The single-hop star-of-stars topology of current Low Power Wide Area Network (LPWAN) is simple and robust but still lack of network coverage because of signal attenuation. We present a forwarding mechanism to increase the coverage and reliability of LPWAN. Two key characteristics of our approach are on-demand and opportunistic forwarding. While most previous studies adopt fixed-path forwarding protocols to realize multi-hop transmission in LPWAN, we adopt opportunistic forwarding for better reliability. Opportunistic forwarding works well in the uncertainties of wireless communication since it does not predefined the forwarding path. Moreover, to reduce redundant transmissions usually found in forwarding approaches, we adopt an on-demand and priority-based forwarding mechanism. That is, a relay can only forward after it receives status feedback from the network server and if it has packets that the server is missing. To overcome the bottleneck problem in on-demand forwarding, we employ Random Linear Network Coding (RLNC) with compressed header overhead for transmitting multiple packets in relays. Compared to fixed-path forwarding, our mechanism shows better performance in terms of end-to-end packet delivery ratio.

Long range (LoRa) has gained popularity in realizing Internet of Things applications by facilitating long-range communication with low-power consumption. However, LoRa faces scalability issues due to its unsophisticated random access (RA) protocol, Aloha, which is vulnerable to collision and not scalable in dense network scenarios. In contrast, distributed queuing (DQ), a collision-free contention-based RA protocol, is a promising candidate for replacing Aloha because of its near-optimal performance and independence from the traffic load and pattern. However, DQ is not compatible with LoRa, as it was initially designed to operate on CableTV that supports the full-duplex (FDX) bidirectional link. In FDX, the contention feedback overhead is negligible. Most wireless networks, such as LoRa, support the half-duplex, for which the feedback overhead is considerable. Furthermore, a reduction in efficiency is aggravated under a multichannel environment because of an unbalanced traffic load. This study proposes a joint distributed queueing protocol to consider contention for LoRa that minimizes control overhead and achieves interchannel load balancing. Our analysis shows that the proposed protocol reduces the control overhead by up to 70% compared to DQ. The protocol performance demonstrates near-optimum throughput and access delay, irrespective of the number of arrivals.

DASH-based multiview video allows users to freely navigate between available viewpoints. However, achieving seamless transition during viewpoint switching is still challenging, especially in free-viewpoint video streaming, where user navigation is unpredictable. When bandwidth is limited, delivering all viewpoints in high quality is nearly impossible. The DASH system needs to prioritize high video quality for the selected viewpoint. The problem arises when the switching event occurs because the new video may not yet be available in the buffer. This paper introduces multiview video quality of experience (QoE) then discusses how viewpoint transition delay and viewpoint quality delay affect the overall user experience. We propose quality-based adaptive bitrate (ABR) for multiview video by specifying viewpoint priority and a target quality of the main viewpoint, namely multiview video priority (MVP). Although it select a lower bitrate level than rate-based ABR, it performs better in perceptual video quality, especially when the viewpoint changes frequently. The simulation results show that the MVP algorithm effectively allocates available bandwidth to the required viewpoints. Hence, it can eliminate transition delay and decrease viewpoint quality delay. As a result, the user gets better video quality, smoothness, continuity, and seamless transition during video switching.

This paper presents a quality of service analysis conducted for real-time multimedia communication services particularly over limited capacity network. This research aims to examine quality of videoconference service based on WebRTC technology. We investigate several implementation challenges of videoconferencing in bandwidth limited network by conducting a thorough experimental study. We analyze videoconference quality from the implementation of WebRTC based system for supporting distance learning between two schools in Cianjur, West Java, Indonesia. Further, we study adaptability feature of WebRTC by performing experiments for several network conditions. Result of the experiment shows that fair quality of video (peak signal-to-noise ratio, PSNR, between 30dB to 33dB) can be reached when bandwidth capacity is 2048kbps. On the other hand, the experiment shows acceptable audio and video stream quality with packet loss rate (PLR) less than 1% and jitter less than 100ms can be reached in network condition with bandwidth capacity higher than 128kbps. Outcome of this research supports understanding and addressing of the challenges of real-time videoconferencing on bandwidth limited network.

Providing acceptable video quality in the Internet of Things (IoT) implementation poses a significant challenge, mainly when the application is performed on low-cost and low-power devices. This research focuses on developing a frame resolution adjustment system that maintains the frame rate value of video delivery in wireless IoT environments with resource-constrained devices. Consistent frame rates prevent motion lag and data loss, improving user experience. The system works by predicting the upcoming throughput values using machine learning methods to adjust the sensing parameter, which is the resolution of the video frame to be captured by camera nodes. Hence, the proposed system is equipped with a file size estimator to estimate the size of the next video frame and then adjust the resolution in accordance with the throughput prediction. In this research, we conducted extensive experiments to evaluate the accuracy of the file size estimator and the throughput prediction. The experiment generated a dataset to evaluate throughput prediction and file size estimator model. The evaluation results for the file size estimator showed a mean absolute percentage error (MAPE) of 6.73% in the experiment using 317 frames with video resolutions between 72p and 720p. Experiments were also conducted to compare several machine learning methods for predicting throughput values. Compared to long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES) outperforms the others with the lowest root mean squared error (RMSE) and mean absolute error (MAE) values. Building upon these findings, we implemented the frame resolution adjustment system using SES as the method for predicting the upcoming throughput values. Finally, we demonstrated that the proposed system can maintain the frame rate according to the threshold set by the system while the resolution is being maximized, thereby addressing the challenges of maintaining video quality in resource-constrained IoT environments.

The single-hop star-of-stars topology in low-power and wide-area networks (LPWAN) exhibits reliability and substandard coverage issues, especially in urban areas where line-of-sight (LoS) communication is difficult to achieve. Moreover, LPWAN trade-off the data rate to achieve longer coverage, preventing other end-devices from using the time resource. Locating other gateways is uneconomical as it requires infrastructure, such as the internet and a power connection. In this study, we propose a forwarding scheme with a relay to increase LPWAN coverage and reliability while not degrading the network’s capacity. A relay tends to incur unnecessary forwarding that degrades the network capacity unless proper countermeasure is prepared. Our works, namely opportunistic and on-demand network coding (OODC), minimize unnecessary forwarding and make good use of multiple-receiving relays. Network coding is also applied in the relay for better transmission efficiency and reliability simultaneously. Because network coding occurs header overhead, we perform a header compression technique to counter it. According to our simulation result, our method shows better reliability than fixed path forwarding. In an adaptive data rate settings, the OODC achieves a 92% packet delivery ratio (PDR), whereas a fixed-path forwarding only achieves an 84% PDR.