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© 2013 IEEE. In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.

In vehicular networks, the location-based services (LBSs) are very popular and essential for most vehicular applications. However, large number of location information sharing may raise location privacy leakage of in-vehicle users. Since the existing privacy protection mechanisms ignore the trajectory information, so that the location privacy of in-vehicle users and the trade-off between privacy and quality of service (QoS) cannot be effectively solved. In order to provide satisfactory privacy and QoS for in-vehicle users, in this paper, we propose an improved geo-indistinguishable location privacy protection scheme (GLPPS). Specifically, we first select an area of service retrieval (ASR) instead of user’s real location to send to LBS, which can protect location privacy while avoiding the leakage of trajectory. Secondly, we establish an income model based on Stackelberg between in-vehicle users and attackers, and design an IM-ASR algorithm based on the Iterative method and Maximin theorem, to achieve optimal trade-off between privacy and QoS. Finally, we prove that GLPPS satisfies \(\alpha \)-differential privacy. Moreover, the simulation results demonstrate that GLPPS can reduce loss of QoS while improving location privacy compared to other methods.

ZnFe2O4 (ZFO) has attracted considerable attention for supercapacitors (SCs), but its performances are severely hindered by intrinsically low electronic conductivity, relatively low mechanical stability, and severe agglomeration during the charge-discharge process. Here, a novel cotton derived active carbon fiber (ACF) with hierarchical porous architecture is used as template/scaffold, ZnFe2O4 nanoparticles/active carbon fiber composites (ZFO-ACFs) are prepared, in which ZFO nanoparticles (∼20 nm) are confined into the larger nanopores (∼20 nm) of the cotton derived active carbon fiber (ACF) while the smaller nanopores (∼2.5 nm) still are preserved. The cotton derived ACF matrix not only controls the size of ZFO particles and avoids self-aggregation but also offers continuous electron pathway and rapid ion transport channel, and prevents the loss of active material (ZFO) during the electrochemical reaction. The ZFO-ACFs electrode exhibits high specific capacitance of 192 F g−1 (58.7 mAh g−1). Moreover, enhanced rate capability are also obtained.

Defective ZnFe2O4/graphene (D-ZFO/G) composites are synthesized by a novel laser irradiation route. D-ZFO/G composites with unique architecture satisfy all the kinetic requirements for a desirable electrode material. Particularly, oxygen vacancies lied in D-ZFO/G provide abundant active sites for electrochemical reactions and enhance the intrinsic conductivity of ZFO for fast electron transport in D-ZFO/G electrode. D-ZFO/G composites then achieve a large specific charge of 474 C g−1 at 1 A g−1, high rate capability (95.8% at 10 A g−1), and excellent cycle life (98.9% capacity retention after 20,000 cycles). Keywords: ZnFe2O4, Laser irradiation, Oxygen vacancies, Graphene, Charge storage

Here, a controllable ZFO/rGO hybrid is prepared with high SCs performance by controlling the ZFO particle size and ZFO mass ratio. Small ZFO nanoparticles (∼15 nm) can be fully utilized and facilitated fast ion transport during electrochemical reactions, whereas the high mass ratio of ZFO (82.3%) will improve the specific capacitance of the ZFO/rGO hybrid electrode. The specific capacitance of this hybrid reach 352.9 F g−1 at current densities of 1 A g−1, which is higher or comparable to other ZFO based electrodes. It also exhibits remarkable cyclic stability (92.3% retention after 10000 cycles) and good rate performance. In addition, the energy density of symmetric supercapacitor based on ZFO/rGO hybrid can reach 6.7 Wh kg−1 at a power density of 300 W kg−1 within a voltage of 1.2 V in a 2 M KOH aqueous electrolyte.

In the past years, the issues of energy efficiency and interference are becoming increasingly serious in wireless local area network (WLAN) since lots of access points (AP) are deployed densely to provide high-speed users access. However, current works focus on solving the two issues separately and the influence of each other is rarely considered. To address these problems, we propose a joint optimization scheme of energy efficiency and interference to reduce energy consumption and interference together without sacrificing users’ traffic demands. Firstly, based on energy consumption measurement of AP and network interference analysis, we establish energy efficiency and interference models respectively. Then, the weighting method is introduced to build the joint optimization to quantify the effects of user-AP association, AP switch, AP transmit power and AP channel on energy consumption and interference. Lastly, we formulate the joint optimization as an Mixed Integer Non-Linear Programming (MINLP) problem. Since the MINLP problem is NP-hard, we proposed an Joint Optimization of Energy Efficiency and Interference (JOEI) algorithm based on greedy method to simplify its computational complexity. The evaluation results show that the proposed algorithm can effectively reduce the network energy consumption while improve the capacity of WLANs.

ZnFe2O4/carbon (ZFO/C) nanofibers are prepared by electrospinning method with subsequent stabilization treatment, high temperature carbonization, and extra induced annealing for high performance supercapacitor electrodes. Previous attempts to incorporate ZFO into electrospun fibers always resulted in ZFO nanofibers without carbon or multiphase components in carbon nanofibers. In this work, the extra induced annealing is introduced, successfully producing ZFO/C nanofibers without any other phases. The continuous carbon nanofibers with a diameter of ~180 nm could make the ion diffusion distance shorter, enhance the conductivity of ZFO, and improve the structural stability of the ZFO/C nanofibers, whereas the small ZFO nanoparticles (~30 nm) can be fully utilized during electrochemical reactions. Therefore, the optimized ZFO/C–B-250-600-280 exhibits a remarkable specific capacitance of 237 F g−1 at the current density of 1 A g−1. Even at the current density of 10 A g−1, 88.2% of capacity is retained.

In this research contribution, the primary objective was to enhance the crystallization behavior of poly(ethylene terephthalate) (PET). To accomplish this tack, three kinds of new nucleating agents SiO2-diethylene glycol-LMPET (PET-3), SiO2-triethylene glycol–LMPET(PET-4) and SiO2-tetraethylene glycol-LMPET (PET-5) nucleating agents were prepared via grafting different oligomers (diethylene glycol; triethylene glycol and tetraethylene glycol) to the surface of nano-SiO2 and then linking to the low molecular weight poly(ethylene terephthalate) (LMPET). These nano-particle nucleating agents facilitated the crystallization of PET. Differential scanning calorimetry (DSC) studies of the composites that pure PET blended with PET-3, PET-4 and PET-5 indicated that the longer ethoxy segment in the nucleating agents exhibited (i) higher degrees of crystallinity; (ii) faster rates of crystallization; and (iii) higher crystallization temperatures. The Jeziorny method was employed to analyze the non-isothermal crystallization kinetics of the composites. These works demonstrated that the PET-3, PET-4 and PET-5 were attractive nucleating agents for poly(ethylene terephthalate), and the longer the chain length of the ethoxy segment in the nucleating agents, the more efficient the nucleation effect.

In this letter, we propose an access point (AP) sleeping and user offloading optimization scheme to improve energy efficiency in densely deployed WLANs. Through real trace analysis, we investigate AP energy efficiency to obtain the sleep-awake threshold, which is used to select sleep or awake APs according to real-time status information monitored on controller. Moreover, we formulate the user offloading problem as a reverse auction process to optimize energy efficiency of APs involved in offloading. Simulation results demonstrate that, comparing to traditional methods, our scheme can achieve up to 20% energy saving while maintaining effective system coverage and throughput.