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Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems and to provide high spatial and temporal resolutions. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated whether NO 2 , which creates PM 2.5 by means of chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM 2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors and that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than 2 years, since July 2017. Based on the operating periods of the PA-II units, we then chose the period of January 2018 to December 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM 2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM 2.5 data. In the per-month analysis, three key features – namely temperature, relative humidity (RH), and NO 2 – were considered. The NO 2 , called collocated NO 2 , was collected from the reliable instrument collocated with the PA-II unit. The per-month analysis showed that the PA-II unit had a good correlation (coefficient of determination R2>0.819) with the BAM-1020 during the months of November, December, and January in both 2018 and 2019, but their correlation intensity was moderate during other months, such as in July and September 2018 and August, September, and October 2019. NO 2 was shown to be a key factor in increasing the value of R2 in the months when moderate correlation based on only PM 2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies, as well as their multiplicative terms. The addition of NO 2 had a much larger effect than that of RH when both PM 2.5 and temperature were considered for calibration in both models. When NO 2 , temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. In addressing the feasibility of utilizing distant NO 2 measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO 2 when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO 2 data proves to be challenging or costly. We assessed the performance of different PA-II units to determine their efficacy. Our investigation reveals a significant enhancement in calibration performance across different PA-II units upon integrating NO 2. Importantly, this improvement remains consistent even when employing models trained with different PA-II units within the same location. Overall, this investigation emphasizes the significance of NO 2 in improving calibration for low-cost PM 2.5 sensors and presents insights into leveraging distant NO 2 measurements as a viable alternative for calibration in the absence of collocated data. [ABSTRACT FROM AUTHOR]

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems to provide high spatial and temporal resolution. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated if NO2, which creates PM2.5 by chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than two years since July 2017. Based on the operating periods of the PA-II units, we then chose the period of Jan. 2018 to Dec. 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM2.5 data. In the per-month analysis, three key features, temperature, relative humidity (RH), and NO2, were considered. The NO2, called colocated NO2, was collected from the reliable instrument colocated with the PA-II unit. The per-month analysis showed the PA-II unit had a good correlation (coefficient of determination, R 2 > 0.819) with the BAM-1020 during the months of Nov., Dec., and Jan. in both 2018 and 2019, but their correlation intensity was moderate during other months, such as July and Sep. 2018, and Aug., Sep., and Oct. 2019. NO2 was shown to be a key factor in increasing the value of R 2 in the months when moderate correlation based on only PM2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies as well as their multiplicative terms. The addition of NO2 had a much larger effect than that of RH when both PM2.5 and temperature were considered for calibration in both models. When NO2, temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. Since it is practically infeasible to colocate a reliable NO2 instrument colocation with high accuracy at low-cost PM sensors, we investigated the effectiveness of using NO2 data (which we call distant NO2), collected from monitoring sites deployed at locations far from the considered low-cost PM sensor for calibration performance enhancement. It was shown that the use of distant NO2 enhances the calibration performance compared to calibration without NO2 when it is highly correlated with colocated NO2. Overall, PA-II units have good agreement with PM2.5 monitoring systems of high quality. Moreover, the calibration performance can be improved by using machine learning algorithms and by considering temperature, RH, and especially NO2. [ABSTRACT FROM AUTHOR]

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations. [ABSTRACT FROM AUTHOR]

The development of novel materials will enable a new generation of prosthetic devices to be built with additive manufacturing (AM). Vacuum infiltrated sandwich structure composites are a promising approach for building prosthetic sockets via AM. In this paper, we test the tensile properties of 18 different composite material configurations using ASTM D638. These composites were manufactured using a custom vacuum infiltration method and had varying filament materials, infiltrated matrix materials, and print directions. Several material-matrix-print composites showed higher ultimate tensile strengths and reduced anisotropy compared to full-infill control samples. However, the mechanical properties of these composites were limited by a large degree of porosity due to the manufacturing method. Still, the results were sufficiently promising to create a proof of concept prosthetic socket via the vacuum infiltration method. Future research should focus on reducing porosity defects and investigating additional material-matrix-print combinations. [ABSTRACT FROM AUTHOR]

