The concept of a smart city is to equip sensors to various objects in urban life to monitor areas and collect sensing data, and make wise decisions based on the collected data. However, some malicious sensor devices may interrupt and interfere with data collection, leading to a reduction in the integrity and availability of information, thereby causing harm to Internet of Things (IoT) applications. Therefore, identifying the credibility of sensor nodes to ensure the credibility of data collection is a challenge. This article proposes a trust-based active game data collection (TAGDC) scheme to collect trust data in the IoT. This TAGDC scheme mainly includes the following parts: (1) An active trust framework plus evolutionary game theory is proposed to encourage high-energy sensors to send detection routes and quickly obtain sensor trust. (2) In order to balance the data security requirements of subnetworks, the number and frequency of detection routes required by subnetworks are estimated through mechanism modeling and fuzzy analytic hierarchy process. (3) The design focuses on the internal trust computing model in the region to evaluate the trust of nodes. The findings of the experiment demonstrate that the TAGDC scheme, as described in this research study, enhances the accuracy of identifying malicious nodes by 20%, reduces the required identification time by 40%, and improves the data collection success rate by 5%.

When a large grid disturbance occurs, the grid-following inverter is prone to cause transient instability due to the weak damping or negative damping induced by a synchronous reference frame phase-locked loop. First, the transient instability mechanism and characteristics of grid-following inverters are analyzed. As the grid disturbance increases, the transient damping will decrease or even become negative, and the transient stability of the system will deteriorate. To address this issue, a constant damping phase-locked loop (CD-PLL) is proposed, which can achieve a constant positive damping control. Meanwhile, the working principle, characteristics, and design method of the proposed CD-PLL are introduced in detail. The proposed CD-PLL can not only enhance the transient stability of the system but also improve the dynamic performance. Finally, the theoretical analysis and the proposed CD-PLL are verified by simulation and experimental studies.

The output voltage of the boost converter is often compromised by unpredictable load and uncertain input voltage disturbance, making the stable control of the output voltage problematic. To address this issue, a dc voltage robust control strategy of the boost converter is proposed with considering system disturbance. First, based on the dynamic model of the boost converter, a Luenberger observer is proposed to estimate the load impedance and input voltage. Subsequently, the nonlinear model of the boost converter is transformed into a linear Brunovsky form by using the feedback linearization method, and a single-loop sliding mode controller is designed based on the system energy function. Since the proposed observer can update the system disturbances for the controller in time, the robustness and dynamic performance of the boost converter are significantly improved. Finally, the effectiveness of the proposed control strategy is verified by simulation and experiment.

The voltage distortion caused by the nonlinear load seriously threatens the safe and stable operation of the droop control-based islanded ac microgrid. To address this issue, a quasi-disturbance current-based voltage robustness control strategy of the inverter-based distributed generator (DG) is proposed, which does not need any filters to extract harmonics. By feedforwarding a quasi-disturbance current in current loop, the voltage robustness of the islanded ac microgrid can be significantly enhanced under load disturbance. In addition, based on the impedance method, the impedance model of the improved DG is built and the voltage robustness of the islanded ac microgrid is analyzed from the perspective of impedance. In the middle-frequency band, the impedance of the DG controlled by the proposed control strategy is obviously smaller than that of the classical DG, which makes the optimized DG more like an ideal voltage source and thus improves the voltage robustness of the islanded ac microgrid. Compared with the traditional harmonic suppression methods, the proposed method is simpler and can suppress time-varying harmonics in the middle-frequency band. Finally, the theoretical analysis and the proposed control strategy are verified with the simulation and experimental study.

In constant voltage and frequency (VF) control-based islanded microgrids, the nonlinear load can easily cause voltage harmonics and degrade the power quality of the islanded microgrids. First, the mechanism and characteristics of the voltage distortion are analyzed based on the impedance method. Due to the large internal impedance of the energy storage inverter, the harmonic voltage generated on the internal resistance is relatively large, leading to the microgrids' voltage distortion. To address this issue, this article proposes an internal voltage robust control of battery energy storage system for suppressing the wideband harmonics, which can achieve the voltage stability control of islanded microgrids under load disturbance. Compared with the traditional energy storage inverter, the impedance amplitude of the improved inverter is relatively small, which is more like an ideal voltage source. Under the nonlinear load and pulse load disturbances, the voltage of the islanded microgrids controlled by the proposed control strategy can operate normally. Finally, the effectiveness of the proposed control strategy and the above theoretical analysis is verified by simulation and experiment.

