Multiple input and multiple output and energy‐aware peering routing protocol for energy consumption in sensor networks

Abstract

Wireless sensor network consumes large number of energy-constrained nodes that are used to monitor the external devices while transferring the information in the sensor networks. At the time of the information transmission process, node contains high energy, and battery of node may be recharged continuously, which leads to reduction of the entire information transmission system performance. This paper introduces the multiple input and multiple output (MIMO) method with energy-efficient protocol for reducing the energy consumption in the network. Initially, the network coverage is determined by applying the shadow fading sensing model, and the clusters are formed with the help of the particle dual clustering process. After the cluster is formed, the information has been transmitted with the help of the energyaware peering routing protocol (EPR), which reduces the network traffic and also improves the energy efficiency with efficient manner. Then, the efficiency of the system is analyzed with the help of experimental results in terms of coverage fraction, accuracy of the cluster, and energy consumption.KEYWORDS multiple input and multiple output (MIMO) method with energy-efficient protocol, particle dual clustering process, shade fading sensing model, the energy-aware peering routing protocol (EPR), wireless sensor network

CapsNet‐based computing in cognitive communications

“…The effectiveness of the proposed technique is achieved by minimizing the set objective function on satisfying the QoS constraints in the MANETs. “Identify the User presence by GLRT and NP Detection Criteria's in Cognitive Radio Spectrum Sensing”—Authored by Budati , Anil Kumar; Valiveti , HimaBindu: This research study [18] has introduced a novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection (PD) is increased, probability of false alarm (Pfa) and probability of missed detection (Pmd) have been reduced when compared with the existing methods. “Multiple Input and Multiple Output and Energy Aware Peering Routing Protocol for Energy Consumption in Sensor Networks”—Authored by Raj , A Sundar; M , Chinnadurai: This paper [19] introduces the MIMO method with energy‐efficient protocol for reducing the energy consumption in the network. Later, the efficiency of the system is analyzed with the help of experimental results in terms of coverage fraction, accuracy of the cluster, and energy consumption. “Day‐Ahead Optimal Scheduling of Micro Grid with Adaptive Grasshopper Optimization Algorithm”—Authored by C , Shilaja; T , Arunprasath; P , Priya: The optimal analysis of MG, this paper [20] has proposed an adaptive grasshopper algorithm (AGOA) with cuckoo search (CS).…”

Dimensionality determination of unknown deployed underwater sensor network (UWSN) using cost function

“…25 However, localization technique has an ultimate goal to find exact location of sensor, which requires high power consumption, and energy usage is an important issue for wireless sensor network. 26,27 In addition, exact location of a sensor mainly is required during the replacement or the recovery of the existing sensors. Furthermore, in the cases where the exact positions of sensors are not desired, only a depiction of the network is enough, this method is suitable as well as it delivers guaranty about the desired dimensionality for such cases proposed in Senel et al 11 Additionally, during the data collection by UWSN, it is required to know about the coverage area of the network and most importantly the coverage area depends on the dimensionality of the network.…”