An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks.
Bottom Line:
The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN].The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks.The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS.
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PubMed Central - PubMed
Affiliation: Department of Information Science & Technology, Anna University, Chennai 600025, India.
ABSTRACT
A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. No MeSH data available. Related in: MedlinePlus |
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Mentions: Various aspects like size of the CDS at various mobility rates, lifetime of the CDS at various mobility rates, and message overhead are observed and compared with existing CDS algorithms. Performance is analysed at sparse as well as dense networks. A sparse network is created with 100 nodes with transmission range of 25 mts at two different speeds 5 mts/s and 10 mts/s. Similarly a dense network is created with 450 nodes with transmission range of 50 mts at two different speeds 5 mts/s and 10 mts/s. A Random walk mobility model is used. The terrain area is about 1000 m × 1000 m. Figure 8 shows the size of CDS versus number of nodes in sparse network with node's transmission range of 25 mts, at node speed of 5 mts/s. Figure 9 shows the size of CDS versus number of nodes in sparse network with node's transmission range of 25 mts, at a node mobility of 10 mts/s. In both cases our algorithm performed well irrespective of speed and obtained optimal CDS size in sparse network when compared to the existing CDS algorithms. Figure 10 shows the size of CDS versus number of nodes in dense network with node's transmission range of 50 mts, at a node mobility of 5 mts/s. Figure 11 shows the size of CDS versus number of nodes in dense network with node's transmission range of 50 mts, at node mobility rate of 10 mts/s. In these cases also our algorithm performed well when compared to other algorithms because of its distributed nature. Figure 12 shows the message overhead versus number of nodes with node's transmission range of 50 mts and speed 10 mts/s. Here our algorithm exchanged optimal number of messages when compared to the existing standard CDS algorithms, where the existing CDS algorithms have not performed activity scheduling, so the CDS repair phase message cost increased. Figure 13 shows the delivery ratio versus number of nodes with transmission range of 25 mts and speed of 10 mts/s. Here also our algorithm performed well when compared to other algorithms. The graph in Figure 14 shows the lifetime of the optimal CDS versus mobility of nodes with node's transmission range of 50 mts. The lifetime of our algorithm is observed at different speeds of nodes 5 mts/s, 10 mts/s, 15 mts/s, and 20 mts/s. The lifetime of our algorithm in case of mobility is also good when compared to the existing standard CDS algorithms because of activity scheduling among neighbouring dominators in phases 3 and 4. So our algorithm is robust, reliable, and fault tolerant. The graph in Figure 15 shows the lifetime of the optimal CDS at various energy threshold values. Here the network is simulated with 200 nodes at various transmission ranges of 50 mts, 25 mts, and 10 mts and at a speed of 5 mts/s with threshold values of 0.05, 0.1, 0.15, and 0.2. The value of threshold at 0.1 gives better results and maintains the tradeoff between network lifetime and message cost. |
View Article: PubMed Central - PubMed
Affiliation: Department of Information Science & Technology, Anna University, Chennai 600025, India.
No MeSH data available.