Information Freshness: How To Achieve It and Its Impact On Low- Latency Autonomous Systems

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Virginia Tech


In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI.

All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective.

In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications.

In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment.

In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions.



wireless communications, age of information, deep reinforcement learning, IoT networks, vehicular networks, smart grid