Optimizing Age of Information in Wireless Networks with Perfect Channel State Information

Age of information (AoI), defined as the time elapsed since the last received update was generated, is a newly proposed metric to measure the timeliness of information updates in a network. We consider AoI minimization problem for a network with general interference constraints, and time varying channels. We propose two policies, namely, virtual-queue based policy and age-based policy when the channel state is available to the network scheduler at each time step. We prove that the virtual-queue based policy is nearly optimal, up to a constant additive factor, and the age-based policy is at-most factor 4 away from optimality. Comparing with our previous work, which derived age optimal policies when channel state information is not available to the scheduler, we demonstrate a 4 fold improvement in age due to the availability of channel state information

Age Optimal Information Gathering and Dissemination on Graphs

We consider the problem of timely exchange of updates between a central station and a set of ground terminals $V$, via a mobile agent that traverses across the ground terminals along a mobility graph $G = (V, E)$. We design the trajectory of the mobile agent to minimize peak and average age of information (AoI), two newly proposed metrics for measuring timeliness of information. We consider randomized trajectories, in which the mobile agent travels from terminal $i$ to terminal $j$ with probability $P_{i,j}$. For the information gathering problem, we show that a randomized trajectory is peak age optimal and factor-$8\mathcal{H}$ average age optimal, where $\mathcal{H}$ is the mixing time of the randomized trajectory on the mobility graph $G$. We also show that the average age minimization problem is NP-hard. For the information dissemination problem, we prove that the same randomized trajectory is factor-$O(\mathcal{H})$ peak and average age optimal. Moreover, we propose an age-based trajectory, which utilizes information about current age at terminals, and show that it is factor-$2$ average age optimal in a symmetric setting

Capacity and delay scaling for broadcast transmission in highly mobile wireless networks

We study broadcast capacity and minimum delay scaling laws for highly mobile wireless networks, in which each node has to disseminate or broadcast packets to all other nodes in the network. In particular, we consider a cell partitioned network under the simplified independent and identically distributed (IID) mobility model, in which each node chooses a new cell at random every time slot. We derive scaling laws for broadcast capacity and minimum delay as a function of the cell size. We propose a simple first-come-first-serve (FCFS) flooding scheme that nearly achieves both capacity and minimum delay scaling. Our results show that high mobility does not improve broadcast capacity, and that both capacity and delay improve with increasing cell sizes. In contrast to what has been speculated in the literature we show that there is (nearly) no tradeoff between capacity and delay. Our analysis makes use of the theory of Markov Evolving Graphs (MEGs) and develops two new bounds on flooding time in MEGs by relaxing the previously required expander property assumption.National Science Foundation (U.S.) (Grant CNS-12170)National Science Foundation (U.S.) (Grant CNS-17137)National Science Foundation (U.S.) (Grant AST-15473

Speed limits in autonomous vehicular networks due to communication constraints

Autonomous vehicles need to be aware of other vehicles in their vicinity in order to avoid collisions and successfully perform their tasks. Such network awareness is ensured by exchanging location and control information over wireless radio channels. However, wireless interference constraints limit the number of messages that can be exchanged between the vehicles. In this paper, we study the impact of such communication constraints on maximum vehicle speed in dense autonomous vehicular networks. We define hazard rate to be the fraction of time a vehicle enters an `uncertainty region', i.e., a region where there is a positive probability of other vehicles being present due to lack of situational awareness. We show that the hazard rate follows a threshold behavior with respect to maximum speed v as the network density n increases to infinity. We show that for a 2D network the hazard rate tends to 1, if the maximum speed v decreases slower than n[superscript -3/2]; and tends to 0, if v decreases faster than n[superscript -3/2]. For the network hazard rate, which is the fraction of time any vehicle enters its uncertainty region, the threshold is n[superscript -2]. Finally, we extend these results to a 3D network and show that the thresholds for the 3D network are larger than in the 2D network