Predictive Power Management for Internet of Battery-Less Things

In this paper, we study how to determine concurrent transmissions and the transmission power level of each link to maximize spectrum efficiency and minimize energy consumption in a wireless ad hoc network. The optimal joint transmission packet scheduling and power control strategy are determined when the node density goes to infinity and the network area is unbounded. Based on the asymptotic analysis, we determine the fundamental capacity limits of a wireless network, subject to an energy consumption constraint. We propose a scheduling and transmission power control mechanism to approach the optimal solution to maximize spectrum and energy efficiencies in a practical network. The distributed implementation of the proposed scheduling and transmission power control scheme is presented based on our MAC framework proposed in [1]. Simulation results demonstrate that the proposed scheme achieves 40% higher throughput than existing schemes. Also, the energy consumption using the proposed scheme is about 20% of the energy consumed using existing power saving MAC protocols.

A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.

Human kinetic energy is regarded as a promising sustainable energy source to solve the energy bottleneck of Internet of Things (IoT). The low power harvested from human motion and scarce hardware resource of IoT severely restrain the operation of kinetic energy harvesting IoT and stress the need for power management strategies to improve the energy efficiency. In this paper, we propose a novel power management framework for kinetic energy harvesting IoT, composed of an off-line inertial harvester optimization algorithm and an on-line joint sink selection and transmission power control module. By analyzing the characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize the power generation from human daily motion. The on-line scheme improves energy efficiency by joint consideration of optimal sink selection (i.e., on-body sink or off-body sink) and transmission power control. The real world human motion data set is used to evaluate the proposed framework. The simulation results indicate that, compared with the existing approach, the proposed kinetic harvester optimization algorithm achieves 83.31% to 135.69% improvement in harvested power from the same human motion trace. In addition, the proposed on-line joint sink selection and transmission power control incurs 7.07% to 34.23% improvement in transmission energy efficiency.

Besides the traditional voice and data services, 5G is expected to support new communication paradigm, i.e., Internet of Things (IoT) and Machine-to-Machine (M2M) services. Nonetheless, the system designs of transmit power control (TPC) and carrier sense threshold (CST) will be crucial when the huge number of these user equipment (UE) are densely engaged in vast number of data traffic transmissions. One practical and effective approach is to study the system design through numerical simulation. In this paper, we formalize the problem of minimizing the energy consumption and maximizing the average end-to-end throughput of massive M2M communication by optimally using a proposed consensus TPC scheme. To validate the effectiveness of our proposed scheme, we also focus the impact of the CST on the trade-off in between UE experienced data rate and network energy efficiency.

For Internet of Things (IoT) networks, it is important to develop energy-efficient communication schemes to extend the operating life of battery-powered IoT devices. Additionally, non-orthogonal multiple access (NOMA) can utilize frequency resources more efficiently than orthogonal multiple access, making it more suitable to support massive connectivity of IoT users. Motivated by these facts, we consider uplink NOMA IoT cellular networks and develop two novel algorithms that jointly optimize sub-band allocation and transmit power control to minimize the total transmit power of all users and the maximum transmit power among all users' transmit power, respectively, while meeting the minimum required data rate for all users. Specifically, we propose a novel two-step approach that sequentially performs sub-band assignment and transmit power control for each IoT user, in which a genetic algorithm-based method is applied for sub-band assignment whereas unsupervised learning (USL) implemented as deep neural network (DNN) models is utilized for transmit power control. Moreover, we propose loss functions that can achieve an appropriate balance between power minimization and rate constraint satisfaction in the process of training. Extensive simulations are performed to evaluate the performance of the proposed algorithm in various aspects, and we show that our proposed two-step algorithm can approach the optimal performance achievable through an exhaustive search with much lower computational complexity.

Techniques for controlling the transmission power of mobile devices have been widely studied in MANETs and cellular networks. However, as mobile applications for WSNs emerge, the unique characteristics of WSNs, such as severe resource constraints, suggest that transmission power control should be revisited from a WSN perspective. In this work, we take an experimental approach to examine the effectiveness of transmission power control for WSNs that involve mobility at human walking speeds. We propose two lightweight transmission power control schemes to improve energy efficiency and spatial reuse. The first is an active probing based scheme that adjusts transmission power based on (the lack of) packet losses and applies to all low-power radios, while the second scheme requires radios that offer link quality indicators (LQI) to estimate the proximity between the transmitter and receiver. We evaluate both schemes using mobile nodes in an indoor and an outdoor environment. Results show that the energy efficiency of the proposed transmission power control schemes can be very close to that of the optimal offline strategy and our schemes significantly reduce the interference for spatial reuse. To our knowledge, this is the first work that evaluates the effect of transmission power control in mobile WSNs.

