The focus of this paper is to extend the network lifetime to maximum and also to balance the entire network in terms of channel throughput and data rates. It is therefore, recommended that sensor nodes within the diameter of 35 to 40 meters be run on BPSK or 8PSK modulation schemes and when the distance between sensor nodes and their respective cluster head and cluster head and border node is above 40 meters 16PSK or 4QAM modulation techniques should be implemented.
With this way we cannot only successfully sense the area for much greater time but also with effective data rates and channel efficiencies. Figure 3 and Figure 4 shows formation of clusters with cluster heads and border nodes of each cluster. Figure 3. Figure 4. Default parameters used during simulation are given as Table 2 under:.
Figure 5 shows the energy consumption Vs Noise of different modulation schemes at a fixed distance of 30 m. It is observed that for any modulation scheme, the probability of receiving a data packet decreases with the increase in packet size. Therefore, small packet sizes may help to increase energy efficiency. However it is true only if no packet overhead is considered.
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Figure 6 shows the trade-off between the energy consumption and varied packet sizes with different packet overheads. The trade off in packet size with or without overhead can be seen in the above figure. When packet overhead is zero or minimum, energy tends to decrease but with the rise in packet overheads, the packet size also increases. Table 2. Parameters used in the models. Figure 5. Energy consumption Vs Noise at a fixed distance of 30 meters. Figure 6. Energy consumption during varied packet sizes different packet overheads. Equations 1 to 4 clearly shows that distance and transmission time are directly proportional to the energy consumed.
Figure 7 gives us the fair idea about energy consumption at varied transmission time for short distance. On the other hand complex circuitry design of 16PSK or 4QAM techniques needs more processing capabilities of sensor nodes which in many cases may not be possible and feasible too. Due to this trade off, it is highly recommended through this paper that to balance the entire network, a heterogeneous modulation technique may be implemented on the sensing area i. This way not only network lifetime can be extended but higher data rates can also be achieved.
To achieve desired results optimization of above mentioned parameters can be done according to the applicability and resources of WSNs. In this paper we explored physical, MAC and routing layer parameters to enhance the network lifetime of wireless sensor networks. Through simulation. Figure 7.
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Energy consumption Vs Transmission time w. Figure 8. Energy consumed by different modulation schemes w. Figure 9. The results presented in this paper may help network designers in a greater way.
The main contributions of this paper are as follows:. Future work: This research work can be further extended by exchanging information between transport and application layers. Other than this our future work may include testing of this analysis on hardware motes and evaluate the results. We are thankful to Dr. Baljeet Singh, Associate Professor Mathematics for his suggestions and valuable insights. A Survey and the Road Ahead. Wireless Communication Mobile Computing, 9, Del, F. Lecture Notes in Control and Information Sciences, , Computer Networks, 55, Eurasip Journal on Wireless Communications and Networking, , A survey and Taxonomy.
International Journal of Distributed Sensor Networks. Hindawi Publishing Corporation, Cairo. Wireless Networks, 5, Pearson Education, London, Share This Article:.
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- A Cross-Layer Optimization Framework for Energy Efficiency in Wireless Sensor Networks.
The paper is not in the journal. Go Back HomePage. Karuna Babber , Rajneesh Randhawa. ABSTRACT We consider the extension of network lifetime of battery driven wireless sensor networks by splitting the sensing area into uniform clusters and implementing heterogeneous modulation schemes at different members of the clusters. A cross-layer optimization has been proposed to reduce total energy expenditure of the network; at network layer, routing is done through uniform clusters; at MAC layer, each sensor node of the cluster is assigned fixed or variable time slots and at physical layer different member of the clusters is assigned different modulation techniques.
Introduction In recent years wireless sensor networks WSNs are gaining momentum in almost every facet of life whether it is health care, home security, forest area supervision, monitoring earth movement or battle field surveillance. A brief overview of recent work on cross-layer optimization for minimizing energy consumption is given below: To form uniform clusters and choosing cluster heads and border nodes, following steps are to be followed: And as the same time states some information about cross-layer design, discussing the architecture of the cross-layer, and focusing on major methods of interfacing with giving one example of data transmission and interfacing which is meant to be very useful in this stage.
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From This Paper Figures, tables, and topics from this paper. Explore Further: Citations Publications citing this paper. Showing of 3 extracted citations. Cariou , Philippe Christin , J-F. Huang, Weilan. Traditional communication systems are designed using a layered approach based on the open system interconnection OSI reference model. According to this design philosophy, each layer offers certain services to the higher layers, by shielding those layers from the details of how the services are implemented. Consequently, each layer optimizes its own goal and the design can hardly be optimal from an overall system point of view.
Because of the increasing demand of wireless communication systems, it is more and more necessary for the system designers to implement more efficient protocols through a cross-layer approach. The nature of a cross-layer design is to provide an innovative insight into the vertical integration of different protocol layers with the ultimate goal of achieving efficient management of system resources.
In this thesis, we present several cross-layer methodologies, primarily in the context of integrating the physical PHY and medium access control MAC layers, to more efficiently support adaptability and optimization in wireless networks. Our numerical and simulation results demonstrate that significant improvement in the system performance such as throughput, power-efficiency, average packet delay, and system stability can be achieved by our cross-layer approaches, compared with conventional schemes where integrated layer adaptation, design, and optimization are not used.
Two types of wireless architectures are investigated: Wireless ad hoc networks and infrastructured wireless networks.