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CDF of battery life, F 0 (B, t) for different threshold values. The dashed line represents the case of no threshold, lines beneath are thresholds at V = 0.1, V = 0.3 and V = 0.5. Note the jump at the start. This is the probability that the initial non-charging period is long enough for the battery life to end with no charging periods having occurred. The parameters here are set to λ = 3, µ h = 2, µ = 1, B = 1 and α = β = 1.
Source publication
We investigate how a power-save mode affects the battery life of a device subject to stochastically determined charging and discharging periods. We use a multi-regime fluid queue, imposing a threshold at some value. When the power level falls below the threshold, (for example, 20% of charge remaining) a power-save mode is entered and the rate of di...
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Context 1
... the examples in this paper we calculate the battery life starting with a full battery in a discharging state 0. It would be straightforward to find the same results for other starting situations. The CDF shown in Figure 2 is plotted for V = 0 (dashed), V = 0.1, V = 0.3 and V = 0.5. The value V = 0 (dashed) corresponds to the situation where the power-save mode is not used, while the V = 0.1, V = 0.3 and V = 0.5 correspond to 10%, 30% and 50% threshold values. ...
Context 2
... we decrease µ this minimum time increases. In Figure 2 we see a strict improvement in battery life performance for any increase in the threshold value V . ...
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Citations
... A Markov fluid queue model of the battery of an IoT device with adaptive sensing was proposed in [21]. Also, Markov fluid queue models of the battery of an IoT device with energy-saving threshold or energy-saving regimes was proposed in [21]- [23]. ...
... They derived the cumulative distribution function of the lifetime of the UE (the time required to decrease the charge level of the battery to zero). The authors in [23] used a similar modelling approach: the device enters an energy-saving regime by reducing its power consumption when a defined charge level threshold is reached. ...
... (3) will change to those in Fig. (4). The mean time to depletion is represented in Fig. 5 as a function of µ and B. In addition to adaptive sensing and duty cycling, energysaving thresholds have been adopted to further increase the energy efficiency of IoT nodes (e.g., [21]- [23], [30]). The node may switch between the various energy consumption regimes over time, e.g. after reaching a certain depletion threshold, it passes to the more energy-efficient operating regime. ...
Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (especially water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery's energy content and to estimate the mean and variance of the device's lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node's lifetime.
... During the energysaving regime, the device may optimise its radio resources (adjustment of transmission power), sleep/wakeup mechanism, and frequency of occurrence of active events (adjust the sleep times) in such a way as to reduce the energy consumption rate of the device. A state-dependent model of an IoT battery with an energy threshold after which the device should be forced into an energy-saving regime was presented in [15]. The mean energy consumption rate depends on the battery's energy state or the number of energy packets in the battery. ...
... When the energy content of the battery goes below a given threshold, K, the IoT device throttles or decreases its rate of energy consumption from µ 2 to µ 1 (µ 2 > µ 1 ) to increase the lifetime of the device. It might be done by reducing the sampling rate of a sensor [15], decreasing the transmission power, increasing the sleep time, and decreasing the frequency of performing measurement (sensing), data transmission & reception, and actuation operations (if possible). It is assumed that the process of consuming energy packets from the battery is exponentially distributed with the mean rate µ 2 in the normal regime, (K, B) and µ 1 in the energy-saving regime, (0, K). ...
The growing concerns about the resource sustainability and environmental impact of deploying billions or trillions of IoT devices in the various sectors of society or economy have given rise to an energy research field known as Green-IoT (G-IoT). G-IoT emphasises the need to prioritise energy efficiency and the reduction of pollution in the IoT ecosystem. This paper presents a Markov model for a self-powered Green IoT device with state-dependent energy consumption. The model evaluates the energy performance of a self-powered Green IoT device. We derive the probability distribution of the energy content of the battery, including the probability that the energy stored in the battery is completely depleted and the density of the lifetime of the IoT device. We investigate the influence of some design parameters on the results. Introducing the threshold at which the device is forced to enter an energy-saving regime significantly increases a G-IoT device's lifetime. Apart from deploying energy-efficient mechanisms to increase the lifetime of a G-IoT device, integrating an efficient energy harvesting system into the device will increase its lifetime and reduce the frequency of battery replacement.
... To solve the above problem, this subsection proposes a waiting time model based on the stochastic fluid model (SFM) for different VoI packets [32][33][34]. Specifically, the process of CHs receiving packets is modeled as a finite-state, continuous-time Markov chain (CTMC) ...
... (3) Average energy consumption Energy refers to the average energy consumption of packets successfully received by the sink node, which reflects the energy consumption of the network to successfully transmit and send unit packets. The calculation method is shown in (34). ...
