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Summary The cluster networking protocols are the roots that embed intelligent decision‐making and enhance the lifespan of wireless sensor networks (WSNs). Wireless sensors with limited capabilities face several challenges due to the heterogeneous application environments. Especially, the mobility‐incorporated sensors in most situations trouble the cluster network's robustness. Many cluster networking protocols have been presented in the past to enhance the network lifespan and data delivery ratio. However, they lack a dedicated and efficient mechanism for mobility assistance, an adequate cluster management process and cluster head selection criteria. To overcome these issues and for the uniform energy load distribution, we propose a mobility‐compatible cache controlled cluster networking protocol (MC‐CCCNP) in this paper. It is an energy‐efficient cluster networking protocol that supports sensor movement. Network resource management and routing are controlled distributively by an optimal number of cache nodes. It defines a new strategy for cache node deployment based on neighbour density as well as a weight formula for cluster head selection and cluster formation based on the residual energy, the distance to the base station and the node velocity. It also includes techniques for detaching and reconnecting a mobile node to an appropriate cluster cache if it crosses the cluster boundary. We simulate and compare the performance of our protocol with the centralised energy‐efficient clustering routing, energy‐efficient mobility‐based cluster head selection protocol and dual tier cluster‐based routing protocols over different network configurations with varying mobility, scalability and heterogeneity. The MC‐CCCNP showed remarkable improvements in energy utilisation uniformity and energy consumption. With the improved network lifespan, it also maintains a higher data throughput rate of 95% or more in almost all network configurations. [ABSTRACT FROM AUTHOR]

Summary: The main aim of this paper is to address the problem of redundant broadcast packets using the extension of rough set concept. In general, a rough set is a mathematical tool for dealing with issues of uncertainty. In MANET, mobile nodes are moving from one place to another place in a simulation environment; hence, it creates uncertainty issues. Due to the fast mobility of mobile nodes, network metrics such as bandwidth and hop count vary. The findings were useful in identifying and improving the network's performance. The proposed NRS‐based route selection approach has been compared with conventional techniques such as rough set, covering rough set (CRS). The reduced control packets are obtained using pruning concept based on selection of one‐hop and two‐hop nodes. Eventually, the proposed approach proved that it attains a higher throughput (85%), reducing the route request packets (3406) and delay of data transmission (0.02274 s). Also, two well‐known ad hoc routing protocols were used such as Dynamic Source Routing (DSR) and Ad Hoc On‐Demand Distance Vector (AODV) protocols. [ABSTRACT FROM AUTHOR]

Summary: A routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on‐demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR. [ABSTRACT FROM AUTHOR]