Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
1
2
3
1
Lecturer (Senior Scale) in Electrical Engineering, Annamalai University,
Annamalai Nagar – 608 002,Tamil Nadu, India. Ph: +91 98941 26771,
2
Professor of Electrical Engineering, Annamalai University
3
Assistant Professor of Electrical Engineering,
Pondicherry Engineering College, Puducherry.
ABSTRACT
This paper attempts to develop a scheme that facilitates data transfer between the various nodes in an energy constrained Wireless Sensor Network (WSN), with a view to increase the life time of the network. The philosophy is based on optimising the product of energy and delay besides ensuring an enhancement in the performance of grid networks. It envisages the network to be in the form of clusters and builds the procedure for transfer of information within the nodes and to the remote Base Station (BS). The approach involves the use of a cluster based protocol and incorporates measures to cater to correlated traffic. It includes the NS2 based simulation results that are compared in terms of its performance indices for four different protocols to highlight the suitability of the proposed strategy for the present day delay sensitive applications.
Keywords: Wireless Sensor Networks, CAODV, Energy*Delay, Network Lifetime
1. INTRODUCTION
A Wireless Sensor Network (WSN) is recognized as an important technology in realizing ubiquitous applications. It is an emerging field with a wide range of potential applications such as environment monitoring, earthquake detection and patient monitoring systems. They are deployed for military applications, such as target tracking, surveillance and security management [1]. It is designed for an unmanned surveillance system, monitoring military services in terrains where access is difficult. The sensor network is used for remote sensing in a number of areas including intelligent traffic system, control of processes or environment control of buildings in a factory [2, 3].
A WSN is a network consisting of distributed self–organized autonomous devices built with sensors to monitor physical and environmental conditions such as vibration, motion, temperature and sound in a coordinated fashion. It is a group of specialized transducers with a communication infrastructure intended to monitor and record conditions at diverse locations [4].
The WSN is quite different from the traditional wireless networks. It has a large number of densely deployed sensor nodes where the distance between neighbouring nodes is shorter as compared to other wireless networks. It normally consists of a large number of distributed nodes that organize themselves into a multi-hop wireless network. Each node has one sensor and relevant circuitry that are usually battery operated. They rely on limited battery energy on account of the remote nature and small size of the individual nodes that cannot be replenished in many applications .The sensors have limited energy resources and allow their functionality to continue until their energy is drained [5,6].
The primary concern in WSN is thus energy efficiency . The nodes in a sensor network may not be charged once their energy is drained. Consequently the lifetime of the network depends critically on energy conservation mechanism. Delay guarantee is particularly significant, when data received by the central controller are used to control a physical process. The network may not be useful without such a bound being guaranteed. [7]. Thus, low power consumption technology associated with a facility to guarantee delays are

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297 sensitive issues in WSNs in order to prolong system lifetime. [8] It is in this perspective that data transfer is envisaged through a protocol based strategy to ensure a minimum energy* delay metric.
An energy efficient routing algorithm with delay guaranteed, by constraining the length of the routing path from each sensor node to the collection node has been formulated for Sensor Networks. A novel optimal routing algorithm has been suggested for WSN to minimize the time delay in transferring a fixed number of data packets in an energy constrained manner [9]. An energy efficient scheduling strategy has been developed so as to minimize the energy consumed by data fusion in WSN [10]. The energy consumption has been found to reduce substantially through a Low-Complexity Fractional – Inverse – Log Scheduling (FILS) algorithm and result in high peak to average power ratio (PAPR). An Energy Efficient and Collision Aware (EECA) nodedisjoint multipath routing algorithm has been proposed for WSN. It has been found to yield an overall good performance through efficient energy saving and data transferring mechanisms. [11]. An adaptive approach for improving the performance of randomly distributed WSN has been detailed [12]. The scheme has been based on a Reinforcing Learning Algorithm (RLA) to minimize the time delay in enabling energy constrained data transfer. An energy efficient joint routing, scheduling and link adaptation strategies have been suggested for
TDMA based Sensor Networks. [13]. An efficient power management technique has been suggested, in order to extend the life time of WSN by addressing its target coverage issue [14]. The objective has been to find the maximum number of set covers and the ranges associated with each sensor, such that each sensor set covers all the targets. The optimum number of cluster head for minimizing the energy expended in transmitting data to a sink in a clustered WSN has been determined [15].
2. SYSTEM MODELLING
A large number of sensor nodes are deployed either inside or very close to it in a WSN. Each node consists of sensing, data processing, and communication components, to process information before being sent to the remote base station. These sensor nodes can self- organize themselves to form a network and communicate with each other in a wireless manner. Each node possesses control of transmitted power and an
Omni-directional antenna and therefore can adjust the area of coverage through its wireless transmission. The nodes collect audio, seismic and other types of data and collaborate to perform a high level task in a sensor web.
The sensor nodes are characterised by a small memory and it is to operate with limited battery power owing to the fact that wireless communications consume significant amounts of battery power. Hence the sensor nodes are required to be energy efficient in transmitting data.
The WSN is geographically arranged in clusters with CDMA/ Non CDMA fifty nodes as shown in
Figure1. It is partitioned such that one particular node is designed to serve as a Cluster Head (CH ), while the remaining nodes are allowed to function as its followers. The process commences with the formation of a number of clusters.
The nodes are allowed to broadcast their Identification (ID) number within a finite time in a pre specified cluster range in order to identify the particular cluster group they belong, depending on the received signal strength. However they finally choose the nearest CH. Several such clusters constituted in a similar fashion are called the primary cluster of the network. The CH nodes thereafter prepares a schedule containing both primary CH id and the serial information that decide the order in which the member nodes will elect themselves as CH in the network lifetime. The schedule works in a round fashion through the lifetime of the sensor nodes in the network.
Figure 1. System Model with Clusters

