arXiv subjects

Akanksha Jaiswal

Publications and source records attributed to Akanksha Jaiswal.

4 recordsLinked to original sources

arXiv2025

Age-of-information minimization under energy harvesting and non-stationary environment

This work focuses on minimizing the age of information for multiple energy harvesting sources that sample data and transmit it to a sink node. At each time, the central scheduler selects one of the sources to probe the quality of its channel to the sink node, and then the assessed channel quality is utilized to determine whether a source will sample and send the packet. For a single source case, we assume that the probed channel quality is known at each time instant, model the problem of AoI minimization as a Markov decision process, and prove the optimal sampling policy threshold structure. We then use this threshold structure and propose an AEC-SW-UCRL2 algorithm to handle unknown and time varying energy harvesting rate and channel statistics, motivated by the popular SWUCRL2 algorithm for non stationary reinforcement learning. This algorithm is applicable when an upper bound is available for the total variation of each of these quantities over a time horizon. Furthermore, in situations where these variation budgets are not accessible, we introduce the AEC-BORL algorithm, motivated by the well known BORL algorithm. For the multiple source case, we demonstrate that the AoI minimization problem can be formulated as a constrained MDP, which can be relaxed using a Lagrange multiplier and decoupled into sub problems across source nodes. We also derive Whittle index based source scheduling policy for probing and an optimal threshold policy for source sampling. We next leverage this Whittle index and threshold structure to develop the WIT-SW-UCRL2 algorithm for unknown time varying energy harvesting rates and channel statistics under their respective variation budgets. Moreover, we also proposed a Whittle index and threshold based bandit over reinforcement learning (WIT-BORL) algorithm for unknown variation budgets. Finally, we numerically demonstrate the efficacy of our algorithms.

eess.SY↗

arXiv2024

Whittle's index-based age-of-information minimization in multi-energy harvesting source networks

We consider the problem of source sampling and transmission scheduling for age-of-information minimization in a system consisting of multiple energy harvesting (EH) sources and a sink node. At each time, one of the sources is selected by the scheduler and the quality of its channel to the sink is measured. This probed channel quality is then used to decide whether a source will sample an observation and transmit the packet to the sink in that time slot. We formulate this problem as a constrained Markov decision process (CMDP) assuming i.i.d. energy arrival and channel fading processes, and relax it using a Lagrange multiplier. We apply a near optimal Whittle's index policy to decide the node to be probed. Next, for the probed node, we derive an optimal threshold policy, which recommends source sampling and observation transmission from the probed source only when the measured channel quality is above a threshold. Our proposed policy is called Whittle's index and threshold based source scheduling and sampling (WITS3) policy. However, in order to calculate Whittle's indices, one must be aware of the underlying processes' transition matrices, which are occasionally concealed from the scheduler. Therefore, we further propose a variant Q-WITS3 of WITS3 based on Q-learning assisted by two timescale asynchronous stochastic approximation, which seeks to learn Whittle's indices and optimal policies for the case with unknown channel states and EH characteristics. Numerical results demonstrate the efficacy of our algorithms over two baseline policies.

eess.SY↗

arXiv2024

Age-of-information minimization via opportunistic sampling by an energy harvesting source

Herein, minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source 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 wireless fading channel. Each time, the EH node decides whether to probe the link quality and then decides whether to sample a process and communicate 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 source node. We use infinite horizon Markov decision process (MDP) to formulate the AoI minimization problem for two scenarios where energy arrival and channel fading processes are: (i) independent and identically distributed (i.i.d.), (ii) Markovian. In i.i.d. setting, after channel probing, the optimal source sampling policy is shown to be a threshold policy. Also, for unknown channel state and EH characteristics, a variant of the Q-learning algorithm is proposed for the two-stage action model, that seeks to learn the optimal policy. For Markovian system, the problem is again formulated as an MDP, and a learning algorithm is provided for unknown dynamics. Finally, numerical results demonstrate the policy structures and performance trade-offs.

eess.SY↗

arXiv2021

Minimization of Age-of-Information in Remote Sensing with Energy Harvesting

In this paper, the 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 probe the link quality using its stored energy and further 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, an infinite horizon Markov decision process 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) single process with channel state information at the transmitter (CSIT), (ii) multiple processes with CSIT. In each scenario, for probed channel state, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process(es), 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.

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