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The proposed approach is designed to boost the IEEE 802.15.4 / ZigBee-based WSN streaming efficiency by introducing multipurpose routing. We investigate how those networks can be designed in order to achieve optimum throughput using computational models to explain intra-track and inter-track interference in multipath routing networks. One mode of action in particular – Spatial-TDMA (S-TDMA), Zig Bee dependent WSNs, will greatly reduce the amount of interference, both in one- and multi-track environments. It is also seen that two-way networks can be stronger than their one-way counterparts by means of deliberately selected implementation parameters. Finally, a greater degree of spatial isolation between the routes used indicates improved average efficiency in multi-path scenarios. The simulation is one of our results.

Visible light communication (VLC) network is an appropriate solution to meet the 5G and 6G. It can overcome the spectrum scarcity and capacity in the radio frequency (RF) networks. This network faces some challenges. Obstacles can break links and disconnect the network. Mobile nodes cannot receive data continuously because the transmitters have few fields of view. Using common MAC solutions in wireless networks does not solve VLC network problems. VLC MAC solutions should solve deafness, hidden node, full-duplex capability, channel utilization, and connectivity. This paper provided a survey in MAC layer solutions in VLC networks based on the systematic literature review (SLR). The methods have been recently published from 2011 to 2021. We analyzed analytically and statistically and the technical taxonomy presented with the SLR process according to the proposed solutions. Solutions were categorized into half-duplex, full-duplex, cooperative, and VLC/RF categories. Also, other proposed methods were described briefly. We explained the features, advantages, and disadvantages of each method. MAC layer design challenges in VLC networks were discussed to fill the gap in the previous literature. By classification and problem analysis, fewer attention topics that need more research were extracted. We found that fairness and reliability metrics received less attention and dynamic resource allocation was the main context in the VLC MAC solutions. Finally, future research challenges and open issues in VLC MAC solutions were proposed.

Cooperative spectrum sensing (CSS) can resolve the hidden terminal problem in cognitive radio (CR) networks. In CSS, the unlicensed users also known as secondary users (SUs) are often battery-operated, hence it is essential to use their energy efficiently. In CSS, the fusion rule threshold, number of CR users, sensing time, and transmission power directly affect the energy efficiency (EE). In this paper, fusion rule threshold and transmission power are optimized and evaluated to maximize the EE of the CR network. Iterative algorithms have been proposed to calculate individual optimum fusion rule threshold and joint optimal transmission power and fusion rule threshold, which maximizes the EE. It is found that EE is maximum at optimum fusion rule threshold = 14 and transmission power 0.81 W at average SNR = − 18 dB. EE at the optimum point is = 4.4598 Mbits/Hz/J.

In vehicular ad hoc networks (VANETs), beacon broadcasting plays a critical role in improving road safety and avoiding hazardous situations. How to ensure reliability and scalability of beacon broadcasting is a difficult and open problem, due to high mobility, dynamic network topology, hidden terminal, and varying density in both the time and location domains. In this paper, wireless resources are divided into basic resource units in the time and frequency domains, and a distributed and adaptive reservation based MAC protocol (DARP) is proposed to solve the above problem. For decentralized control in VANETs, each vehicle's channel access is coordinated with its neighbors to solve the hidden terminal problem. To ensure the reliability of beacon broadcasting, different kinds of preambles are applied in DARP to support distributed reservation, detect beacon collisions, and resolve collisions. Once a vehicle reserves a resource unit successfully, it will not release it until collision occurs due to topology change. The protocol performance in terms of access collision probability and access delay are analyzed. Based on the analysis, protocol parameters, including transmission power and time slots duration, can be adjusted to reduce collision probability and enhance reliability and scalability. Using NS-3 with vehicle traces generated by simulation of urban mobility (SUMO), simulation results show that the proposed DARP protocol can achieve the design goals of reliability and scalability, and it substantially outperforms the existing standard solutions.

