Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 26 references
Abstract
The expansion of the Internet of Things (IoT) has considerably intensified the demand for radio spectrum, which remains a limited and highly valuable resource. Cognitive Radio (CR) technology provides an effective approach by enabling the dynamic identification of unoccupied frequency bands and enabling opportunistic spectrum access, thus improving spectral efficiency. This paper addresses the problem of spectrum sensing in Cognitive Radio-based IoT (CR-IoT) networks using real IoT signals transmitted by LoRa modules and captured with an RTL2832U software-defined radio receiver. Energy detection is employed as the sensing method due to its low computational complexity and suitability for unknown signals. The effectiveness of the proposed method is assessed using Receiver Operating Characteristic (ROC) analysis, highlighting its ability to reliably distinguish between occupied and idle frequency bands. The results confirm the feasibility of deploying energy detection-based CR-IoT systems in real-world scenarios, providing a practical foundation for future opportunistic spectrum access techniques in IoT networks.
This survey provides an exhaustive analysis of the existing routing protocols, clustering, optimization and energy management techniques in WSNs and their benefits and drawbacks, with emphasis on EH-WSNs, Cognitive WSNs, and EH-CWSNs.
A. V. Anuja, B. Meena Preethi· International Journal of Sci...· 0 citations
Research on low-power Internet of Things (IoT) systems has gained significant momentum within the broader context of green and sustainable IoT. In this setting, batteryless (BL) IoT has emerged as a promising solution to reduce maintenance costs and environmental impact by eliminating the need for battery replacements. As large-scale IoT deployments for monitoring and sensing applications continue to expand, important challenges arise in the design and operation of sustainable BL IoT networks, including long-term reliability, performance evaluation, and the analysis of energy-harvesting behavior under real-world conditions. To help address these challenges, this article presents a comprehensive dataset capturing the behavior of BL IoT devices deployed in an indoor environmental sensing network. The dataset comprises 100 days of measurements collected from sensors installed throughout an office building and includes data from two types of IoT devices: 1) plugged-in (PI) sensors with continuous power supply; and 2) BL sensors powered exclusively by energy harvested from indoor light. This dual-device deployment enables direct comparison of sensing performance, reliability, and energy dynamics between powered and energy-harvesting systems. The final dataset contains over three million samples collected from 28 sensors (14 PI and 14 BL) deployed across approximately 300 m<inline-formula><tex-math notation="LaTeX">${}^{2}$</tex-math></inline-formula> of office space spanning nine rooms. The dataset provides a valuable resource for the systematic investigation of BL IoT systems, enabling rigorous analysis of deployment strategies, spatial–temporal sensing dynamics, energy-harvesting behaviors, system performance, and long-term sustainability considerations in next-generation self-powered IoT sensing networks.</p> <p><bold>IEEE SOCIETY/COUNCIL</bold> Communications Society (COMSOC)</p> <p><bold>DATA TYPE/LOCATION</bold> Comma Separated Values (CSV); KU Leuven, Belgium</p> <p><bold>DATA DOI/PID</bold> <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.21227/TF0N-SB66">10.21227/TF0N-SB66</ext-link>
Jimmy Fernandez Landivar, Ihsane Gryech, A. Colpaert et al.· IEEE Data Descriptions· 0 citations
Internet of Things (IoT) devices equipped with electromagnetic sensors, such as millimeter-wave radars, generate massive amounts of computation-intensive and latency-sensitive sensing data. However, their limited computational capability and energy supply hinder real-time local processing. Edge computing can alleviate this limitation by offloading electromagnetic sensing tasks to nearby edge servers. Nevertheless, the broadcast nature of wireless channels exposes sensing data to eavesdropping, while the heterogeneity of multi-source electromagnetic sensing data further complicates sensing-data offloading optimization. To address this issue, this paper develops secure edge-assisted sensing-data offloading framework for heterogeneous electromagnetic sensors deployed on IoT devices. Considering the energy consumed by electromagnetic sensing, wireless transmission, and edge computing, together with the end-to-end latency constraint, a secrecy energy efficiency maximization problem is formulated by jointly optimizing spectrum assignment, transmit power, and computing resource allocation. The simulation results demonstrate that the proposed algorithm improves secrecy energy efficiency at least 31% compared with benchmark schemes within a specific range, demonstrating its effectiveness for secure and energy-efficient electromagnetic sensing-data offloading.
Tong Xue, Yang Xia, Xiaolong Chen· Italian National Conference...· 0 citations
The rapid expansion of the Internet of Things (IoT) has led to the proliferation of interconnected devices that generate and exchange vast amounts of data. Efficient data transmission in IoT networks is a crucial factor in ensuring energy conservation, as most IoT devices operate on battery power and are often deployed in remote locations. Energy-efficient data transmission protocols are essential in prolonging the lifespan of these devices, reducing operational costs, and improving network efficiency. This paper explores various energy-efficient data transmission protocols tailored for IoT networks, focusing on mechanisms such as duty cycling, data aggregation, adaptive transmission power, and sleep scheduling. The discussion encompasses recent advancements in the field, comparative analyses of different protocols, and their effectiveness in real-world IoT applications. Additionally, it presents an evaluation of trade-offs between energy efficiency and data reliability, latency, and security. A detailed methodology section outlines experimental setups used to assess protocol performance, followed by a results and discussion section that highlights key findings. The study concludes with insights on future directions and potential improvements in energy-efficient transmission protocols for IoT networks.
I. Moore· International Journal of Mod...· 0 citations
The rapid expansion of the Internet of Things (IoT) has led to the proliferation of interconnected devices that generate and exchange vast amounts of data. Efficient data transmission in IoT networks is a crucial factor in ensuring energy conservation, as most IoT devices operate on battery power and are often deployed in remote locations. Energy-efficient data transmission protocols are essential in prolonging the lifespan of these devices, reducing operational costs, and improving network efficiency. This paper explores various energy-efficient data transmission protocols tailored for IoT networks, focusing on mechanisms such as duty cycling, data aggregation, adaptive transmission power, and sleep scheduling. The discussion encompasses recent advancements in the field, comparative analyses of different protocols, and their effectiveness in real-world IoT applications. Additionally, it presents an evaluation of trade-offs between energy efficiency and data reliability, latency, and security. A detailed methodology section outlines experimental setups used to assess protocol performance, followed by a results and discussion section that highlights key findings. The study concludes with insights on future directions and potential improvements in energy-efficient transmission protocols for IoT networks.
Emily Johnson· International Journal of Dat...· 0 citations
Recent demand for Internet of things (IoT) based sensing devices has increased in many significant areas, such as greenhouse monitoring, remote patient monitoring, military applications, wild-life tracking and habitat monitoring, precision agriculture, etc. Moreover, some critical issues are present in the perpetual sensor network like node energy consumption rate, coverage/connectivity, quality of service, network throughput and charging cycle capacity of sensor node batteries. Thereby, various network issues can arise, especially regarding coverage holes, proper connectivity, and network life-time. In this paper, we have studied these problems well and have shown the necessary constraints for the connectivity of the IoT sensor network. Moreover, we have also presented various helpful IoT applications and energy harvested IoT sensor networks. Any network can perform well for a long time because of energy management which is very crucial. This paper presents various feature scopes used in wireless sensor networks (WSNs). It also illustrates distinct approaches like relay node placement, efficient communication, machine-learning systems i.e. reinforcement learning, and heuristic techniques. It also discusses real-world deployments to highlight practical connectivity challenges and failures.
Ramesh Kumar, D. Sah, R. Bhan· Peer-to-Peer Networking and...· 0 citations
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