This paper proposes a downlink (DL)/uplink (UL) transmission power control (TPC) scheme for dynamic time division duplex (TDD) based small cells under multi-cell environment. In the dynamic TDD, an eNB (evolved node B) selects an adequate UL-DL configuration according to the ratio of DL to UL data bits in each cell. However, in this case, eNB- eNB and user equipment (UE)-UE interferences could be additional interference since the transmission directions can be different among cells. Especially, eNB-eNB interference significantly degrades the UL transmission performances. Therefore, we investigate the DL TPC which is applied to subframes which can be different directions among cells and the benefits to decrease eNB-eNB interference by this scheme. We also investigate the different UL TPC parameters that are applied according to the subframe types in order to alleviate the impact of eNB-eNB interference. Computer simulation confirms that the proposed DL/UL TPC scheme can achieve 21.6 % gain at maximum for UL throughput without significant DL throughput degradation.

To reduce interference and to save a significant amount of energy, a control of transmission power is employed in Mobile Ad hoc Network. Many researchers have reported numerous transmission power control schemes to achieve the objective. Some of those techniques use higher transmission power for control packets (Request To Send/Clear To Send) and lesser power for Data and ACK packets. These schemes, though save some amount of energy, achieve least aggregate throughput due to poor spatial reuse and hidden terminal interference. In this paper, an efficient Power Controlled Medium Access Control (EPCM) scheme is evinced, which uses uniform interference aware and minimum transmission power for both Control and Data packet. The performance of EPCM is evaluated and compared with three reported Medium Access Control protocols which are based on transmission power control schemes and is observed that the proposed protocol achieves better throughput and minimal energy consumption while avoiding the hidden terminal problem.

In this paper, we investigate the impact of a fuzzy logic control-based transmission power control in a wireless sensor network on the quality of service aspects like average power, energy efficiency, throughput and average end-to-end delay. The transmission power control algorithm is implemented in the medium access control layer as an enhancement for wireless MAC Standard IEEE 802.15.4. For a given fading channel, observations are made regarding received signal strength variations and link quality variations for different distances of separation between transmitter and receiver. The membership functions for the fuzzy logic are then formulated for efficient transmission power control. The transmission power of the source node along with the received signal strength indicator and link quality indicator values measured at the receiving node is used for deciding the required transmission power. The performance of this fuzzy logic-based transmission power control (FTPC_MAC) scheme is compared with the standard IEEE 802.15.4 MAC protocol (IEEE_MAC) and also with location prediction-based transmission power control (LPTPC_MAC). Analytical and simulation results show that fuzzy logic-based transmission power control performs better than the other two MAC protocols.

Ultra dense network (UDN) is considered as a promising solution to support high throughput applications with lower operational cost. However, user mobility is even tricky for UDNs, especially in high-speed cases. While handover issues have been widely studied, the impact of high mobility on resource allocation needs to be further studied. In this paper, we investigate the impact of user mobility on transmit power control (TPC) and propose two kinds of mobility-aware TPC schemes which are adaptive to user's speed intelligently: mobility-aware constant TPC (MC-TPC), mobility-aware sub-slotted TPC (MS-TPC). Investigating the distance distribution between the moving user and its access base station (BS), we derive the analytical expressions of system outage probability (OP) and energy efficiency (EE) in the mobile environment to characterize the performance of the proposed and referenced TPC schemes. Numerical results show that the proposed mobility-aware TPCs significantly improve system reliability in terms of OP and data rate, especially with the proper sub-slots division. Meanwhile, in exchange for high reliability, EE degrades within a reasonable range with the increase of moving speed.

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT). IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithmto extend the network lifetime in IoMT networks for healthcare applicationsby eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%.

Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.

The energy efficiency paradigm is a major bottleneck for the development of wireless sensor networks (WSNs) and Internet of Things (IoT) architectures and technologies. This edited book presents comprehensive coverage of energy harvesting sources and techniques that can be used for WSN and IoT systems

Energy consumption is a key issue in wireless body area network (WBAN) since the wearable sensor devices are severely resource-constrained. In this paper, we present a novel transmission power control scheme, which exploit the regular body motion in dynamic wireless channel for WBAN. We first investigate the relationship between the link state and body movement with experiment, and give the evidence that the path loss is strongly related to the body movement. We then propose the motion aware transmission power control (M-TPC) scheme which takes advantage of the information from activity recognition algorithm and arranges transmission when the transmitter is at the desire location. Finally, we implement and evaluate the proposed scheme on ZigBee platform with CC2530 radio, and also compare with the real-time reactive scheme in walking scenario. The experimental results show that the M-TPC scheme reduces transmission power by 43.27% and enhances the link reliability by reducing the packet loss rate by 75%.

In recent years, energy-harvesting (EH) technology is rapidly developing and served as a promising solution to enable the energy sustainability of Internet of things (IoT) networks. In this work, we study an uplink (UL) cellular-based energy harvesting IoT network where a joint energy supply strategy is employed to ensure energy continuity. To characterize both the spatial randomness of IoT devices, and spatio-temporal random arrival of traffic and harvested energy, we develop a spatio-temporal analytical framework to evaluate the IoT network performance by leveraging stochastic geometry and queueing theory. We first derive the traffic queue void probability of a random IoT device, and obtain the service rate and mean packet throughput (MPT). Then we derive the availability of the rechargeable battery and the energy efficiency (EE) of an IoT device. By studying the effect of key parameters on the network performance, we observe that the growth in UL traffic arrival rate leads to a reduction in service rate, MPT, and EE. A higher successful charging probability always enhances EE.