Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.

Network selection is one of the hot issues in the fusion of heterogeneous wireless networks (HWNs). However, most of previous works only consider selecting single-access network, which wastes other available network resources, rarely take account of multi-access. To make full utilization of available coexisted networks, this paper proposes a novel multi-access selection algorithm based on joint utility optimization for users with multi-mode terminals. At first, the algorithm adopts exponential smoothing method (ESM) to get smoothed values of received signal strength (RSS). Then we obtain network joint utility function under the constraints of bandwidth and number of networks, with the consideration of trade-off between network benefit and cost. At last, Lagrange multiplier and dual optimization methods are used to maximize joint utility. Users select multiple networks according to the optimal association matrix of user and network. The simulation results show that the proposed algorithm can optimize network joint utility, improve throughput, effectively reduce vertical handoff number, and ensure Quality of Service (QoS).

In order to reduce the convergence time in an iterative procedure, some gradient based preliminary processes are employed to eliminate outliers. The adaptive variable block size is also introduced to balance the accuracy and computational complexity. Moreover, the use of Canberra distance instead of Euclidean distance illustrates higher performance in measuring motion similarity.

The dynamic competition between two bounded rational mobile virtual network operators (MVNOs) in a duopoly spectrum market is investigated. A two stage game is employed to model the interaction of the MVNOs and the quality of service of the secondary users is taken into account. The evolutionary game theory is introduced to model the dynamic strategy selections of MVNOs. Using replicated dynamics, the proposed evolutionary game algorithm can converge to a unique evolutionary stable strategy. Simulation results verify that the proposed algorithm can make the MVNOs adaptively adjust the strategies to approximate optimal solution.

This paper presents a design method of a two-hop wireless power transfer (WPT) system for installing on a robot arm. The class-E inverter and the class-D rectifier are used on the transmission and receiving sides, respectively, in the proposed WPT system. Analytical equations for the proposed WPT system are derived as functions of the geometrical and physical parameters of the coils, such as the outer diameter and height of the coils, winding-wire diameter, and number of turns. Using the analytical equations, we can optimize the WPT system to obtain the design values with the theoretically highest power-delivery efficiency under the size limitation of the robot arm. The circuit experiments are in quantitative agreement with the theoretical predictions obtained from the analysis, indicating the validity of the analysis and design method. The experimental prototype achieved 83.6% power-delivery efficiency at 6.78MHz operating frequency and 39.3W output power.

This paper proposes a new robust adaptive processing algorithm that is based on the extended least squares (ELS) method with running spectrum filtering (RSF). By utilizing the different characteristics of running spectra between speech signals and noise signals, RSF can retain speech characteristics while noise is effectively reduced. Then, by using ELS, autoregressive moving average (ARMA) parameters can be estimated accurately. In experiments on real speech contaminated by white Gaussian noise and factory noise, we found that the method we propose offered spectrum estimates that were robust against additive noise.

Vehicular Ad hoc Network (VANET) is an important part of the Intelligent Transportation System (ITS). VANETs can realize communication between moving vehicles, infrastructures and other intelligent mobile terminals, which can greatly improve the road safety and traffic efficiency effectively. Existing studies of vehicular ad hoc network usually consider only one data transmission model, while the increasing density of traffic data sources means that the vehicular ad hoc network is evolving into Heterogeneous Vehicular Network (HetVNET) which needs hybrid data transmission scheme. Considering the Heterogeneous Vehicular Network, this paper presents a hybrid transmission MAC protocol including vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I/I2V). In this protocol, the data are identified according to timeliness, on the base of the traditional V2V and V2I/I2V communication. If the time-sensitive data (V2V data) fail in transmission, the node transmits the data to the base station and let the base station cooperatively transmit the data with higher priority. This transmission scheme uses the large transmission range of base station in an effective manner. In this paper, the queueing models of the vehicles and base station are analyzed respectively by one-dimensional and two-dimensional Markov Chain, and the expressions of throughput, packet drop rate and delay are also derived. The simulation results show that this MAC protocol can improve the transmission efficiency of V2V communication and reduce the delay of V2V data without losing the system performance.

This paper proposes a novel greedy algorithm, called Creditability-Estimation based Matching Pursuit (CEMP), for the compressed sensing signal recovery. As proved in the algorithm of Stagewise Orthogonal Matching Pursuit (StOMP), two Gaussian distributions are followed by the matched filter coefficients corresponding to and without corresponding to the actual support set of the original sparse signal, respectively. Therefore, the selection for each support point is interpreted as a process of hypothesis testing, and the preliminarily selected support set is supposed to consist of rejected atoms. A hard threshold, which is controlled by an input parameter, is used to implement the rejection. Because the Type I error may happen during the hypothesis testing, not all the rejected atoms are creditable to be the true support points. The creditability of each preliminarily selected support point is evaluated by a well-designed built-in mechanism, and the several most creditable ones are adaptively selected into the final support set without being controlled by any extra external parameters. Moreover, the proposed CEMP does not necessitate the sparsity level to be a priori control parameter in operation. In order to verify the performance of the proposed algorithm, Gaussian and Pulse Amplitude Modulation sparse signals are measured in the noiseless and noisy cases, and the experiments of the compressed sensing signal recoveries by several greedy algorithms including CEMP are implemented. The simulation results show the proposed CEMP can achieve the best performances of the recovery accuracy and robustness as a whole. Besides, the experiment of the compressed sensing image recovery shows that CEMP can recover the image with the highest Peak Signal to Noise Ratio (PSNR) and the best visual quality.