each advanced node is Eₒ*(1+α) The total energy of the networks [13, 14] is given by:

[image: ](3)

The total energy of the networks [13, 14] is given by the factor of (1+m*(α+mₒ*β))In multi-level H-HEED protocol, initial energy of sensor nodes is randomly distributed over the close set[Eₒ,Eₒ *(1+ ax)], where E0 is the lower bound and amax determine the value of maximal energy. Initially, the node si is equipped with the initial energy of Eₒ*(1+ai ) which is ai times more energy than the lower bound E0. The total initial energy of the network [11] is

[image: ](4)

During the Cluster formation phase, every node will have its own Emax value in case of heterogeneity while computing the cluster head probability of the sensor node.

[image: ]

Figure 1 Cluster head formation by H-HEED protocol

5. Simulation Results

The simulation is done in Matlab. Let us assume the heterogeneous sensor network with 100 sensor nodes are randomly distributed in the 100m*100m area. The base station is located at the center (50, 50). We have set the minimum probability for becoming a cluster head (pmin) to 0.0001 and initially the cluster head probability for all the nodes is 0.05.In the process of transmitting an l-bit message over adistance d, the energy expended by the radio is given by:

[image: ](5)

And to receive the message, the radio expends:

[image: ](6)

The electronics energy, Eelec depends on factors such as the digital coding, modulation, filtering, and spreading of the signal, whereas the amplifier energy, Efs d^2 or Emp d^4, depends on the distance to the receiver and the acceptable bit-error rate. There are other factors like noise, physical obstacles or collision may affect the received power are ignored. We have introduced the advanced nodes to the HEED protocol, so as to assess the performance of HEED protocol in the presence of heterogeneity. Let us consider the case for 2-level H-HEED, 30% of the nodes are advanced nodes (m=0.3) and equipped with 150% more energy than normal nodes (a=1.5). For 3-level H-HEED, 30% of the nodes are advanced nodes and 20% of the nodes are supernodes are equipped with 150% and 300% more energy than the normal nodes (a=1.5 and b=3, m=0.5 and m0=0.4). For multilevel H-HEED, each node in the sensor network is randomly assigned different energy between a closed set [0.5, 2].

[image: ]Figure 2. The Number of Alive Nodes per Round

In Fig. 2, a detailed view of the behavior of HEED and H-HEED protocol is illustrated; it shows the number of alive nodes perround. The number of nodes die in HEED is more than H-HEED over the same number of rounds. The number of normal node dies very fast and as a result the sensing field becomes sparse very fast. On the other hand, advanced nodes and super nodes die in a very slow fashion. But in multi-level H-HEED, all the sensor nodes are having different energy as a result nodes will die randomly. In this we can say that multi-level H-HEED prolongs lifetime and shows better performance than other level of HHEED and HEED protocol.

[image: ]Figure 3 The total Remaining Energy in each Round

Figure 3, represents the total remaining energy of the network in each round. In this both HEED and H-HEED, the energy depletes very fast at constant rate. We can conclude that both 3-level H-HEED and multi-level H-HEED is more energy efficient. more residual energy so more packets will be sent to the base station. Thus, the H-HEED sends more effective data packets to the base station.

[image: ]

Figure 4. The Number of Packets Sent to the BS in each

Round

Figure 4 represents the number of packets sent to the BS in each round. In this, more packets is sent in the H-HEED in comparison with HEED, as advanced nodes and supernodes will be having more probability of becoming the cluster heads, due to more residual energy so more number of packets will be sent to the base station. Thus, the H-HEED sends more effective data packets to the base station.

6. Conclusion

In this paper, the life of sensor nodes are increased, and it is more helpful for the government to monitor the tree poaching in the forest . This was done by using the protocol called H-HEED. In this, we introduced different level of heterogeneity: 2-level, 3-level and multi-level in terms of the

node energy.

7. Acknowledgment

The authors would like to thank the anonymous reviewers and people who helped in carrying out this work successfully.

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