Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorithms in which each cluster is managed by a leader called Cluster Head. Each other node must communicate with this CH to send the data sensing. The nearest base station nodes must also send their data to their leaders, this causes a loss of energy. In this paper, we propose a new approach to ameliorate a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding closest nodes to the base station in the clustering process. We show by simulation in MATLAB that the proposed approach increases obviously the number of the received packet messages and prolongs the lifetime of the network compared to TDEEC protocol.


INTRODUCTION
Wireless technologies have made significant progress in recent years, allowing many applications in addition to traditional voice communications and the transmission of high-speed data with sophisticated mobile devices and smart objects.In fact, they also changed the field of metrology especially the sensor networks and the smart sensors.The establishment of an intelligent sensor system requires the insertion of wireless communication which has changed the world of telecommunications.It can be used in many situations where mobility is essential and the wires are not practical.
Today, the emergence of radio frequency wireless technologies suggests that the expensive wiring can be reduced or eliminated.Various technologies have emerged providing communication differently.This difference lies in the quality of service and in some constraints related on the application and it environment.The main constraints to be overcome in choosing a wireless technology revolve around the following conditions [1], [2]: In this work, we studies using a comparative analysis, the different parameters which influence the performance and quality of a wireless communication system based on intelligent sensors taking into our consideration the cost and the application requirements.
We can classify the requirements of applications using smart sensors into three main categories as shown in table I. www.ijacsa.thesai.org

II. RELATED WORK
In the related work, many research studies in [3][4][5][6][7][8] have been focused on wireless sensor networks to improve communication protocols in order to solve the energy constraint, to increase the level of security and precision and to expand autonomy for accuracy, feasibility and profitability reasons.On the other side, the field of intelligent sensors remains fertile and opens its doors to research and innovation, it is a true technological challenge in so far as the topology and the infrastructure of the systems based on intelligent sensors are greatly different compared to wireless sensor networks, particularly in terms of size (number of nodes) and routing.In fact, to preserve the quality of these networks, it is very difficult even inconceivable to replace regularly the faulty nodes, which would result in a high cost of maintenance.The concept of energy efficiency appears therefore in communication protocols, [5][6][7][8][9].Thus, it is very useful to search the optimization of data routing and to limit unnecessary data sending and the collisions [6], [9].The aim challenge for intelligent sensors systems is to overcome the physical limitations in data traffic such as system noise, signal attenuation, response dynamics, power consumption, and effective conversion rates etc… This paper emphasis on the metrics of performance for wireless protocols which stands for superior measurement, more accuracy and reliability.The object of this study is for realizing an advanced intelligent sensors strategy that offers many system engineering and operational advantages which can offer cost-effective solutions for an application.

III. NEW CONSTRAINTS OF INTELLIGENT SENSORS SYSTEM
An intelligent sensor is an electronic device for taking measurements of a physical quantity as an electrical signal, it intelligence lie in the ability to check the correct execution of a metrology algorithm, in remote configurability, in its functions relating to the safety, diagnosis, control and communication.

1) A measuring chain controlled by microcontroller 2) A bidirectional communication interface with the network, providing the connection of the sensor to a central computer
The communication part reflects all the information collected by an intelligent sensor and allows the user to configure the sensor for operation.It is therefore absolutely essential that this interface be robust and reliable.Figure 1 illustrates the intelligent sensor with its wireless communicating component.A variety of communication interfaces (wireless modules) is available, but not all sensors support these interfaces.The designer must select an interface that provides the best integration of the sensor with the others components of the system taking in our account the costs and the constraints of reliability required for a particular application.
There are others solutions to collect remote measurements such mobile and satellite communications.The main problems related to the quality of communications are: attenuation problems (distance, obstacles, rain ...), interference and multipath.The realization of the systems based on smart sensors dedicated to the applications mentioned in section I, requires the techniques and the protocols that take into account the following constraints [3]: The sensors are limited in energy, in computing capacity, and in memory.In ad-hoc networks, energy consumption was considered as an aim factor but not essential because energy resources can be replaced by the user.These networks are more focused on the QoS than the energy consumption.Contrariwise, in sensor networks, the transmission time and energy consumption are two important performance metrics since generally the sensors are deployed in inaccessible areas.