Nodes in fog computing are easily captured since they are generally resource-constraint and usually work in open and unprotected environments. In this article, a multidimensional trust fusion preference (MTFP) strategy is proposed to detect the malicious node, which mines and utilizes multiple factors that affect trust evaluation. First, the service rating gaps between the self-evaluation and other evaluation in a historical sliding window are calculated and differentially used to alleviate the behavior sparse problem of direct trust evaluation. In addition, a multiperspective weight algorithm is designed to improve the rationality of recommendation trust. Moreover, a reward and punishment factor are designed and incorporated into global trust calculation to enhance the reliability of trust evaluation. Compared with the existing mechanism model method of trust evaluation, the proposed MTFP strategy uses more node information, which can improve the detection precision and recall of malicious nodes at the same time. Finally, the experimental results validate the effectiveness of the proposed trust evaluation method.

Power grid disturbance usually degrades the performance of phase-locked loop (PLL), which may cause the stability issue of grid-connected inverter. Considering the power grid disturbance, including voltage amplitude, grid frequency, and grid stiffness, a double-loop inertia phase-locked loop (DLI-PLL) with antidisturbance ability is proposed. In addition, the mechanism and characteristics of the proposed DLI-PLL are analyzed based on the unit step response of transfer function. The inertia of the proposed DLI-PLL can be controlled by adjusting the control parameters. Compared with the single-loop inertia phase-locked loop, the proposed DLI-PLL have better grid synchronization performance in case of grid frequency fluctuation. Meanwhile, due to the inertial control of angular frequency, the designed DLI-PLL has good antidisturbance ability. Finally, experimental results validate the effectiveness of the proposed DLI-PLL.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

In this paper, unmanned aerial vehicles (UAVs) are used to efficiently collect information in an areas of interest. Based on the matrix completion, an optimal UAV data collection trajectory (OUDCT) scheme is proposed for improving energy efficiency and reducing redundant data by optimizing the trajectory of the UAV. With the proposed scheme, the backbone sampling points can be selected as follows. First, sampling points with higher degrees are selected as dominator sampling points. Second, sampling points with lower degrees are selected as virtual dominator sampling points to ensure that the information in all rows and columns is collected. Third, sampling points with lower degrees are selected as follower sampling points until the total number of selected sampling points satisfies the minimum requirement of the matrix completion. Thus, all the information in the monitoring area can be recovered by using the matrix completion. Finally, the optimal simulated annealing algorithm is used to plan the path of UAV based on the selected sampling points. The experimental results indicate that the performance of the OUDCT scheme is better than those in previous studies. Extensive simulation results are provided, which demonstrate that the OUDCT scheme can reduce data redundancy by 50%-52% and increase the lifetime by 17% compared with the random selection sampling points scheme.

For the cyber-physical systems (CPS) in the smart industrial environments, a larger amount of smart sensor nodes, processors and actuators are deployed to sense information from physical world, which is vulnerable to be attacked because the nodes are deployed in unattended areas. Security is a pivotal issue for CPS, therefore, a trust-based active detection (TBAD) scheme is proposed for improving the reliability of collecting data packets and reducing the data redundancy. In TBAD scheme, the trust of the nodes is evaluated by the neighboring nodes and the evaluation results are added into the header of data packets. Thus, according to the reliability of data packets collected by the unmanned aerial vehicle (UAV), the trust of sensor nodes is evaluated by the UAV. In addition, the evaluation trust of nodes stored in the header of data packets will be detected when the UAV suspects the stored trust of sensor nodes. Then, the trust of corresponding sensor nodes is adjusted according to the detection results. The sensor nodes with higher trust are selected to form movement trajectory. A series of simulation experiments are conducted to evaluate the performance of the scheme. The results illustrate that the proposed scheme can greatly improve the efficiency and security of data routing in CPS.

Security is a pivotal issue for the development of Cyber Physical Systems (CPS). The trusted computing of CPS includes the complete protection mechanisms, such as hardware, firmware, and software, the combination of which is responsible for enforcing a system security policy. A Trust Detection-based Secured Routing (TDSR) scheme is proposed to establish security routes from source nodes to the data center under malicious environment to ensure network security. In the TDSR scheme, sensor nodes in the routing path send detection routing to identify relay nodes’ trust. And then, data packets are routed through trustworthy nodes to sink securely. In the TDSR scheme, the detection routing is executed in those nodes that have abundant energy; thus, the network lifetime cannot be affected. Performance evaluation through simulation is carried out for success of routing ratio, compromised node detection ratio, and detection routing overhead. The experiment results show that the performance can be improved in the TDSR scheme compared to previous schemes.

