In recent years, with the acceleration process of smart cities, the Artificial Intelligence of Things (AIoT) has become a novel approach for emergency rescue. However, due to small capacity and heterogeneity, AIoT nodes are facing various energy limitations and complex routing challenges. Therefore, emergency AIoT often have issues, such as data transmission failures, energy consumption, reduced network lifespan, and latency. To overcome these problems, this study proposes an energy efficiency emergency air–ground integrated heterogeneous AIoT (E3-HetAIoT) structure to improve the energy management efficiency of smart cities. First, we adopt a cell-based clustering mechanism, and the multiobjective zebra optimization algorithm (M-ZOA) is used to select cluster heads (CHs) and super nodes (UNs). The UN uses an urgency level-driven energy efficient sleep scheduling (U-ESS) mechanism to balance the remaining energy of sensors, especially in scheduling sleep time slots for sensors have energy below typical threshold. Second, an air–ground integrated data transmission mechanism is adopted, in which the generator de bits pseudo aleatorios (GBPA) is used to eliminate redundant data in CH and improve security. Then, the data packets are divided into normal packets and emergency ones. The normal packets wait for transmission of unmanned aerial vehicle (UAV) UAVs as intelligent mobile agents (U-iAgents), the trajectory of U-iAgents are dynamically predicted by dueling double deep Q-Network (Dueling-DDQN), meanwhile emergency data packets are immediately transmitted through inter cluster routing. Simulation results demonstrate that compared to existing algorithms, our proposed E3-HetAIoT framework achieves lower energy consumption and higher network lifetime, respectively.

Wireless sensing systems have been employed in the field of healthcare, environment monitoring, and smart agriculture, etc. Since the freshness and accuracy indicators of the sensing data are critical to wireless sensing systems, it is of great significance to ensure their performances simultaneously, i.e., the Age of Information (AoI) and information entropy of the sensing data should be jointly optimized. In this regard, we first establish the wireless sensing system models, including AoI and information entropy expressions. Next, from the information entropy viewpoint, we theoretically analyze an impact of the AoI on data accuracy. Then, we formulate the joint optimization problem of AoI, information entropy, and sensing energy consumption. Furthermore, we propose two numerical algorithms to solve the formulated problem in the known or unknown transmission environment, respectively. Finally, we evaluate the correctness and effectiveness of our proposals under various parameter settings, where the proposed scheme can obtain a better sum-weighted performance on AoI, information entropy, and sensing energy consumption than baselines in the literature.

Emergency Communication Network (ECN) is improving network quality of service (QoS) performance via numerous resource allocation and management technologies, such as network slicing, in order to meet the requirements of heterogeneous users in various types of emergency events. Unmanned aerial vehicles (UAVs) serving as NodeB, a.k.a., UxNB, can assist ground base stations (GBSs) to extend the coverage range and network utility of Heterogeneous ECN (HetECN), but make the resource allocation issues for different slice demands in HetECN more complex. This paper investigates the dynamic resource allocation problem of HetECN with a goal of maximizing traffic efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Firstly, in order to model the dynamic and uncertain environment of HetECN, we describe the long-term resource allocation problem as a stochastic game, which is an extension of game theory in Markov decision process-like environment. Subsequently, we develop an independent Q-learning based multi-agent reinforcement learning (IQ-MARL) framework, for which all agents execute decision algorithm independently but share a common structure. Simulation results demonstrate that our proposed IQ-MARL algorithm achieves a good balance between performance gains and information exchange overheads in HetECN, which is superior to those of other benchmark schemes.

