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Wireless powered communication technology has a great potential to power low-power wireless sensor networks and Internet of Things (IoT) for real-time applications in future 5G networks, where age of information (AoI) plays a very important performance metric. This paper studies the system average AoI of a wireless powered network, where a wireless...
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... consider a wireless powered network, as depicted in Figure 1, where a user (e.g., a sensor node) desires to transmit its data to its AP (e.g., a sink node). Since the user is lack of energy, it has to harvest energy from a WPS that deployed in the system and used to charge the wireless devices via wireless power transfer. ...
Citations
... [27] 2018 Minimization of age and synchronization of information across multiple flows Distributed cyber-physical systems A novel age-based scheduler is developed with inter-arrival times in its decision [28] 2018 Optimizing AoI using the switch or drop of new arrived packets A source sending updates to a destination through a rate-limited link A Markov chain is formulated and optimized using MDP with value iteration. [29] 2018 AoI minimization in a network with wireless powered source with Rayleigh fading A wireless powered energy harvesting user transmitting through a block Rayleigh fading channel to the access point A closed-form expression is derived for average AoI using queuing theory and probability models [8] 2019 ...
Ambient intelligence (AmI) represents the future vision of intelligent computing that can bring intelligence to our daily life through various domains. In such applications, AmI is often subject to the freshness of information collected, which is commonly quantified by a relatively newer metric called age of information (AoI). In the data aggregation and analytics for Internet-of-Things (IoT) AmI, AoI should be well managed, because information update should be as timely as possible to achieve optimal performances. AoI has been studied in various applications using different queuing policies, scheduling algorithms, and multiple access schemes, in which each component of communication and information systems are designed and analyzed to improve the AoI. This paper provides a comprehensive overview of literature on the AoI and its variants in large-scale networks. AoI in IoT systems depends on the arrival rate at the source nodes, queuing policy adopted at the nodes, the scheduling of nodes for information transmission and the access scheme adopted by the nodes. To better design and operate the AmI applications that require the freshness of information, we discuss the impacts of the queuing policy, stochastic modeling, scheduling, and multiple access schemes. In particular, non-orthogonal multiple access (NOMA), which is regarded as one of the key technologies in beyond 5G and 6G, and it hybrid version combined with the conventional orthogonal multiple access (OMA) are discussed in the context of AoI. In addition, we identify promising research opportunities in potential age-sensitive applications. Thus, compared to the existing surveys on AoI, this paper provide more practical and up-to-date design guidelines for the applications with the information freshness requirements.
... The importance of such a tool is presented in [1]. In [2], the AoI is measured as the elapsed time since the last received update was generated. Upon receiving a new packet with updating information, the AoI drops to the elapsed time since the packet generation; otherwise, it grows linearly in time. ...
... In [5], the peak Age of Information (PAoI) is minimized in a network with packet delivery error, i.e., update packets can get lost during transmissions to their destination. Different methods and constraints are used for AoI optimization such as interference constraints in [11], throughput constraints in [12], update generation rate in [2], and battery capacity in [9]. In [13], AoI minimization is performed with perfect channel state information. ...
... φ and θ are the irradiance and incidence angles, respectively. In addition, φmax represents the semi-angle at halfpower of the LED, m is the order of the Lambertian model and is given by m = − ln (2) ln(cos(φmax)) , Ts(θ) is the gain of the optical filter, and g(θ) is the concentrator gain, which is assumed to be a constant depending on the concentrator design. The exponential Beers-Lambert Law [37] is ...
div>This paper presents an analysis for the Age of Information (AoI) in a wireless sensor network consisting of multiple VLC/RF IoT sensors. In this network, we use an energy harvesting method, and all sensors utilize the stored energy in their batteries and transmit update packets with a specific power. We present two main optimization problems based on PD-NOMA scheme for average AoI with the sensor's transmit power constraint. By optimizing these problems, the average AoI for each sensor and the total average AoI in the proposed system are reduced by 10% and 15%, respectively.</div
... The importance of such a tool is presented in [1]. In [2], the AoI is measured as the elapsed time since the last received update was generated. Upon receiving a new packet with updating information, the AoI drops to the elapsed time since the packet generation; otherwise, it grows linearly in time. ...
... In [5], the peak Age of Information (PAoI) is minimized in a network with packet delivery error, i.e., update packets can get lost during transmissions to their destination. Different methods and constraints are used for AoI optimization such as interference constraints in [11], throughput constraints in [12], update generation rate in [2], and battery capacity in [9]. In [13], AoI minimization is performed with perfect channel state information. ...
