A major problem with Wireless Sensor Networks (WSNs) is the maximization of effective network lifetime through minimization of energy usage in the network nodes. A modified k-means (Mk-means) algorithm for clustering was proposed which includes three cluster heads (simultaneously chosen) for each cluster. These cluster heads (CHs) use a load sharing mechanism to rotate as the active cluster head, which conserves residual energy of the nodes, thereby extending network lifetime. Moreover, it reduces the number of times reclustering has to be done and significantly increases the number of data packets sent during network operation. The results show that Mk-means (modified k-means) algorithm was found to outperform the existing clustering algorithms owing to its unique multiple cluster head methodology.

Utilizing the available spectrum in a more optimized manner and selecting a proper routing technique for transferring the data, without any data collision, from the sensor node to the base station play a major role in any network for increasing their network lifetime. Cognitive radio techniques play a major role to achieve the same, and when combined with wireless sensor networks the above-said requirements can be greatly accomplished. In this article, a novel energyefficient distance-based clustering and routing algorithm using multi-hop communication approach is proposed. Based on distance, the given heterogeneous cognitive radio-based wireless sensor networks are divided into regions and are allocated with a unique spectrum. Dynamic clustering through distance calculation and routing of data through multi-hop communication is done. The simulation results illustrate that the proposed algorithm has improved energy efficiency and is more stable. The first node death and 80% node death illustrate the improved scalability. Also, the increased throughput aids in maintaining the residual energy of the network, which further solves the problem of load balancing among nodes. All the above results combined with half node death analysis show that the proposed algorithm also has an improved network lifetime.

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Wireless sensor networks (WSNs) are expected to find extensive applicability and accelerating deployment in the future. However, the main challenge faced in WSN is its perishing lifetime. The process of clustering a network is a popular mechanism employed for the purpose of extending the lifespan of WSNs and thereby making efficient data transmission. The main aim of a clustering algorithm is to elect an optimal cluster head (CH). The recent research trend suggests meta‐heuristic algorithms for the selection of optimal CHs. Meta‐heuristic algorithms possess the advantages of being simple, flexible, derivation‐free, and avoids local optima. This research proposes a novel hybrid grey wolf optimiser‐based sunflower optimisation (HGWSFO) algorithm for optimal CH selection (CHS) under certain factor constraints such as energy spent and separation distance, such that the network lifetime is enhanced. Sunflower optimisation (SFO) is employed for a broader search (exploration) where the variation of the step‐size parameter brings the plant closer to the sun in search of global refinement, thus increasing the exploration efficiency. Grey wolf optimisation (GWO) is employed for a narrow search (exploitation), where the parameter coefficient vectors are deliberately required to emphasise exploitation. This balances the exploration‐exploitation trade‐off, prolongs the network lifetime, increases the energy efficiency, and enhances the performance of the network with respect to overall throughput, residual energy of nodes, dead nodes, alive nodes, network survivability index, and convergence rate. The superior characteristic of the suggested HGWSFO is validated by comparing its performance with various other existing CHS algorithms. The overall performance of the proposed HGWSFO is 28.58%, 31.53%, 48.8%, 49.67%, 54.95%, 70.76%, and 87.10%, better than that of GWO, SFO, particle swarm optimisation (PSO), improved PSO, low‐energy adaptive clustering hierarchy (LEACH), LEACH‐centralised, and direct transmission, respectively.

<p>The main interesting aspect of the digital era is the widely spread ease of communication from one end of the world to the other end of the world. There is a revolution in communication, digitalization, globalization, video calling, wireless data transfer and this is possible due to networking. Initially computer networks is the data sharing where data such as documents, file, reports, presentation files, videos, images etc can be shared within a local network or remotely connected networks. Traditional data networking is to empower end-to-end information transfer. The data in such networks are carried across point-to-point links and the intermediate nodes just forward the packets, where the payload of the packets is not modified. Traditional LANs need wires, which may be difficult to set up in some situations.</p><p>It is very much understandable and clearly visible that wired communication is being completely overtaken by wireless technologies in the recent past. Wireless LANs, by its very nature, empowers with increased mobility and flexibility. Wi-Fi devices get connected to the internet through WLAN and access points. 2.4 GHz and 5 GHz ISM bands are used by Wi-Fi. Also, it is to be understood that, a wireless adhoc network is distributed in its nature. It is also to be noted that, the adhoc nature makes these network to rely on any of the pre-existing infrastructure. The data forwarding shall happen from the nodes very much dynamically based on the connectivity and the routing algorithm used.  </p>