We study the type of distributions that Restricted Boltzmann Machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they impose on observed binary nodes. We report an exact expression for these marginals in the form of a model of interacting binary variables with the explicit form of the interactions depending on the hidden node activation function. We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distributions over binary variables depends on the hidden node activation function and on the number of hidden nodes. When the inferred RBM parameters are weak, an intuitive pattern is found for the expression of the interaction terms which reduces substantially the differences across activation functions. We show that the weak parameter approximation is a good approximation for different RBMs trained on the MNIST dataset. Interestingly, in these cases, the mapping reveals that the inferred models are essentially low order interaction models., Comment: Supplemental material is available as ancillary file and can be downloaded from a link on the right

Cognitive radio (CR) is an emergent communication platform that offers solutions for spectrum scarcity issues. Cognitive radio networks (CRNs) will offer increased bandwidth to mobile consumers through wireless heterogeneous architectures and dynamic spectrum acquisition mechanisms. However, CRNs enforce challenges because of the fluctuating behaviour of the spectrum available and the diverse requirements for a varied range of applications. The functions of spectrum management can resolve those challenges to realize a new paradigm of the network. Secondary users (SUs) can opportunistically explore and employ the blank spaces present in licensed channels. This makes the SU evacuate the licensed channel and then switch to a vacant channel, when an incumbent primary user (PU) interferes with the channel, it causes degradation of SUs because of the frequent switching of channels. Also, the deafness problem is commonly seen in a CRN, where the QoS is critically affected due to the hidden interferences. This research proposes a Genetic Algorithm Optimized Fuzzy decision system for performing channel selection, channel switching, and spectrum allocation in a multi-channel multi-hop CRN. The proposed scheme acts as a decision support system (DSS), focusing on reducing the channel switching rate, hidden node interferences, and efficient spectrum allocation. Meta-heuristic genetic algorithm (GA) optimizes the parameters of the fuzzy decision system (FDS), for obtaining optimized decisions. The proposed DSS in the CR environment is simulated in the MATLAB platform and the results show improved performance concerning throughput and channel utilization.

Connectivity provisioning in large-scale Internet-of-Things (IoT) networks raises several challenges, one of which is the high collision rate among the transmission attempts of IoT devices. Transmission collisions result in prolonged data delivery delay and increased energy consumption of IoT devices. Contention-based wireless communication technologies such as WiFi is particularly vulnerable to this issue. As a remedy, a new standard IEEE 802.11ah is designed and such methods as RAW (Restricted Access Window) grouping is incorporated in order to curb the high collision rate. Meanwhile, in IEEE 802.11ah the transmission range and the number of devices that can be accommodated are also drastically increased. As a result, the impact of the hidden terminal problem is significantly amplified, which causes transmission collisions. In this paper, we propose an approach to avoid the high collision rate at the phase of the AP(Access Point) selection for IEEE 802.11ah networks. The proposed scheme considers the traffic load balancing and the hidden terminal issue simultaneously, without incurring any burden to the IoT devices. No modification of the IEEE 802.11ah standard is needed. Via extensive NS-3 simulations, we have verified that the proposed AP selection scheme outperforms the existing schemes in terms of data delivery delay and energy consumption by reducing transmission collisions.

The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.

Long-Term Evolution in unlicensed spectrum (LTE-U) is a promising solution to address the spectrum scarcity in the cellular networks. However, the LTE-U and WiFi systems may interfere with each other and degrade the performance. Thus, many works have focused on coexistence design and proposed various solutions. Nevertheless, the hidden terminal problem has not been well studied in the LTE-U system. In this paper, we investigate joint interference detection and resource allocation in the LTE-U system to maximize the overall system throughput while guaranteeing each user's quality-of-service (QoS) requirements. Firstly, to solve the hidden terminal problem and avoid the co-channel interference between the WiFi and LTE-U users, we adopt a cluster-based algorithm to find the potential WiFi interference for each LTE-U user according to their channel quality indicator (CQI) feedback and location information. Secondly, we formulate a joint subcarrier and power allocation problem over the licensed and unlicensed bands with each user's QoS provision. To make the original problem more tractable, we separate it into licensed and unlicensed subproblems and then derive the optimal solution. Moreover, we propose a low-complexity resource allocation scheme. The simulation results show that the proposed schemes can significantly improve the system performance and outperform the existing schemes.

The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node problem (HNP) and exposed node problem (ENP) cannot be effectively handled when both LAA and WiFi nodes attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNP and ENP, based on LBT, we firstly analyze HNP and ENP by means of mathematical approach. The analytical results surprisingly reveal that the hidden node and exposed node probabilities are as high as 41% and 39.33%, respectively. Then, a Give And p-persistent Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure, namely LBR-GAT, is proposed to cope with LBT to reduce the collision caused by HNP as well as to retrieve the bandwidth sacrificed by the ENP. With LBR-GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi. Evaluation results show that the proposed LBR-GAT could conditionally obtain better network performance comparing to legacy LBT.

