International Journal of Electronics and JOURNALEngineering & Technology (IJECET), ISSN 0976 –
INTERNATIONAL Communication...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
Upcoming SlideShare
Loading in …5
×

40120130406001

192 views
106 views

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
192
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

40120130406001

  1. 1. International Journal of Electronics and JOURNALEngineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL Communication OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December, 2013, pp. 01-13 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME AN EFFICIENT CLUSTER HEAD SELECTION SCHEME FOR DYNAMIC SLEEP TIME IN WIRELESS SENSOR NETWORK Rashid M. Awadi1, Rawya Y. Rizk2, Mohamed I. Habib2, and Amira A. M. Elsonbaty3 1 Dept. of Communication, Faculty of Engineering, Mansoura University, Egypt. 2 Electrical Engineering Department, Port Said, Egypt. 3 Higher Institute of Computer and Business Administration, El zarka, Damietta, Egypt. ABSTRACT Energy consumption is the key challenge in the Wireless Sensor Networks (WSNs). Recent advances in WSNs enable us to develop minimum energy consumption clustering algorithms. Clustering is an effective way for reducing energy consumption of sensor nodes as well as the cost of transmission. One of the other main sources of energy waste in a WSN is idle listening, i.e., nodes consuming energy to sample an idle channel. The sleep time is an approach utilizes known traffic statistics and optimally controls the sleep interval between consecutive wake-ups of the receiver so that the expected total energy spent during each transmission is minimized. This paper presents a complete solution of the energy consumption problem in WSNs. It combines a dynamic sleep time approach, and an efficient cluster head selection scheme to save energy in WSNs. Simulation results are included to compare fixed sleep times to dynamic control policy time. This study classifies the lifetime into different types and gives the corresponding cluster head selection method to achieve the life-time extension objective. Simulation results show that the energy is significantly reduced compared with the previous clustering based routing algorithm for the sensor networks. Keywords: Data aggregation, Clustering based algorithms, dynamic sleep, Energy efficiency, Wireless Sensor Networks, WSNs. 1. INTRODUCTION Due to existing and emerging applications in various situations, wireless sensor networks (WSNs) have recently emerged as a premier research topic [1, 2]. A WSN consists of a number of small-sized sensor nodes spreading over a geographical area and a sink node where the end user can access data. All nodes are equipped with capabilities of sensing, data processing, and communicating with each other by means of a wireless ad hoc network. A wide range of tasks can be performed by these tiny devices, such as condition-based maintenance and the monitoring of a large area with respect to some given physical quantity [3]. 1
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME One of the limitations of wireless sensor nodes is their inherent limited energy resource. Besides maximizing the lifetime of the sensor node, it is preferable to distribute the energy dissipated throughout the WSN in order to minimize maintenance and maximize the overall system performance. To support scalability, nodes are often grouped into disjoint clusters. Each cluster would have a leader, often referred as cluster head (CH). A CH is responsible for not only the general request but also assisting the general nodes to route the sensed data to the target nodes. The power-consumption of a CH is higher than of a general (non-CH) node. Therefore, the CH selection will affect the lifetime of a WSN. Low Energy Adaptive Clustering Hierarchy (LEACH) is a well known clustering based protocol that utilizes randomized rotation of local cluster base stations to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data aggregation into the routing protocol to reduce the amount of information that must be transmitted to the base station. The cluster heads are randomly chosen in order to randomize the distribution of the