Enhanced Double Cluster Head Selection using Ant-Colony Optimization for Energy-Efficient Routing in Wireless Sensor Network

Abstract

The energy-efficient routing strategies regulate the network lifetime for wireless sensor networks (WSNs). The researchers have continuously improved the WSN lifetime using their algorithms and strategies since the sensor world. Clustering significantly improves the network lifetime and energy efficiency in WSN. In this paper, the concept of double cluster heads (CHs) is introduced in a single cluster of the WSN and Ant Colony Optimization as the nature-inspired algorithm is applied for a CH selection between double CHs. The Ant Colony Optimization is based on the natural behavior of the ant. The simulation result presents that the proposed algorithm enhances the network lifetime and residual energy because a double cluster head significantly reduces the workload of a single cluster head. The other nature-inspired algorithms can be applied for the energy efficiency of WSN in the future.

A Review of Power Management Approaches for Mobile Ad Hoc Networks

“…Some methods support specific MANET parameters, whereas others do not.OPC approaches presented in study achieve the performance elements of power management, delay, energy, overhead, and congestion control but do not achieve PDF, load management, or security characteristics.Rahmani et al [16] presented the M-AODV approach, which covers delay, energy, and overhead but does not support other parameters. The AUTOMATA approach is provided by study [43] yields just energy. The POR methods proposed by study [36] support only power, delay, and energy parameters, whereas the OLSR methods proposed by study [44] achieve only security characteristics.…”

“…Finally Sridhar et al [18], Rao and Singh [3], Musthafa et al [19], Jim et al [22], Abirami and Sumithra [21], Ponnusamy [23], Rahmani et al [16], Singh et al [17], Ramesh et al [24], Hasani et al [25], Nobahary and Babaie [8], Hadi et al [27], Kumar et al [28], Venkatesh and Chakravarthi [29], Raj Kumar and Bala [31], Sahu and Patil [43], Al-Najjar [32], Devika and Sudha [33], Braik et al [35], Saravanan et al [39], Bisen et al [40], Arulprakash et al [41], Sivapriya and Mohandas [38] achieves only one parameters are fails to other parametersby using the methods TESAODV, KF-MAC, SNDA, AIS, NCV-AODV, EE, AUTOMATA, OLSR, MSD-SNDT, FSN, SNMN, SNAODV, QOS, CHLD, EECAO, ACO, CLU, C-SEWO, AGS-ROA, E-CFSA, E-√ √ √ √…”

“…Research on power management is still ongoing to accomplish all kinds of parameter metrics. TESAODV √ Rao and Singh [3] KF-MAC √ Musthafa et al [19] SNDA √ Jim and Gregory [22] AIS √ Abirami and Sumithra [21] NCV-AODV √ Ponnusamy [23] EE √ Rahmani et al [16] AUTOMATA √ Singh et al [17] OLSR √ Ramesh et al [24] MSD-SNDT √ Hasani and Babaie [25] FSN √ Nobahary and Babaie [8] SNMN √ Hadi et al [27] SNAODV √ Kumar et al [28] QOS √ Venkatesh and Chakravarthi [29] CHLD √ Raj Kumar and Bala [31] EECAO √ Sahu and Patil [43] ACO √ Al-Najjar [32] CLU √ Devika and Sudha [33] C-SEWO √ Braik et al [35] AGS-ROA √ Saravanan et al [39] E-CFSA √ Bisen et al [40] E-MAVMMF √ Arulprakash et al [41] EBDC √ Sivapriyaand Mohandas [38] E-CFSA √ Vij et al [20] MKMPE √ Nobahary and Babaie [8] GAME THEORY √ √ Venkatasubramanian [36] CREDIT-BASED √ √ Hamza and Vigila [34] EPO-FGA √ √ Kumar et al [28] EEMST √ √ Thanappan and Perumal [45] CC √ √ Reddy and Mungara [46] GAME THEORY √ √ Phakathi et al [47] QOS √ √ Ravi et al [48] FUZZY √ √ Alghamdi [49] HFO √ √ Satyanarayana et al [50] FRAMEWORK √ √ Vinayakan et al [51] CONJUNCTION √ √ Saraswathi et al [52] AOMDV √ √ Veeraiah and Krishna [9] HGFNN √ √ Namdev and Mishra [15] IDSM √ √ √ Sri et al [6] M-AODV √ √ √ Goyal et al …”