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The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.

LoRa is a popular communication technology in the Internet of Things (IoT) domain, providing low-power and long-range communications. Most LoRa IoT applications use the LoRaWAN architecture, which builds a star topology between LoRa end nodes and the gateway they connect to. However, LoRa can also be used for the communication between end nodes themselves, forming a mesh network topology. In this paper, we present a library that allows to integrate LoRa end nodes into a LoRa mesh network, in which a routing protocol is used. Thus, an IoT application running on these nodes can use the library to send and receive data packets to and from other nodes in the LoRa mesh network. The designed routing protocol is proactive, and maintains the routing table at each node updated by sending routing messages between neighboring nodes. The implemented library has been tested on embedded boards featuring an ESP32 microcontroller and a LoRa single-channel radio. By using our LoRa mesh library, nodes do not need to connect to a LoRaWAN gateway, but among themselves. This opens the possibility for new, distributed applications solely built upon tiny IoT nodes.

The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive-routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques. [ABSTRACT FROM AUTHOR]