energy consumption and load among the sensors, and therefore taking the first step towards evenly distributing the energy consumption through the system’s lifetime. It has the disadvantage of irrationality of choosing cluster head and of unbalanced energy consumption of nodes [4]. The sleep time is an approach utilizes known traffic statistics and optimally controls the sleep interval between consecutive wake-ups of the receiver so that the expected total energy spent during each transmission is minimized. This paper proposes a data aggregation operation based on a scalable cluster architecture whose basic idea is as follows: First, position information is used to select some sensors as CHs. Then further it organizes those nodes into clusters. As in LEACH approach, which is broken up into rounds, where each round begins with a set-up phase when the clusters are organized, followed by a steady-state phase when data transfers to the base station occur. In order to minimize overhead, the steady-state phase is long compared to the set-up phase. In each cluster, dynamic sleep time is applied with data aggregation. In this paper, a complete solution of the energy consumption problem in WSNs is presented. An efficient cluster head selection scheme to select the cluster-heads based on the required energy to do the transmission to the sink is presented. A dynamic sleep time approach is also proposed to save energy in WSNs. The rest of this paper is organized as follows. Section 2 presents the proposed approach. Section 3 presents the simulation results of the proposed protocol and comparisons with the closely related protocols. Finally, Section 4 introduces the main conclusions and the future work. 2. THE PROPOSED SOLUTION TO ENERGY EFFICIENT PROBLEM One of the most important things that affect the performance of the system and to reduce the energy is the optimal choice for the CH, according to a number of factors (residual energy, distance, etc,). Another energy saving solution is to use a sleep time. This paper suggest two algorithms. First algorithm for head selection is the Associated Cluster Head Array (ACHR). The second algorithm is the Dynamic Sleep time for Aggregationed Data (DSDA). It uses dynamic sleep time rather than fixed sleep time to reduce the time wasted and thus wasted energy. 2-1. The Proposed ACHR Algorithm This paper applies head selection algorithm where the whole network is separated into a number of clusters. Each cluster has a CH. Fig (1) shows the pseudo code of the head selection algorithm. 2
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME In set-up phase, the sensor nodes form the constant cluster and the cluster-head elected based on the residual energy of the individual node calculation with constant clustering and the node scheduling scheme is adopted in each cluster. In this paper, static clusters are formed using virtual points whose locations are determined by the sink. First of all, all sensor nodes check their locations using the low-power GPS device. Then, they send their coordinates to the sink. The CH communicates with the local base station, then the local base station feed data to the entire network of base stations, and terminal user can access useful information. The cluster formation of the proposed approach aims at balancing the energy load of CHs. Each CH is responsible only for received data from the cluster members, performs aggregation process over the received data and then to the BS. BS chooses the cluster heads according the current information of nodes at the beginning of each round. In each cluster, termed as Ci, one of sensor nodes in Ci is elected as the cluster head CHi. A CH is responsible for receiving the sensed data of the other sensor nodes in cluster and routing to the sink. The CH is selected from the sensor nodes in the same cluster, where the selection is performed round-by-round. Therefore, the energy consumption of the CH is higher than of the other nodes. In order to balance the energy consumption for elongating the lifetime of this WSN, the CH in a cluster is alternate among sensor nodes. Therefore, the CH selection manner will affect the lifetime of this network. When a residual energy of CH node falling