Information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

Age of Transmission-Optimal Scheduling for State Update of Multi-Antenna Cellular Internet of Things

Timely information updates are critical for real-time monitoring and control applications in the Internet of Things(IoT). In this paper, we consider a multi-antenna cellular IoT for state update where a base station(BS) collects information from randomly distributed IoT nodes through time-varying channel.Specifically, multiple IoT nodes are allowed to transmit their state update simultaneously in a spatial multiplex manner. Inspired by age of information(AoI),we introduce a novel concept of age of transmission(AoT) for the sceneries in which BS cannot obtain the generation time of the packets waiting to be transmitted. The deadline-constrained AoT-optimal scheduling problem is formulated as a restless multi-armed bandit(RMAB) problem. Firstly, we prove the indexability of the scheduling problem and derive the closed-form of the Whittle index. Then, the interference graph and complementary graph are constructed to illustrate the interference between two nodes. The complete subgraphs are detected in the complementary graph to avoid inter-node interference. Next, an AoT-optimal scheduling strategy based on the Whittle index and complete subgraph detection is proposed.Finally, numerous simulations are conducted to verify the performance of the proposed strategy.

Timeliness optimization of unmanned aerial vehicle lossy communications for internet-of-things

Information freshness is a key factor for Internet-of-Things(IoT)to make appropriate decisions and operations.This paper proposes an analytical framework for evaluating the timeliness performance of the IoT system based on Unmanned Aerial Vehicle(UAV)lossy communications.The performance analysis consists of the outage probability analysis and the Age-of-Information(AoI)analysis with outages.To begin with,we solve a lossy coding problem formulated from the UAV communication system,and derive a closed-form expression of the outage probability based on Shannon's lossy source-channel separation theorem.Then,we characterize the Peak AoI(PAoI)for the considered system,and further minimize the PAoI by deriving the optimal rate for generating information.Moreover,we analyze the system performance through theoretical calculations and simulations.The results indicate that the optimal server utilization ratio is always no larger than 0.5.In practical applications,we can utilize the proposed analytical framework to determine the system parameters which guarantee the timeliness performance of UAV lossy communications.