Power Aware Routing in Mobile Ad Hoc Networks

Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

(1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

https://link.springer.com/content/pdf/10.1057%2Fjors.1987.184.pdf

Applied Probability and Queues

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.

/pdf/energy-harvesting-wireless-communications-a-review-of-recent-22doeox995.pdf

Energy Harvesting Wireless Communications: A Review of Recent Advances

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

Optimal energy management policies for energy harvesting sensor nodes

We consider the problem of optimizing video de- livery for a network supporting video clients streaming stored video. Specifically, we consider the joint optimization of network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) re- alizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffer- ing and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use in the DASH framework. Further, NOVA can be extended for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.

https://users.ece.utexas.edu/~gustavo/papers/JoD14j.pdf

NOVA: QoE-driven optimization of DASH-based video delivery in networks

5G NR is designed from the ground up to provide support for mission critical communication with ultra-reliability and low latency (URLLC) in addition to other use cases such as enhanced mobile broadband. URLLC allows 5G network performance to approach that of wired networks (e.g., Ethernet) and paves the way for new use cases. In this paper, we focus on the end-to-end design of 5G networks for industrial factory automation. Providing URLLC wireless communication in these environments is especially challenging due to highly dynamic RF variations with possible signal blockage or reflections from moving metal objects. Coordinated Multipoint (CoMP) communication, which leverages spatial diversity to improve reliability without relying on packet retransmission, is a promising approach in these environments. We present a design and performance trade-offs between different CoMP techniques and their implication to capacity, robustness, and architecture. Subsequently, we describe architecture and protocol design aspects that allow 5G to operate as part of a time sensitive network including Ethernet bridging, time synchronization, and QoS. A prototype system that validates certain aspects of the design is also presented.

5G Industrial Networks With CoMP for URLLC and Time Sensitive Network Architecture

User perceived video quality depends on a variety of only partially understood factors, e.g., the application domain, content, compression, transport mechanism, and most importantly psycho-visual systems determining the ultimate Quality of Experience (QoE) of users. This paper centers on two key observations in addressing the problem of joint rate adaptation for video streams sharing a congested resource. First, we note that a user viewing a given video will experience temporal variations in the dependence of perceived video quality to the compression rate. Intuitively this is due to the possibly changing nature of the content, e.g., from an action to a slower scene. Thus, in allocating rates to users sharing a congested resource, in particular a wireless system where additional temporal variability in users' capacity may be high, content dependent tradeoffs can be realized to deliver a better overall average perceived video quality. Second, we note that such adaptation of users' rates, may result in temporal variations in video quality which combined with perceptual hysteresis effects will degrade users' QoE. We develop an asymptotically optimal online algorithm, requiring minimal statistical information, for optimizing users' QoE by realizing tradeoffs across mean, variance and fairness. Simulations show that our approach achieves significant gains in viewers' QoE. The novelty of this work lies not only in tackling the fundamental problem of achieving fair allocations of perceived video quality across a user population with time varying sensitivities and capacity, but, in addition, in integrating the deleterious impact that variations in perceived quality has on their QoE.

https://users.ece.utexas.edu/~gustavo/VariabilityAwareVideoRateAdapt.pdf

Jointly optimizing multi-user rate adaptation for video transport over wireless systems: Mean-fairness-variability tradeoffs

We study sensor networks with energy harvesting nodes The generated energy at a node can be stored in a buffer A sensor node periodically senses a random field and generates a packet These packets are stored in a queue and transmitted using the energy available at that time at the node For such networks we develop efficient energy management policies First, for a single node, we obtain policies that are throughput optimal, ie, the data queue stays stable for the largest possible data rate Next we obtain energy management policies which minimize the mean delay in the queue We also compare performance of several easily implementable sub-optimal policies A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay Next using the results for a single node, we develop efficient MAC policies

Vinay Joseph

Papers

Optimal energy management policies for energy harvesting sensor nodes

NOVA: QoE-driven optimization of DASH-based video delivery in networks

5G Industrial Networks With CoMP for URLLC and Time Sensitive Network Architecture

Jointly optimizing multi-user rate adaptation for video transport over wireless systems: Mean-fairness-variability tradeoffs

Efficient energy management policies for networks with energy harvesting sensor nodes