DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things

We propose a new energy harvesting strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency (RF) energy harvesting powered Internet of Things. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the energy harvesting device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming (DP) algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term

Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution

Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes. In contrast, current reinforcement learning (RL) studies mainly focus on training an agent with a fixed morphology (e.g., skeletal structure and joint attributes) in a fixed environment, which can hardly generalize to changing environments or new tasks. In this paper, we optimize an RL agent and its morphology through ``morphology-environment co-evolution (MECE)'', in which the morphology keeps being updated to adapt to the changing environment, while the environment is modified progressively to bring new challenges and stimulate the improvement of the morphology. This leads to a curriculum to train generalizable RL, whose morphology and policy are optimized for different environments. Instead of hand-crafting the curriculum, we train two policies to automatically change the morphology and the environment. To this end, (1) we develop two novel and effective rewards for the two policies, which are solely based on the learning dynamics of the RL agent; (2) we design a scheduler to automatically determine when to change the environment and the morphology. In experiments on two classes of tasks, the morphology and RL policies trained via MECE exhibit significantly better generalization performance in unseen test environments than SOTA morphology optimization methods. Our ablation studies on the two MECE policies further show that the co-evolution between the morphology and environment is the key to the success

Adapting Mobile Beacon-Assisted Localization in Wireless Sensor Networks

The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs

Proteomic analysis of the biomass hydrolytic potentials of Penicillium oxalicum lignocellulolytic enzyme system

Additional file 2: Table S1. The functional annotations of proteins identified in the proteome of SP. Mass spectrometry-based proteomics study was performed to comprehensively dissect the lignocellulolytic enzyme profile of SP. Accession, Protein name, PSM, Calc. MW, CBM, Calc. pI and CAZy family of identified proteins were shown

Hydrological and biogeochemical controls on absorption and fluorescence of dissolved organic matter in the northern South China Sea

Author Posting. © American Geophysical Union, 2017. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 122 (2017): 3405–3418, doi:10.1002/2017JG004100.The Kuroshio intrusion from the West Philippine Sea (WPS) and mesoscale eddies are important hydrological features in the northern South China Sea (SCS). In this study, absorption and fluorescence of dissolved organic matter (CDOM and FDOM) were determined to assess the impact of these hydrological features on DOM dynamics in the SCS. DOM in the upper 100 m of the northern SCS had higher absorption, fluorescence, and degree of humification than in the Kuroshio Current of the WPS. The results of an isopycnal mixing model showed that CDOM and humic-like FDOM inventories in the upper 100 m of the SCS were modulated by the Kuroshio intrusion. However, protein-like FDOM was influenced by in situ processes. This basic trend was modified by mesoscale eddies, three of which were encountered during the fieldwork (one warm eddy and two cold eddies). DOM optical properties inside the warm eddy resembled those of DOM in the WPS, indicating that warm eddies could derive from the Kuroshio Current through Luzon Strait. DOM at the center of cold eddies was enriched in humic-like fluorescence and had lower spectral slopes than in eddy-free waters, suggesting inputs of humic-rich DOM from upwelling and enhanced productivity inside the eddy. Excess CDOM and FDOM in northern SCS intermediate water led to export to the Pacific Ocean interior, potentially delivering refractory carbon to the deep ocean. This study demonstrated that DOM optical properties are promising tools to study active marginal sea-open ocean interactions.National Natural Science Foundation of China Grant Numbers: U1305231, 412760642018-06-2