The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor’s battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.

In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle- (UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately 42% in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by 27% using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different.

Background The objective of this study was to assess the accessibility and content of the Accreditation Council for Graduate Medical Education (ACGME)-accredited general cardiology fellowship websites. Methods Using the online information provided by the Electronic Residency Application Services (ERAS), we compiled a list of ACGME-accredited cardiac fellowship programs. The program links on ERAS were evaluated followed by a standard Google search of the program name + “cardiology fellowship”. Each program website was evaluated on the basis of program content, applying/recruiting and education. Results At the time of this study, we reviewed 231 general cardiology fellowship programs provided through ERAS. Of the 231 programs, 12 were excluded due to broken links, repeated links on ERAS, and websites with a general lack of content. We analyzed the data collected from the remaining 219 programs to assess the availability and general content of those websites. Data collected revealed a general lack of information regarding application processing and educational services but were sufficient in providing program descriptions and contact information. Conclusions ERAS can be used to locate general cardiology fellowships participating in the current match; however, the links provided by the program websites on ERAS are lacking in general content and accessibility. Although most websites did contain enough information about their program, there was a distinct lack of key information provided typically in the education services and application process.

Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs’ aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs).

Wireless body area networks (WBANs) have revolutionized healthcare by enabling remote supervision, prior detection, and disease interception using invasive and wearable sensor devices. The limited battery capacity of the sensors coupled with the poor channel condition (that may arise from body postures) require cooperative transmission strategies that can prolong the sensors' life time and associated functionalities. Therefore, in this article, a cooperative scheme based on single-stage relaying is presented for spectrum and energy efficiency. The relay operating for two different scenarios, i.e. network coding and hierarchical modulation, is discussed. The general trend for bit error rate (BER) is observed by modeling a Rayleigh faded link catering path loss. The results are further studied for actual channel models, defined in WBAN standard. The effect of hop-length variation on BER and packet error rate (PER) are discussed. Simulation results show that both cooperative schemes outperform direct communication. A hybrid switching scheme is proposed to enhance efficiency.