Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

Abstract

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.

Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization

“…Small values of p and d, however, make c(α) close to 1 [cf. (38)], thus involving a larger asymptotic bound (41). On the other hand, minimizing the asymptotic bound (41) requires larger values of p and d, thus sacrificing optimality and constraint satisfaction.…”

“…An illustrative example is provided for the flow control problem in communications systems [36]; the proposed approach can also be applied to stochastic routing problems and energy-harvesting communication networks [38]. Consider a communication network modeled as a directed graph G = (N , E), with N the set of nodes and E the set of directed edges, which are dictated by (possibly time varying) routing matrix.…”

“…However, the framework is generally applicable to a number of settings where the objective is to drive the operation of physical and logical systems as well as networked systems to optimal operating points in real time. Application domains include, for example, wireless communication systems [36]- [38], vehicle control [39], water systems [40], and robotic sensor networks [41]. It is also worth pointing out that the general topic of online convex optimization and the associated (dynamic) regret analysis has been extensively studied in the theoretical machine learning literature; see, e.g., [42]- [45] and references therein.…”

Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

“…We also design a low‐complexity online algorithm without a priori knowledge of energy harvesting by employing Lyapunov optimization. The Lyapunov approach is a particular form of a dual stochastic algorithm, which can be used to solve the optimization problems involving online resource allocation 13 . Our online algorithm determines the energy consuming rates only according to the current energy state of the device and result of activity recognition, without demanding for any prior knowledge about the statistics of energy arrivals.…”

Packet Dropping Minimization in Energy Harvesting-Based Wireless Sensor Network With Linear Topology

“…Therefore, there is no need for selecting one neighbor as the next hop. Authors in [22] proposed a routing protocol based on a stochastic subgradient method to stabilize an energy harvesting network, but similar to the above-mentioned works, they did not take into account packet dropping due to the channel effect.…”