IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to provide quantitative results that help maximize battery life in IoT transportation systems. Although is well known that MQTT consumes less power than HTTP, a comparative analysis of their power consumption with long-time tests and different conditions has not yet been conducted. In this sense, a design and validation of an electronic cost-efficient platform system for remote real-time monitoring is proposed using a NodeMCU module, in which experimentation is carried out for HTTP and MQTT with different QoS levels to make a comparison and demonstrate the differences in power consumption. Furthermore, we characterize the behavior of the batteries in the systems and compare the theoretical analysis with real long-time test results. The experimentation using the MQTT protocol with QoS 0 and 1 was successful, resulting in power savings of 6.03% and 8.33%, respectively, compared with HTTP, demonstrating many more hours in the duration of the batteries, which could be very useful in technological solutions for the transport industry.

This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN.

The continuous technological advances in favor of mHealth represent a key factor in the improvement of medical emergency services. This systematic review presents the identification, study, and classification of the most up-to-date approaches surrounding the deployment of architectures for mHealth. Our review includes 25 articles obtained from databases such as IEEE Xplore, Scopus, SpringerLink, ScienceDirect, and SAGE. This review focused on studies addressing mHealth systems for outdoor emergency situations. In 60% of the articles, the deployment architecture relied in the connective infrastructure associated with emergent technologies such as cloud services, distributed services, Internet-of-things, machine-to-machine, vehicular ad hoc network, and service-oriented architecture. In 40% of the literature review, the deployment architecture for mHealth considered traditional connective infrastructure. Only 20% of the studies implemented an energy consumption protocol to extend system lifetime. We concluded that there is a need for more integrated solutions specifically for outdoor scenarios. Energy consumption protocols are needed to be implemented and evaluated. Emergent connective technologies are redefining the information management and overcome traditional technologies.

In IoT-based supply chain transportation, low rates for data loss, minimizing time to destination, and optimizing energy consumption are paramount. These factors can be influenced by variable parameters, data volume, logging procedures, positioning complexities, and communication hiccups during transit. This study introduces an adaptive data logging algorithm for a cost-effective IoT node, addressing these challenges. This innovation enables real-time data acquisition and remote display via a web interface. Experimental tests demonstrate the prototype's reliability in both controlled indoor and dynamic outdoor environments, particularly in environmental and GPS data collection. Results reveal 5.24% data loss indoors and 23.24% via the web interface. Outdoors, data loss peaks at 55.34%, increasing to 82.76% with the web interface. However, the obtained information is adequate for prototype validation. The algorithm reduces data by 74%, leading to lower data processing and power transmission needs. Moreover, determining the distance from GPS coordinates is essential for predicting travel times and monitoring vehicle velocity to maximize efficiency. The results from this prototype are expected to enhance the development of advanced models, thus enriching future scientific research initiatives that aim to incorporate IoT technology into transportation systems.

The recognition of clinical manifestations in both video images and physiological-signal waveforms is an important aid to improve the safety and effectiveness in medical care. Physicians can rely on video-waveform (VW) observations to recognize difficult-to-spot signs and symptoms. The VW observations can also reduce the number of false positive incidents and expand the recognition coverage to abnormal health conditions. The synchronization between the video images and the physiological-signal waveforms is fundamental for the successful recognition of the clinical manifestations. The use of conventional equipment to synchronously acquire and display the video-waveform information involves complex tasks such as the video capture/compression, the acquisition/compression of each physiological signal, and the video-waveform synchronization based on timestamps. This paper introduces a data hiding technique capable of both enabling embedding channels and synchronously hiding samples of physiological signals into encoded video sequences. Our data hiding technique offers large data capacity and simplifies the complexity of the video-waveform acquisition and reproduction. The experimental results revealed successful embedding and full restoration of signal’s samples. Our results also demonstrated a small distortion in the video objective quality, a small increment in bit-rate, and embedded cost savings of −2.6196% for high and medium motion video sequences.