In wireless sensor networks, real-time data delivery schemes typically achieve a desired delivery speed by proactively performing one-hop lookahead. Recently, to reduce the deadline miss ratio against the desired delivery speed, a study has proposed a real-time routing protocol based on proactively performing twohop lookahead. However, the recent study might cause heavy message exchange overhead and high computing complexity to proactively carry out obtainment of two-hop neighborhood speed information in the entire sensor network whether data are delivered or not. Moreover, although multi-hop lookahead provides the least deadline miss ratio, due to the restriction from the overhead and the complexity, the recent study merely adopts the two-hop lookahead manner. In this paper, we propose a novel real-time routing protocol. The proposed routing protocol adopts on-demand neighborhood information obtainment only around data forwarding paths. The on-demand information obtainment is performed in multi-hop lookahead. Hence, the proposed routing protocol is able to provide the least deadline miss ratio to realtime data delivery. Simulation results prove that the proposed routing protocol offers better performances with respect to deadline miss ratio, total communication costs, energy efficiency, and network lifetime.

Modern healthcare systems are under growing strain from aging populations, urbanization, and rising chronic disease burdens, creating an urgent need for real-time monitoring and informed decision-making. This survey examines how the convergence of Integrated Sensing and Communication (ISAC) and digital-twin technologies can meet that need by analyzing how ISAC unifies sensing and communication to gather and transmit data with high timeliness and reliability and how digital-twin platforms use these streams to maintain continuously updated virtual replicas of patients, devices, and care environments. Our synthesis compares ISAC frequency options across sub-6 GHz, millimeter-wave, and terahertz bandswith respect to resolution, penetration depth, exposure compliance, maturity, and cost, and it discusses joint waveform design and emerging 6G architectures. It also presents reference architecture patterns that connect heterogeneous clinical sensors to ISAC links, data ingestion, semantic interoperability pipelines using Fast Healthcare Interoperability Resources (FHIR) and IEEE 11073, and digital-twin synchronization, and it catalogs clinical and operational applications, together with validation and integration requirements. We conduct a targeted scoping review of peer-reviewed literature indexed in major scholarly databases between January 2015 and July 2025, with inclusion restricted to English-language, peer-reviewed studies already cited by this survey, and we apply a transparent screening and data extraction procedure to support reproducibility. The survey further reviews clinical opportunities enabled by data-synchronized twins, including personalized therapy planning, proactive early-warning systems, and virtual intervention testing, while outlining the technical, clinical, and organizational hurdles that must be addressed. Finally, we examine workflow adaptation; governance and ethics; provider training; and outcome measurement frameworks such as length of stay, complication rates, and patient satisfaction, and we conclude that by highlighting both the integration challenges and the operational upside, this survey offers a foundation for the development of safe, ethical, and scalable data-driven healthcare models.

Based on distributed sensing via energy-constrained sensor devices, the key of large-scale and shape-dynamic object monitoring is how to reduce communication costs which is the highest related factor to energy consumption. For achieving this goal, we propose a novel detection mechanism based on the convex hull algorithm that has a strong aspect for reducing the number of (geographic) sensing points to be transmitted to base stations for figuring out a large-scale object. As the convex hull only could recognize convex shapes, we develop methods to not only detect shape loss being a convex but recovery original boundary. That is, our mechanism could take both low communication cost and high detection reliability of large-scale objects.

Industrial IoT is a key field of the 4th Industrial Revolution and wireless network technology is essential to support strict industrial requirements. Industrial wireless networks have reliable and real-time data transmission as the main requirement, and to achieve this, the network exploits two schemes: routing graph generation which calculates the data forwarding paths of each node, and scheduling, which allocates the communication time and frequency resources of each node. However, since conventional scheduling approaches allocate resources for all the calculated graphs, specific paths including routes close to the sink are used more redundantly. This phenomenon leads to unnecessary resource wastage and poor scalability in the industrial environment where the number of equipment used increases. To mitigate these problems, we propose a shared path-based scheduling scheme for efficient resource management and scalability improvement by setting the frequently overlapping sections of data flows as resource-sharing paths. The proposed scheme designates the resource sharing path and allocates resources for the shared path in consideration of network statuses, such as congestion and reliability. Simulation results show the proposed scheme is effective in terms of scalability and resource efficiency compared to traditional scheduling.