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Emergency communication network (ECN) can provide fast, efficient and high-capacity communication services for specific areas by using mmWave transmission and unmanned aerial vehicles (UAVs) serving as aerial base stations (ABSs) or relay nodes. Now, in order to satisfy diverse demands, ECN should support different types of nodes, access methods, and traffic distributions, which is referred to as heterogeneous ECN (HetECN). Therefore, inappropriate trajectory planning and unbalanced traffic loading can lead to UAV flight collisions and network congestion. In this article, we jointly optimize UAV jamming avoidance trajectory and user association strategy aimed to load balancing, to maximize the utilization of HetECN. Specifically, an improved artificial potential field (APF) method along with mmWave beam forming technology is used to obtain the jamming avoidance trajectory of UAVs, and the optimal deployment location of UAVs are determined based on the distribution of ground users (GUs). Subsequently, the matching game and alliance game are comprehensively used to determine the load balancing based GU-UAV associated strategy under various GU demands, thereby ensuring traffic load balancing and resource optimization allocation. In addition, altitude fine-tuning have been made to further power consumption, thereby improving overall network efficiency. Simulation results demonstrate that the proposed method can achieve the expected performance in network utilities such as coverage rate, network capacity, load balancing effect of mmWave UAV-assisted HetECNs.

Green communication has attracted lots of attention recently, where NOMA (Non-Orthogonal Multiple Access) is one of the most promising technologies to realize energy-efficient communication. Specifically, by making massive wireless devices connect to the same time-frequency resource, NOMA can enhance the spectrum efficiency. In this paper, to investigate the freshness of the sensing data, we analyze the Age of Information (AoI) performance in NOMA-enabled Mobile Crowdsensing (MCS) circumstance, where the stochastic geometry theory is adopted. Firstly, we focus on the data submission process between mobile workers (MWs) and service provides (SPs), which drives to establish a model of the NOMA-enabled MCS. Then, given the transmission schemes of NOMA and OMA (Orthogonal Multiple Access), the mathematical expressions of the AoI metric are derived in the closed form respectively. Furthermore, simulation experiments are conducted to obtain AoI numerical results under various parameter settings (e.g., power strategies, queue models, and transmission protocols). Finally, the evaluation results not only prove the validness of the established models, but also provide some efficient solutions to achieve the optimal AoI value under the considered NOMA-enabled MCS scenario.

To efficiently and reliably obtain the target data, mobile crowdsensing (MCS) is widely used to provide the sensing data collection service. Currently, despite concerns of sensing network scale and mobility can be addressed to some degree in the MCS manner, the freshness and privacy goals of the sensing data are still not considered simultaneously. We combine the Age of Information (AoI) with security-enhanced technique to establish a novel MCS system, which can guarantee data freshness and security, respectively. Hence, the problem is formatted as a joint AoI optimization and privacy-preservation process. To solve this problem, we utilize game theory to achieve AoI-oriented spectrum access, and homomorphic encryption to encrypt communication data. Finally, security analysis and numerical results show that our proposed approach can effectively ensure the security level and improve the AoI performance at the same time in MCS.

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAVs) are playing a more and more important role in Mobile Edge Computing Networks (MECN), such as ground sensor communication assistance, user data collection, edge computation offloading and remote control services. In UAV-aided MECN, the timeliness of exchange data is a key factor that influences the real-time data-driven decisions at the server-side. Simultaneously, the Age of information (AoI) is also an indicator that reflects the freshness of data in terms of the destination during the communication process. Hence, AoI minimization is a vital goal in the MECN. The most recent work overlooks the possible security issues in the AoI minimization process, especially the revealed channel access attacks (CAAs), which aim to deteriorate network performance from ground to air channels. To overcome this research gap, in this paper, we improve the AoI-oriented channel access problem under CAA from the perspective of game theory. Firstly, a system model with active probability consideration is established to obtain a MECN-based AoI indicator under CAA. Subsequently, by utilizing Ordinary Potential Game (OPG), we formulate the AoI-based channel access optimization problem. Then, to reach the Nash Equilibrium (NE) of the OPG, a learning algorithm called Distributed Channel Access Strategy Determination (DCASD) is proposed to determine the channel access strategies. Finally, we conduct experiments under different parameters to present the better performance of our algorithm as compared with related work.

