In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.
To achieve a balance between throughput and data collection fairness, a scheduling strategy is proposed to allocate the power transfer among users and the proportion of the time between energy harvests @cite_13 . @cite_14 , a data rate selection in RWSN is studied, which considers fairness of data rate and WPT duration among the nodes. Moreover, a set of algorithms are developed to obtain a optimal rate assignment with the Water-Filling-Framework under different routing types. A WPT system is studied to balance a tradeoff between spectral and energy efficiencies by jointly designing beamforming for WPT and data transmission @cite_20 . Moreover, both WPT and data transmission beamforming are adaptive to channel fading by exploiting the benefits of channel state information.
@cite_14 @cite_13 @cite_20
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In this paper, we propose a spacial time slot scheduling algorithm for relay operation to improve the throughput performance of millimeter-wave wireless personal area network (mmWave WPAN) systems which employ directional antenna. The upcoming mmWave WPAN is designed for high definition TV (HDTV) transmission, high speed wireless docking and gaming, etc. Based on the fact that the significant path loss of millimeter-wave environments provides good space isolation, we have proposed a coexistence mechanism by sharing time slots for relay with direct transmission to guarantee throughput for the above data-rate-greedy applications. This paper is an extension that addresses spacial time slot scheduling for relay operation taking the effect of directional antenna into consideration. We model the throughput maximization with scheduling as an integer optimization and solve it by transforming the problem to a max-weight matching problem of a bipartite graph. We propose a scheduling algorithm based on the Kuhn-Munkres algorithm which can be used to solve the max weight matching problem. Simulation results show that there is up to 25 throughput improvement achieved compared with random scheduling method. Communication over a broadband fading channel powered by an energy harvesting transmitter is studied. Assuming non-causal knowledge of energy data arrivals and channel gains, optimal transmission schemes are identified by taking into account the energy cost of the processing circuitry as well as the transmission energy. A constant processing cost for each active sub-channel is assumed. Three different system objectives are considered: i) throughput maximization, in which the total amount of transmitted data by a deadline is maximized for a backlogged transmitter with a finite capacity battery; ii) energy maximization, in which the remaining energy in an infinite capacity battery by a deadline is maximized such that all the arriving data packets are delivered; iii) transmission completion time minimization, in which the delivery time of all the arriving data packets is minimized assuming infinite size battery. For each objective, a convex optimization problem is formulated, the properties of the optimal transmission policies are identified, and an algorithm which computes an optimal transmission policy is proposed. Finally, based on the insights gained from the offline optimizations, low-complexity online algorithms performing close to the optimal dynamic programming solution for the throughput and energy maximization problems are developed under the assumption that the energy data arrivals and channel states are known causally at the transmitter. Wireless power transfer (WPT) provides a novel solution to the painstaking power-charging issue in wireless sensor networks. However, due to the propagation loss, the fast attenuation in energy transfer efficiency over the transmission distance is the main impediment to the WPT application. In this paper, we apply the simultaneous wireless information and power transfer (SWIPT) to a wirelessly powered sensor network, where each node has two circuits, which operate on energy harvesting mode and information decoding mode separately. We propose a novel cooperative SWIPT scheme (CSS) for this system. First, we present a conflict-free schedule initialization algorithm for CSS. For a given conflict-free schedule, we formulate a resource allocation problem to maximize the network energy efficiency, which is then transformed to an equivalent convex optimization problem and resolved via dual decomposition. Finally, a heuristic algorithm is presented to achieve the transmission schedule with the maximum energy efficiency and the corresponding resource assignment policy. Simulation results indicate that the CSS can significantly improve the energy efficiency of the wirelessly powered sensor network. 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.