Affiliations: [a]
Department of Computer Science and Engineering>, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India
| [b]
Department of Computer Science, School of Computing, National University of Singapore, Singapore
Correspondence:
[*]
Corresponding author. Senthilkumar Mathi, Department of Computer Science and Engineering, Amrita School of
Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India. E-mail: [email protected].
Abstract: The Internet of Things lies in establishing low-power and lossy networks created by interconnecting many wireless devices with limited resources. Fascinatingly, an IPv6 routing protocol for low-power and lossy networks has become a common practice for these applications. Even though this protocol addresses the challenges of low-power networks, many issues concerning the quality of service and energy consumption are open to the research community. The protocol relies on a destination-oriented directed acyclic graph, and the root selection depends on some constraints and metrics associated with an objective function (OF). The conventional OFs select parents based on a single metric, such as the expected transmission count or the number of nodes to travel. The current paper proposes an enhancement to the OF metric, aiming to decrease node energy and enhance the quality of service. This improvement is achieved by the factors, including the received signal strength indicator, node distance, power, link quality indicator, and expected transmission count, to select reliable communication links. The minimum power needed for reliable communication is predicted from the received signal strength indicator, node distance, receiver power, and link quality indicator using a nonlinear support vector machine. The OF value of the candidate node is computed from the power level and expected transmission count combined using the Takagi-Sugeno fuzzy model. The proposed OF is implemented in the Cooja simulator and compared against minimum rank with hysteresis OF and OF zero. A considerable improvement in the packet delivery ratio and a 37.5% reduction in energy consumption is obtained.
Keywords: Classification, fuzzification, power prediction, received signal strength
indicator, transmission power, link quality indicator, low power networks, TSK fuzzy
model
