Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

The Network-on-Chip (NoC) has been emerging as a very promising paradigm for scalable on-chip communication architectures in many fields. To achieve excellent results in Network on Chip (NoC) systems application the load balanced in the routing has to be achieved. Normally, the routing is based on the probability function using the data of the past and current, which means the route may exist or may not, because of which the size of the routing table also increases. To overcome this drawback in the NoC systems, we propose K-Step Look Ahead in Load Balanced Adaptive Routing, where all the details about the routing the routing table is maintained by the every router are achieved by ACO-based Cascaded Adaptive Routing (ACO-CAR). These routing tables are formed based on the probability of pheromones left behind by the ant packets in the past. Then we implement a k-Step Look Ahead (KSLA) algorithm which finds the exact number of steps for the ant packet to reach the destination using the information of the ACO-CAR routing tables and updates the routing table by eliminating the route information which does not exists.

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<abstract> <p>In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.</p> </abstract>