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1. Optimizing Timely Coverage in Communication Constrained Collaborative Sensing Systems

   https://doi.org/10.1109/TCNS.2022.3203806  

   Rahal, Jean Abou; de Veciana, Gustavo; Shimizu, Takayuki; Lu, Hongsheng (September 2022, IEEE Transactions on Control of Network Systems)

   We consider a collection of distributed sensor nodes periodically exchanging information to achieve real- time situational awareness in a communication constrained setting, e.g., collaborative sensing amongst vehicles to improve safety-critical decisions. Nodes may be both con- sumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers broadcast updates on what they are currently able to see. Accordingly, we introduce and explore optimiz- ing trade-offs between the coverage and the space-time in- terest weighted average “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers that maximizes the cover- age of the consumers preferred regions and minimizes the average age of these regions given that producers provide updates at a fixed rate. The second addresses the mini- mization of the interest weighted average age achieved by a fixed subset of producers with possibly overlapping cov- erage by optimizing their update rates. The first problem is shown to be submodular and thus amenable to greedy op- timization while the second has a non-convex/non-concave cost function which is amenable to effective optimization using the Frank-Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information sharing among obstructed sensing nodes. 

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2. Optimizing Networked Situational Awareness

   Jean Abou Rahal, Gustavo de (June 2019, IEEE WiOPT RAWNET Workshop)

   This paper proposes a framework to explore the op- timization of applications where a distributed set of nodes/sensors, e.g., automated vehicles, collaboratively exchange information over a network to achieve real-time situational-awareness. To that end we propose a reasonable proxy for the usefulness of possibly delayed sensor updates and their sensitivity to the network re- sources devoted to such exchanges. This enables us to study the joint optimization of (1) the application-level update rates, i.e., how often and when sensors update other nodes, and (2), the transmission resources allocated to, and resulting delays associated with, exchanging updates. We first consider a network scenario where nodes share a single resource, e.g., an ad hoc wireless setting where a cluster of nodes, e.g., platoon of vehicles, share information by broadcasting on a single collision domain. In this setting we provide an explicit solution characterizing the interplay between network congestion and situational awareness amongst heterogeneous nodes. We then extend this to a setting where such clusters can also exchange information via a base station. In this setting we characterize the optimal solution and develop a natural distributed algorithm based on exchanging congestion prices associated with sensor nodes’ update rates and associated network transmission rates. Preliminary numerical evaluation provides initial insights on the trade-offs associated with optimizing situational awareness and the proposed algorithm’s convergence. 

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3. Multicast with Prioritized Delivery: How Fresh is Your Data?

   https://doi.org/10.1109/SPAWC.2018.8446018  

   Zhong, Jing; Yates, Roy D.; Soljanin, Emina (June 2018, Signal Processing Advances in Wireless Communications (SPAWC), IEEE Workshop on)

   We consider a multicast network in which real-time status updates generated by a source are replicated and sent to multiple interested receiving nodes through independent links. The receiving nodes are divided into two groups: one priority group consists of k nodes that require the reception of every update packet, the other non-priority group consists of all other nodes without the delivery requirement. Using age of information as a freshness metric, we analyze the time-averaged age at both priority and non-priority nodes. For shifted-exponential link delay distributions, the average age at a priority node is lower than that at a non-priority node due to the delivery guarantee. However, this advantage for priority nodes disappears if the link delay is exponential distributed. Both groups of nodes have the same time-averaged age, which implies that the guaranteed delivery of updates has no effect the time-averaged freshness. 

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4. Age-Optimal Low-Power Status Update over Time-Correlated Fading Channel

   https://doi.org/10.1109/TMC.2022.3160050  

   Yao, Guidan; Bedewy, Ahmed; Shroff, Ness B. (March 2023, IEEE Transactions on Mobile Computing)

   In this paper, we consider transmission scheduling in a status update system, where updates are generated periodically and transmitted over a Gilbert-Elliott fading channel. The goal is to minimize the long-run average age of information (AoI) under a long-run average energy constraint. We consider two practical cases to obtain channel state information (CSI): (i) without channel sensing and (ii) with delayed channel sensing. For (i), CSI is revealed by the feedback (ACK/NACK) of a transmission, but when no transmission occurs, CSI is not revealed. Thus, we have to balance tradeoffs across energy, AoI, channel exploration, and channel exploitation. The problem is formulated as a constrained partially observable Markov decision process (POMDP). We show that the optimal policy is a randomized mixture of no more than two stationary deterministic policies each of which is of a threshold-type in the belief on the channel. For (ii), (delayed) CSI is available via channel sensing. Then, the tradeoff is only between the AoI and energy. The problem is formulated as a constrained MDP. The optimal policy is shown to have a similar structure as in (i) but with an AoI associated threshold. With these, we develop an optimal structure-aware algorithm for each case. 

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5. Optimizing resource constrained distributed collaborative sensing

   G. de Veciana M. Stecklein, H. Beytur (June 2021, IEEE ICC Workshop on Spectrum Sharing Technology for Next Generation Communications)

   null (Ed.)

   Collaborative sensing of spatio-temporal events/processes is at the basis of many applications including e.g., spectrum and environmental monitoring, and self-driving cars. A system leveraging spatially distributed possibly airborn sensing nodes can in principle deliver better coverage as well as possibly redundant views of the observed processes. This paper focuses on modeling, characterising and quantifying the benefits of optimal sensor activation/scanning policies in resource constrained settings, e.g., constraints tied to energy expenditures or the scanning capabilities of nodes. Under a natural model for the process being observed we show that a periodic sensor activation policy is optimal, and characterize the relative phases of such policies via an optimization problem capturing knowledge of the sensor geometry, sensor coverage sets, and spatio-temporal intensity and event durations. Numerical and simulation results for simple different sensor geometries exhibit how performance depends on the underlying processes. We also study the gap between optimal and randomized policies and how it scales with the density of sensors and resource constraints. 

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