Large-scale Internet of things (IoT) networks have shown great advantages and attracted much attention in 5G networks due to its high-speed Internet connectivity, high data rate, ultra reliability and low latency. However, the diversity of ubiquitous IoT devices and complex transmission environment lead to the difficulty of theoretical analysis of delay performance. In order to capture the characteristics of large IoT networks, a novel integrated analysis framework is developed to build the performance model and analyze the statistical delay performance in this paper. Specifically, we model the spatial distribution of IoT devices with heterogeneous Poisson point processes. With the Laplace transform of signal-to-interference-plus-noise ratio, the service capability of the wireless channel with multiple interferes is determined with a mathematical stochastic-network-calculus aided approach in the exponential domain for Poisson bipolar and cellular networks. Then, the upper bound of delay is achieved by the Mellin transform of the service incremental process. We extend our work to a $k$ -tier heterogeneous network and apply the analysis results to a practical two-tier cognitive radio IoT network. Simulation results validate the theoretical analysis of delay performance with various network parameters. [ABSTRACT FROM AUTHOR]

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay. [ABSTRACT FROM AUTHOR]

This paper proposes a new packet rate scheduling scheme for a non-real time data services over the uplink of a burst switching based direct sequence code division multiple access (DS/CDMA) system to support integrated voice/data services. We consider the most general form of optimization problem formulation to determine the optimal number of transmission time groups along with their data rates, which minimizes the average packet transmission delay. An ordered packet length based groupwise transmission (OLGT) scheme is proposed as a simple heuristic solution to this problem, and we present some analytical results for performance comparison with other possible schemes. Index Terms--Direct sequence CDMA, optimal scheduling, order statistics, packet scheduling algorithm, radio resource management.

Digital healthcare technologies are transforming the face of prosthetic care. Millions of people with limb loss around the world do not have access to any form of rehabilitative healthcare. However, digital technologies provide a promising solution to augment the range and efficiency of prosthetists. The goal of this review is to introduce the digital technologies that have the potential to change clinical methods in prosthetic healthcare. Our target audience are researchers who are unfamiliar with the field of prostheses in general, especially with the newest technological developments. This review addresses technologies for: scanning of amputated limbs, limb-to-socket rectification, additive manufacturing of prosthetic sockets, and quantifying patient response to wearing sockets. This review does not address biomechatronic prostheses or biomechanical design practices. Digital technologies will enable affordable prostheses to be built on a scale larger than with today's clinical practices. Large technological gaps need to be overcome to enable the mass production and distribution of prostheses digitally. However, recent advances in computational methods and CAD/CAM technologies are bridging this gap faster than ever before. We foresee that these technologies will return mobility and economic opportunity to amputees on a global scale in the near future. [ABSTRACT FROM AUTHOR]

This paper jointly addresses the problem of power control and scheduling in ad hoc networks supporting multicast traffic. First, we present a distributed algorithm which, given the set of multicast transmitters and their corresponding receivers, provides an optimal solution to the power control problem, if there is any. The transmit power levels obtained by solving the optimization problem minimize the network power expenditure while meeting the requirements on the SINR at the receivers. Whenever no optimal solution can be found for the given set of multicast transmitters, we introduce a joint scheduling and power control algorithm which eliminates the strong interferers, thus allowing the other transmitters to solve the power control problem. The algorithm can be implemented in a distributed manner. Although the proposed scheme provides a suboptimal solution, simulation results show that the obtained solution is close to the global optimum, when it exists. When instead there is no optimal solution, our algorithm allows for a high number of successful multicast transmissions.

