A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

Abstract

In WSN-assisted IoT environment, the sensors are resource constrained. The energy, computing and storage resources of deployed sensors in the sensing area are limited. Clustering is the key method for saving energy in wireless sensor networks. A hybrid protocol named as an Energy Efficient Centroid-based Ant colony Optimization (EECAO) protocol is proposed in this paper to improve the performance of the sensor network in WSN-assisted IoT environments. The protocol uses the concept of centroid based clustering to gather the information of local clusters and ant colony optimization to relay that information to the base station. proposed hybrid protocol includes multiple clustering factors such as energy cost, channel consistency and cognitive sensor throughput to select cluster heads and a new distributed cluster formation for self-organizing deployed sensors. Selection of the super cluster head among the cluster heads is based on the energy centroid position for a defined coverage area. In EECAO protocol, the energy level of cognitive sensors is the key parameter for defining the position of centroid. To reduce the long-distance communication, path optimization between the super cluster heads and the base station is carried out using an ant routing model. Our simulation results indicate that EECAO protocol performs better when benchmarked against existing ETSP and EECRP protocols. The proposed hybrid protocol EECAO is well-suited for networks that requires long lifetime when the base station is placed at either center, border or outside the network.

A Review of Power Management Approaches for Mobile Ad Hoc Networks

“…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-√ √ √ √…”

“…Hadi et al [27] applied the AODV Using a Wireless Network technique to improve packet delivery, although the findings yield a lower packet delivery ratio. ORS Better Throughput, Lower Latency, Lower Jitter, PDR -Venkatesh and Chakravarthi [29] HAMBOCHLD Energy Waste Reduced -Goyal et al [30] HAODV PDF, END, Routing overhead -Raj Kumar and Bala [31] EECAO -Lengthy Lifetime Al-Najjar [32] ACO Network Lifespan and Residual Energy Two Cluster Heads PDR and NLT metrics Uniform Distribution of Energy -Devika and Sudha [33] C-SEWO Innovative Design -…”

“…Goyal et al [30] employs HAODV approaches to improve packet delivery ratio, end-to-end delay, and reduce routing overhead. Raj Kumar and Bala [31] employ the EECAO approach, but the study fails by yielding the Lengthy Lifetime. Al-Najjar [32] employ the ACO technique to increase network lifespan and residual energy; however the best results require two cluster heads.…”

“…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 …”

A Polynomial Series-based data aggregation and spectrum aware clustering technique for a combined model of WSN and cognitive radios in IoT applications

“…This scheme helps improve the inter cluster data aggregation, improving the overall performance of the network. Raj Kumar et al [16] discussed an optimization based approach for energy efficient clustering, which uses centroid based ant colony optimization method to improve the network performance. During the initial phase, it uses centroid based clustering for information collection and later ant colony optimization is presented to transmit the gathered data to the base station.…”