Abstract— Efficient utilization of energy is the most
important issue in the research area of WSNs. Sensor nodes in the network are energy restricted as their functioning depends on the batteries fixed into them. Hence, there arises a need of best suited methods for routing sensed data from the network to the sink. The present work attempts to optimize the energy related issues by introducing a new routing protocol that enhances lifetime of the network and data packet delivery, by adopting the concept of cluster formation, cluster heads (CHs) and temporary CHs (TCHs). MATLAB simulation is performed to demonstrate the results of proposed protocol as compared to some existing protocol applying similar concepts.
Index Terms—Cluster Head, Temporary Cluster Head,
Homogeneous WSN.
I. INTRODUCTION
A wireless sensor network is a network of hundreds of sensor devices employed in an area of interest for monitoring purposes to perform a predefined task. The sensor nodes can be deployed for monitoring various physical phenomenon or natural conditions including temperature, pressure, wind, pollution, motion, vibration etc., into the regions or localities which are not easily accessible by any means. The major tasks performed by the sensor nodes are: sensing data based on surrounding conditions and transmitting it to a particular point known as the sink or base station (BS) [1, 2]. The BS acts as a gateway between the monitored region and the end user or server. Initially, WSNs were used for surveillance inside the battle field or other military applications [3-4] such as intrusion detection system (IDS), but now they are used excessively in various other areas ranging from earth sensing [5], to industry, agriculture and food [6-7], low cost transportation [8-9] and home automation [10] and health care. Earth monitoring includes air pollution monitoring, fire detection [11-12], landslide detection, water quality monitoring, flood detection etc. Industrial applications include machine health monitoring, waste water monitoring, data logging, rapid emergency response etc. In health care monitoring, certain wearable devices are equipped with sensor nodes or they can be implanted directly to the human body for overall monitoring known as body area network.
Manuscript received August, 2018.
Harendra S. Jangwan, Faulty of Engineering & Technology, Jayoti Vidyapeeth Women’s University, Jaipur, India.
Ashish Negi, Dept. of Computer Science & Application, G.B. Pant Engineering College, Pauri Garhwal, India.
The deployment of sensor nodes into the region under study can be structured or randomized using mathematical tools such as random graphs. Certain other deployment issues include connectivity and coverage metrics if it is application specific [13]. Several other challenges faced by WSNs are energy-efficiency, responsiveness, robustness, self-configuration and adaptation, scalability, heterogeneity, systematic design, security and privacy [14, 15].
On the other hand, WSNs carry some major unavoidable disadvantages. Firstly, they are adopted over the areas which are almost inaccessible and secondly, their network topology is not known [16]. This makes the sensor nodes resource constrained in nature and as a result, their energy cannot be replenished. Due to these limitations, energy conservation has become an essential and foremost requirement as on this basis, the working lifetime of the overall network can be increased.
A.S. Jahmati et al. introduced Energy-Efficient Protocol with Static Clustering [17] in 2007. EEPSC introduced the concept of TCHs and eliminated the dynamic clustering overhead by exploiting the idea of static clustering approach. In EEPSC, cluster formation is performed only once during the whole network operation. In set up phase, the base station broadcasts (k-1) different messages with different transmission powers. Here, k represents the number of clusters in the network field. All of the sensor nodes that hear the broadcasted message by BS, set there cluster ID as k and send information to BS that they are the member of cluster k via transmitting the join-request message back to the BS. At last, all the sensor nodes which are not the member of any cluster set their ID as k and inform the BS. Further, the BS does a random selection of one temporary cluster head for each cluster and set this rule to the entire network. Base station also prepares the TDMA schedule for nodes and transmits that to the nodes in each cluster and the node with lowest energy level is selected as temporary-CH for the next round and sends a round-start packet that includes the new responsible node ID for the current round. This packet also indicates the beginning of round to other nodes.
In responsible node selection phase, all the nodes in the cluster send their energy level information to temporary-CH in their time-slots. Afterwards, temporary-CH choose the sensor node with utmost energy level as CH for the current round; and the node with lowest energy level is selected as temporary-CH for the next round and sends a round-start packet including the new responsible sensor ID for the current round. This packet also indicates the beginning of round to other nodes.