IV.
SENSORS TECHNOLOGY AND OPTIMAL TOPOLOGY The communication topology of the intelligent sensor systems is divided into two categories:

A. Direct Communication
The intelligent sensors deployed in a capture zone communicate directly with the base station via a radio link as shown in figure 2, the server collect and processes the measurements data and stores it in a database.

B. Indirect Communication
In this case, the intelligent sensor communicates, via a GPRS network providing Internet connectivity, with the server of the monitoring center as shown in figure 3.With this architecture, it is possible to establish communications for applications that have a wider monitoring area which spreads for kilometers or when the application requires large dimensions. V.

THE COMPARATIVE PERFORMANCE ANALYSIS
In this section, we put importance with a comparative study the following wireless protocols: Bluetooth, UWB, ZigBee, ZigBeeIP, Wi-Fi, Wi-Max, GSM/GPRS which correspond to the standards IEEE 802.15.1, 802.15.3, 802.15.4,802.11a/b/g, 802.16 and 850-900 DCS PCS respectively [14], [15].Based on the characteristics of each standard, obviously noticed that the UWB, Wi-Fi and Wi-Max protocols provides a higher data rate, while Bluetooth, ZigBee and GPRS provide a lower level.
Contrariwise, Bluetooth, UWB and ZigBee are intended for WPAN communication due to their range of coverage which reaches 10 m, while Wi-Fi is oriented WLAN with a range of about 100 m.However, Wi-Max and GPRS have a coverage radius of a few tens of kilometers for a WMAN network.Table II summarizes the main differences between the mentioned protocols.

A. Network Size
The size of the GPRS network can be balanced according to the interference level, the size of data packets during traffic, the transmission protocols implemented and the number of users connected to the GSM voice services, this influences the number of GPRS open sessions which can reach 1000 to a single cell.ZigBee star network take the first rank for the maximum number of nodes that exceeds 65000, in second place there is the Wi-Fi network with a number 2007 of nodes in the BSS structure, while the Wi-Max network has a size of 1600 nodes, UWB allows connection for 236 nodes in the piconet structure, finally we found the Bluetooth which built its piconet network with 8 nodes.All these protocols have a provision for more complex network structures built from basic cells which can be used to extend the size of the network.

B. Transmission Time
The transmission time depends on the data rate, the message size, and the distance between two nodes.The formula of transmission time in (µs) can be described as follows: The typical parameters of the different wireless protocols used to evaluate the time of transmission are given in Table III.From the figure 4, it is noted that the transmission time for the GSM/GPRS is longer than the others, due to its low data rate (168 Kb/s) and its long range reasons, while UWB requires less transmission time compared to the others because its important data rate.
It clearly shows that the required transmission time is proportional to the data payload size N data and it is not proportional to the maximum data rate.www.ijacsa.thesai.org

C. Transmission power and range
In wireless transmissions, the relationship between the received power and the transmitted power is given by the Friis equation as follows [1], [33], [36][37][38][39][40]: P t the transmitted power P r the received power G t the transmitting omni basic antenna gain G r the receiving antenna gain D the distance between the two antennas λ the wavelength of the signal From equation (2) yields the formula the range of coverage as follows: We note that as the frequency increases, the range decreases.The figure 5 shows the variation of signal range based on the transmission frequency for a fixed power.The most revealing characteristic of this graph is the non-linearity.The signals of GSM/GPRS with 900MHz propagate much better than ZigBee, Wi-Fi, Bluetooth with 2.4GHz and UWB with 3.1GHz vice to vice coverage area.

D. Energy consumption
The energy consumption for intelligent sensor involves three steps: acquisition, communication, computation and data aggregation.This consumption in the acquisition operation depends on the nature of the application [3].Data traffic, particularly in the transmission, consumes more energy than the other operations.It also depends on the distance between the transmitter and receiver [4], [5].According to the radio energy model, [6], [38][39][40][41][42][43][44] the transmission power of a k bit message to a distance d is given by: The electronic energy E Elec depends on several factors such as digital coding, modulation, filtering, and signal propagation, while the amplifier energy depends on the distance to the receiver and the acceptable bit error rate.If the message size and the range of communication are fixed, then if the value of α grow, the required energy to cover a given distance increase also.

The model governing the energy consumption E(p) of an intelligent sensor p depending on the communication range d(p) is given as follows
The figure 6 illustrates the evolution of the energy consumption for ZigBee protocol based on the signal range.We can say that an increase in data packet size allows then an increase of the transmission energy.The equations ( 4) and ( 5) can be generalized for the all wireless mentioned protocols.The simulation parameters are given in table IV.The predicted received power by an intelligent sensor for each data packet according to the communication range d is given by the Two-Ray Ground and the Friss free space models [3], [35], [40] as follows: L the path loss h t the height of the transmitter antenna h r the height of the receiver antenna d the distance between transmitter and receiver The figure 7 shows the evolution of the reception power based on the signal range for the different studied protocols for a fixed data packet size: According to this figure, it is noted that when the distance between the transmitter and the receiver increases, the received power decreases, this is justified by the power loss in the path.The ZigBee, UWB and Bluetooth have low power consumption while Wi-Max, Wi-Fi and GPRS absorb more power due to theirs high communication range reason.

E. Chipset power consumption
To compare practically the power consumption, we are presents in the table VI the detailed representative characteristics of particular chipset for each protocol [44][45][46][47][48][49].The figure 8 shows the consumption power in (mW) for each protocol.Obviously we note that Bluetooth and ZigBee consume less power compared to UWB, Wi-Fi, Wi-Max and a GPRS connection.The difference between the transmission power and reception power for the protocols GPRS and Wi-Max is justified by the power loss due to the attenuation of the  Based on the data rate of each protocol, the normalized energy consumption in (mJ/Mb) is shown in the figure 9, shows clearly in this figure that the UWB, Wi-Fi and Wi-Max have better energy efficiency.In summary, we can say that Bluetooth and ZigBee are suitable for low data rate applications with a limited battery power, because of their low energy consumption which promotes a long lifetime.Contrariwise for implementations of high data rate, UWB, Wi-Fi and Wi-Max would be the best solution due to their low normalized energy consumption.While for monitoring and surveillance applications with low data rate requiring large area coverage, GPRS would be an adequate solution.

F. Bit error rate
The transmitted signal is corrupted by white noise AWGN (Additive White Gaussian Noise) to measure the performance of the digital transmissions (OQ-B-Q-PSK, 4PAM, 16QAM, GMSK, GFSK, 8DPSK, 8PSK and OFDM), seen in the table II, by calculating the bit error probability.The purpose of a modulation technique is not only the transfer of a data packet by a radio channel, but also achieves this operation with a better quality, energy efficiency and less bandwidth as possible.The BER for all systems decreases monotonically with increasing values of E b /N 0 , the curves defining a shape similar to the shape of a waterfall [36], [38].The BER for QPSK and OQPSK is the same as for BPSK.We note that the higher order modulations exhibit higher error rates which thus leads to a compromise with the spectral efficiency.
QPSK and GMSK seem the best compromise between spectral efficiency and BER followed by other modulations.These two robust modulations are used in Wi-MAX, ZigBee, Wi-Fi and in GPRS network, can be employed in the noisy channels and in the noisy environments.However, because of their sensitivity to noise and non-linearities, the modulations 4PAM and 8DPSK remain little used compared to other modulations.
Concerning the QAM modulation, it uses more efficiently the transmitted energy when the number of bits per symbol increases; this provides a better spectral efficiency and a high bit rate.As for the frequency hopping FSK modulations, the increase of the symbols will enable reduction of the BER but also increase the spectral occupancy.The main fault of these FSK modulations is their low spectral efficiency.www.ijacsa.thesai.orgOn the other side, the GMSK modulation has been developed in order to increase the spectral efficiency [50].It has a satisfactory performance in terms of BER and noise resistance.This modulation is applied in the data transmission systems (MODEM), in The GSM networks [9], [35], [37], [39], [41].The table VII gives the values of E b /N 0 which cancel the BER for each modulation.Furthermore, the lower bit error probability is obtained to the detriment of the number of users.We must investigate the relationship between the transmission quality and the number of users served [50].