Industrial communication systems (ICSs) have become an important part of many automated applications. Smart sensors for ICSs have attracted much attention. Using smart sensors for ICSs can allow the surrounding world to connect with the production equipment to reduce human resource requirements. However, due to their operating nature, smart sensors are often unattended and prone to different kinds of attacks. An on-off attack is one behavior that can cause serious damage to wireless sensor networks in ICSs. To ensure network security, a trust joint light probe based defense (TLPD) mechanism is proposed in which each smart sensor node selects a smart sensor node with high trust as the next hop to improve network security. The TLPD scheme is constituted by three major components, including light probe routing, trust estimation, and trust-aware routing. Light probe routing is adopted for increasing the operation times of malicious nodes, and a trust evaluation scheme evaluates nodes trust according to operation behavior of nodes. Thus, the discrimination degree of node trust between temporary errors and disguised malicious behaviors can be increased compared to existing trust evaluation schemes. Thus, a malicious smart sensor node can be quickly and easily identified. The analytical results of the TLPD mechanism assessed are with a simulation experiment.

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross‐layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of near‐sink area is high and that in far‐sink area is low, a cross‐layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission‐only mechanism.

Rapid, reliable and energy-efficient programing code dissemination is a challenging issue and offers a programmable and flexible network architecture for software-defined wireless networks (SDWNs). However, many schemes for programing codes in large-scale network incur a longer dissemination convergence time (DCT) and lower energy efficient in loss nature of wireless channels. In this paper, an adaptive broadcast dissemination (ABD) scheme is proposed to achieve low DCT and high network lifetime for SDWNs. An ABD scheme makes full use of energy left of nodes in areas far from the sink. In an ABD scheme, codes are broadcast constantly few times by nodes near the sink to save energy, and many times by nodes in areas far from the sink to rapidly spread software codes. Thus, an ABD scheme can reduce transmission delay for spreading software code while retaining network lifetime. Theoretical analysis and experimental results show that DCT in an ABD scheme is reduced by 28.12–29.86% compared with the hybrid scheme, while retaining network lifetime.

A Semi Random Circle routing for mobile Sink joint Ray Routing for data (SRCRR) scheme is proposed for preserving sink-location privacy with a delay guaranteed. In the SRCRR scheme, the data are directionally routed along ray paths and stored at intermediate nodes probabilistically. The Sink moves in a semirandom circular pattern to collect data from the local nodes occasionally, which guarantees that the data will be collected with an acceptable delay and prevents attackers from predicting their locations and movements. The experimental results indicate that the performance of the SRCRR scheme is better than that of the previous schemes.

A multi-representative re-fusion (MRRF) approximate data collection approach is proposed in which multiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.

Rapid, reliable and energy efficient programing code dissemination is a challenging issue and offer a programmable and flexible network architecture for software defined wireless networks (SDWNs). Many schemes for programing codes in large-scale network incur longer dissemination convergence time (DCT) in loss nature of wireless channels. In this paper, an integrated adjusted broadcast (TAB) scheme is proposed to achieve lower dissemination convergence time and longer network lifetime for SDWNs.

Abstract Currently, the analysis result of fair probability marking approach can only be got in the single‐source linear and single‐source tree network, which cannot meet the requirement of applications. This paper fills in this gap by providing an accurate marking probability analysis model for linear network, tree network, and planar network. Compare with the marking packets mechanism with equal marking probability, the analysis result of this paper shows that FPM mechanisms cannot only reduce convergence time up to 20%–60% for medium‐sized plane sensor networks, but also reduces the amount of data, so the network lifetime is 1.2 times to 3.37 times than previous methods. The mark probability τ of each node is given and its performance is comprehensively analyzed for FPM approach. The results have important guiding significance for designing traceback scheme. Copyright © 2016 John Wiley & Sons, Ltd.

In cyber-physical systems (CPS), service organizers (SOs) aim to collect service from service entities at lower price and provide better combined services to users. However, each entity receives payoffs when providing services, which leads to competition between SOs and service entities or within internal service entities. In this paper, we first formulate the price competition model of SOs where the SOs dynamically increase and decrease their service prices periodically according to the number of collected services from entities. A game based services price decision (GSPD) model which depicts the process of price decisions is proposed in this paper. In the GSPD model, entities game with other entities under the rule of “survival of the fittest” and calculate payoffs according to their own payoff-matrix, which leads to a Pareto-optimal equilibrium point. Numerous experiments demonstrate that the GSPD model can explain the price dynamics in the real world, and also can help decision makers a lot under various scenarios.