Enhanced Energy-Efficient Static Clustering
Protocol for WSNs
In steady state phase, the nodes send their data to CH during pre-allocated time slots. These data contain node IDs and sensed parameters. In this scheme, the Direct Transmission approach is made for the communication between a CH and the BS. A node can transmit its data in a fixed time slot which is always constant. Therefore, the time required to send a data frame is fully dependent on the number of nodes in a cluster. The energy dissipation can be reduced by keeping „ON‟ the radio of all non-cluster head nodes until their allocated time slot for data transmission reaches.
Jangwan et al. introduced Energy Efficient Balanced Clustering protocol for WSN [18] in 2016. In EEEBCP also, cluster formation occurs only once during the network operation. The network operation consists of many rounds where each round is further divided into three phases, namely, set-up phase, steady state phase and responsible node selection phase. In set-up phase, cluster formation is done and in responsible node selection phase, CHs and TCHs are selected on the basis of two parameters i.e. distance from the central position of the cluster and residual energy of member nodes. Steady state phase involves in data collection through CHs and transmission from CHs to the BS.
II. SYSTEM MODEL
A. Assumptions
We consider the following properties for the proposed routing protocol of a WSN as defined in [19]:
Every deployed sensor node is stationary and homogeneous in terms of energy.
The nodes capability of controlling power enables to vary their transmission power.
WSN operates on continuous data flow model rather than event driven model.
Position of BS is static and BS is positioned outside the sensing field.
Sensor nodes are aware about their position in the network.
BS is unaware about the position of any node placed in the network.
Sensing nodes are having limited battery power.
Sensing nodes are arbitrarily placed in a uniform fashion. Sensing nodes can calculate their residual energy.
B. Network Model
We assume a set of sensing nodes „S‟ is arbitrarily placed in a 100 m*100 m region and the BS node is located in the middle of the network. Every sensing node Si (i=1, 2, 3…)
has its own location information (Xi, Yi). The
communication between sensor nodes and CH is performed using single-hop communication architecture. The sleep mode is utilized to save energy during idle period. The BS node contains unlimited battery power, memory and computation capability. The BS node is fixed and positioned outside the sensing field.
C. Energy Dissipation Model
The first order radio model [19] has been used for energy dissipation analysis. According to the first order radio model shown in Fig. 1, the energy required for transmitting K-bits at a distance d is given as:
.
Fig.1 Radio Energy Dissipation Model
The energy required for receiving K-bit message is given as:
Here, the distance of a node from its CH or a CH from the BS is represented by d and the default distance (threshold) is represented by d0, transceiver energy dissipation is
represented by Eelec and Efs, Eamp are transmitter-amplifier
energy-expenses by a node when d < d0, and d d0
respectively.
D. Performance Matrices
Network Lifetime: The time span of alive nodes during network operation can be considered as the network lifetime. Some authors define the network lifetime as the time when the first node dies in the network or when the loss of coverage and connectivity occurs.
Energy Consumption: It is a power dissipation of a sensor node during data transmission, data receiving, computation and sleeping mode. The data routing protocol computes the energy dissipation on the basis of radio energy dissipation model. It shows the efficiency of a protocol in terms of energy consumption in a network.
Throughput: More data packets received at the base station indicate that greater numbers of alive nodes are present in the network, hence achieving a longer network lifetime.
III. THE PROPOSED PROTOCOL
On the basis of the assumptions made above, the network operation of the proposed protocol is discussed in this section. EEESCP avoids dynamic clustering and exploits the benefits of both load balance as well as static clustering. The whole network operation is composed of several rounds and each round is further divided into three phases, namely, set-up phase, steady state phase and responsible node selection phase. Each of these phases is discussed in the following sub sections.
A. Set-up Phase
sensor nodes send their location detail to the base station, which is acknowledged by the base station. After receiving the respective location detail of sensor nodes in the network field, base station selects k number of nodes for the role of TCHs in such a way that the distance between any two TCHs is M/2 and diagonally opposite pair of such selected nodes be at least but approximately at M/√2 distance while ensuring that the dimension of sensing field is M*M. Here, k is the desired number of clusters, assumed to be fixed (k=4). In this way the TCHs are selected so that the network field can be portioned into four equal portions. Once the TCHs are selected, they start broadcasting their status throughout the network. Based on the received signal strength, nodes send JOIN-REQ message to the respective TCH, hence forming the clusters. TCHs send acknowledgement to the nodes about their membership along with the TDMA schedule of the nodes too.