G. Data coding efficiency
The coding efficiency can be calculated from the following formula: Based on the figure 11, the coding efficiency increases when the data size increase.For small data size, Bluetooth and ZigBee is the best solution while for high data sizes GPRS, UWB, Wi-Max and Wi-Fi protocols have efficiency around 94%.
In the applications point of view, for the automation industrial systems based on intelligent sensors, since most data monitoring and industrial control have generally a small size, such the pressure or the temperature measurements that don't pass 4 bytes and that don't require an important data rate, Bluetooth, ZigBee and GPRS can be a good choice due to their coding efficiency and their low data rate.On the other hand, for applications requiring a large cover zone as the borders monitoring, the persons tracking or the environmental monitoring or the event detection, GPRS and Wi-Max are an adequate solution, whereas for the multimedia applications requiring an important data rate such the video monitoring, Wi-Fi, UWB and Wi-Max form a better solution.CONCLUSION We have presented in this paper a comparative performance analysis of six wireless protocols: Bluetooth, UWB, ZigBee, Wi-Fi, Wi-Max and GSM/GPRS.However, it exists others wireless protocols as 6LoWPAN, DASH, HiperLAN…We have chosen therefore to land just the most popular ones.A quantitative evaluation of the transmission time, the data coding efficiency, the bite error rate, and the power and the energy consumption in addition of the network size permitted us to choose the best protocol which is suitable for an application based on intelligent sensor.Furthermore, the adequacy of these protocols is influenced strongly by many others factors as the network reliability, the link capacity between several networks having different protocols, the security, the chipset price, the conformity with the application and the cost of installation that must be taking in consideration.Facing the fact that several types of wireless technologies can coexist in a capture environment, the challenge which requires is to develop a gateway (multistandard transceiver) that enables the data exchange between these heterogeneous infrastructures with a good quality of service.This approach would allow the implementation of solutions for maintaining and for monitoring while minimizing the necessary resources and avoiding the costs associated to the compatibility testing.Solving this challenge is a

Fig. 1 .
Fig. 1.Block diagram of an intelligent sensor communication

Fig. 2 .Fig. 3 .
Fig. 2. Direct communication with the monitoring center between two nodes to be neglected in this paper

Fig. 4 .
Fig. 4. Comparison of transmission time relative to the data size

Fig. 7 .
Fig. 7.The received power depending on the signal range with fixed message size GPRS f=900 MHz www.ijacsa.thesai.orgsignal in the communication path since both of these protocols have a large coverage area.

Fig. 9 .
Fig. 9. Comparing the chipset normalized energy consumption for each protocolThe bit error rate is a very good way to measure the performance of the modulation used by a communication system and therefore helps to improve its robustness.It is calculated by the following formula:

Fig. 11 .
Fig. 11.Coding efficiency depending on data size VII.CONCLUSION We have presented in this paper a comparative performance analysis of six wireless protocols: Bluetooth, UWB, ZigBee, Wi-Fi, Wi-Max and GSM/GPRS.However, it exists others wireless protocols as 6LoWPAN, DASH, HiperLAN…We have chosen therefore to land just the most popular ones.A quantitative evaluation of the transmission time, the data coding efficiency, the bite error rate, and the power and the energy consumption in addition of the network size permitted us to choose the best protocol which is suitable for an application based on intelligent sensor.
GSM/GPRS www.ijacsa.thesai.orgperspective and a continuation of this work.It turns out that the choice of a modulation type is always determined by the constraints and the requirements of the application.The BER is a parameter which gives an excellent performance indication of a radio data link.

TABLE I .
NEEDS BASED APPLICATIONS

Types of application Specifications and Needs
 Permanent connection www.ijacsa.thesai.org

TABLE III
+ Where the data is 10 Kbytes.*For TCP/IP Protocol

TABLE VI .
POWER CONSUMPTION CHARACTERISTICS OF CHIPSETS