Description

Highlights

• This article discusses the optimized improved TEEN (Threshold-Sensitive Energy Efficient Sensor Network protocol) routing protocol in WSN.
• A MATLAB code is written for improved TEEN and evaluation is done on the criteria of residual energy, total alive nodes and total packet received.
• The TEEN clustering routing protocol specifies two thresholds known as the Hard threshold and the Soft threshold.
• The formulation of the TEEN routing protocol is completed in two phases [4]. Problem FormulationThe initial step is known as the setup phase for the cluster formation in WSN.
• The TEEN routing protocol is coming from the hierarchical clustering routing protocol that reduced the energy consumption and packet delivery ratio among the sensor nodes.

This article discusses the optimized improved TEEN (Threshold-Sensitive Energy Efficient Sensor Network protocol) routing protocol in WSN. A MATLAB code is written for improved TEEN and evaluation is done on the criteria of residual energy, total alive nodes and total packet received. An extensive comparison with the conventional TEEN with improved TEEN is also presented after code execution.

A wireless sensor network (WSN) has been detected as an economical solution for monitoring applications. The working principle of WSN is sensing physical quantities like temperature, humidity, radiation and pressure. The consumption of energy in WSN operation is high due to its sensing and transmission capabilities.

Introduction

Several numbers of sensor nodes built a WSN environment for environmental monitoring. The recorded information of WSN is passed to the base station in the transmission stage. The battery source powers each sensor node. A large amount of energy consumes for the sensing and transmission task. The routing protocols can manage the energy consumption during the sensing, processing and transmission stage. Media Access Protocol MAC reduces the sensing stage consumption energy. The hierarchical type routing protocols are used to reduce energy consumption during the processing and transmission stage. The LEACH (Low-energy adaptive clustering hierarchy), Threshold-Sensitive Energy Efficient Sensor Network protocol (TEEN) [1] and Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network (APTEEN) minimize energy utilization during processing and transmission.

Thesis statement

In this work, we select TEEN routing protocol to reduce the energy consumption of data transmission. The TEEN routing protocol improves the energy efficiency and lifetime of the network. A cluster formation process is arriving in the TEEN protocol for the sensor nodes. The sensor node has maximum residual energy selected as the cluster head (CH). The cluster sensor nodes transmit the data to CH, and CH transmits the received data to the base station. The cluster head selection in the improved TEEN protocol is made by the BAT optimization algorithm [5].

The testing of the BAT optimization algorithm for different benchmark function MATLAB code is available at free-thesis.com

Threshold-Sensitive Energy Efficient sensor Network(TEEN)

A TEEN protocol is coming from the category of the hierarchical clustering routing protocol.  The transmission process in the TEEN protocol is decided by hard threshold and the soft threshold value. The cluster head spread soft and hard threshold values to their sensor nodes with several attributes. The absolute value is known as the hard threshold value if a sensor nodes sense the value below a hard threshold value than the nodes update their newel detected value with the cluster head value. The difference between the two consecutive values of sense parameters is known as the soft threshold value. The nodes of the soft threshold value can send and transmit the attribute value to the cluster head. The time operation of the TEEN protocol is shown in figure 1

Figure 1 Time diagram for TEEN

The cluster head selection procedure of the TEEN protocol is similar as in LEACH protocol. The TEEN clustering routing protocol specifies two thresholds known as Hard threshold and Soft threshold. These two specific values represent the range of calculated values and variation of measurement value before and after. Some reactive and active modes are added, which can’t collect the data only to inform base station but reacts quickly during the changing environment. The formulation of the TEEN routing protocol is completed in two phases [4].

Problem Formulation

The initial step is known as the setup phase for the cluster formation in WSN. The transmission phase is known as the steady phase that sends data information to the sink nodes. The sensor nodes present in the TEEN protocol has threshold energy

P is cluster head percentage in all nodes,  is the round and set of nonselected cluster head nodes. The energy model is followed by TEEN protocol with the two-channel model; free space (d²) for single hope path and multipath fading (d⁴) for the multihop path. So the energy consumption of  bit packets over distance d is estimated as

Where e_fs = free space energy loss, e_mp = multipath fading loss, d= distance between source and destination node, d₀= crossover distance =square root of (e_fs/e_mp). The energy variable depends on the node distance, so via optimizing node distance, we can minimize the energy consumption at every sensor node in WSN. The Hard Threshold (H_T) and Soft threshold (S_T) are the limits of the constraints to the transfer data. If the sensed value of nodes is beyond the attribute value, then it is on the transmitter way and reaches to the cluster head. The small variation in the sensed attribute is known as the soft threshold, which means the node is a trigger and transmits the value.

Objective function

An objective function is formulated with the help of the energy consumption equation, which should satisfy all the limits of the network. The node’s energy in the WSN environment depends on the distance between them. So the objective function is designed with the nodes distance and energy variables. The fitness function used for our work is as:

The α₁ and α₂ are tradeoff factors and decide the weightage of distance and energy variables.

BAT optimized TEEN

We proposed a BAT optimization algorithm to minimize the TEEN protocol objective function as written in equation 3. The BAT is a Metaheuristic optimization algorithm developed in 2010. The echolocation nature of microbats inspires with increasing pluses rate of emission and loudness.

