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Congestion in Wireless Sensor Networks (WSN) get worse when there are multiple and random flows of data in which some have superior significance over the others requiring fidelity in terms of packet delivery, QoS, energy efficiency and throughput. In node-level, congestion leads to impairment of packets that obviously reduces the QoS. In this paper, we present a Cluster based congestion control with Rate Adjustment based on Priority (CRAP) protocol, which self-organizes the sensor nodes into clusters. These clusters monitor congestion in a proactive manner within its confined range which exchanges information among them and adjusts the traffic rate when one cluster has high priority flow over the other. This rate adjustment is based on the exchange of traffic rate estimate among the clusters that reduces packet re-transmissions and energy loss. Our simulation expedites system wide rate control resulting in good throughput, very low packet loss probability and delay that deals with multiple, random flows of data.

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Summary In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time‐consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr‐SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator‐based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr‐SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr‐SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr‐SSLO approach. Moreover, various fitness functions are also considered for computing better performance.

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