PurposeIn wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.Design/methodology/approachThe proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.FindingsIn this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.Originality/valueThe proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.

Speech-based Interaction systems contribute to the growing class of contemporary interactive techniques (Human-Computer Interactive system), which have emerged quickly in the last few years. Versatility, multi-channel synchronization, sensitivity, and timing are all notable characteristics of speech recognition. In addition, several variables influence the precision of voice interaction recognition. However, few researchers have done a significant study on the five eco-condition variables that tend to affect speech recognition rate (SRR): ambient noise, human noise, utterance speed, and frequency. The principal strategic goal of this research is to analyze the influence of the four variables mentioned earlier on SRR, and it includes many stages of experimentation on mixed noise speech data. The sparse representation-based analyzing technique is utilized to analyze the effects. Speech recognition is not noticeably affected by a person’s usual speaking pace. As a result, high-frequency voice signals are more easily recognized (∼∼98.12%) than low-frequency speech signals in noisy environments. By performing the experiments, the test results may help design the distributive controlling and commanding systems.