below the threshold value, it triggers a new CH candidacy event by informing the BS that it is unable to perform its duties as a CH any more. Subsequently the BS will inform this to all other CHs thus initiating a CH rotation phase. The next round cluster head is selected in advance to avoid the deadlock when an old cluster head dies. The proposed algorithm suggested associated CH array which contains Node- ID and Location-ID. It orders decreasing by residual energy of sensors. Cluster nodes gathering data from environment and send it to the CH. While processing of Cluster head node the energy is reduced. So if the energy of CH becomes below to the non-cluster head nodes energies means next round should be processed. ACHR calls the next CH for each cluster to start the new round. In this algorithm the formation cluster and members of each cluster is fixed. It doesn't need re_clustering. Pseudo code 1: The ACHR algorithm 1 Setup ( ) 2 Sensor nodes send information about its current location to the base station 3 Geographical group the data 4 Determining good clusters 5 In each cluster, all sensor nodes sent energy level to BS 6 If sensor node with highest energy level then 7 Choose CH and unicast the information to the member node 8 Else 9 Choose nodes as member node 10 End if 11 Nodes send data to CH (transmission uses a minimal energy) 12 The radio of each non CH can be turned off until the nodes allocated transmission time 13 CH performs data aggregation 14 Aggregated data is sent to BS (transmission takes high energy) 15 Decreasing ordered array according the energy level 16 If (CH's residual energy < the threshold value) 17 New round without selection the CH using ACHR 18 New rounds without re_clustering 19 With each new round choose the next ID. 20 End. Fig (1): Pseudo code 1: Head selection algorithm. 3
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Energy Consumption This paper assumes that nodes operate in a periodic sleep schedule where each sleep period consists of a sleep interval and a wake-up interval. The sleep interval is the time duration when the node’s radio is off in each sleep period. The wake-up interval is the time duration that a node has its radio on to transmit packet. This paper applied a simple model of [5] for the radio hardware energy dissipation where the transmitter dissipates energy to run the radio electronics and the power amplifier, and the receiver dissipates energy to run the radio electronics. Both the free space and the multipath fading channel models were used, depending on the distance between the transmitter and receiver. Power control can be used to invert this loss by appropriately setting the power amplifier, if the distance is less than a distance threshold, the free space (fs) model is used; otherwise, the multipath (mp) model is used. Fig. (20 show the four major components and associated energy cost parameters of a typical sensor node. To compute the energy consumption, the equations (1-10) are considered. Table (10 presents the parameters used in the head selection algorithm. The following assumptions are considered in the network model. • All micro-sensor nodes deployed within a square area are homogeneous. • All micro-sensors and the BS are stationary after deployment. • All micro-sensor nodes use only the initial battery power and are not recharged. • All micro-sensor nodes are location-aware (e.g., sensor nodes are equipped with GPS receivers or location detection devices). Table (1): The Parameters of the head selection algorithm. L ‫ܧ‬௘௟௘௖ ‫ܧ‬௔௠௣ ‫ܧ‬௙௦ ‫ܧ‬஽஺ ‫ܧ‬௜௡௧௘௥ ‫்ܧ‬௫௜,௝ ‫ܧ‬ோ௫௜ ECH Emem Ecluster N N D d0 ଶ ݀௧௢஼ு The number of bits in each data message The per bit energy dissipation for running the transceiver circuitry The amplifier parameter for the multipath propagation The amplifier parameter for the free space propagation The energy for data aggregation The energy dissipation of data transmission from the cluster head to the base station The energy dissipation for transmitting an l bit message from the transmitter to receiver Energy required for receiving an l bits message The energy dissipation of the cluster head ECH The energy consumed by one member node The average energy dissipated in cluster Number of nodes Number of clusters Distance from the transmitter to receiver Threshold distance Distance from the member node to the cluster head 4
