Currently, Internet of Things (IoT) has become common in various applications, including smart factories, smart cities, and smart homes. In them, wireless local-area networks (WLANs) are widely used due to their high-speed data transfer, flexible coverage ranges, and low costs. To enhance the performance, the WLAN configuration should be optimized in dense WLAN environments where multiple access points (APs) and hosts exist. Previously, we have studied the active AP configuration algorithm for dual interfaces using IEEE802.11n and 11ac protocols at each AP under non-channel bonding (non-CB). In this paper, we study the algorithm considering the channel bonding (CB) to enhance its capacity by bonding two channels together. To improve the throughput estimation accuracy of the algorithm, the reduction factor is introduced at contending hosts for the same AP. For evaluations, we conducted extensive experiments using the WIMENT simulator and the testbed system using Raspberry Pi 4B APs. The results show that the estimated throughput is well matched with the measured one, and the proposal achieves the higher throughput with a smaller number of active APs than the previous configurations.

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.

Handheld devices have recently become an indispensable part of our daily lives. These devices always require an end-to-end connection for proper message transmission. Message transmission will be halted if the connection fails. In this case, delay-tolerant networks (DTNs) are preferable. One of the most important applications of DTN is the transmission of emergency messages among rescue personnel in the aftermath of a disaster. Previously, we created a simulation map in the Rajshahi University (RU) campus area of Bangladesh and studied the performance of DTN routing protocols. This paper used our developed simulation map to evaluate the performance of DTN and social-based routing protocols for emergency message circulation in the RU campus area when traditional communication networks are unavailable. We conducted extensive experiments with the opportunistic network environment (ONE) simulator for evaluations. The performance analysis is based on the delivery ratio, average latency, transmission cost, and average hop count of each group when the message size and node density are changed. According to the simulation results, dLife outperforms all other routing protocols in terms of delivery ratio, while the Spray-and-Focus routing protocol outperforms all other performance metrics.

Nowadays, the IEEE 802.11 wireless local area network (WLAN) has been widely used for Internet access services around the world. Then, the unfairness or insufficiency in meeting the throughput request can appear among concurrently communicating hosts with the same access point (AP), which should be solved by sacrificing advantageous hosts. Previously, we studied the fairness control method by adopting packet transmission delay at the AP. However, it suffers from slow convergence and may not satisfy different throughput requests among hosts. In this paper, we propose a throughput request satisfaction method for providing fair or different throughput requests when multiple hosts are concurrently communicating with a single AP. To meet the throughput request, the method (1) measures the single and concurrent throughput for each host, (2) calculates the channel occupying time from them, (3) derives the target throughput to achieve the given throughput request, and (4) controls the traffic by applying traffic shaping at the AP. For evaluations, we implemented the proposal in the WLAN testbed system with one Raspberry Pi AP and up to five hosts, and conducted extensive experiments in five scenarios with different throughput requests. The results confirmed the effectiveness of our proposal.

Conceptual summary showing LSTM and GRU outperform ML models under meteorological-only inputs, while RFR and LGBMR dominate when lagged water level data are available, across five scenarios in the Old Brahmaputra River, Bangladesh. Accurate river water level forecasting is critical for flood management in Bangladesh's monsoon-dominated deltaic environment, yet optimal model selection under varying data availability remains unexplored. This study systematically compared six machine learning (ML) and deep learning (DL) algorithms, specifically linear regression, support vector regression, random forest regression (RFR), light gradient boosting machine regression (LGBMR), long short-term memory (LSTM), and gated recurrent unit (GRU), for water level prediction at Islampur station using 25 years of daily hydrometeorological data (1999–2024) across five input scenarios employing 5-day lag configurations. DL architectures dominated meteorological-only scenarios, with LSTM achieving R2 = 0.8809, explained variance score (EVS) = 0.8874, and symmetric mean absolute percentage error (SMAPE) = 4.83%, confirming unbiased symmetrical predictions. ML algorithms excelled with autoregressive inputs, where LGBMR achieved R2 = 0.9960 and RFR attained R2 = 0.9984; the latter's EVS = 0.9984 and SMAPE = 0.40% confirmed purely stochastic residuals. Principal component analysis (PCA)-based composite scoring of nine complementary metrics provided objective model rankings. Zero-shot spatial transferability at Sarishabari confirmed reproducible hierarchies without retraining, with violin plots and PCA composite scoring corroborating near-zero prediction bias across all transferred configurations. Results demonstrate that LSTM and GRU enable reliable forecasting under input-limited, meteorological-only conditions while ensemble ML provides superior accuracy when water level observations are available, offering actionable guidance for flood forecasting in data-constrained environments.