Fig. 2 CH and TCH Selection in Proposed Protocol
Along with these ACK packets and TDMA schedule, TCHs also request the member nodes to send their location coordinates. After having such information, TCHs, compute the central point of their respective cluster by calculating the mean position (Xmean, Ymean) and appoint the node as cluster
head (CH) nearest to the central position for the very first round.
B. Steady State Phase
In the steady-state phase, all the member nodes transmit their sensed data to their respective CH according to their pre-allocated time slot. Direct transmission approach is used for communication among CHs and the bases station. In a cluster, radio of the member nodes are kept off until their allocated timeslot but radio of cluster-head is kept on always to receive data from all the nodes.
C. Responsible Node Phase
In this phase, cluster-heads (CHs) and the temporary-cluster-heads (TCHs) for the next round are selected in each cluster. At the beginning of each round, member nodes in each cluster send information about their location coordinates and residual energy (Eresidual) to the
corresponding TCH. In each cluster, TCH declares a cluster head (CH) node on the basis of two parameters. First, the node must have highest possible value of residual energy for the current round, and secondly, it must be as nearest to the central position as possible. In order to achieve this, the nodes with their residual energy higher than the average residual energy of member nodes are identified and the node nearest to the mean position is selected. In each cluster the nodes with the least value of residual energy and nearest to
the central position are selected as TCHs for the next round. Once the CHs are selected for the current round, CHs start broadcasting their status so that the nodes may send their measured data to CHs.
IV. SIMULATION OUTCOMES
The performance of proposed protocol (EEESCP) is evaluated by using MATLAB 7.1 as a simulation tool. We consider that the sensor nodes are deployed randomly across a plain area. Each node is equipped with equal amount of energy at the beginning of the simulation. Table 1 represents various parameters and their values used in simulation.
TABLE I SIMULATION PARAMETERS
Parameter Name Value
Network Area 100 m *100 m
Base Station‟s Position (50m, 175m) Number of deployed sensors 100
Initial energy for nodes 2 Joule Size of data message 4000 bits
5 nj
50 nj 10 pj/bit/
0.0013 pj/bit/
A set of experiments is conducted to test the performance of EEESCP, EEEBCP and EEPSC. From the results of various simulations performed as depicted in Fig. 3 to Fig. 6, it can be firmly stated that the proposed scheme, An Enhanced Energy-Efficient Static Clustering Protocol (EEESCP) outperforms EEPSC and EEEBCP in term of network lifetime, energy consumption and throughput.
[image:3.595.317.547.538.713.2]Fig. 3 shows the comparative plot of number of nodes alive over time. In EEPSC, EEEBCP and EEESCP the last node die at 780 seconds, 1000 seconds and 1060 seconds respectively. Hence, an improved network life time has been achieved in EEESCP as compared to EEPSC and EEEBCP as clearly shown in fig. 3.
Fig. 3 Number of Nodes Alive Over Time
Fig. 5 clearly shows that the energy consumption rate in EEESCP is significantly lower than EEPSC and EEEBCP resulting in a lower decay rate of the sensor nodes too and hence achieving a longer network lifetime.
Fig. 4 Number of Data Packets Received at BS
Fig. 5 Energy Consumed in Network over Time
[image:4.595.58.288.546.714.2]Fig. 6 describes that the number of messages received at base station for any amount of energy consumed in the network is greater in EEESCP as compared to EEPSC and EEEBCP. EEESCP results in greater data packets received at the BS at less cost of network-energy in a consistent manner.
Fig. 6 Packets Received at Bs per Amount of Energy
V.CONCLUSION
The article presents an enhanced energy-efficient static clustering protocol (EEESCP) for WSN with a target of delaying network lifetime. EEESCP provides the mechanism
for intelligent selection of cluster head and temporary cluster head at set-up phase and responsible node selection phase. The simulation outcomes are compared with the existing protocols on the basis of different matrices as network lifetime, energy consumption and data packets received at base station. On the basis of simulation matrices and results, we can conclude that the proposed scheme, Enhanced Energy-Efficient Static Clustering Protocol outperforms the existing protocols EEPSC and EEEBCP in terms of network life time, energy consumption and throughput.
