The population would reach ten billion by 2050, and experts believe that the agricultural sector needs to boost production by 70% to satisfy the demand. Traditional farming practices rely on primitive technology that creates a yield gap with low productivity. A paradigm shift towards merging new technologies in the agriculture sector would enhance productivity, optimize cost, and encourage sustainable development. In this paper, we review the necessity for the fusion of the Fourth Industrial Revolution technological approach in the agricultural domain. We discuss the gap in supply chain management for the Industrial sector and Agricultural sector and identify the issues of vendor-specific production systems. We propose a multimodal communication model for the systematic integration of multi-vendor agricultural production systems. Our model utilizes the Data Distribution Service (DDS) middleware to enable communication between heterogeneous production systems to perform farming operations in a coordinated manner. Experimental work is conducted on a small-scale hydroponic farm to evaluate the system performance in terms of throughput, latency, and packet delivery ratio (PDR). The throughput for our proposed DDS system has significantly improved with the use of the BATCH QoS policy for payload size less than 1024 bytes. However, we incur an average latency of approximately 235 microseconds for any payload size. The value of PDR is 1 for any payload size ensuring our system to be reliable. The results suggest that our model can enable interoperability between multi-vendor production systems in real-time while incurring minimum latency.

The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity of data received by healthcare decision-makers, leading many to develop Distributed Decision Support Systems (DDSSs) to help them make accurate and timely decisions. This research is a systematic review of DDSSs in healthcare using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, and blockchain enhance clinical decision-making processes. It highlights key innovations in DDSSs, including hybrid imaging techniques for comprehensive disease characterization. It also examines the role of Case-Based Reasoning (CBR) frameworks in improving personalized treatment strategies for chronic diseases like diabetes mellitus. It also presents challenges of applying DDSSs in the healthcare sector, such as security and privacy, system integration, and interoperability issues. Finally, it discusses open issues as future research directions in the field of DDSSs in the healthcare sector, including data structure standardization, alert fatigue for healthcare workers using DDSSs, and the lack of adherence of emerging technologies like blockchain to medical regulations.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

The ever-increasing number of network-capable devices places a massive burden on modern networks. Communication infrastructure should provide quality-of-service essentials in terms of high-bandwidth capacity, scalability, resiliency, and security. Programmable networks are viewed as the prevailing method of encountering the challenges introduced by the accelerated expansion. The ability of software-defined networking (SDN) to separate the control plane from the data plane and enable the programmability of the network creates new ways to architect the network. The centralization of control logic introduces complexities in large-scale, distributed networks such as performance bottlenecks and reliability. Distributed SDN controllers have been proposed to overcome the performance concerns. The lack of a communication standard among distributed controllers, referred to as the East/West interface, presents a challenge in the adoption of SDN in large-scale, distributed networks. In this paper, we propose Distributed SDN control plane Framework (DSF) - a framework for the East/West interface for heterogeneous, distributed SDN controllers to synchronize topologies using a standardized data-centric real-time publish/subscribe paradigm known as the Data Distribution Service (DDS). Distributed control plane architectures are proposed using DSF: flat, hierarchical, and T-model. The DSF interface is implemented on multiple SDN control plane platforms to evaluate performance: Floodlight and Open Network Operating System (ONOS) controllers. Test cases with different configurations are designed for performance evaluation of the proposed interface in homogeneous and heterogeneous SDN control planes. In addition, a performance comparison is presented of DSF-based ONOS controllers versus Atomix-based ONOS cluster solutions.

There are a vast number of researches in sensor networks, medical devices, wireless communication, middleware software and software applications that help advance improvements in the healthcare systems. Health monitoring systems deliver health status reports to actors such as people under monitoring, practitioners and coaches for several purposes. In this paper, we propose E-Ambulance framework, which is a smart ambulance system model that provides health monitoring of patients for remote medical professionals. As well as provide an automatic responses of suggestions and warnings to paramedic staff inside an ambulance. Sensor networks record and deliver health status information to other elements of system. Due to the availability of information from sensors networks, an auto response can take place by alarms supervised by Decision Maker unit. Remote decisions can be made in a medical center after receiving inputs generated inside an ambulance. Building a distributed real time system can handle all aspects of time critical systems and hide heterogeneity between elements and different types of data. Data Distribution Service (DDS) standard is used to build the proposed model. To demonstrate the effectiveness of the system in terms of latency and throughput, experiments are performed over DDS middleware. Additionally, to have efficient configuration different QoS policies configurations of DDS are tuned

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions.