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Abstract Objective This study, based on the China Hainan Centenarians Cohort Study (CHCCS), aims to comprehensively describe the characteristic of daytime, night and total sleep duration, sleep quality and different sleep mode of Hainan centenarians and their associations with activity of daily living (ADL) functions. Method The baseline data of CHCCS was used. ADL function was evaluated the Bathel index, sleep quality was evaluated by Pittsburgh sleep quality index (PSQI), sleep status including daytime, night and total sleep duration as well as sleep quality and sleep mode. Multivariate logistic regression model was used to explore the association between sleep status and ADL disability and ADL moderate & severe disability. Results A total of 994 centenarians were included in this study with the age range 100–116 years old. Compared with the centenarians who sleep 6–9 h at night and 9 h at night and sleep ≥ 2 h in the daytime and ADL disability was 2.93 (95% CI: 1.02–8.44), and adjusted OR of ADL moderate & severe disability was 2.75 (95% CI: 1.56–4.83). Compared with centenarians who sleep for 7–9 h and have good sleep quality, centenarians who sleep for > 9 h and have poor sleep quality have an increased risk of ADL moderate & severe disability (OR = 3.72, 95% CI: 1.54–9.00). Conclusion Relation between sleep duration and ADL disability was more significant compared with sleep quality in Hainan centenarians. Poor sleep quality can aggravate the relationship between sleep duration and ADL moderate & severe disability.

Objective: This study, based on the China Hainan Centenarians Cohort Study (CHCCS), aims to comprehensively describe the characteristic of daytime, night and total sleep duration, sleep quality and different sleep mode of Hainan centenarians and their associations with activity of daily living (ADL) functions. Method: The baseline data of CHCCS was used. ADL function was evaluated the Bathel index, sleep quality was evaluated by Pittsburgh sleep quality index (PSQI), sleep status including daytime, night and total sleep duration as well as sleep quality and sleep mode. Multivariate logistic regression model was used to explore the association between sleep status and ADL disability and ADL moderate & severe disability. Results: A total of 994 centenarians were included in this study with the age range 100–116 years old. Compared with the centenarians who sleep 6–9 h at night and < 2 h in the daytime, the adjusted OR between sleep > 9 h at night and sleep ≥ 2 h in the daytime and ADL disability was 2.93 (95% CI: 1.02–8.44), and adjusted OR of ADL moderate & severe disability was 2.75 (95% CI: 1.56–4.83). Compared with centenarians who sleep for 7–9 h and have good sleep quality, centenarians who sleep for > 9 h and have poor sleep quality have an increased risk of ADL moderate & severe disability (OR = 3.72, 95% CI: 1.54–9.00). Conclusion: Relation between sleep duration and ADL disability was more significant compared with sleep quality in Hainan centenarians. Poor sleep quality can aggravate the relationship between sleep duration and ADL moderate & severe disability. Key points: 1. The median duration of daytime sleep (Q1, Q3) was 1.00 (0.50,1.50) hour, while those of night sleep and total sleep (Q1, Q3) were 8.00 (7.00,9.00) hours and 9.00 (8.00,10.50) hours in the centenarians, respectively. And 35.5% of them had poor sleep quality. 2. Compared with sleep quality, the relationship between sleep duration and activity of daily living (ADL) disability was more significant. 3.Centenarians with long sleep duration both at night and in the daytime have high risk of ADL disability and ADL moderate & severe disability. 4. Poor sleep quality could aggravate the relationship between sleep duration on ADL moderate & severe disability. [ABSTRACT FROM AUTHOR]

As the communication industry develops, issues such as how to conserve energy, reduce environmental pollution, and realize more sustainable communication are becoming more pressing. In this paper, a type of energy saving strategy for edge computing networks is proposed. In this energy saving strategy, links will transfer to the sleep mode from the active mode as soon as there are no packets ready for transmission, i.e., the link will be idle once the output buffer is empty. In the active mode, the link transmission rate is adaptively adjusted based on the number of packets in the output buffer with two thresholds. In order to capture the digital nature of modern communication and numerically evaluate the system performance, we establish a discrete-time multiple-vacation queue with variable service rates to model the proposed energy saving strategy. By using the method of a matrix-geometric solution, we analyze the queueing model in the steady state, and obtain the formulas for the performance measures in terms of the energy saving rate of the system, the average latency of packets and the utilization of the edge server. Moreover, we provide numerical results with analysis and simulation to investigate the influence of the system parameters on the system performance. Finally, we construct a cost function and develop an intelligent searching algorithm to give the jointly optimal values for the two buffer thresholds and the lower transmission rate of packets. Note: The affiliation of the third author Wuyi Yue has been updated. [ABSTRACT FROM AUTHOR]

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

The traffic activity of fifth generation (5G) networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation (4G) network technologies that demand always for varied control and data signalling based on control base station (CBS) and data base station (DBS). Hence, this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network. As the new radio (NR) based 5G network is configured to transmit signal blocks for every 20 ms, the proposed algorithm implements withstanding capacity of on or off based energy switching, which in-turn operates in wide range control by carrying out reduced computational complexity. The proposed Wide range of control for base station in green cellular network using sleep mode for switch (WGCNS) algorithm toon and off the base station will work in heavy load with neighbouring base station. For reducing the overhead duration in air, heuristic versions of the algorithm are proposed at the base station. The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings. The proposed algorithm reduces 40% to 83% of residual energy based on the traffic pattern of the urban scenario. [ABSTRACT FROM AUTHOR]

Nocturnal syncope is a common presentation in the emergency department, often related to orthostatic hypotension and subsequent loss of cerebral perfusion when patients get up from sleep to use the restroom faster than their cardiac output and vascular tone can accommodate. Poor hydration status and antihypertensive medications can increase this risk. Patients with syncope who present to the emergency department with a pacemaker are usually evaluated with a pacemaker interrogation to evaluate for runs of nonperfusing rhythms (e.g., ventricular tachycardia or fibrillation). Sleep rate mode (SRM) is a relatively new feature of modern pacemakers and is not currently recognized by emergency physicians. It was implemented to accommodate more physiologic fluctuations in heart rate during rapid eye movement sleep. There is a paucity of evidence supporting the clinical benefit of SRM and similarly no documentation of prior complications of SRM in the current literature. We report the case of a 92-year-old woman with a Medtronic Avisa pacemaker presenting with recurrent nocturnal syncope and bradycardia resulting in multiple emergency department visits. These episodes ultimately resolved by turning off SRM on her pacemaker. SRM is not currently flagged on interrogation report summaries provided to emergency physicians. This report highlights the importance of recognizing this mode as a potential etiology of nocturnal syncope related to chronotropic incompetence in patients with pacemakers. [ABSTRACT FROM AUTHOR]

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems. [ABSTRACT FROM AUTHOR]

The inter-cycle sleep design previously proposed as a power-saving scheme for next-generation Ethernet passive optical networks (NG-EPONs) is able to effectively decrease the power consumption of heavy-load traffic. However, with a medium to high traffic load, these networks may still suffer from a high level of power consumption if no enhanced mechanisms are used. It was noted that the optical line terminal (OLT) often fully opens all communication channels at a medium to heavy load. Moreover, the number of opened channels was changed cyclically according to the traffic loading status. Accordingly, optical network units (ONUs) may be frequently allocated to different channels with changing loads. This may lead to inefficient operation and result in significant channel tuning delays. We thus propose a loading-aware time-division multiple access (TDMA) mechanism that allows the OLT to reserve a maximum bandwidth for each ONU when the network experiences heavy-load traffic. The performance results of our simulations reveal that the proposed scheme is able to reduce power consumption for targeted medium to high loads since ONUs under such loads can extend their sleep time because the cycle length is maximized. Moreover, the total delay is maintained at a relatively low level after applying the proposed scheme since the tuning delay is reduced significantly; however, the transmission delay is slightly increased due to the increased cycle length. [ABSTRACT FROM AUTHOR]

The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting technology into IoT devices to extend their lifetime and sustainably effectively. However, this requires improvements in different IoT protocol stack layers, especially in the MAC layer, due to its high level of energy consumption. These improvements are essential in critical applications such as IoT medical devices. In this paper, we simulated a dense solar-based energy harvesting Wi-Fi network in an e-Health environment, introducing a new algorithm for energy consumption mitigation while maintaining the required Quality of Service (QoS) for e-Health. In compliance with the upcoming Wi-Fi amendment 802.11be, the Access Point (AP) coordination-based optimization technique is proposed, where an AP can request dynamic resource rescheduling along with its nearby APs, to reduce the network energy consumption through adjustments within the standard MAC protocol. This paper shows that the proposed algorithm, alongside using solar energy harvesting technology, increases the energy efficiency by more than 40% while maintaining the e-Health QoS requirements. We believe this research will open new opportunities in IoT energy harvesting integration, especially in QoS-restricted environments. [ABSTRACT FROM AUTHOR]

An improved low-power hiccup-mode technology for short-circuit protection is proposed in this paper, which can effectively suppress short-circuit currents and greatly minimize the power dissipation of hiccup mode. The circuit can start normally after the short circuit is recovered, and there is no voltage overshoot. At the same time, the proposed pre-charge circuit can effectively suppress the large initial inrush current in the pre-charge stage. These technologies are used in a Peak-Current-Mode-Control (PCMC) Pulse-Width-Modulation (PWM) DC-DC boost converter designed with a 0.35 standard CMOS process. Compared with the conventional structure, the post-simulation results show that the initial inrush current during the start-up phase in the proposed structure is reduced by about 90%. When the output short circuit occurs, the inductor current drops to approximately zero and the power dissipation of the converter is very low at this time. The converter repeatedly detects the state of the output load after a period of about 24 ms. Eventually, the converter will restart after the short circuit is recovered and there is no voltage overshoot. [ABSTRACT FROM AUTHOR]

Age-of-Information (AoI), or simply age, which measures the data freshness, is essential for real-time Internet-of-Things (IoT) applications. On the other hand, energy saving is urgently required by many energy-constrained IoT devices. This paper studies the energy-age tradeoff for status update from a sensor to a monitor over an error-prone channel. The sensor can sleep, sense and transmit a new update, or retransmit by considering both sensing energy and transmit energy. An infinite-horizon average cost problem is formulated as a Markov decision process (MDP) with the objective of minimizing the weighted sum of average AoI and average energy consumption. By solving the associated discounted cost problem and analyzing the Markov chain under the optimal policy, we prove that there exists a threshold optimal stationary policy with only two thresholds, i.e., one threshold on the AoI at the transmitter (AoIT) and the other on the AoI at the receiver (AoIR). Moreover, the two thresholds can be efficiently found by a line search. Numerical results show the performance of the optimal policies and the tradeoff curves with different parameters. Comparisons with the conventional policies show that considering sensing energy is of significant impact on the policy design, and introducing sleep mode greatly expands the tradeoff range. [ABSTRACT FROM AUTHOR]

The surging mobile traffic poses serious challenges for mobile operators and the advancement of information and communication technologies (ICT). The cost and energy consumption cannot support affordable mobile services and sustainable growth. As a new architecture, cloud-based radio access network (C-RAN) is proposed to confront these challenges. C-RAN is a deployment paradigm that seeks to isolate baseband unit (BBU) from its remote radio unit (RRU) in base station (BS), consolidating the BBUs into a common place (i.e. BBU pool). In the BBU pool, the computing resources provided by the BBUs can be dynamically assigned to RRUs on demand by the BBU controller. Thus, with the fluctuation of data traffic from RRUs, a part of BBUs can be dynamically turned on or off. As a result, the energy consumption of the BBU pool can be reduced correspondingly. In this paper, we design a Markov chain-based calculation model to formulate the energy-saving operation of the BBU pool with an active/sleep mode under dynamic RRU traffic load. On this basis, we propose two different BBU state transition strategies for the BBU pool, i.e., “Cautious ON, Bold OFF (CO-BF)” strategy and “Bold ON, Cautious OFF (BO-CF)” strategy. We formulate the key performance indicators in terms of the energy saving efficiency (ESE), the consequent packet queuing delay (PQD) and the normalized tradeoff cost as well. In extensive simulations, we investigate the effect of these key parameter indicators on the system performances of the BBU pool. Achieving the minimum normalized cost, an optimal operating strategy for the BBU pool can be determined by adjusting the parameter values of the ( $N$ , $M$ ). The simulation results show when RRU traffic load is light (e.g., $\lambda =0.1$ ), the proposed BO-CF strategy can lead to the minimum normalized cost, which corresponds to ESE of up to 75.4% and PQD of 0.0097ms. For heavier load (e.g., $\lambda =0.4$ ), the minimum normalized cost can be obtained along with ESE of up to 74.1% and PQD of 0.1241ms, when the CO-BF strategy is adopted. Hence, we observe that the BO-CF strategy is slightly better than the CO-BF for the light traffic load of the BBU pool, while the CO-BF strategy would be more suitable for the BBU pool with the heavier traffic load. It proves the efficacy of our proposed Markov chain-based calculation model to pursuit an optimal operating strategy for the delay-aware and energy-efficient BBU pool.