Wireless Multimedia Sensor Networks (WMSNs) have emerged as the new class of wireless sensor networks (WSNs) to meet the stringent Quality of Service (QoS) requirements of emerging applications. Multipath routing with cross-layer approach appears to be a potential solution for supporting the distinct characteristics of WMSNs. However, due to the broadcast nature of the underlying medium, multiple paths are exposed to inter-path interference. In addition, low-power wireless links are asymmetric, error-prone and unreliable in nature. Consequently, an accurate and stable link quality estimation is essential to guarantee the performance of routing protocol. This paper proposes Triangle link quality metric and minimum inter-path Interference based Geographic Multipath Routing (TIGMR) protocol which finds multiple node-disjoint paths in IEEE 802.15.4 compliant network. This cross-layer routing protocol selects forwarding node based on a triangle link quality metric, remaining energy, and distance while anticipating minimum adjacent path interference effect. In addition, TIGMR protocol avoids Hidden Node Problem (HNP) at the sink node without using Request-To-Send/Clear-To-Send (RTS/CTS) handshake mechanism. Simulation results indicate TIGMR protocol optimizes overall performance and improves network lifetime as compared with state-of-the-art Two-Phase Geographic Forwarding (TPGF) and Link Quality and Energy-Aware Routing (LQEAR) protocols.

The IEEE 802.15.4-2015 TSCH (Time Slotted Channel Hopping) standard provides high-reliability, deterministic delay, and energy efficiency with the channel hopping and the TDMA. The standard does not specify the scheduling method, which is an essential part of the solution. One of the representative studies in TSCH scheduling is Orchestra. It is an autonomous scheduling method with minimal overhead that does not require a negotiation process between nodes. However, with high traffic load, performance of the Orchestra is greatly degraded because of its fixed schedule. If a node has children, continuous collisions occur in the slot of the node. We propose a novel algorithm called SRCA (Slot Reallocation for Collision Avoidance), as a solution for this problem. The SRCA autonomously updates the schedule. When a child requests a slot reallocation, the parent allocates a new slot with a low probability of collision. Another algorithm called SJCA (Slot reJection for Collision Avoidance) is further proposed in this work. It improves the SRCA to operate in an environment with interference where a sending node can disturb a transmission from a hidden node. In the SJCA, a node determines whether a collision due to interference will occur in the slot newly allocated by the parent. If so, it rejects the slot and requests again. We compare the performance of the SRCA and the SJCA against the Orchestra and e-TSCH-Orch through simulations. It is confirmed that the SJCA is robust to interference. In most cases, the SJCA shows better performance of PAR (Packet Acknowledgement Ratio), PLR (Packet Loss Rate), and latency than others. It is also verified that the SJCA provides high-reliability and low latency compared to existing technologies.

Previous studies showed that the packet collision probability in the 802.11 Distributed Coordination Function (DCF) can approach zero by adopting Centralized Random Backoff (CRB), which improves the total throughput performance. Moreover, it was reported that wireless networks using CRB maintain a high level of compatibility to legacy wireless local area networks (WLANs) when they coexist. However, little attention has been paid to relations between the performance of CRB and the retransmission limit, although the number of retransmission limit is an essential parameter that must be carefully chosen when implementing CRB for efficient WLANs. This article presents more realistic Markov chain models, and studies the impact of retransmission limit on the performance of CRB. These relations are also investigated by Monte Carlo simulation in more practical scenarios. Results show the trade-off that the total throughput increases with the retransmission limit when the number of nodes is larger than 15; whereas, the channel access opportunities in the mixed network scenario are more unevenly distributed between nodes as the retransmission limit increases. It is also shown in the simulation results that the throughput degradation caused by the hidden node problem is alleviated as the retransmission limit increases.

To extend the modeling area with artificial neural networks (ANNs) from finite periodic arrays to thinned arrays, where spacings between adjacent elements are crucial for array performance, an efficient model is proposed in this letter. Considering the spacings, a novel hidden node interconnection-radial basis function neural network (HNI–RBFNN) is developed to map the relationship between the array electromagnetic (EM) responses and the element ones. The element EM responses are obtained with the traditional RBFNN only involving the element geometry, while the connected weights of hidden layer nodes are determined by mutual coupling and array environment with the HNI scheme. A numerical example of the thinned phased array is used to evaluate the validity of the proposed model.

