Who meets national early childhood sleep guidelines in Aotearoa New Zealand? A cross-sectional and longitudinal analysis

Study Objectives To investigate the proportion of children in Aotearoa New Zealand (NZ) who do or do not meet sleep duration and sleep quality guidelines at 24 and 45 months of age and associated sociodemographic factors. Methods Participants were children (n=6,490) from the Growing Up in New Zealand longitudinal study of child development with sleep data available at 24 and/or 45 months of age (48.2% girls, 51.8% boys; 22.4% Māori [the Indigenous people of NZ], 12.9% Pacific, 13.4% Asian, 45.2% European/Other). Relationships between sociodemographic factors and maternally-reported child sleep duration (across 24 hours) and night wakings were investigated cross-sectionally and longitudinally. Estimates of children in NZ meeting sleep guidelines were calculated using a range of analytical techniques including Bayesian linear regression, negative binomial multiple regression, and growth curve models. Results In NZ, 29.8% and 19.5% of children were estimated to have a high probability of not meeting sleep duration guidelines and 15.4% and 8.3% were estimated to have a high probability of not meeting night waking guidelines at 24 and 45 months respectively, after controlling for multiple sociodemographic variables. Factors associated cross-sectionally with children’s sleep included ethnicity, socioeconomic deprivation, material standard of living, rurality and heavy traffic, and longitudinal sleep trajectories differed by gender, ethnicity and socioeconomic deprivation. Conclusions A considerable proportion of young children in NZ have a high probability of not meeting sleep guidelines but this declines across the ages of 24 and 45 months. Sleep health inequities exist as early as 24 months of age in NZ.

Approximations and Optimal Control for State-Dependent Limited Processor Sharing Queues

The paper studies approximations and control of a processor sharing (PS) server where the service rate depends on the number of jobs occupying the server. The control of such a system is implemented by imposing a limit on the number of jobs that can share the server concurrently, with the rest of the jobs waiting in a first-in-first-out (FIFO) buffer. A desirable control scheme should strike the right balance between efficiency (operating at a high service rate) and parallelism (preventing small jobs from getting stuck behind large ones). We use the framework of heavy-traffic diffusion analysis to devise near optimal control heuristics for such a queueing system. However, although the literature on diffusion control of state-dependent queueing systems begins with a sequence of systems and an exogenously defined drift function, we begin with a finite discrete PS server and propose an axiomatic recipe to explicitly construct a sequence of state-dependent PS servers that then yields a drift function. We establish diffusion approximations and use them to obtain insightful and closed-form approximations for the original system under a static concurrency limit control policy. We extend our study to control policies that dynamically adjust the concurrency limit. We provide two novel numerical algorithms to solve the associated diffusion control problem. Our algorithms can be viewed as “average cost” iteration: The first algorithm uses binary-search on the average cost, while the second faster algorithm uses Newton-Raphson method for root finding. Numerical experiments demonstrate the accuracy of our approximation for choosing optimal or near-optimal static and dynamic concurrency control heuristics.

An Enhanced Packet Transmission Framework for Handling Real Time Burst Traffic in Wireless Sensor Networks

Congestion control for real time traffic is an important network measure to be handled in case of repeated event triggers, continuous packet re-transmissions, node interference, node deaths and node failures in Wireless Sensor Networks (WSNs). Network modelling for transmission of packets from source node to sink using probabilistic M/Pareto and Poisson processes have been examined in the past. The existing methodologies are deficit in designing a queuing framework considering other network parameters such as energy consumption and delay for alleviating congestion and thereby efficiently routing packets to sink by reducing packet drops. To overcome this fall back, a Minimum Weight Estimation for Mitigating Congestion during Real Time Burst Traffic (MWCBT) framework is proposed. This gives a precautionary solution against heavy traffic occupancy among the interim and sink-neighbouring nodes in WSNs is proposed. Routing of packets using a congestion-free path is required to increase the node lifespan. An optimal M/Pareto stochastic traffic generator is used in combination with traffic factors such as energy and delay to predict amount of traffic across nodes. A simpler congestion prediction mechanism is performed to control the occurrence of heavy-tailed traffic distributions. A torrent weight value for incoming traffic is generated at each node periodically that directs routing of data packets to sink. The devised MWCBT framework supervises real-time traffic congestion and is found to be more optimal than the existing approaches for network traffic modelling. The proposed approach achieves greater packet delivery ratio and less node congestion compared to the existing network modelling techniques.