... φ and θ are the irradiance and incidence angles, respectively. In addition, φmax represents the semi-angle at halfpower of the LED, m is the order of the Lambertian model and is given by m = − ln (2) ln(cos(φmax)) , Ts(θ) is the gain of the optical filter, and g(θ) is the concentrator gain, which is assumed to be a constant depending on the concentrator design. The exponential Beers-Lambert Law [37] is ...
div>This paper presents an analysis for the Age of Information (AoI) in a wireless sensor network consisting of multiple VLC/RF IoT sensors. In this network, we use an energy harvesting method, and all sensors utilize the stored energy in their batteries and transmit update packets with a specific power. We present two main optimization problems based on PD-NOMA scheme for average AoI with the sensor's transmit power constraint. By optimizing these problems, the average AoI for each sensor and the total average AoI in the proposed system are reduced by 10% and 15%, respectively.</div
... On the other hand, a number of papers have considered AoI minimization problem under EH setting: [8] for derivation of average AoI for a single source having finite battery capacity, [9] for derivation of the minimal age policy for EH two hop network, [10] for average AoI expression for single source EH server, [11] for AoI minimization for wirelessly powered user, [12] for sampling, transmission scheduling and transmit power selection for a single source single sink system over infinite time horizon where delay is dependent on the packet transmission energy. The authors in [13] considered two source nodes (power grid node and EH sensor node) sending different data packets to a common destination by using multiple access channel, and derived the delay and AoI of two source nodes respectively. ...
Herein, minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source equipped remote sensing setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants, and sends the status updates to a sink node over a time-varying wireless link. At any discrete time instant, the EH node decides whether to ascertain the link quality using its stored energy (called probing) and then, decides whether to sample a process and communicate the data based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the energy buffer of the source node. To this end, infinite horizon Markov decision process (MDP) theory is used to formulate the problem of minimization of time-averaged expected AoI for a single energy harvesting source node. The following two scenarios are considered: (i) energy arrival process and channel fading process are independent and identically distributed (i.i.d.) across time, (ii) energy arrival process and channel fading process are Markovian. In the i.i.d. setting, after probing a channel, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process, the available energy in the buffer and the instantaneous channel quality as the decision variables. Also, for unknown channel state and energy harvesting characteristics, a variant of the Q-learning algorithm is proposed for the two-stage action taken by the source, that seeks to learn the optimal status update policy over time. For Markovian channel and Markovian energy arrival processes, the problem is again formulated as an MDP, and a learning algorithm is provided to handle unknown dynamics. Finally, numerical results are provided to demonstrate the policy structures and performance trade-offs.
... Compared with traditional natural energy sources [4,5], RF signals are easy to control, can provide steady power, and have relatively low requirements on the deployment environment [6]. Evidently, we have seen many works on AoI-based wireless powered communication networks (WPCN) powered by RF EH [7][8][9][10][11][12][13]. In [7], an optimal online state update strategy to minimize the AoI over long-term time scale with energy constraints was studied. ...
... Evidently, we have seen many works on AoI-based wireless powered communication networks (WPCN) powered by RF EH [7][8][9][10][11][12][13]. In [7], an optimal online state update strategy to minimize the AoI over long-term time scale with energy constraints was studied. In [8], the performance of AoI in WPCN was analyzed, and it proved that the smaller the probability of packet generation, the smaller the average AoI. In [9], the emergency AoI (U-AoI) in WPCN was minimized. ...
This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.
... On the other hand, a number of papers have considered AoI minimization problem under energy harvesting setting: [7] for derivation of average AoI for a single source having finite battery capacity, [8] for derivation of the minimal age policy for energy harvesting two hop network, [9] for average AoI expression for single source energy harvesting server, [10] for AoI minimization for wirelessly powered user, [11] for sampling, transmission scheduling and transmit power selection for single source single sink system over infinite time horizon where delay is dependent on packet transmit energy. ...
In this paper, minimization of time-averaged age-of-information (AoI) in an energy-harvesting sensor equipped remote sensing setting is considered. An energy harvesting (EH) sensor generates energy packets according to a Bernoulli process at discrete time instants. These energy packets are used by the sensor to make measurements of physical processes and send the observation packets to a remote estimator or a sink node. The trade-off is between the freshness of information available at the sink node and the available energy at the energy buffer of the sensor, which requires the sensor to opportunistically sample and communicate the observations to the sink node. To this end, infinite horizon Markov decision process theory is used to formulate the problem of minimization of time-averaged expected AoI for a single energy harvesting sensor. The following progression of scenarios is considered: (i) single process, perfect communication channel between sensor and sink node, (ii) single process, fading channel with channel state information at transmitter (CSIT), (iii) multiple processes, perfect channel, (iv) multiple processes, fading channel with CSIT. In each scenario, the optimal sensor sampling policy is shown to be a threshold policy involving the instantaneous age of the process, the available energy in the buffer and the instantaneous channel quality as the decision variables. Finally, numerical results are provided to demonstrate the policy structures and trade-offs.