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high ynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter $V$ and the decrease of grid power consumption cost is at the speed of $1/V$. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named ${M}$ time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other ${M}-{1}$ time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks. [ABSTRACT FROM AUTHOR]

The self-organizing network is envisioned as a key technology to future wireless networks, especially for densely deployed small cell scenarios. Self-healing (SH) is an essential functionality to allow the networks to automatically detect and compensate for cell outages, which typically occur when unexpected network failures arise. In this paper, reaping the benefits of machine learning, we propose a novel SH framework in ultra dense small cell networks for meeting the demands of low-cost and fast network operation, quality of service (QoS), and energy efficiency. The proposed SH scheme comprises small cell outage detection (SCOD) and small cell outage compensation (SCOC) to enable self-healing in ultra dense small cell networks. Based on the context information of the partial key performance indicator (KPI) statistics, we propose a novel SCOD algorithm to detect the outage by applying support vector data description (SVDD) approach. The SCOD algorithm detects a small cell outage efficiently considering two situations: KPIs available situation and non-KPIs available situation. Furthermore, in order to compensate the small cell outage, SCOC is formulated as a network utility maximization problem to optimally compensate for the outaged zone in small cell network. A distributed compensation algorithm with low computational complexity is developed to balance the load of small cell networks, considering the QoS provision for users. Simulation results demonstrate that the proposed SH scheme can detect the small cell outage efficiently and can achieve an optimized QoS performance when compensating for the detected small cell outage. [ABSTRACT FROM AUTHOR]

Recently, millimetre-wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C-RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time-frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C-RANs is investigated. The TDMA-based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed-integer non-linear fractional programming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes. [ABSTRACT FROM AUTHOR]

Obesity is a chronic disease that can lead to an increased risk of other serious chronic diseases and even death.We present switching and time-delayed feedback-based model-free control methods for the dynamic management of body mass and its major components. The estimation of body composition based on human body weight dynamics is proposed using a soft switching-based observer. Additionally, this paper addresses the control allocation problem for optimal body weight management using linear algebraic equivalence of the nonlinear controllers based on dynamic behaviour of body composition described in literature. A control allocator system computes the required energy intake and energy expenditure from a controlling range of inputs to track the desired trajectory of body mass by optimizing a weighted quadratic function. Simulation results validate the performance of the proposed controllers and the observer under disturbances in energy intake and energy expenditure. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the throughput of wireless systems in the presence of random data arrivals and quality-of-service (QoS) requirements, which are statistically characterized by the queueing-bound violation probability. By combining the concepts of effective capacity and effective bandwidth, we propose a unified analytical framework to investigate the achievable throughput. Employing the proposed unified framework, we further acquire the explicit expressions of the throughput of QoS guaranteed wireless systems with the channel state information (CSI) known/unknown at the transmitter. Specifically, the acquired throughput is characterized by the first- and second-order statistics of the random data arrivals, and it is shown that the QoS requirements affect the throughput by the second-order statistics of the random data arrivals and the random data transmissions. In addition, our theoretical analysis demonstrates that the throughput under QoS constraints is tighter than the conventional stable throughput, and that the first-order statistic of the random data arrivals is sufficient to characterize the throughput when the system can tolerate an arbitrarily long queueing delay. In particular, we prove that the queueing-bound violation probability decays exponentially with the queueing bound. Finally, simulation results corroborate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the power–delay tradeoff for the newly emerging wireless powered communication networks, in which wireless powered devices (WPDs) harvest energy from a power station via wireless energy transfer in the downlink and then communicate with an information receiving station in the uplink. Each WPD is equipped with an energy buffer and a data buffer to store the random harvested energy and the bursty data arrivals, respectively. To minimize the time-averaged power consumption, a stochastic optimization problem is formulated subject to both constraints of data queue stability and harvested energy availability. By employing Lyapunov optimization theory, we transform the stochastic optimization problem into a series of deterministic problems, where each is further proved to become the standard convex optimization problem and, hence, can be effectively and optimally solved by standard convex optimization techniques. Correspondingly, we propose an Online Power and TIMe Allocation (Optima) algorithm, which requires no a priori distribution knowledge of channel states and data arrivals. Most importantly, the proposed algorithm achieves the power–delay tradeoff as [\mathcalO(\text1/V),\mathcalO(V)], with $V$ being a system control parameter, and provides a significant method to control the power–delay performance on demand in system design. Simulation results verify the theoretical analysis and validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]