Recently, vehicle-to-vehicle communication has been envisaged to be one of the technologies for realizing highly safe connected and automated driving. One of the approaches for predicting the radio environment is the use of a measurement-based spectrum database, which stores various pieces of information on the radio environment of data received and collected by vehicles; however, prediction of an accurate packet delivery ratio (PDR) with consideration of packet collisions is difficult if the vehicle density changes after the generation of PDR maps. This paper proposes a method for predicting the PDR with consideration of packet collisions, including the influence of hidden nodes, by using the positions and number of vehicles.

Vehicular Adhoc Networks (VANETs) is one of the prominent and most important technologies that have reached a milestone in today’s modern world. One of the important key functions of VANET is known to be data dissemination that attracted most researchers to deal the problem and technical aspects towards its development. As collective perception technique is used by VANETs for expanding the perceptual range for the road traffic by sharing the sensor data regarding the objects in region using Vehicle-to-Vehicle communication. However, hidden terminal problem and packet collision occurs due to high vehicle density which makes data dissemination more difficult to deliver its messages which contains sensor data. In this paper, the proposed Epidemic and Transmission Control Priority (ETCP) based data dissemination model is presented. The proposed technique is used for controlling the transmission frequency of the sensor data or messages to avoid collision and to find the infected number of vehicles which are affected by the message without network overload. Furthermore, the vehicles are selected automatically with high probability for broadcasting sensor data. The simulation results of proposed ETCP algorithm is compared with existing Epic and Condensation Based Forwarding (CBF) algorithms, where transmission delay is as low as 214 millisecond which is lower when compared to other existing models. Packet delivery ratio of the proposed model is 99.12% which is significantly higher when compared to other existing models. The performance evaluation shows that the proposed model can deliberately avoid collision accidents and minimize the overhead and delay by comparing with other existing techniques.

Wireless technologies are continuously evolving, including features such as the extension to mid- and long-range communications and the support of an increasing number of devices. However, longer ranges increase the probability of suffering from hidden terminal issues. In the particular case of Wireless Local Area Networks (WLANs), the use of Quality of Service (QoS) mechanisms introduced in IEEE 802.11e compromises scalability, exacerbates the hidden node problem, and creates congestion as the number of users and the variety of services in the network grow. In this context, this paper presents a configurable Colored Petri Net (CPN) model for the IEEE 802.11e protocol with the aim of analyzing the QoS support in mid- and long-range WLANs The CPN model covers the behavior of the protocol in the presence of hidden nodes to examine the performance of the RTS/CTS exchange in scenarios where the QoS differentiation may involve massive collision chains and high delays. Our CPN model sets the basis for further exploring the performance of the various mechanisms defined by the IEEE 802.11 standard. We then use this CPN model to provide a comprehensive study of the effectiveness of this protocol by using the simulation and monitoring capabilities of CPN Tools.

Cooperative spectrum sensing (CSS) is a technique where multiple cognitive radio users cooperate among themselves to make binary decisions about the presence of a primary user. The single cognitive user often faces the hidden terminal problem. However, CSS tackles this problem by sending local sensing-based decisions to the fusion centre. A major drawback of conventional energy detection is the poor performance at low SNR regime. In this work, likelihood ratio statistics is considered as a test-statistic due to its highest statistical power. An improved likelihood ratio statistic-based CSS scheme is proposed by considering several past sensing events. The proposed scheme mitigates the poor detection at low SNR regime and misdetections arising due to sudden drops in signal energy. Furthermore, the generalised Byzantine attack is taken into account considering a security aspect. The proposed scheme is also shown to outperform Anderson Darling-based malicious user detection in CSS at a low SNR regime. The proposed scheme is verified and validated over empirical spectrum data. The performance improvement is at the cost of computational time, which in practice is very low and is justified by the significant performance improvements of the proposed scheme at low SNR regime.

In cognitive radio networks, the identification of a primary user (PU) with the help of spectrum sensing is very important for a secondary user (SU), such that interference free transmission can be performed. In recent times, the cooperative spectrum sensing that improves the reliability of the decisions made about the presence of a PU and overcoming hidden terminal problem by obtaining sensing results from different sensors is gaining much attention. However, the selfish SUs are sometimes not willing to cooperate. This paper proposes a contract theory-based approach, which is an incentive design mechanism where the participants are offered appropriately designed reward to encourage cooperation. The secondary base-station (BS)–SU interaction is modeled as a labor market using contract theory. Contract theory helps in analyzing the case of incomplete information where the BS is not aware of the SU’s private information. Further, the contract is formed in such a way that only the limited number of noncorrelated SUs can participate in sensing such that unnecessary energy consumption is reduced. Simulation results show that the contract can effectively incentivize SU’s cooperation, and outperforms the considered benchmark scheme in terms of PU detection probability.