All BATS [5] use echolocation to measure distance and also know the difference between food and background barriers. Bat flies randomly have a velocity at a fixed position, with fixed frequency and increasing wavelength and loudness to search for food. The wavelength of bats emitted pluses is adjusted automatically and adjusts the rate of pulse emission. The loudness of bats varied from a higher range to a constant minimum range. The main task of optimization is that allocate cluster heads in each cluster. The node which consumed less energy in data transmission and reception is selected as the cluster head. The BAT algorithm minimizes the objective function written in equation 3. The position of Bats allocates the position of cluster head in each cluster.

Steps of the proposed method

All the scripts and WSN environment are developed in MATLAB 2018 software. The TEEN clustering routing protocol specifies two thresholds known as Hard threshold and Soft threshold. These two specific values represent the range of calculated values and variation of measurement values before and after. Some reactive and active modes are added, which can’t collect the data only to inform the base station but reacts quickly during the changing environment. The formulation of the TEEN routing protocol is completed in two phases. The initial step is known as the setup phase for the cluster formation in WSN. The transmission phase is known as the steady phase that sends data information to the sink nodes. Following steps are involved in the optimization of TEEN using BAT algorithm;

1. We develop a 100*100 geographical area in MATLAB 2018 software.
2. Initialize the parameters of TEEN routing protocol, number of nodes and total round.
3. Randomly deployed the 100 nodes in the developed area of wireless sensor network
4. Formulate an objective function based on the residual energy with respect to the node’s distance and placement as in equation 3.
5. Initialize the BAT optimization algorithm parameters and find the optimal value of an objective function with Hard and Soft Threshold limits of the TEEN protocol.
6. The optimal value of the objective function is providing a cluster head which has more residual energy.
7. The energy consumption via sensor nodes is minimized and improved residual energy of nodes after the optimization of the TEEN protocol using BAT algorithm.

A similar product Modified LEACH in WSN to Reduce Energy Consumption is available on the Free-thesis.com

Results

In this work, we developed a WSN network for the optimal transmission and reception of the data information. The residual energy of sensor nodes is minimized via the TEEN routing protocol implementation. The TEEN routing protocol is coming from the hierarchical clustering routing protocol that reduced the energy consumption and packet delivery ratio among the sensor nodes. All the scripts are developed in the MATLAB 2018 software.

We consider a 100*100 geographical area of WSN in MATLAB, as shown in figure 2. 100 sensor nodes are deemed to be placed in the selected area with the condition of the TEEN protocol. In the TEEN protocol, cluster formation is taking place for the efficient transmission of energy from one node to another node. The cluster head selection plays an essential role in the energy consumption of sensor nodes. The BAT optimization algorithm is proposed for the cluster head selection in the TEEN protocol.

Figure 2 WSN environment for 100 nodes placement in 100*100 geographical area

Figure 3 Residual energy comparison among BAT optimized TEEN and standard TEEN environment

BAT optimization reduces the energy consumption among the sensor nodes in the TEEN protocol. The residual energy is maximum in the case of BAT optimize TEEN protocol than the standard TEEN protocol. Total 2500 rounds show the proposed BAT optimized TEEN protocol achieves the better residual energy. In the first 100 rounds, the residual energy goes from 10 joules to 9.5 joules and 8 joules reaching to the 500 rounds in case of standard TEEN.

Figure 4 Alive nodes comparison among BAT optimized TEEN and TEEN routing protocol

Figure 5 Packets received comparison among BAT optimized TEEN and standard TEEN routing protocol

Conclusion

In this code, efficient TEEN routing is performed by BAT optimization algorithm. The improvement in residual energy and network lifetime is provided by BAT optimized TEEN protocol. The cluster head selection plays an essential role in terms of energy consumption.  In TEEN protocol the cluster head selection is made with the BAT algorithm. The proposed BAT optimized TEEN algorithm is compared with the standard TEEN routing protocol.

Published Paper similar to this work

1. Agrawal, Deepika, Muhammad Huzaif Wasim Qureshi, Pooja Pincha, Prateet Srivastava, Sourabh Agarwal, Vikram Tiwari, and Sudhakar Pandey. “GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs.” International Journal of Communication Systems 33, no. 8 (2020): e4344.
2. Sharawi, Marwa, and Eid Emary. “Impact of grey wolf optimization on WSN cluster formation and lifetime expansion.” In 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), pp. 157-162. IEEE, 2017.
3. Indrapandi, A., and S. Rizwana. “Energy Efficient Cluster based Data Aggregation using GWO Optimization with SPFA Technique for Wireless Sensor Networks.” Solid State Technology 63, no. 6 (2020): 24069-24083.
4. Pratha, S. Jaya, V. Asanambigai, and S. R. Mugunthan. “Grey wolf optimization based energy efficiency management system for wireless sensor networks.” (2021).

References

1. Ge, Yanhong, Shubin Wang, and Jinyu Ma. “Optimization on TEEN routing protocol in cognitive wireless sensor network.” EURASIP Journal on Wireless Communications and Networking2018, no. 1 (2018): 27.
2. Manjeshwar, Arati, and Dharma P. Agrawal. “TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks.” In ipdps, vol. 1, p. 189. 2001.
3. Khan, M. N., S. O. Gilani, M. Jamil, A. Shahzad, and A. Raza. “Efficient energy utilization in wireless sensor networks: an algorithm.” (2019).
4. Wang, Minghao, Shubin Wang, and Bowen Zhang. “APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm.” Ad Hoc Networks(2020): 102138.
5. Chawla, Mridul, and Manoj Duhan. “Bat algorithm: a survey of the state-of-the-art.” Applied Artificial Intelligence29, no. 6 (2015): 617-634.