With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in a WSN. In a nutshell, localization is the process in which the position of a sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

To deal with the multimodality, nonlinearity, and nonconvexity of the maximum likelihood (ML) cost function when solving the localization problem with received signal strength (RSS) in wireless sensor networks, we propose a localization approach based on the differential evolution algorithm, opposition-based learning, and adaptive redirection in a unified way. In the proposed approach, we need neither to approximate the ML cost function nor to provide a good initial point while most conventional approaches, including those based on the semidefinite programming, second-order cone programming, and unscented transformation. The performance and computational complexity of the proposed approach are analyzed and compared with those of other approaches for three practical scenarios. Simulation results confirm that the proposed approach performs better than other approaches with relatively low computational complexity.

Unlike most studies assuming exact knowledge of the transmit power and path-loss exponent, we address the localization problem under a more practical assumption that the exact values of the transmit power and/or path-loss exponent are unavailable. We propose an algorithm based on the differential evolution, opposition based learning, and adaptive redirection that jointly estimates the position of the target node and the parameters. Results from simulation and indoor experiments show that the proposed algorithm provides higher localization accuracy and requires less computational time than conventional algorithms especially when the exact values of the transmit power and path-loss exponent are unavailable simultaneously.

Recent advances in wearable technology have led to the development of various methods for stress sensing in both controlled laboratory and real-life environments. However, existing methods often rely on specialized or expensive sensors that may not be easily accessible to the general population. In this study, we investigate the feasibility of using off-the-shelf smartwatches for stress detection in real-life scenarios. To achieve this, we propose SOSW, a comprehensive methodology for robust sensor data processing by considering both physiological and contextual data. SOSW employs a two-layer machine learning (ML) architecture. The first-layer ML model is trained and validated using carefully collected data under controlled laboratory conditions. The second-layer ML model is trained and validated using data collected in real-life settings. We conducted evaluations with 26 and 18 participants in controlled laboratory and real-life conditions, respectively. The results indicate that our methodology can successfully detect stressful events with an F-1 score of up to 0.84 in laboratory conditions and 0.71 in real-life scenarios using off-the-shelf smartwatches. The results are comparable to those achieved by the state-of-the-art methods that rely on dedicated wearables.

This paper introduces a comprehensive microgrid roadmap for the Korea Institute of Energy Technology (KENTECH), an energy specialized institute in South Korea, aligning with the country's overarching objective of achieving carbon neutrality by the year 2050. The roadmap outlines the integration of diverse energy resources-primarily renewables-to enhance sustainability and energy efficiency on campus. The paper also describes key elements for achieving autonomous energy operations through advanced technologies such as energy management systems, network gateways for system interoperability, static transfer switches, intelligent electronic devices, and power condition systems. It also presents details on our microgrid management system and customized data collection platform that acquires data from internet of things devices, advanced metering infrastructure, and automated revenue measurement. Additionally, by using a stochastic two-stage optimization model, we evaluate 1,320 scenarios to identify the optimal energy mix, focusing on the integration of renewable sources. Our findings reveal that a 15 MW solar capacity, coupled with an advanced energy storage system (ESS), can significantly enhance energy self-sufficiency, achieving up to 100% self-sufficiency in specific scenarios. The study demonstrates that increasing the solar installation from 5 MW to 15 MW, while integrating ESS, reduces dependency on imported power by 35% and enhances operational efficiency. Moreover, the optimization model indicates a potential 15% reduction in operational costs when demand response strategies are effectively implemented, particularly during peak activities. Furthermore, the microgrid design is projected to reduce CO2 emissions by up to 60%, contributing to KENTECH's role as a leader in sustainable energy management. These results highlight the roadmap's capability to transform KENTECH into a model of energy independence, with significant implications for similar institutions globally.