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
3. PROBLEM DESCRIPTION
The Sensor Web is constructed with fifty nodes and each node has packets of data around it. Besides, the nodes are allowed to transmit directly to any other node in the base station through wireless means. The objective is to develop a scheduling algorithm that incur minimum energy and delay cost, as measured by the product of energy * delay through the use of a suitable protocol such that it delivers acceptable performance metrics.
4. PROPOSED APPROACH
The Scheduling scheme is articulated so as to avail the use of a cluster based AODV (CAODV) protocol in order to traverse a data packet in a WSN constituted of CDMA/Non CDMA nodes, from a known source to a defined destination. The sensor nodes have power control so that they can transmit their data directly to the base station or to any other nodes in the network. Besides, the nodes are assumed to be homogeneous and constrained with uniform energy. An important operation in a sensor network is systematic gathering of data from the field, where each node has a packet of information in each round of communication. The data sensed by the nodes are fused into a single message and sent to a distant base station. The amount of energy spent in transmitting a packet has both fixed and variable costs that depend on the distance of transmission. Similarly receiving a data packet also has a fixed energy cost. It is precisely to conserve energy as to why short distance transmissions are always preferred. It is designed in such a way that the nodes take turns in transmitting data to the BS, in order to balance the energy spent in the nodes. The cost of transmission from BS to any node is high as the BS is usually located far away and consequently the total energy cost will also be high. The process of transmitting fused data acquired from different sensor measurements serves to reduce the rate of transmission.
Wireless communications among pairs of nodes is possible only if there is minimal interference among different transmissions. CDMA technology is used to achieve simultaneous wireless transmissions with low interference. It is possible to incorporate parallel communications with a view to reduce the overall delay on account of the fact that the sensor nodes are CDMA capable. However, the energy cost may go up slightly as there will still be a small amount of interference from other unintended transmissions. Alternatively, with a single radio channel and non-CDMA nodes, simultaneous transmissions are possible only among spatially separated nodes. The energy costs and delay per transmission for these two types of nodes are quite different and therefore energy * delay reduction is considered separately for these two cases.
Numerous protocols have emerged for use in general ad hoc wireless networks to accomplish efficient routing between the nodes in the network so that messages can be delivered. Shortest hop routing is the most common approach used in table-driven protocol DSR (Dynamic Source Routing), source-initiated protocols such as AODV (Ad Hoc On-Demand Distance Vector), Improved AODV (IAODV) and CAODV. However routing based on shortest hop is not preferred for sensor networks, where there are a lot of data sources and only a few data sinks. It therefore appears that cluster based approaches are becoming significant in such applications.
The gathered data moves from node to node, get fused, and eventually a designated node transmits to the BS, so that the average energy spent by each node per round is reduced. It is envisaged to implement the scheduling exercise through all the four protocols. The development of algorithm is based on the following flowchart shown in the Figure 2.

