An evolutionary computing‐based energy‐efficient solution for IoT‐enabled software‐defined sensor network architecture

Abstract

Software-defined networking (SDN) has emerged as an evolving technique in wireless sensor networks (WSNs). SDN enables WSNs with programmable control to manage network functions dynamically and efficiently. In Internet of Things (IoT) applications, smart sensors suffer from the low battery issue, generally deployed in harsh network environments where regular recharge is not feasible. Moreover, integrating SDN with IoT-enabled sensor network puts forward several challenges, for example, control nodes' selection, load balancing, and energy cost optimization while aggregating the collected data, focusing on heterogeneous traffic data. Thus, an energy-efficient data collection technique via definite sensing control in two-level IoT-enabled softwaredefined heterogeneous WSN (2SD-HWSN) is formulated as an optimization problem, with transmission distance from smart sensors, residual energy of sensors, and load based on node density. The proposed algorithm is divided into two: set-up and transmission phases. In the set-up phase, the control server (CS) elects the best-suited control nodes (CNs) and sets up a schedule for coordinating data transmission. Further, normal nodes join appropriate CNs based on distance and residual energy. This way, CNs form clusters and route sensed data during the transmission phase. Therefore, an alternative nature-inspired algorithm, that is, grey wolf optimization (GWO), is hybridized with particle swarm optimization using a low-level co-evolutionary technique to improve its overall performance. This hybrid variant of GWO, known as HGWO-BC, offers balanced clustering (BC) via novel fitness function design. An exhaustive simulation study is performed in different scenarios considering homogeneous and heterogeneous sensors. Comparative results show that the HGWO-BC outperforms state-of-the-arts concerning network lifetime, instability period, residual energy, throughput, and computational efforts.

Opportunistic coding across on‐route dominating nodes in a flow‐orientedSDNenabledWSN

“…Furthermore, the controller exploits coding opportunities at dominant nodes to reduce transmission counts during communication in the network. Repeated packet encoding during transmissions through sensory nodes reduces packet delay which is demonstrated by Szab et al 24 In the literature, various applications of SDN‐enabled WSN are deployed in a range of environments and confirmed the improvement in different evaluation metrics 25–29 . Network coding is susceptible to security issues, even though opportunistic coding with SDN architecture is better suitable for futuristic networks, such as 5G 30,31 .…”

“…Repeated packet encoding during transmissions through sensory nodes reduces packet delay which is demonstrated by Szab et al 24 In the literature, various applications of SDN-enabled WSN are deployed in a range of environments and confirmed the improvement in different evaluation metrics. [25][26][27][28][29] Network coding is susceptible to security issues, even though opportunistic coding with SDN architecture is better suitable for futuristic networks, such as 5G. 30,31 The usage of SDN somehow addresses the security issues in the candidate wireless ad hoc networks.…”

A Mechanism for Load Balancing Routing and Virtualization Based on SDWSN for IoT Applications

“…An energy-efficient data collection technique via definite sensing control in two-level IoT-enabled software-defined heterogeneous WSN (2SD-HWSN) is formulated in [26] as an optimization problem, with transmission distance from smart sensors, residual energy of sensors, and load based on node density. The proposed algorithm is divided into two: set-up and transmission phases.…”

Flow‐rule integration for quality of service enhancement in software‐defined vehicular network

“…A hybrid gray wolf optimization approach was proposed by Mishra et al 10 to improve the overall performance of the network by offering balanced clustering using the design of fitness function. Kim et al 11 address the problem of response delay of the SDN controller to that of the flow table capacity constraint based on path discovery cache methods, and it satisfies the delay requirements by using a heuristic algorithm.…”