Extreme learning machine (ELM) application to model the shear stress of magnetorheological (MR) fluids has superiority over the existing methods, such as Herschel-Bulkley. Although the shear stress has been successfully predicted, the hidden node numbers are too high reaching up to 10,000 that will hinder the application of the models. Furthermore, the existing works have tried to determine the hidden node number only by trial and error method. Therefore, this paper aims to reduce the hidden node number by employing the particle swarm optimization (PSO) considering the accuracy and the hidden node numbers. The ELM based-shear stress model was firstly defined by treating the magnetic field and shear rate as the inputs and shear stress as output. The objective function optimization method was then formulated to minimize the normalized error and the hidden node numbers. Finally, the proposed methods were tested at various ELM activation functions and samples. The results have shown that the platform has successfully reduced the hidden node numbers from 10,000 to 571 while maintaining the error of less than 1%. In summary, the proposed objective function for PSO optimization has successfully built the optimum shear stress model automatically.

Objectives: To implement energy detection and eigenvalue based cooperative spectrum sensing in NI-USRP hardware platform and to obtain its performance.Cooperative spectrum sensing is to be implemented using O and AND fusion rules. Methodology: The hardware is implemented using one primary user transmitter and two cognitive radio users. The implementation is done using LABVIEW and detection performance is analyzed. In cooperative spectrum sensing (CSS), CR system shares its own sensing information with other sensing nodes and utilizes the sensing outcomes of others to give a decision. Findings: Energy detection performs well in high SNR region and eigenvalue based detection performs well even with low SNR region. Energy detector detects the signal completely with SNR greater than 10dB. Eigenvalue detector detects signal completely at SNR of -9dB. Detection probability with OR Cooperative rule is better than AND rule. It increases with number of samples and signal to noise ratio. It is found that cooperative spectrum sensing performs well than the noncooperative sensing by decreasing the chances of miss detection. Novelty: CSS is robust in sensing errors due to hidden node or fading channels and also it decreases the probabilities of false alarm as well as probabilities of miss detection. Keywords: Cognitive radio; Cooperative spectrum sensing; Primary users; Secondary users; Energy detection

Vehicular safety applications based on DSRC/802.11p have strict reliability requirement (greater than 0.99). However, it is difficult to achieve high reliability in wireless medium as the transmission is vulnerable to various wave propagation issues. To the best of our knowledge, none of the existing emergency message dissemination schemes in the literature, achieves a predefined reliability in lossy channel. In this paper, we propose a novel scheme, called reliable emergency message dissemination scheme (REMD), which achieves a predefined reliability for message dissemination while satisfying delay requirements, for various channel conditions. We aim to guarantee very high reliability (e.g., 99%) in each hop, with low control overhead while keeping low end-to-end latency for time-critical applications. We employ zero-correlated unipolar orthogonal codes to combat hidden terminal problem. We exploit periodic beacons, to accurately estimate reception quality of 802.11p wireless link in each cell; then, we use this information to determine the optimal number of broadcast repetitions in each hop. In addition, to ensure reliability in multi-hop, we utilize cooperative communication. The simulation results show that REMD outperforms the existing well-known schemes in the literature. Furthermore, REMD satisfies latency requirements for time-critical vehicular applications and has less network overhead than the existing schemes.

We propose three different methods to determine the optimal number of hidden nodes based on $L_{1}$ regularization for a multilayer perceptron network. The first two methods, respectively, use a set of multiplier functions and multipliers for the hidden-layer nodes and implement the $L_{1}$ regularization on those, while the third method equipped with the same multipliers uses a smoothing approximation of the $L_{1}$ regularization. Each of these methods begins with a given number of hidden nodes, then the network is trained to obtain an optimal architecture discarding redundant hidden nodes using the multiplier functions or multipliers. A simple and generic method, namely, the matrix-based convergence proving method (MCPM), is introduced to prove the weak and strong convergence of the presented smoothing algorithms. The performance of the three pruning methods has been tested on 11 different classification datasets. The results demonstrate the efficient pruning abilities and competitive generalization by the proposed methods. The theoretical results are also validated by the results.

