Optimal Adaptive Random Multiaccess in Energy Harvesting Wireless Sensor Networks

Abstract

Wireless sensors can integrate rechargeable batteries and energy-harvesting (EH) devices to enable long-term, autonomous operation, thus requiring intelligent energy management to limit the adverse impact of energy outages. This work considers a network of EH wireless sensors, which report packets with a random utility value to a fusion center (FC) over a shared wireless channel. Decentralized access schemes are designed, where each node performs a local decision to transmit/discard a packet, based on an estimate of the packet's utility, its own energy level, and the scenario state of the EH process, with the objective to maximize the average long-term aggregate utility of the packets received at the FC. Due to the non-convex structure of the problem, an approximate optimization is developed by resorting to a mathematical artifice based on a game theoretic formulation of the multiaccess scheme, where the nodes do not behave strategically, but rather attempt to maximize a common network utility with respect to their own policy. The symmetric Nash equilibrium (SNE) is characterized, where all nodes employ the same policy; its uniqueness is proved, and it is shown to be a local maximum of the original problem. An algorithm to compute the SNE is presented, and a heuristic scheme is proposed, which is optimal for large battery capacity. It is shown numerically that the SNE typically achieves near-optimal performance, within 3% of the optimal policy, at a fraction of the complexity, and two operational regimes of EH-networks are identified and analyzed: an energy-limited scenario, where energy is scarce and the channel is under-utilized, and a network-limited scenario, where energy is abundant and the shared wireless channel represents the bottleneck of the system

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

“…Two main approaches have emerged in this context. Tools from stochastic optimization are used to develop design protocols assuming that the statistics of the energy process are known [125]- [127]. Alternatively, approaches based on learning theory provide the means to design energy harvesting systems by having the users adapt to the environment based on past observations [128], [129].…”

Lifetime maximization for sensor networks with wireless energy transfer

“…The vertical axis is the probability of the WSN to be immortal, whose criterion follows Proposition 1. The line with circles, squares, and diamonds represents packet generation rate λ to be uniform randomly determined from [1,6], [1,11], and [5,15] per minute respectively. When the WSN consists of 5 sensor nodes with λ ∼ U (5, 15), the WSN can be immortal with probability about 0.9.…”

“…The results are shown in Fig. 3 (a), (b), and (c), where the packet generation rates of sensor nodes are uniform randomly chosen from [1,11], [5,15], and [10,20]. The horizontal axis is the network size, and the vertical axis is the average network lifetime.…”

“…Every node can determine whether it should transmit or discard the data, according to the data utility, energy level, and the energy harvesting process. When the knowledge of state-of-charge is imperfect, a decentralized algorithm based on game theory has been proposed in [15]. However, since the arrival of ambient energy is uncontrollable, the performance of the WSN may be compromised.…”

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

“…Multi-hop link [65], [66] Convex optimization [85]- [87], [91], [131], [132] Lagrange multiplier [85]- [87], [91], [131] Water filling method [85], [86] Dynamic Programming [131] Game-theory based method [88], [89] [91]…”