Keywords

1 Introduction

A sensor network is composed of a large no of sensor nodes, which are densely deployed either inside the phenomenon or very close to it. The no of nodes in sensor network can be several orders of magnitude higher than the nodes in an ad hoc network. Sensor network are densely deployed [9]. This tiny system, which consist of sensing, data processing and communication components are known as wireless sensor network [6].Sensor nodes are limited to power, computational capacities and memory. Sensor nodes mainly use broadcast, most ad hoc network are based on Point to Point. These nodes, with limited computing, communicating, and sensing capabilities as well as Limited energy can make the best use of themselves together data from sensor node to Base Station (BS) by using excellent network topologies, optimized routing scheme [8]. In simplest method these node get data from sensor to BS by using topology, the energy loss in LEACH is more than the Fix Cluster Head Selection (FCHS). WSN has limited supply of energy hence an energy conserving routing protocol is important for these networks. Therefore, while conventional network aim to achieve high quality of service provisions, sensor network focus primarily on power consumption. There are several power-aware routing protocol explained in the literature [2, 7, 8, 10]. Although the clustering can reduce the energy consumption, the main problem is that the energy consumption is concentrated on the cluster heads. In order to overcome this demerit, the issue in cluster routing is how to distribute the energy consumption. The representative solution is LEACH [3] which is a localized clustering method based on the probability model. All sensor nodes evenly elect itself as a cluster head based on the probability model to distribute the energy consumption. However, in some cases, in-efficient cluster heads can be elected. Because LEACH is only depend on probability model, some cluster heads may be very close each other and can be located in the edge of the WSNs. These in-efficient cluster heads could not maximize the energy efficiency [4]. Recently, Gupta [4] introduced a cluster head election method using fuzzy logic to overcome the defects of LEACH.

2 FCHS Algorithm Details

The FCHS was created with particular criteria such as Fixed cluster head selection, fixed location of sensor node that will cause less energy dissipation of cluster head and base station so that the protocol becomes computational simple as the static route could be used. FCHS is source initiates protocol with time driven reporting, so the sensor node would always have data to send to the base station. FCHS would also apply data aggregation to avoid information overload or access of data, in this aspect FCHS shares the same features as LEACH [5]. FCHS shows an analytic architecture for obtaining the stable probability with which a node would become a cluster head in order to minimize the network’s energy consumption. The analysis presented for a small network. FCHS change the choice of cluster head over time for minimum energy dissipation between the threshold level 0 and 1 to become clusters head (Table 1).

2.1 Proposed Fchs Algorithm for Cluster Head

Step1: Initialization

Cluster head change over time for minimum energy dissipation Decision is made by random number between 0 and 1 node become Cluster Head.

\(\mathrm{{p}}=\mathrm{{percentage}}\) of cluster head.

\(\mathrm{{r}}=\mathrm{{current}}\) round.

Table 1 System lifetime using different amounts of initial energy for the sensors
Full size table

Step2: Header Selection

  • If the number is less than the following threshold:

  • if(temp_rand\(< {=}\, \)(p/(1- p*mod(r,round(1/p))))

  • if(countCHs\(<{=}\,5\))

  • \(\mathrm{{countCHs}}=\mathrm{{countCHs}}+1\);

Step 3: Advertisement

  • After the selection of Cluster Head choose in between the threshold

  • The node broadcast a CHs_ADV advertisement to normal node.

  • Formation and function of Cluster head.

  • Next round is executed until rmax value.

Step 4: Management

  • Member nodes of each cluster send data to CHs.

  • CHs collects the data

  • CHs send the collected data to the BS.

Step5: Header Switch

  • If E(CH)\(<\)E’, the node become a header

  • If E of the node greater than E’, go to step2 total energy of the network

  • Remaining_\(\mathrm{{Energy}}\,{=}\,0\);

  • Total_\(\mathrm{{Energy}}=\mathrm{{n}}\)*((1-m)*Eo+m*(1+a)*Eo);

  • for \(\mathrm{{i}}=1\):n

  • Remaining_\(\mathrm{{Energy}}=\mathrm{{Remaining}}\)_Energy + S(i).E;

  • end

  • Energy_\(\mathrm{{Consumed}}=\mathrm{{Total}}\)_Energy-Remaining_Energy;

2.2 Simulation Results for FCHS and LEACH

figure a1

3 Comparison of FCHS and LEACH When the First Node Dies

Performance graph of System lifetime when the First node die using Different Energy Level with fixing no of cluster head shows the comparison of the FCHS and LEACH Initially, when clusters are being created, each node decides whether or not to become a cluster-head based on the suggested percentage of cluster heads for the network (determined a priori) and here in this graph Dotted (\(\text{-}\text{-}\text{-}\)) line shows when the first node of LEACH gets die shows the life time of the network system the number of times the node has been a cluster-head so far. This decision is made by the node n choosing a random number between 0 and 1. If the number is less than a threshold T(n)[1], the node becomes a cluster-head for the current round. The threshold is set as:

$$\begin{aligned} T(n)=\left\{ \begin{array}{l@{\quad }l}\dfrac{p}{1-P*(rmod\frac{1}{P})}&{\text{ if}}\ n\in G\\ 0&{\text{ otherwise}} \end{array}\right. \end{aligned}$$
figure a2

Where P \(=\) the desired percentage of cluster heads (e.g., \(\mathrm{{P}} = 0.10\)), \(\mathrm{{r}} = \mathrm{{the}}\) current round, and G is the set of nodes that have not been cluster-heads in the last 1/P rounds. Using this threshold, each node will be a cluster-head at some point within 1/P rounds. During round 0 (\(\mathrm{{r}} = 0\)), each node has a Clustering and Cluster-Head Selection Techniques used in WSN probability P of becoming a cluster-head. In the graph the rigid line shows the lifetime of the cluster head in the FCHS. Hence, the FCHS increase the lifetime of the network by fixing the choice of cluster head.

3.1 Comparison of FCHS and LEACH When the Last Node Dies

Performance graph of System lifetime when the Last node die using Different Energy Level with fixing no of cluster head shows the comparison of the FCHS and LEACH Here in this graph Dotted (\(\text{-}\text{-}\text{-}\text{-}\)) line shows when the Last node of LEACH gets die shows the life time of the network system the number of times the node has been a cluster-head so far. In the graph the rigid line shows the lifetime of the cluster head in the FCHS. Hence, the FCHS increase the lifetime of the network by fixing the choice of cluster head by increase the value by 1000 rmax [4].

figure a3

4 Conclusions

In WSN power efficiency is an important performance metrics that must be considered when designing the routing protocol. Objective of this paper is to design an energy aware routing protocol based on LEACH. Wireless Sensor networks (WSN) have emerged as a promising tool for monitoring (and possibly actuating) the physical world, we also compared the routing protocols according to different parameters since one single Routing protocol is not applicable in all the situations. We have performed detailed analysis of the LEACH protocol and the Direct Communication Protocol by running the simulations in MATLAB and by fixing the no of cluster head it enhanced the System Life time also the less energy dissipation. We have found that the LEACH protocol outperforms the Direct Communication Protocol in terms of Energy Dissipation. To form constant number of clusters Results show that FCHS is an efficient routing protocol when compared to conventional routing protocol LEACH, which has shown less lifespan of network. Simulation results show that FCHS reduces communication energy by as much as 105 % compared to LEACH. This is due to the fix cluster head selection, which results in optimal probability of becoming a cluster head. It could be concluded that an effective selection of a cluster head could reduce the usage of consumption power. Simulation also shows by minimizing the transmission distance of FCHS and LEACH would result in increase the lifetime of the network.