The traditional goal in remote tracking of a dynamic source is to keep the current estimate at the destination as close as possible to the true state. However, in domains such as surveillance applications, the destination is also interested in reconstructing the past trajectory of states for further processing. This requires striking a balance between providing current versus past state information so that the destination can optimize the trade-off between the metrics of freshness and reconstruction queue length. In this work, we propose a randomized update policy that decides between head-of-line versus tail-of-line packets in the update queue. As such, our policy combines the strength of Last-Come-First-Serve (LCFS) service discipline (which aims at reducing the age) with the strength of First-Come-First-Serve (FCFS) service discipline (which aims at reducing the reconstruction delay). We evaluate the performance of our proposed policy in terms of its randomization parameter, which can be optimized given the system parameters to achieve a better trade-off.

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance and healthcare. In such systems, maintaining both real-time state awareness (for online decision making) and accurate reconstruction of historical trajectories (for offline post-processing) are essential. While the Age of Information (AoI) metric has been extensively studied as a measure of freshness, it does not capture the accuracy with which past trajectories can be reconstructed. In this work, we investigate reconstruction error as a complementary metric to AoI, addressing the trade-off between timely updates and historical accuracy. Specifically, we consider three policies, each prioritizing different aspects of information management: Keep-Old, Keep-Fresh, and our proposed Inter-arrival-Aware dropping policy. We compare these policies in terms of impact on both AoI and reconstruction error in a remote tracking system with a finite buffer. Through theoretical analysis and numerical simulations of queueing behavior, we demonstrate that while the Keep-Fresh policy minimizes AoI, it does not necessarily minimize reconstruction accuracy. In contrast, our proposed Inter-arrival-Aware dropping policy dynamically adjusts packet retention decisions based on generation times, achieving a balance between AoI and reconstruction error. Our results provide key insights into the design of efficient update policies for resource-constrained IoT networks.

In this work, we perform a comparative study of centralized and decentralized update strategies for the basic remote tracking problem of many distributed users/devices with randomly evolving states. Our goal is to reveal the impact of the fundamentally different tradeoffs that exist between information accuracy and communication cost under these two update paradigms. In one extreme, decentralized updates are triggered by distributed users/transmitters based on exact local state-information, but also at a higher cost due to the need for uncoordinated multi-user communication. In the other extreme, centralized updates are triggered by the common tracker/receiver based on estimated global state-information, but also at a lower cost due to the capability of coordinated multi-user communication. We use a generic superlinear function to model the communication cost with respect to the number of simultaneous updates for multiple sources. We characterize the conditions under which transmitter-driven decentralized update policies outperform their receiver-driven centralized counterparts for symmetric sources, and vice versa. Further, we extend the results to a scenario where system parameters are unknown and develop learning-based update policies that asymptotically achieve the minimum cost levels attained by the optimal policies.

In this work, we perform a comparative study of centralized and decentralized update strategies for the basic remote tracking problem of many distributed users/devices with randomly evolving states. Our goal is to reveal the impact of the fundamentally different tradeoffs that exist between information accuracy and communication cost under these two update paradigms. In one extreme, decentralized updates are triggered by distributed users/transmitters based on exact local state-information, but also at a higher cost due to the need for uncoordinated multi-user communication. In the other extreme, centralized updates are triggered by the common tracker/receiver based on estimated global state-information, but also at a lower cost due to the capability of coordinated multi-user communication. We use a generic superlinear function to model the communication cost with respect to the number of simultaneous updates for multiple sources. We characterize the conditions under which transmitter-driven decentralized update policies outperform their receiver-driven centralized counterparts for symmetric sources, and vice versa. Further, we extend the results to a scenario where system parameters are unknown and develop learning-based update policies that asymptotically achieve the minimum cost levels attained by the optimal policies.

In this work, we consider a network, where n distributed information sources whose states evolve according to a random process transmit their time-varying states to a remote estimator over a shared wireless channel. Each source generates packets in a decentralized manner and employs a slotted random access mechanism to transmit the packets. In particular, we are interested in networks with a large number of low-complexity devices that share low-capacity random access channels. Accordingly, we investigate update strategies for remote tracking of source states that require each update to constitute as few bits as possible. To that end, we develop update strategies requiring only one-bit of information per update. We first consider a natural benchmark update policy and reveal that the benchmark policy cannot guarantee stability under all conditions. We then introduce an improvement of the benchmark policy that employs a local cancellation strategy, which makes the system always stable. We further analyze and optimize the performance of the cancellation-enabled update policy to bound the estimation error at the receiver. Through simulations, we compare the proposed cancellation-enabled one-bit update policy with zero-wait sampling and threshold-based sampling policies that require more than one-bit of information per update. The comparisons show that the cancellation-enabled update policy at its optimal threshold level outperforms the multi-bit update policies. This suggests that the cancellation-enabled one-bit update policy could be greatly beneficial for applications where transmission power or shared channel capacity are limited.

In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of$O(N)$and$O(NK)$, respectively, where$N$denotes the number of users and$K$denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.

In Cognitive Radio Networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utility. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. A reward of a channel is stochastic with unknown distribution, and statistically different for each user. Each user either explores a channel to learn the channel statistics, or exploits the channel with the highest expected reward based on information collected so far. Further, a channel should be accessed exclusively by one user at a time due to a collision. Using multi-armed bandit framework, we develop a provably efficient solution whose computational complexity is linear to the number of users and channels.

Cognitive Radio (CR) networks have received significant attention as a promising approach to improve the spectrum efficiency of current license-based regulatory system. In CR networks, a Secondary User (SU) can use a spectrum vacancy that can be detected by either sensing-before-transmission or database access. However, it is often difficult to detect a vacant spectrum opportunity because of inaccuracies due to sensing and delays to update and/or the database that holds this information. In this paper, we develop a hybrid detection framework in multi-channel CR networks, where an SU can selectively sense a channel for spectrum vacancy by accessing the spectrum history of Markovian channels. We focus on the value of the channel history information offered by the Primary Provider (PP) of each channel, and consider a market for the information exchange between multiple PPs and SUs. We investigate the interplay between of the PPs and the SUs through their pricing and buying decisions for this information, in the presence of sensing inaccuracy, i.e., false alarm and miss detection.

Cognitive Radio (CR) networks have received much attention as a solution to the spectrum inefficiency problem of current license-based regulatory management. In CR networks, Secondary User (SU) can use a spectrum vacancy that can be detected by either sensing-before-transmission or database access. However, sensing inaccuracy or long access time to database often becomes a major obstacle to timely detect the spectrum vacancy. In this paper, we develop a hybrid detection framework in multi-channel CR networks, where an SU can selectively sense a channel for spectrum vacancy by accessing the spectrum history of Markovian channels. We focus on the value of the channel history information offered by the Primary Provider (PP) of each channel, and consider a market for the information between multiple PPs and SU. We investigate the interplay between of the PPs and the SU through their pricing and buying decisions for the information, in the presence of sensing inaccuracy, i.e., false alarm and miss detection.