... Warehouse layout plays an important role in the logistics activities of the whole warehouse [1][2][3][4][5][6]. As a result, enterprises can obtain benefits by changing the layouts of their warehouses [7][8][9][10][11][12][13][14][15][16]. Reference [17] first questioned the traditional layout of shelves, where the fishbone layout was proposed and, under certain assumptions, it was verified that the fishbone layout reduced costs by an average of 23.5% compared with the traditional layout. ...
The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for logistics benefits is becoming more important. The appropriate use of technologies related to IoT to improve logistics efficiency, such as cloud computing, mobile computing and data mining, has become a topic of considerable research interest. Picking operations are currently an extremely important and cumbersome aspect of logistics center tasks. To shorten the picking distance and improve work efficiency, this paper uses the genetic algorithm, ant colony algorithm and cuckoo algorithm to optimize the picking path in a fishbone-layout warehouse and establishes an optimized model of the warehouse picking path under the fishbone layout. Data-mining technology is used to simulate the model and obtain the simulation data under the condition of multiple orders. The results provide a theoretical basis for the study of the fishbone-layout picking path model and has certain practical significance for the efficient operation of logistics enterprises. Through optimization, it is conducive to the sustainable development of enterprises and to achieving long-term profitability.
... Moreover, harvesting energy from man-generated RF sources is controllable and relatively reliable. Thus, RF-based EH is suitable for low-power devices and is considered as a promising solution to power the IoTs and wireless sensor networks (WSNs) by deploying dedicated power stations [14][15][16]. ...
This paper studies the optimal design of the fog computing assisted wireless powered network, where an access point (AP) transmits information and charges an energy-limited sensor device with Radio Frequency (RF) energy transfer. The sensor device then uses the harvested energy to decode information and execute computing. Two candidate computing modes, i.e., local computing and fog computing modes, are considered. Two multi-objective optimization problems are formulated to minimize the required energy and time for the two modes, where the time assignments and the transmit power are jointly optimized. For the local computing mode, we obtain the closed-form expression of the optimal time assignment for energy harvesting by solving a convex optimization problem, and then analyze the effects of scaling factor between the minimal required energy and time on the optimal time assignment. For the fog computing mode, we derive closed-form and semi-closed-form expressions of the optimal transmit power and time assignment for offloading by adopting the Lagrangian dual method, the Karush–Kuhn–Tucker (KKT) conditions and Lambert W Function. Simulation results show that, when the sensor device has poor computing capacity or when it is far away from the AP, the fog computing mode is the better choice; otherwise, the local computing is preferred to achieve a better performance.
... In [14], optimal online status update policies for an EH-enabled sensor were proposed with various battery sizes. In [15], the average AoI was analyzed for wireless powered networks in low SNR region. However, in these works, , the energy arrivals were described as to occur as a point process. ...
... By submitting (15) and (16) into Problem (9), solving Problem (9) is equivalently transformed into the following problem. ...
This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with time between two received updates. Due to EH, some waiting time is required at the sensor before transmitting the status update. To find the optimal transmission policy, an optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. As the problem is non-convex and with no known solution, a two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression of the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results shows that our proposed optimal transmission policy outperforms the the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are non-congested. It is also observed that in order to achieve the lower U-AoI, the sensor should transmit the next update without waiting when the network is congested while should wait a moment before transmitting the next update when the network is non-congested. Additionally, it also shows that the system U-AoI first decreases and then keep unchanged with the increments of EH circuit's saturation level and the energy outage probability.
This paper investigates the age of information (AoI)-based online scheduling in multi-sensor wireless powered communication networks (WPCNs) for time-sensitive Internet of Things (IoT). Specifically, we consider a typical WPCN model, where a wireless power station (WPS) charges multiple sensor nodes (SNs) by wireless power transfer (WPT), and then the SNs are scheduled in the time domain to transmit their sampled status information with their harvested energy to a mobile edge server (MES) for decision making. For such a system, we first derive a closed-form expression of the successful data transmission probability in Nakagami-
${m}$
fading channels. To pursue an efficient online scheduling policy that minimizes the Expected Weighted Sum AoI (EWSAoI) of the system, a discrete-time scheduling problem is formulated. As the problem is non-convex with non-explicit expression of the EWSAoI, we propose a Max-Weight policy based on the Lyapunov optimization theory, which schedules the SNs at the beginning of each time in terms of the one-slot conditional Lyapunov Drift. Simulations demonstrate our presented theoretical results and show that our proposed scheduling policy outperforms other baselines such as the greedy policy and random round-robin (RR) policy. Especially, when the number of SNs is relatively small, the gain achieved by the proposed policy compared to the greedy policy is considerable. Moreover, some interesting insights are also observed: 1) as the number of SNs increases, the EWSAoI also increases; 2) when the transmit power is relatively small, the larger the number of SNs, the smaller the EWSAoI; 3) the EWSAoI decreases with the increment of transmit power of the WPS and then tends to be flat; 4) the EWSAoI increases with the increment of the distance between the SNs and the MES.
