| 1. |
- Abd-Elmagid, Mohamed A., et al.
(författare)
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A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems
- 2020
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Ingår i: IEEE Transactions on Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0090-6778 .- 1558-0857. ; 68:8, s. 4747-4760
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age of Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI. To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP) with finite state and action spaces. Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem even for reasonable-scale settings. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy. Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.
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| 2. |
- Abd-Elmagid, Mohamed A., et al.
(författare)
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AoI-Optimal Joint Sampling and Updating for Wireless Powered Communication Systems
- 2020
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Ingår i: IEEE Transactions on Vehicular Technology. - : IEEE. - 0018-9545 .- 1939-9359. ; 69:11, s. 14110-14115
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Tidskriftsartikel (refereegranskat)abstract
- This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy.
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| 3. |
- Abd-Elmagid, Mohamed A., et al.
(författare)
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On the Role of Age of Information in the Internet of Things
- 2019
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Ingår i: IEEE Communications Magazine. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0163-6804 .- 1558-1896. ; 57:12, s. 72-77
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT). We start by summarizing the concept of AoI and its variants with emphasis on the differences between AoI and other well-known performance metrics in the literature, such as throughput and delay. Building on this, we explore freshness-aware IoT design for a network in which IoT devices sense potentially different physical processes and are supposed to frequently update the status of these processes at a destination node (e.g., a cellular base station). Inspired by recent interest, we also assume that these IoT devices are powered by wireless energy transfer by the destination node. For this setting, we investigate the optimal sampling policy that jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices with the goal of minimizing long-term weighted sum-AoI. Using this, we characterize the achievable AoI region. We also compare this AoI-optimal policy with the one that maximizes average throughput (throughput-optimal policy), and demonstrate the impact of system state on their structures. Several promising directions for future research are also presented.
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| 4. |
- Abd-Elmagid, Mohamed A., et al.
(författare)
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Online Age-minimal Sampling Policy for RF-powered IoT Networks
- 2019
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Ingår i: 2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). - : IEEE. - 9781728109626
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study a real-time Internet of Things (IoT)-enabled monitoring system in which a source node (e.g., IoT device or an aggregator located near a group of IoT devices) is responsible for maintaining the freshness of information status at a destination node by sending update packets. Since it may not always be feasible to replace or recharge batteries in all IoT devices, we consider that the source node is powered by wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy that minimizes the long-term average Age-of-Information (AoI), referred to as the age-optimal policy. The age-optimal policy determines whether each slot should be allocated for WET or update packet transmission while considering the dynamics of battery level, AoI, and channel state information (CSI). To solve this optimization problem, we model this setup as an average cost Markov Decision Process (MDP). After analytically establishing the monotonicity property of the value function associated with the MDP, the age-optimal policy is proven to be a threshold based policy with respect to each of the system state variables. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy. Afterwards, we analytically demonstrate that the structures of the age optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average AoI.
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| 5. |
- Mankar, Praful D., et al.
(författare)
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A Spatio-temporal Analysis of Cellular-based IoT Networks under Heterogeneous Traffic
- 2021
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Ingår i: 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). - : IEEE. - 9781728181042
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider a cellular-based Internet of things (IoT) network consisting of IoT devices that can communicate directly with each other in a device-to-device (D2D) fashion as well as send real-time status updates about some underlying physical processes observed by them. We assume that such real-time applications are supported by cellular networks where cellular base stations (BSs) collect status updates over time from a subset of the IoT devices in their vicinity. We characterize two performance metrics: i) the network throughput which quantifies the performance of D2D communications, and ii) the Age of Information which quantifies the performance of the real-time IoT-enabled applications. Concrete analytical results are derived using stochastic geometry by modeling the locations of IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another Independent PPP. Our results provide useful design guidelines on the efficient deployment of future IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.
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| 6. |
- Mankar, Praful D., et al.
(författare)
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Throughput and Age of Information in a Cellular-Based IoT Network
- 2021
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Ingår i: IEEE Transactions on Wireless Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1536-1276 .- 1558-2248. ; 20:12, s. 8248-8263
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the Johnson-Mehl (JM) tessellation, such that the IoT devices located in the JM cells are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.
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