Named data networking (NDN) has emerged as a promising communication paradigm, proposed to deal with the shortcomings of the traditional IP‐based model. NDN introduces new name‐based routing, receiver‐based service, caching, and self‐certifying contents features that obviously improve data delivery efficiency and reliability. Moreover, NDN offers lightweight forwarding rules that suits constrained devices. These features makes NDN as highly promising communication model for the Internet of Things (IoT). On the other hand, one of the widely adopted networking specification for IoT is the IEEE 802.15.4 standard. This latter proposes interesting energy saving functionalities. To take full advantages of the two technologies, we propose an NDN over IEEE 802.15.4 communication solution that meets the requirements of low‐data rate and low‐power‐consumption monitor and control IoT applications. The proposed solution includes two modules: a reliable energy‐aware forwarding strategy that selects the next hop forwarder based on its residual energy level and a sleep mode scheduling algorithm that schedules the sleep/wake‐up mode according to the role of the node in the forwarding and the path repair processes. Extensive simulations and analyses have been conducted to confirm the viability and effectiveness of our proposal in terms of energy consumption, network lifetime, delivery ratio, retrieval delay, and scalability. [ABSTRACT FROM AUTHOR]

Passive optical networks (PONs) are a preferred technology for implementing fiber-to-the-home networks. Though PONs minimize power consumption compared to digital subscriber loops (DSL), they still constitute a significant portion of the power consumed in the telecommunication network. Several research efforts have thus focused on minimizing power consumption in a PON network, e.g., optimal PON dimensioning, sleep modes, and designing next-generation power efficient PON candidates, like bit-interleaved PON (Bi-PON), wavelength split time and wavelength division multiplexed PON (TWDM-PON) and wavelength-switched TWDM-PON. A natural question thus arise is what are the power savings of these various mechanisms and are there some synergy gains if these efforts can be combined? In this work, we propose analytical models for evaluating the power saving potentials of optimal PON dimensioning, sleep modes, and next-generation PON candidates like Bi-PON, wavelength split and wavelength switched TWDM-PON. For optimal PON dimensioning, we consider a promised grade of service to the users, while to explore the sleep mode functionality, we consider state-of-the-art dynamic bandwidth allocation (DBA) algorithms like sleep mode aware (SMA) and hybrid sleep mode aware (HSMA). We then propose the power models to assess the power efficiency of sleep modes in combination with the optimal dimensioning. In addition, we further extend these mechanisms to the next-generation PON candidates and evaluate the power saving potentials. Furthermore, in order to show the accuracy of the proposed models, we validate all these analytical models with the simulation results. [ABSTRACT FROM AUTHOR]

Power over fiber (PoF) with sleep mode operation in centralized radio access networks (C-RAN) of low power Remote Radio Heads (RRH) helps to reduce power consumption. This proposal includes bundles of single mode optical fibers (SMF) as part of the 5G C-RAN front-haul solution for providing control on power consumption by selectively activating some parts of the RRH. We experimentally demonstrate a PoF system based on 14.43 km of SMF feed by 2.24 W giving 226 mW electrical power at the RRH for control, battery charge, load operation and communication purposes. A bidirectional control channel is integrated in the central office and the RRH for providing the capability of entering in sleep mode operation and to provide information about the status of the battery and sensing elements at RRH. The optical data uplink/downlink operates over separate optical fibers shared by various RRHs and achieves low power consumption below 33 mW with low data rates. The measured PV cells conversion efficiency is above 30%. The RRH has two sleep modes of operation with a minimum power consumption of 5.8 mW. [ABSTRACT FROM AUTHOR]

