Graphene-based gas/vapor sensors have attracted much attention in recent years due to their variety of structures, unique sensing performances, room-temperature working conditions, and tremendous application prospects, etc. Herein, we summarize recent advantages in graphene preparation, sensor construction, and sensing properties of various graphene-based gas/vapor sensors, such as NH3, NO2, H2, CO, SO2, H2S, as well as vapor of volatile organic compounds. The detection mechanisms pertaining to various gases are also discussed. In conclusion part, some existing problems which may hinder the sensor applications are presented. Several possible methods to solve these problems are proposed, for example, conceived solutions, hybrid nanostructures, multiple sensor arrays, and new recognition algorithm.

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Abstract-In relay-aided wireless transmission systems, one of the key issues is how to manage the energy resource at the source and each individual relay, to optimize a certain performance metric. This paper addresses the sum rate maximized resource allocation (RA) problem in an orthogonal frequency division modulation (OFDM) transmission system assisted by multiple decode-and-forward (DF) relays, subject to the individual sum power constraints of the source and the relays. In particular, the transmission at each subcarrier can be in either the direct mode without any relay assisting, or the relay-aided mode with one or several relays assisting. We propose two RA algorithms which optimize the assignment of transmission mode and source power for every subcarrier, as well as the assisting relays and the power allocation to them for every relay-aided subcarrier. First, it is shown that the considered RA problem has zero Lagrangian duality gap when there is a big number of subcarriers. In this case, a duality based algorithm that finds a globally optimum RA is developed. Most interestingly, the sensitivity analysis in convex optimization theory is used to derive a closed-form optimum solution to a related convex optimization problem, for which the method based on the Karush-Kuhn-Tucker (KKT) conditions is not applicable. Second, a coordinate-ascent based iterative algorithm, which finds a suboptimum RA but is always applicable regardless of the duality gap of the RA problem, is developed. The effectiveness of these algorithms has been illustrated by numerical experiments.Index Terms-Orthogonal frequency division modulation, resource allocation, decode and forward, relaying, Lagrangian duality gap, dual decomposition method, energy efficiency.

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This paper develops a sum-power minimized resource allocation (RA) algorithm subject to a sum-rate constraint for cooperative orthogonal frequency division modulation (OFDM) transmission with subcarrier-pair based opportunistic decode-and-forward (DF) relaying. The improved DF protocol first proposed in [1] is used with optimized subcarrier pairing. Instrumental to the RA algorithm design is appropriate definition of variables to represent source/relay power allocation, subcarrier pairing and transmission-mode selection elegantly, so that after continuous relaxation, the dual method and the Hungarian algorithm can be used to find an (at least approximately) optimum RA with polynomial complexity. Moreover, the bisection method is used to speed up the search of the optimum Lagrange multiplier for the dual method. Numerical results are shown to illustrate the power-reduction benefit of the improved DF protocol with optimized subcarrier pairing. Index Terms-Cooperative communication, resource allocation, decode and forward, OFDM.

This paper investigates the optimum energy efficiency (EE) and the corresponding spectral efficiency (SE) for a communication link operating over a flat-fading channel. The EE is evaluated by the total energy consumption for transmitting per message bit. Three channel cases are considered, namely static channel with channel state information available at transmitter (CSIT), fast-varying (FV) channel with channel distribution information available at transmitter (CDIT), and FV channel with CSIT. The link's circuit power is modeled as ρ+κφ(R) Watt, where ρ > 0 and κ ≥ 0 are two constants and φ(R) is a general increasing and convex function of the transmission rate R ≥ 0. For all the three channel cases, the tradeoff between the EE and SE is studied. It is shown that the EE improves strictly as the SE increases from 0 to the optimum SE, and then strictly degrades as the SE increases beyond the optimum SE. The impact of κ, ρ and other system parameters on the optimum EE and corresponding SE is investigated to obtain insight. Some of the important and interesting results for all the channel cases include: (1) when κ increases the SE corresponding to the optimum EE should keep unchanged if φ(R) = R, but reduced if φ(R) is strictly convex of R; (2) when the rate-independent circuit power ρ increases, the SE corresponding to the optimum EE has to be increased. A polynomial-complexity algorithm is developed with the bisection method to find the optimum SE. The insight is corroborated and the optimum EE for the three cases are compared by simulation results.

This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems, to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs states change, and relay stations (RSs) states change. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves the better performance at linear complexity compared to the existing algorithms under different dynamic environments, while guaranteeing the fairness to a proper level.Index Terms-Cooperative communication, dynamic optimization, discrete particle swarm optimization, orthogonal frequency division multiple access (OFDMA), relay, resource allocation, utility function. 0018-9545 (c)

The blockchain technology becomes a key facilitator for Intelligent Manufacturing as it enables intelligent nodes to participate in global manufacturing networks with secure ledgers and smart contracts features. However, Traditional centralized storage cannot meet performance and security requirements and fully distributed storage consumes a large amount of computing, storage and network resources, which is inefficient and difficult to implement. In this paper, we propose a clustering strategy on node community clustering by constructing a trust model based on the decentralization of blockchain technology. We introduce a multi-chain storage structure. Our experiments show that the proposed strategy reduces data synchronization time and storage space, improves system performance by enabling efficient parallel processing.