REFERENCES
[1] I. F. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Communication Magazine, vol. 40, no. 8, pp. 104-112, Aug. 2002.
[2] G. Wei, Y. Ling, B. Guo, B. Xiao, and A. V. Vasilakos, “Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter,” Computer Communications, vol. 34, no. 6, pp. 793-802, May 2011.
[3] S. M. Diamond and M. G. Ceruti, “Application of wireless sensor network to military information integration,” Proceedings of 5th IEEE Int. Conf. Ind. Inform., Vienna, Austria, vol. 1, pp. 317-322, 23 June 2007. [4] I. Bekmezci, and F. Alagöz, “Energy Efficient, Delay Sensitive, Fault
Tolerant Wireless Sensor Network for Military Monitoring,”
International Journal of Distributed Sensor Networks, vol. 5, no. 6, pp. 729-747, Oct. 2009.
[5] A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for habitat monitoring,” Proceedings of 1st ACM International Workshop on Wireless sensor networks and applications, New York, NY, USA, pp. 88-97, 28 Sept. 2002.
[6] L. Ruiz-Garcia, L. Lunadei, P. Barreiro, and I. Robla, “A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends,” Sensors, vol. 9, no. 6, pp. 4728-4750, June 2009.
[7] N. Wang, N. Zhang, and M. Wang, “Wireless sensors in agriculture and food industry– recent development and future perspective,” Computers and electronics in agriculture, vol. 50, no. 1, pp. 1-14, Jan. 2006. [8] W. Chen, L. Chen, Z. Chen, and S. Tu, “WITS: A wireless sensor network
for intelligent transportation system,” First International Multi-Symposiums on Computer and Computational Sciences, IMSCCS'06, Hangzhou, China, vol. 2, pp. 635-641, 20 June 2006. [9] B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E.
Shih , H. Balakrishnan, and S. Madden S., “Cartel: A distributed mobile sensor computing system,” Proceedings of the 4th international conference on Embedded networked sensor systems, New York, NY, USA, pp. 125-138, 31 Oct. 2006.
[10] A. Wheeler, “Commercial applications of wireless sensor networks using zigbee,” IEEE communications magazine, vol. 45, no. 4, pp. 70-77, Apr. 2007.
[11] J. Zhang, W. Li, Z. Yin, S. Liu, and X. Guo, “Forest fire detection system based on wireless sensor network,” Proceedings of 4th IEEE Conference on Industrial Electronics and Applications, ICIEA, Xi’an, China, pp. 520-523, 25 May 2009.
[12] L. Yu, N. Wang, and X. Meng, “Real-time forest fire detection with wireless sensor networks,” Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China, vol. 2, pp. 1214-1217, 23 Sept. 2005.
[13] B. Diop, D. Diongue, and O. Thiare, “Target coverage management in wireless sensor networks,” IEEE Conference on Wireless Sensors (ICWiSE), pp. 25-30, 26 Oct 2014.
[14] J. A. Stankovic, “Research challenges for wireless sensor network,” ACM SIGBED Review- Special issue on embedded sensor networks and wireless computing, vol.1, no. 2, pp. 9-12, Jul 2004.
[15] B. Krishnamachari, “Networking Wireless Sensors,” Cambridge University Press, 2005.
[16] P. M. Wightman, A. Fabregas, and M. A. Labrador, “A mathematical solution to the MCDS problem for topology construction in wireless sensor networks,” IEEE Latin America Transactions, vol. 9, no. 4, pp. 1-6, Jul 2011.
[18] H. S. Jangwan, and A. Negi, “Enhanced Energy-Efficient Balanced Clustering Protocol for WSN,” International Journal of Applied Engineering Research, vol. 11, no. 5, pp. 3619-3623, 2016.
[19] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, Oct. 2002.
Harendra S. Jangwan is a research scholar in the department of Computer Science and Engineering at G.B. Pant engineering college, Ghurdauri, Pauri Garhwal. Currently, he is working as an Assistant Professor in the Faculty of Engineering & Technology at Jayoti Vidhyappeth Women‟s University Rajasthan. His research interests include wireless sensor networks, network security and fault tolerant systems.
