In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.

Air pollution is a critical issue in cities in developing countries like Hanoi, Vietnam. An efficient and comprehensive air quality monitoring system may reduce the harmfulness and improve the cities' sustainability. This paper presents a novel approach to realize such a system in which the air monitoring sensors are mobile. More specifically, we introduce a three-tier architecture for the air quality system, including sensing, communication, and application layers. Initially, we discuss each layer concept to bypass the limitation of the traditional stationary monitoring system. We then describe our design and implementation of air quality monitoring devices installed on vehicles, such as buses. The device is carefully designed to satisfy the conditions of impedance matching and power integrity. Besides, it fully functions in measuring parameters from the ambient environment. The device is aware of its location (using GPS) and uses Wi-Fi and 4G (LTE) to transmit sensing data on the Internet. We have conducted various experiments, including a trial deployment of the devices on a vehicle running in Hanoi. The results show our device achieves sensing data transmission with high-reliability levels (i.e., 97%, 100% on Wi-Fi, 4G (LTE), respectively). Moreover, the trial deployment confirms the feasible operation of our device in actual condition.

We first design an $\mathcal{O}(n^2)$ solution for finding a maximum induced matching in permutation graphs given their permutation models, based on a dynamic programming algorithm with the aid of the sweep line technique. With the support of the disjoint-set data structure, we improve the complexity to $\mathcal{O}(m + n)$. Consequently, we extend this result to give an $\mathcal{O}(m + n)$ algorithm for the same problem in trapezoid graphs. By combining our algorithms with the current best graph identification algorithms, we can solve the MIM problem in permutation and trapezoid graphs in linear and $\mathcal{O}(n^2)$ time, respectively. Our results are far better than the best known $\mathcal{O}(mn)$ algorithm for the maximum induced matching problem in both graph classes, which was proposed by Habib et al.

So far, air quality monitoring is usually handled by monitoring stations located at fixed locations. However, due to the cost of installation, deployment, and operation, the number of monitoring stations deployed is often tiny; thus, the monitored area is limited. To deal with this problem, in this paper, we consider a mobile air quality monitoring system that relies on sensors mounted on buses to broaden the monitoring area. Specifically, we investigate the optimal buses to place the sensors as well as the optimal monitoring timings to maximize the number of critical regions that are monitored. We mathematically formulate the targeted problem and prove its NP-hardness. Then, we exploit the greedy and dynamic programming approaches to propose a polynomial-time 1/2-approximation algorithm. We use the data of real bus routes in Hanoi, Vietnam, for the experimentation and show that the proposed algorithm guarantees an average performance ratio of 72.68%.

In this paper, we focus on broadening the monitoring area of a mobile air quality monitoring system, in which the sensors mounted on buses. In particular, we investigate the optimal buses to place the sensors and the optimal monitoring timings to maximize the number of monitored critical regions. We mathematically formulate the targeted problem. Then, we leverage the greedy approach to propose a polynomial-time$\frac{e-1}{2e-1}$approximation algorithm. We use the data of real bus routes in Hanoi, Vietnam, for the experimentation and show that the proposed algorithm guarantees an average performance ratio of 63.87%.