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Fig (2): Four major components and associated energy cost parameters of a typical sensor node. ‫א‬௙௦ (1) d0=ට‫א‬௔௠௣ ସ ‫்ܧ‬௫௜,௝ ൌ ‫ܧ‬௘௟௘௖ . ݈ ൅ ‫ܧ‬௔௠௣ . ݀௜.௝ . ݈ (Multipath d<d0) (2) ଶ ‫்ܧ‬௫௜,௝ ൌ ‫ܧ‬௘௟௘௖ . ݈ ൅ ‫ܧ‬௙௦ . ݀௜.௝ . ݈ (Free space d≥d0) (3) ECH=݈‫ܧ‬௘௟௘௖ ቀ ௡ െ 1ቁ ൅ ݈‫ܧ‬஽஺ ௡ ൅ ‫ܧ‬௜௡௧௘௥ (5) ‫ܧ‬ோ௫௜ ൌ ݈. ‫ܧ‬௘௟௘௖ ே (4) ே ଶ ‫ܧ‬௠௘௠ ൌ ‫ܧ‬௘௟௘௖ . ݈ ൅ ‫ܧ‬௙௦ . ݀௧௢஼ு . ݈ Ecluster=ECH+( ே ௡ Etotal =n Ecluster (6) െ 1ሻ‫ܧ‬௠௘௠ (7) The average area of the cluster is approximately The area is Square=m2….m=ට ௡ ௅మ ௅మ ௡ (8) (9) (10) 2-2. the proposed DSDA algorithm This work dynamically adjusts the sleep interval of each node based on the delay constraint and the network condition changes. The dynamic sleep time between following channel polling uses available statistical network traffic information and prior observations. It allows the sleep time to dynamically change in each sleep interval according to a message sent to the receiver. In [6], a dynamic sleep time control method is presented. However, the sleep time obtained is a conservative one since it guarantees the expected delay conditioning on the arrival occurs during the sleep time, which is a strict constraint. Also this approach only focuses on the next message. In this paper, a dynamic sleep time control is proposed where it can batch a few messages together and share the same preamble, and overcome expected delay constraint. The original fixed sleep time is divided into two parameters: a fixed “expected delay” and the corresponding dynamic sleep time. Table (2) presents the list of parameters used in the dynamic sleep algorithm. 5
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Table (2): The list of parameters used in the dynamic sleep algorithm. Variables Definition F(x) Uniform distribution function Zt Sleep time D Random preamble delay denoted [a, b] Inter_arrival time distribution is uniform V Max (t, a) data Time (i) Interarrival time time2listen(i), time2sleep (i) The time remaining for a node to be in the listen or sleep state Tagg The actual aggregation period Taggr Τ Deferred period for aggregation cycle Time The duration of the frame (cycle) Sleep _ state Nodes have turned off their radio for communication; Can still sense and process data; Nodes cannot transmit and receive packets from other nodes in the network. Listen _state Nodes send and receive data packets. During that time aggregation can be performed. The following equations are used in the dynamic time sleep control. A uniform distribution U [a, b], where 0 ≤ a < b is considered [7]. z୲ =ቐ ഥ 2D ൅ v െ t, ഥ ൑ ଵ ሺb െ vሻ D ଶ ଵ ഥ ଵ D ൅ ଶ ሺb െ tሻ ൅ ଶ ሺv െ tሻ, 0, ‫ۓ‬ ۖx െ a Fሺxሻ ൌ , ‫۔‬b െ a ۖ ‫ە‬ 1, x൏ܽ (11) ഥ ൐ ଵ ሺb െ vሻ D ଶ (12) a൑x൏ܾ x൒b Data Aggregation Data aggregation aims to combine responses from multiple sensors into a single message. Reducing the number of messages transmitted in a network can greatly reduce the amount of energy consumed [8, 9]. Fig. (2) shows the Pseudo code that determines the values of the data aggregation period, Taggr, for the different states and cases. 6
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Pseudo code 2: Data aggregation time calculation. 1 BEGIN 2 data Time (i) 3 time2listenTime (i) 4 time2sleep (i) 5 Tagg = 0 6 If τ > listen Period AND status = sleep 7 Tagg = time2listenTime + cycle Time 8 else if τ > listen Period AND status = listen 9 Tagg = time2listenTime 10 else if τ < listen Period AND status = sleep 11 Tagg = time2listenTime + τ 12 else if τ < listen Period AND status = listen (if sufficient time to be sent in the same cycle) 13 Tagg = τ 14 Else Tagg = time2listenTime 15 END Fig (3): Pseudo code 2: The data aggregation time calculation. 