In this paper, we validate that the deterministic distance-based analytical model can be used to estimate the reliability of one-dimensional (1-D) 802.11 broadcast wireless networks compared with the interference-based analytical model. Therefore, we propose a deterministic distance-based reliability analytical framework for such networks in d-dimensional (d-D, $$d \ge 1$$ ) scenarios. This framework takes into account the fading channel and hidden terminal problem and makes three commonly used reliability metrics able to be resolved, including point-to-point packet reception probability (NRP), packet delivery ratio (PDR), and packet reception ratio (PRR). There are two key factors involved in deducing the effect of hidden terminals. One is to measure the hidden terminal transmission probability during the vulnerable period, which can be calculated based on the approximate solution of the semi-Markov process model capturing the channel contention and the back-off behavior. Another is the challenge to determine the size of the area to which the hidden terminals belong. First, we give a general mathematical expression on the size of the hidden terminal coverage affecting NRP which is an important part of the closed-form solution of NRP/PRR. Second, we adopt the Monte-Carlo method to solve the size of general hidden terminal coverage affecting PDR, making it possible to approximate PDR, as well as control the efficiency and accuracy by constraining the relative error. Finally, we adopt a multi-parameter optimization scheme to find the optimum settings for the network to ensure the quality of service and maximize channel utilization. A series of experimental results show that the framework can be used to access the reliability of 802.11 based d-D broadcast wireless network and pave the way for further optimization.

The in-band full-duplexing is a promising technique to boost wireless network throughput by allowing a node to transmit and receive simultaneously. This paper provides a comprehensive investigation on the hidden-node problem that arises in the full-duplex (FD) enabled carrier-sensing multiple-access (CSMA) networks. In particular, we first provide the fundamental conditions that guarantee successful receptions for all the FD transmission cases, and propose an ellipse interference model and an ellipse carrier-sensing model to capture the interference relations and the carrier-sensing mechanism in FD CSMA networks, respectively. We further establish the hidden-node-free design in FD CSMA networks. Specifically, we show the sufficient conditions on the carrier-sensing power threshold that can eliminate hidden-node collisions. We show that compared with half-duplex CSMA networks, the FD CSMA network needs a much smaller carrier-sensing power threshold to prevent hidden-node collisions, which leads to poor network spatial reuse. This motivates us to further propose a new medium access control (MAC) protocol with Full-duplex Enhanced Carrier-Sensing (FECS) mechanism. The FECS-MAC enables the secondary carrier-sensing before starting the secondary transmission. We show that with the secondary carrier-sensing design, the required carrier-sensing power threshold can be increased while keeping the network hidden-node free. Therefore, the network spatial reuse and throughput can be significantly improved. Simulation results demonstrate that the FECS-MAC can improve the throughput of dense three-node FD networks by more than 30 percent, compared with relay full-duplex (RFD) MAC protocol proposed in [1] .

Electricity theft behavior has serious influence on the normal operation of power grid and the economic benefits of power enterprises. Intelligent anti-power-theft algorithm is required for monitoring the power consumption data to recognize electricity power theft. In this paper, an adaptive time-series recurrent neural network (TSRNN) architecture was built up to detect the abnormal users (i.e., the electricity theft users) in time-series data of the power consumption. In fusion with the synthetic minority oversampling technique (SMOTE) algorithm, a batch of virtual abnormal observations were generated as the implementation for training the TSRNN model. The power consumption record was characterized with the sharp data (ARP), the peak data (PEA), and the shoulder data (SHO). In the TSRNN architectural framework, a basic network unit was formed with three input nodes linked to one hidden neuron for extracting data features from the three characteristic variables. For time-series analysis, the TSRNN structure was re-formed by circulating the basic unit. Each hidden node was designed receiving data from both the current input neurons and the time-former neuron, thus to form a combination of network linking weights for adaptive tuning. The optimization of the TSRNN model is to automatically search for the most suitable values of these linking weights driven by the collected and simulated data. The TSRNN model was trained and optimized with a high discriminant accuracy of 95.1%, and evaluated to have 89.3% accuracy. Finally, the optimized TSRNN model was used to predict the 47 real abnormal samples, resulting in having only three samples false predicted. These experimental results indicated that the proposed adaptive TSRNN architecture combined with SMOTE is feasible to identify the abnormal electricity theft behavior. It is prospective to be applied to online monitoring of distributed analysis of large-scale electricity power consumption data.