Ensuring the freshness of high Value of Information (VoI) data has a significant practice meaning for marine observations and emergencies. The traditional forward method with an auv-aid is used to ensure the freshness of high VoI data. However, the methods suffer from two issues: an insufficient high VoI data throughput and random forwarding for cluster heads (CHs). The AUV (Autonomous Underwater Vehicle) with limited energy cannot meet the demand for the random generation of high VoI data. Low VoI data packets compete with high VoI data packets for channels, resulting in an insufficient high VoI data throughput and a low freshness. To address the above issues, we propose the Data Access Channel Scheme based on High Value of Information (DACS-HVOI), which is suitable for prioritizing the transmission packets with a high VoI. First, according to the level of VoI, the packets are divided into K classes, and the packets that are collected and forwarded by the AUV are defined as the highest K+1 class. Second, based on prior knowledge in the network, a Markov chain algorithm-based method is employed to predict which nodes should preferentially use the channel, to avoid conflict between a low and high VoI. Third, based on the stochastic fluid theory, a multilevel queueing system for CHs are constructed to avoid random forwarding. Last, compared with state-of-art protocols, experimental simulation shows that the proposed scheme has a low latency and high network throughput, while improving the throughput of high-VoI packets and ensuring the priority transmission of high-VoI packets.
... In References [34,35], the authors develop fluid flow models with rates driven by a Markov chain to analyse the battery lifetime of user equipment in LTE networks (first) and to evaluate various energy-management policies (second). Furthermore, in Reference [36] multi-regime fluid queue models where charging and discharging rates depend on the energy level are used to study the lifetime of a battery. ...
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... This may enable more types of energy-harvesting sources being considered, for instance solar. Beyond using discretized energy units, continuous fluid models [9] could be employed for future investigation, where the theoretical findings may be further validated by simulation studies under realistic settings. ...
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... Further, in [24] the optimal active power flow model for UEs is discussed. In [25], the authors have investigated battery life of a UE subject to the stochastically determined charging and discharging periods. A multi-regime fluid queue model, imposing a threshold at some value is proposed. ...
... Since the authors did not find any existing literature having such implementation in LTE-A networks then, the authors have taken these values in consideration of the analysis performed in [25], and are mentioned in Table 2. Further, one can use the different values and can get the results from the proposed analytical model. ...
During the Discontinuous Reception (DRX) mechanism, a User Equipment (UE) turns off most of its components for a prolonged period. The battery charge of a UE, which is a continuous quantity, arises as a fluid model. In this scenario, we represent the system as the reservoir where battery charge gets accumulated or is depleted gradually over the time, subject to a random environment constructed by the DRX mechanism. Therefore, the DRX mechanism acts as the background process for the fluid queue model of the battery charge. In a UE, battery charge available at any time t is considered as the fluid available in the reservoir. Different discharging rates are considered for the various states of the DRX mechanism. Using the fluid queue model, cumulative distribution function for the battery life of a DRX enabled UE is derived. Further, the expression for the mean battery life is obtained and analyzed numerically.
... Energy harvesting has received much attention in the wireless communication literature, with a large body of work focusing on techniques that allow communication networks to operate optimally with limited energy. In this context, mathematical models capturing the interactions between the information and energy planes have been proposed based on Markov processes and queueing theory [27,28,35,37], risk theory [30] and diffusion processes [31]. The models enable the study of networks and operational conditions that can balance the energy needs for sensing, communications and computing, with the availability of renewable and intermittent sources of energy, so as to achieve energy neutrality [32]. ...
Mobile communications are a powerful contributor to social and economic development worldwide, including in less developed or remote parts of the world. However they are large users of electricity through their base stations, backhaul networks and Cloud servers, so that they have a large environmental impact when they use the electric grid. On the other hand, they could operate with renewable energy sources and thus reduce their CO2 impact and be accessible even in areas where the electric grid is unavailable or unreliable. The counterpart is that intermittent sources of energy, such as photovoltaic and wind, can affect the quality of service (QoS) that is experienced by mobile users. Thus in this paper we model the performance of mobile telecommunications that use intermittent and renewable energy sources. In such cases to analyse the performance of such systems, both the energy supply and the network traffic, can be modeled as random processes, and we develop mathematical models using the Energy Packet Network paradigm, where both data and energy flows are discretised. QoS metrics for the users are computed based on the traffic intensity and the availability of energy.
... Energy harvesting has received much attention in the wireless communication literature, with a large body of work focusing on techniques that allow communication networks to operate optimally with limited energy. In this context, mathematical models capturing the interactions between the information and energy planes have been proposed based on Markov processes and queueing theory [26,27,34,36], risk theory [29] and diffusion processes [30]. The models enable the study of networks and operational conditions that can balance the energy needs for sensing, communications and computing, with the availability of renewable and intermittent sources of energy, so as to achieve energy neutrality [31]. ...
Mobile communications are a powerful contributor to social and economic development worldwide, including in less developed or remote parts of the world. However they are large users of electricity through their base stations, backhaul networks and Cloud servers, so that they have a large environmental impact when they use the electric grid. On the other hand, they could operate with renewable energy sources and thus reduce their CO2 impact and be accessible even in areas where the electric grid is unavailable or unreliable. The counterpart is that intermittent sources of energy, such as photovoltaic and wind, can affect the quality of service (QoS) that is experienced by mobile users. Thus in this paper we model the performance of mobile telecommunications that use intermittent and renewable energy sources. In such cases to analyse the performance of such systems, both the energy supply and the network traffic, can be modeled as random processes, and we develop mathematical models using the Energy Packet Network paradigm, where both data and energy flows are discretised. QoS metrics for the users are computed based on the traffic intensity and the availability of energy.