3. SIMULATION RESULTS 3-1. ACHR Algorithm This section presents the performance of the proposed head selection algorithm. The simulation is done with different numbers of nodes randomly deployed between (x=0, y=0) and (x=100, y=100) by meter. Simulation carries out a comparison between the proposed algorithm, and the efficient cluster head selection scheme for data aggregation (ECHSSDA) that was presented in [10]. Table (3) shows the required time needed for completing both ACHR algorithm and ECHSSDA algorithm. It is clear that the proposed ACHR greatly saves the time with respect to the ECHSSDA algorithm. Table (3): The required time needed for completing ACHR algorithm and ECHSSDA algorithm. N_nodes Algorithm 100 500 100 500 100 ECHSSDA 1000 2400 3800 5200 1000 100 1000 100 ECHSSDA 2000 2400 6400 8600 1600 100 1600 100 ECHSSDA 2000 4200 6400 8600 1800 100 1800 100 ECHSSDA 3600 7200 1080 14400 ACHR 80 cluster4 ACHR 60 cluster3 ACHR 40 cluster2 ACHR 20 cluster1 ACHR 2500 100 2500 100 ECHSSDA 14400 10800 7200 3600 7
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Figures (4-6) show that the total required time spent in ECHSSDA algorithm is larger than the total required time spent in ACHR algorithm. Only one cluster is created in ACHR algorithm and remains constant throughout the time so it does not consume extra time with each cycle for the establishment of a group as it happens with ESSCHDA with more required time. Fig (4): The required time (ARCH.vs.ECHSSDA) for 20 nodes. Fig (5): The required time (ARCH.vs.ECHSSDA) for 40 nodes. 8
  9. 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Fig (6): The required time (ARCH.vs.ECHSSDA) for 100 nodes. 3-1. DSDA Algorithm This section presents the performance of the proposed DSDA algorithm with uniform distribution function. In this simulation, our experiment model performed on 100 nodes which were randomly deployed and distributed in a 100×100 m2 area. Sensor nodes contain two kinds of nodes: sink nodes (no energy restriction) and common nodes (with energy restriction). All nodes have no mobility since the nodes are fixed in most of the applications of WSNs. Aggregation time were applied in dynamic sleep time equations. Long preamble time is double the event's arrival times, but short preamble is the event's arrival time. Tables (4) and (5) present the sleep time control obtained under uniform distribution (short long) variable preamble. Figures (7-10) show how applied the principle of dynamic sleep time is much better than fixed sleep time. As well as, the use of long or short variable preamble is better than fixed preamble periods because it helps to reduce power consumption and also idle times. It is also shown that the principle of data collection helps to enlarge sleep time so reduce the energy consumed in the data transfer. Table (4): The sleep time control obtained under uniform distribution (short - long) variable preamble. Zt(sleep) AGE (Ti) 4.5 46 22 38 36 51 16 19 59 30 54 12 39 Long preamble µs(2×y) Downlink 9 92 44 76 72 102 32 38 118 60 108 24 78 Short preamble µs(y) Uplink 4.5 46 22 38 36 51 16 19 59 30 54 12 39 9 short preamble Long preamble 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 59.5 64 105.5 81.5 97.5 95.5 110.5 75.5 78.5 118.5 89.5 113.5 71.5 98.5
  10. 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Fig (7): The sleep time control obtained under uniform distribution. Fig (8): The sleep time control obtained under uniform distribution (short - long) variable preamble. 10
  11. 11. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Table (5): The sleep time control obtained under uniform distribution (aggregation messages) for (short-long) variable preamble. AGE (Ti) 0.19 9.5 4.5 46 22 38 36 7.55 51 16 19 59 30 54 12 39 31 Long preamble µs(2×y) Downlink 0.38 19 9 92 44 76 72 15.1 102 32 38 118 60 108 24 78 62 Short preamble µs(y) Uplink 0.19 9.5 4.5 46 22 38 36 7.55 51 16 19 59 30 54 12 39 31 Tagg long preamble short preamble 30.19 39.5 34.5 210 52 210 210 37.55 210 46 49 210 60 210 42 210 210 89.88 108.5 98.5 315.5 133.5 307.5 305.5 104.6 320.5 121.5 127.5 328.5 149.5 323.5 113.5 308.5 300.5 89.69 99 94 269.5 111.5 269.5 269.5 97.05 269.5 105.5 108.5 269.5 119.5 269.5 101.5 269.5 269.5 Fig. (9): The sleep time control obtained under uniform distribution (aggregation messages) for (short-long) variable preamble. 11
