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This paper presents to study the performance of machine learning techniques consisting of multivariate adaptive regression spline (MARS), feed forward neural network-back propagation (FFNN-BP), and decision tree regression (DTR) for estimating the physico-chemical properties of groundwater in the coastal plain area in Vinh Linh and Gio Linh districts of Quang Tri province of Vietnam. With 290 groundwater samples collected in two districts, this study has identified three main elements CO2, Ca, CaCO3 for simulation. Quantitative analysis results have shown that these three components are such as CaCO3 with from 0 to 25.8 mg/l, Ca from 0 to 87.55 mg/l and CO2 from 0 to 12 mg/l. In the present examination, groundwater quality index (GQI) values and their representative categories have been referred by the Vietnam Groundwater Standard (QCVN01). Furthermore, the statistical accuracy parameters were used to compare among models. To deploy FFNN-BP and DTR, different types of transfer and kernel functions were tested, respectively. Determining the results of MARS, FFNN-BP and DTR showed that three models have suitable carrying out for forecasting water quality components. Comparison of outcomes of MARS model with the FFNN-BP and DTR models indicated that this model has good performance for forecasting the elements of water quality, its level of accuracy was slightly more than the other. To assess the accurate values of the models according to the measurement parameters for training phase illustrated that the order of the models was MARS to give the best result, followed by DTR and finally FFNN-BP, respectively.

This work investigates the security and reliability analysis for a novel satellite-terrestrial (SatTer) network. Specifically, a satellite attempts to transmit confidential information to a ground user (GU) via the support of multiple relay nodes in the presence of an eavesdropper that tries to overhear the information. A friendly jammer is deployed to improve the secure transmission between the satellite and the relays. Furthermore, satellite-to-relay generalized Rician fading channels and imperfect channel state information (CSI) are deployed to examine a general system model. In this context, the closed-formed expressions for the outage probability (OP) and intercept probability (IP) are derived corresponding to an amplify-and-forward (AF)-based relaying scheme, which is challenging and has not been studied before. Finally, the exactness of the mathematical analyses is validated through Monte Carlo simulations. Furthermore, the effects of various key parameters (e.g., channel estimation errors, satellite's transmit power, relay's transmit power, number of relays, and fading severity parameter) are examined., 12 pages, 9 figures. Accepted by the IEEE Systems Journal

Mobile-edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinder IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this article investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its nonconvexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), the bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.

In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search's performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the state-of-the-art benchmarks in terms of both energy consumption and outage performance.

Blockchain technology is emerging and has high potential to improve and transform the agricultural supply chain. This study investigates the critical barriers to blockchain technology adoption in the Vietnamese agricultural supply chain using a novel interval-valued hesitant fuzzy Decision-Making Trial and Evaluation Laboratory (IVHF-DEMATEL) approach. The IVHF-DEMATEL technique is applied to identify cause-and-effect relationships and draw the influence-relations map of the barriers. In contrast to prior work, which converts fuzzy sets into crisp sets and then uses crisp set operations, this study is the first study to investigate the Vietnamese agricultural supply chain that uses fully hesitant fuzzy operations representing experts’ assessment without information loss during the conversion. Our results show that ‘lack of government regulation’, ‘lack of scalability and system speed’, ‘a large amount of resource and capital requirements’, and ‘lack of trust among agro-stakeholder or public perception’ are the main barriers. Consistent with previous studies, ‘lack of government regulation’ is the most significant barrier. The results also indicate the hesitant degree of each barrier and better inform decision-makers about uncertain situations. Moreover, a priority order for tackling barriers is proposed to accelerate blockchain adoption in the Vietnamese agricultural supply chain.

Full-duplex (FD) with simultaneous wireless information and power transfer (SWIPT) in wireless ad hoc networks has received increased attention as a technology for improving spectrum and energy efficiency. This paper studies the outage performance for a SWIPT-based decode-and-forward (DF) FD relaying network consisting of a single-antenna source S, a two-antenna relay R, and a multi-antenna destination D. Specifically, we propose four protocols, namely static time-switching factor with selection combining (STSF-SC), static time-switching factor with maximal ratio combining (STSF-MRC), optimal dynamic time-switching factor with selection combining (ODTSF-SC), and optimal dynamic time-switching factor with maximal ratio combining (ODTSF-MRC) to fully investigate the outage performance of the proposed system. In particular, the optimal time-switching factor from the ODTSF-SC and ODTSF-MRC methods is designed to maximize the total received data at the destination. In this context, we derive exact closed-formed expressions for all schemes in terms of the outage probability (OP). Finally, the Monte Carlo simulations are conducted to corroborate the theoretical analysis's correctness and the proposed schemes' effectiveness. Web of Science 21 11 art. no. 3847

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to analyze and compare against analytic results in finite samples. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks., Comment: 5 pages, 1 figure. Submitted for publication

This work studies unmanned aerial vehicle (UAV) relay-assisted Internet of Things (IoT) communication networks in which a UAV is deployed as an aerial base station (BS) to collect time-constrained data from IoT devices and transfer information to a ground gateway (GW). In this context, we jointly optimize the allocated bandwidth, transmission power, as well as the UAV trajectory to maximize the total system throughput while satisfying the user’s latency requirement and the UAV’s limited storage capacity. The formulated problem is strongly non-convex which is very challenging to solve optimally. Towards an appealing solution, we first introduce new variables to convert the original problem into a computationally tractable form, and then develop an iterative algorithm for its solution by leveraging the inner approximation method. Numerical results are given to show significant performance improvement over benchmark schemes.

In order to support a massive number of resource-constrained Internet of Things (IoT) devices and machine-type devices, it is crucial to design future beyond 5G/6G wireless networks in an energy-efficient manner while incorporating the network extension methodologies. To this end, this letter proposes a novel two-hop hybrid active-and-passive relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both the Time-Switching (TS) and Power-Splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated for the joint optimization of throughput, harvested energy, and transmit power of a SWIPT-enabled systemwith the proposed hybrid scheme, and is solved using a nonlinear optimizer. Our numerical results in terms of weighted utility function show the superior performance of proposed hybrid scheme over passive repeater-only and active relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems with the fixed-TP.

In order to support the massive number of resource-constrained Internet of Things (IoT) devices and machine-type devices, it is crucial to design future beyond 5G/6G wireless networks in an energy-efficient manner. To this end, this letter proposes a novel two-hop hybrid backscatter-and-relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both the Time-Switching (TS) and Power-Splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated for the joint optimization of throughput, harvested energy, and transmit power of a SWIPT-enabled system with the proposed hybrid scheme, and is solved using a nonlinear optimizer. Our numerical results in terms of weighted utility function show the superior performance of proposed hybrid scheme over backscatter-only and relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems designed with the fixed-TP.