Current low earth orbit (LEO) satellite network display poor power efficiency, running network devices at full capacity all the time regardless of the traffic matrix and the distribution of the population over the Globe. Most of the research on energy efficiency of LEO satellites has focused on component level or link level. Therefore, this kind of research is not holistic to try to look at the satellite system as a single node. To enhance the energy efficiency. The solution should exploits multipath routing and load balancing. LEO network is overprovisioned, and hence selectively shutting down some satellite nodes and links during off-peaks hours seems like a good way to reduce energy consumption. In this paper, we exploit the fact that due to geographical and climatic conditions, some satellite links are expected to be loaded with data while others remain unused. Our approach is to power down satellite nodes and links during period of low traffic, while guaranteeing the connectivity and QoS. Finding the optimal solution is NPproblem and therefore, we explore in this work two heuristic algorithms. We evaluate our heuristics on a realistic LEO topology and real traffic matrices. Simulation results show that the power saving can be significant. [ABSTRACT FROM AUTHOR]

The next-generation 5G networks are being developed with high promised capabilities. Beyond just multitudes faster data speed, 5G is expected to serve billions of connected devices and the Internet of Things (IoT), with the right trade-offs between speed, latency, and energy at an affordable cost. 5G radio networks will strongly depend on using ultra-dense integrated Small Cells (SCs) beside the Macro Cells (MCs). This kind of Ultra-Dense Networks (UDN) consisting of a large number of MCs and SCs will significantly increase network energy demands. A practical method to control energy consumption is by dynamically controlling power-saving modes in radio networks. In this paper, we propose a novel cooperative energy management framework for 5G UDN using graph theory. The 5G network is first modeled as a graph, then graph theory methods are exploited to determine the order of nodes at which power-off/on procedure is applied. We also show that significant power savings are achievable by considering only a subset of network nodes and thus reduce traffic migration and control plane signaling. We evaluated the proposed algorithm at different network densification levels and several load factors including two real-life networks. We also present the convergence of the proposed algorithm and the robustness of networks optimized using it. We also show that power savings up to 25% at full load and 65% during off-peak can be achieved using the proposed algorithm. These power savings increase further if no constraints are imposed on traffic migration and control signaling.

In order to reduce the energy consumption of wireless sensor networks and control the workload of necessary topology maintenance, the sleep mode and the threshold activation process in the energy saving strategy are considered. Combining with practice, factors such as environmental interferences and physical damages are considered. A repairable M/M/2 vacation queueing model with negative customers, feedback, N-strategy and working breakdown is established. Using quasi birth-and-death process and Gauss-Seidel iterative method, the expressions of performance indicators are given. Then, using MATLAB software for numerical analysis, the influence of system parameters on performance indicators is analysed. Finally, the social optimal parameters are found by constructing benefit functions. Under certain conditions, when λ = 0.64 and β 1 = 1.1 , the social benefit F can take the maximum value F = 12.7729 . [ABSTRACT FROM AUTHOR]

Recently, there has been a lot of interest on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting system made up of solar panels, batteries, charge controllers, and inverters is another way to further reduce the network environmental impact, and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is introduced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations has to be done jointly.

In energy harvesting cloud radio access networks, the sleep mode switching of small radio remote head (SRRH) and the functional splitting between SRRH and baseband unit are two important challenges for improving energy efficiency and throughput. In this paper, we propose an energy-efficient mode switching mechanism (E2MSM) with flexible functional splitting in which a controller determines the modes of SRRHs and functional splitting level by considering the renewable energy levels and populations of SRRHs. To optimize the performance of E2MSM, a constraint Markov decision process problem is formulated and a joint optimal policy on the sleep scheduling and the functional splitting level is obtained by a linear programming. Evaluation results demonstrate that E2MSM with the optimal policy can achieve comparable throughput to the throughput-oriented scheme while consuming no non-renewable energy.

Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users' deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user's delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm. [ABSTRACT FROM AUTHOR]