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
Figure 2: CDMA Protocol
Model for Energy Calculations
A node dissipates Eelec = 1000 j/bit In the first order model, to operate the transmitter or receiver circuitry and Îamp =100 pJ/bit/m2 for the transmitter amplifier. The nodes exercise power control and can expend the minimum required energy to reach the intended recipients. It can be turned off to avoid receiving unintended transmissions. It is assumed that there is an energy loss due to channel transmission. The equations used to calculate transmission and receiving costs for a k-bit message over a distance d are shown below:
Transmission
ETx (k, d) = ETx– elec (k) + ETx–amp (k,d)
ETx (k, d) = Eelec*k + Îamp * k* d2
Reception
ERx(k) = ERx-elec (k)
ERx(k) = Eelec * k
A packet length k of 1000 bits is used. With these parameters, for a defined distance, the energy spent in the amplifier part equals the energy spent in the electronics part, and therefore, the cost to transmit a packet will be twice the cost to receive. It is assumed that the channel is symmetric so that the energy required to transmit a message from node i to node j is the same as that required to transmit a message from node j to node i. When there are multiple simultaneous transmissions, the transmitted energy should be increased to ensure that the same SNR as that through a single transmission is maintained. With CDMA nodes using 64 or 128 chips per bit the interference from other transmissions are calculated as a small fraction of the energy from other unintended transmission. This effectively increases the energy cost to maintain the same SNR. With non-CDMA nodes, the interference is assumed to be equal to the amount of energy seen at the receiver from all other unintended transmitters. Therefore, only few spatially distant pairs can communicate simultaneously.
CDMA may not be applicable for all sensor networks as these nodes may be expensive. Therefore, a protocol that will achieve a minimal energy * delay with non-CDMA nodes is also implemented. It may not be possible to use the binary scheme in this case as the interference will be higher at lower levels. Either the energy cost has to be increased significantly or more time steps at lower levels of the hierarchy allowed both of which will lead to much higher energy * delay cost. Therefore, in order to improve energy*delay a protocol that allows simultaneous transmissions that are far apart to minimize interference while achieving reasonable delay cost is preferred. The transmission schedule can be programmed once at the beginning so that the nodes know in advance as to where to send the data in each round of communication.
5. SIMULATION RESULTS
A network of randomly distributed fifty sensor nodes along with the BS in a place of 1000 m X 1000 m is considered. Data message of size 1000 bytes is to be transmitted. The proposed scheme is implemented through NS2 simulation. It is assumed that energy dissipation does not occur when the nodes are idle. The node

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297 with a higher battery energy level is elected as the CH. The performance indices are computed over a range of two hundred seconds for the chosen source and destination and the results presented.
The response of both CDMA and Non CDMA nodes in terms of indices throughput, PDR (Packet
Delivery Ratio), number of packets received, delay, packet loss and energy obtained through the use of CAODV protocol are depicted in Figures 3 to 8.
Figure 3: Throughput vs Time
It is seen from Figure 3 that the CDMA as compared to Non CDMA nodes offer a much higher output for the same size of the data packets transmitted.
Figure 4: Packet Delivery Ratio vs Time
The Packet Delivery Ratio (PDR) of the network under study is seen in Figure 4. It follows that the
CAODV protocol facilitates the CDMA nodes an increase of nearly 200 percent over that of Non CDMA nodes.

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
Figure 5: Packets Received vs Time
The Figure 5 shows the number of packets received and evinces the fact that the CDMA nodes enable the network to transfer the maximum number of packets between the identified source and destination.
Figure 6: Routing Delay vs Time
The routing delay of both CDMA and Non CDMA nodes in the network is seen in Figure 6. The minimum delay associated with transmission through CDMA nodes explains the reason for the overall improvement in the performance of the network.

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
Figure 7: Packet Loss vs Time
The CDMA nodes suffer from minimum loss of packets when compared to their Non CDMA counterparts as portrayed in Figure 7 and contributes to an increase in the network efficiency.
Figure 8: Energy Consumed vs Time
It is observed from Figure 8 that the CDMA nodes consume lower energy than Non CDMA entities and enjoy higher network lifetime besides minimising the routing overhead.
Table.1 : Perfomance Metrics

Santhana Krishnan B et. al. / International Journal of Engineering Science and Technology
Vol. 2(5), 2010, 1289-1297
The performance metrics seen in Table 1 are obtained for both CDMA and Non CDMA nodes in the network when a data of size 1000 KB are allowed to be transmitted between the same source and destination through different protocols. The analysis establishes that the algorithm is consistent in its mission and serves to justify the advantages of the use of CDMA nodes and the preference for COADV protocol.
Figure 9: Energy*Delay for diffenent protocol
The bar chart shown in Figure 9 brings out the superiority of CDMA over NonCDMA nodes and reveals the viability for large scale transmission besides illustrating the emphasising the merits of COADV realised through its minimum Energy * Delay metric.
Table 2: Energy*Delay for various Packet Size
The response of the CDMA nodes in the network viewed though its Energy * Delay metric, with an increase in the number of packets transferred for the four protocols under study is presented in Table 2. It explains the capability of the network to handle the growing traffic and the reason for CAODV to be the preferred choice.
6. CONCLUSION
It has been identified that performance metrics widely influence the performance of Communication
Networks. A scheduling algorithm suitable for correlated traffic has been coined and evaluated through the use of four different protocols for a network with both CDMA and Non CDMA nodes. It has been brought out that
CAODV outperforms other protocols in terms of higher output, PDR and number of packets received in addition to accomplishing data transmission with minimum delay, packet loss and energy consumption.
The NS-2 simulation graphs have been portrayed to reveal the suitability of CDMA nodes for efficiently encompassing traffic through an optimum energy *delay metric . A comparative study has been made to highlight the merits of the proposed scheme and project its significance to innovative usage in present day applications.
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Vol. 2(5), 2010, 1289-1297
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ACKNOWLEDGEMENT
The authors thank the authorities of Annamalai University for providing the necessary facilities in order to accomplish this piece of work.