  12. 12. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Fig. (10) shows different cases of sleep time control obtained under uniform distribution. 4. CONCLUSION This paper provides a complete solution of energy consumption in WSNs. Two algorithms are proposed. First one for head selection is the Associated Cluster Head Array (ACHR) that helps to reduce the energy due to the optimal choice for the CH. The second algorithm is the Dynamic Sleep time for Aggregationed Data (DSDA). It uses dynamic sleep time rather than fixed sleep time to reduce the time wasted and thus wasted energy. Simulation results conclude that the total required time spent in the proposed ACHR algorithm is much smaller than it in the case of ECHSSDA algorithm. Simulation results also show that the proposed dynamic sleep time with data aggregation model consumes less power less than a fixed sleep time in WSNs, either with single message, or aggregation message. Use long preamble period gives a 10% longer than short preamble period, but the use of long periods of preamble without purpose gives more consumption energy. Future versions of this work will include an energy-based threshold to account for nonuniform energy nodes. The duration of the steady-state phase, and the complexity of the environment will be researched in the future. REFERENCES [1] [2] [3] A. Willig, K. Matheus, and A.Wolisz, "Wireless technology in industrial networks", Proceedings of the IEEE, vol. 93, no. 6, pp. 1130–1151, 2005. A. Wheeler, "Commercial applications of wireless sensor networks using zigbee", IEEE Communications Magazine, vol. 45, no. 4, pp.70 –77, 2007. X. Jia, J. Wu, and Y. He," Minimum Data Aggregation Time Problem in Wireless Sensor Networks",MSN 2005, LNCS 3794, pp. 133–142, 2005. 12
  13. 13. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] Stella.k, Symeon .p, "Energy efficient framework for data gathering in wireless sensor network via the combination of sleeping mac and data aggregation strategies", Int. J. Sensor Networks, vol. 10, nos. 1/2, 2011. Z. Wei-hua, L. La-yuan, Z.Liu-min, W. Xuan-zheng, "Energy Consumption Balance Improvement of LEACH of WSN", Chinese Journal of Sensors and Actuators, 2008. X. Ning, Christos, and G. Cassandras "Dynamic sleep time control in wireless sensor networks", ACM Transactions on Sensor Networks, vol. 6, no. 3, June 2010. J. S. Chen, Z. W. Hong, N. Chung, "Efficient Cluster Head Selection Methods for Wireless Sensor Networks", journal of networks ,vol. 5, no.8, 2010. I. Solis, K. Obraczka. “In-network aggregation trade-offs for data collection in wireless sensor networks,” Computer engineering department, University of California, Santa Cruz. August 2003. K. Maraiya, K. Kant, N. Gupta I "Efficient Cluster Head Selection Scheme for Data Aggregation in Wireless Sensor Network", International Journal of Computer Applications, vol. 23, no.9, June 2011. R. Saravanakumar, S. G. Susila, and J. Raja, "Energy efficient constant cluster node scheduling protocol for wireless sensor networks", in. Proc. of Wseas Transactions on Communication, vol. 10, no. 4, April 2011. Syed Abdul Sattar, Mohamed Mubarak.T, Vidya Pv And Appa Rao, “Corona Based Energy Efficient Clustering In WSN” International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 233 - 242, ISSN Print: 0976-6480, ISSN Online: 0976-6499, Published by IAEME. Mr.Yogesh V Patil, Mr. Pratik Gite and Mr.Sanjay Thakur, “Automatic Cluster Formation and Assigning Address For Wireless Sensor Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 116 - 121, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375, Published by IAEME. Wategaonkar D.N and Deshpande V.S., “On Improvement of Performance For Transport Protocol Using Sectoring Scheme In WSN”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 275 - 281, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375, Published by IAEME. Bharathi M A, Vijaya Kumar B P , Manjaiah D.H, “Power Efficient Data Aggregation Based on Swarm Intelligence and Game Theoretic Approach In Wireless Sensor Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 184 - 199, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375, Published by IAEME Mohanaradhya, Sumithra Devi K A and Andhe Dharani, “Distance Based Cluster Head Section in Sensor Networks For Efficient Energy Utilization” International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 50 - 58, ISSN Print: 0976-6480, ISSN Online: 0976-6499, Published by IAEME 13

×