We consider an energy harvesting information update system where a sensor is allowed to choose a transmission mode for each transmission, where each mode consists of a transmission power-error pair. We also incorporate the battery phenomenon called battery recovery effect where a battery replenishes the deliverable energy if kept idle after discharge. For an energy-limited age of information (AoI) system, this phenomenon gives rise to the interesting trade-off of recovering energy after transmissions, at the cost of increased AoI. Considering two metrics, namely peak-age hitting probability and average age as the worst-case and average performance indicators, respectively, we propose a framework that formulates the optimal transmission scheme selection problem as a Markov Decision Process (MDP). We show that the gains obtained by considering both battery dynamics and adjustable transmission power together are much higher than the sum gain achieved if they are considered separately. We also propose a simple methodology to optimize the system performance taking into account worst-case and average performances jointly.

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Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP.

Fixed-point models have already been successfully used to analytically study networks consisting of persistent TCP flows only, or mixed TCP/UDP flows with a single queue per link and differentiated buffer management for these two types of flows. In the current study, we propose a nested fixed-point analytical method to obtain the throughput of persistent TCP and UDP flows in a network of routers supporting class-based weighted fair queuing allowing the use of separate queues for each class. In particular, we study the case of two classes where one of the classes uses droptail queue management and is intended for only UDP traffic. The other class targeting TCP, but also allowing UDP traffic for the purpose of generality, is assumed to employ active queue management. The effectiveness of the proposed analytical method is validated in terms of accuracy using ns-3 simulations and the required computational effort. Keywords TCP • Active queue management • UDP • Class-based weighted fair queuing • Fixed-point analysis This work is supported by the Science and Research Council of Turkey (Tubitak) under projects no. 111E106 and 115E360.

With the high data rates and ultra-low latency it provides, millimeter-wave (mmWave) communications will be a key enabler for future vehicular networks. However, due to high penetration losses and high mobility, mmWave links experience frequent blockages. We present an analytical framework to evaluate the performance of vehicular relaying, where vehicles on a highway exchange data with the network, either over direct vehicle-to-infrastructure (V2I) links with roadside units or a combination of a vehicle-to-vehicle (V2V) sidelink and a V2I link. Both V2V and V2I line-of-sight links can be blocked by other vehicles. We establish continuous-time Markov chain models of the blockage events that V2I links and vehicular relays experience, and use their steady-state solution to obtain analytical expressions for the blockage probability, average blockage duration and the SINR distribution. We demonstrate through numerical examples that relays are helpful especially when the traffic density is high since they can provide intermittent but more frequent connection opportunities and reduce the blockage duration. We show that relays that are far from a vehicle only have a marginal benefit since they are blocked with higher probability, compared to the closer relays. The proposed analytical framework enables fast and accurate assessment of a given deployment scenario, which will benefit researchers exploring mmWave-enabled vehicular networks.