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

Analysis of the Relationship between the Density and Lane-Changing Behavior of Circular Multilane Urban Expressway in Mixed Traffic

The behavior of changing lanes has a great impact on road traffic with heavy traffic. Traffic flow density is one of the important parameters that characterize the characteristics of traffic flow, and it will also be affected by the behavior of changing lanes, especially in the case of each lane. The penetration of autonomous vehicles can effectively reduce lane-changing behavior. Studying the relationship between traffic flow density and lane-changing behavior under different autonomous vehicle penetration rates is of great significance for describing the operation mechanism of mixed traffic flow and the control of mixed traffic. In this article, we use empirical, simulation, and data-driven methods to analyze the urban expressway of autonomous vehicles with penetration rates of 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation experiment was carried out on the road, and data related to density, the rate of changing into the lanes, and the rate of changing out lanes were collected. The analysis of the experimental results found the following: (1) The increase in penetration of autonomous vehicles leads to a certain degree of downward trend in density, the rate of changing into the lanes, and the rate of changing out lanes. (2) Different lanes have different effects on the penetration of autonomous vehicles. In a 4-lane road, the two lanes farther from the entrance and exit are closer in appearance, while the two lanes closer to the entrance and exit are similar. (3) The relationship between density and the rate of changing into the lanes and the rate of changing out lanes shows a linear relationship with the penetration of autonomous vehicles. Although the performance of each lane is slightly different, in general, it can be carried out by a multiple regression model. The given parameter value range is relatively close under different permeability. In summary, autonomous vehicles effectively reduce the traffic density and lane-changing behavior of each lane. There is a linear relationship between traffic flow density and lane-changing behavior with the penetration of autonomous vehicles. The density-lane-changing behavior model proposed in this paper can better describe the relationship between the density of the circular multilane urban expressway and the lane-changing behavior in the case of a large traffic flow in mixed traffic.

Biomonitoring of Heavy Metal Air Pollution in Warsaw Using Two Moss Species Pleurozium schreberi and Sphagnum palustre

The aim of the study was to assess air pollution with heavy metals in Warsaw, on the basis of the concentrations of selected elements in moss samples. The active biomonitoring method (moss-bag technique) was applied using two moss species Pleurozium schreberi and Sphagnum palustre. Moss samples were collected in the Kampinos National Park, and the prepared moss bags were distributed and exposed on seven sites in Warsaw. The analysis of metals accumulated in mosses was performed twice in 2020, after two (August-September) and four months (August-November) of exposure. The concentrations of seven heavy metals (Cr, Cu, Pb, Ni, Fe, Cd and Zn) in the mosses were determined, using an Inductively Coupled Plasma Optical Emission Spectrometer (ICP OES). Our results showed a clear dependence of heavy metal accumulation in the mosses on the location of the exposition site and the exposure period. Both species of mosses were found to accumulate the most metals in the vicinity of pollutant emitters, such as the ArcelorMittal Warsaw smelter, exit roads or roads in the city with heavy traffic, petrol stations, or construction works. After 4 months of exposure, in both moss species, the highest increases in the concentrations were found for four elements: Cr, Pb, Ni and Cd. Higher concentrations of some heavy metals in the mosses in 2020, as compared to previous studies, indicate a negative influence of progressing urbanisation on air pollution in Warsaw.

ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network

The Internet of Things (IoT) interconnects massive cyber-physical devices (CPD) to provide various applications, such as smart home and smart building. Bluetooth Mesh is an emerging networking technology, which can be used to organize a massive network with Bluetooth Low Energy (BLE) devices. Managed-flooding protocol is used in Bluetooth Mesh to route the data packets. Although it is a highly desirable option when data transmission is urgent, it is inefficient in a larger and denser mesh network due to the collisions of broadcast data packets. In this paper, we introduce ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. ACE relies on the existing Bluetooth Mesh messages to distribute routes without additional traffic overhead and conducts a beacon-aware routing update adaptively as the topology evolves. In ACE, BLE channel resources can be efficiently utilized by a channel scheduling scheme for each node locally and autonomously without any neighborly negotiation. We implement ACE on the nRF52840 from Nordic Semiconductor and evaluate its effectiveness on our testbed. Compared to the Bluetooth Mesh, our experiments proved that ACE could reduce the end-to-end latency by 16%, alleviate packets collisions issues, and increase the packet delivery ratio (PDR) by 30% under heavy traffic. Moreover, simulation results verified that ACE has better scalability when the size and density of networks become larger and denser.

Node Importance Based Protection in Power-Grid Optical Backbone Communication Networks

Power-grid optical backbone communication network is a special communication network serving for power system. With the development of new internet technology, there are more and more services carried by power-grid optical backbone communication networks. It plays an important role in the protection of nodes, especially important nodes which often carry important information of the network, when the network is under heavy traffic load. Hench, to solve this problem, we propose the concept of node importance and design a node importance-based protection algorithm under heavy traffic load scenario in power-grid optical backbone communication networks. Simulation results show that the proposed node importance based protection algorithm can obviously reduce blocking probability of the important nodes and improve the performance of the entire network in terms of blocking probability.