Efficient grant-free uplink transmission is critical in minimizing Age of Information (AoI) in multichannel Internet of Things (IoT) networks. But less attention has been paid to this topic especially when dynamic channel access attacks (DCAAs) exist. To bridge this gap, this article formulates the distributed channel access problem in AoI-oriented IoT networks, and then a reinforcement learning-based solution is put forward based on the theoretical results of the game theory. First, a utility maximization problem is formulated for each sensor node based on its average AoI under DCAAs with probabilistic ACK feedback. Second, the problem is transformed into two ordinary potential game (OPG) models, which are both proved to have at least one nash equilibrium (NE); and a distributed learning algorithm is proposed to reach the NE. Finally, extensive simulations are conducted to evaluate the proposal’s performance. Simulation results verify the effectiveness of the proposed algorithm in various parameters settings.

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

This article investigates the problem of synchronous randomized neighbor discovery with directional antennas. Due to the long tail effect, it will take long time to discover the last few neighbors, which increases overall neighbor discovery time. This effect is due to small proportion of remaining undiscovered neighbors. Moreover, improper choices of reception probabilities make the discovery even worse. In this article, a cognitive framework is proposed to minimize the expectation of neighbor discovery time. We present a scheme in which reception probabilities are dynamically adjusted. We consider an ideal scenario and a practical scenario. In an ideal scenario where perfect information about the number of neighbors is available, reception probabilities are adjusted according to the number of neighbors. A method of dynamic programming is used to recursively calculate the optimal reception probabilities. In an actual scenario where perfect information about number of neighbors is unavailable, a neighbor estimation method based on maximum-likelihood estimation is executed before probability adjustment. Simulation results show that when perfect information about neighbor is available and total transmission probability is within a proper range (between 0.1 and 0.2), the average neighbor discovery time can be significantly reduced (by 38% to 43%, respectively) compared with an existing probability-fixed scheme. With imperfect information, the scheme also works well and realizes appreciable reduction in average neighbor discovery time compared with existing self-adaptive schemes.

Indoor robots, in particular AI-enhanced robots, are enabling a wide range of beneficial applications. However, great cyber or physical damages could be resulted if the robots’ vulnerabilities are exploited for malicious purposes. Therefore, a continuous active tracking of multiple robots’ positions is necessary. From the perspective of wireless communication, indoor robots are treated as radio sources. Existing radio tracking methods are sensitive to indoor multipath effects and error-prone with great cost. In this backdrop, this paper presents an indoor radio sources tracking algorithm. Firstly, an RSSI (received signal strength indicator) map is constructed based on the interpolation theory. Secondly, a YOLO v3 (You Only Look Once Version 3) detector is applied on the map to identify and locate multiple radio sources. Combining a source’s locations at different times, we can reconstruct its moving path and track its movement. Experimental results have shown that in the typical parameter settings, our algorithm’s average positioning error is lower than 0.39 m, and the average identification precision is larger than 93.18% in case of 6 radio sources.

This paper focuses on the joint optimization of the Age of Information (AoI) and Signal to Interference plus Noise Ratio‐ (SINR‐) oriented channel access problem under attack in the Wireless Sensor Networks (WSNs). Firstly, to overcome the uncertain, dynamic, and incomplete information constrains, an active probability model and a controlling channel model are proposed for the sensors and the receiving end, respectively. Secondly, to ensure the AoI and SINR of the data generated by the sensors when transmitted under attack, one utility function based on average AoI and SINR is defined. Then, considering the distributed feature of the channel access process, the joint optimization problem is formulated under the game theory structure. Then, a distributed learning algorithm is proposed to reach the Nash Equilibrium (NE) of the game. Finally, simulation results have verified the correctness and effectiveness of the proposed method.

As one of the important steps in the process of self-organization of wireless ad hoc networks, neighbor discovery has substantial influence on the network performance. Especially in the modern warfare environment, the time consumption of neighbor discovery is very important for holding the battlefield situation. In this paper, we address the problem of neighbor discovery in ad hoc network with double-face phased array radar. And we take advantage of double-face phased array radar to improve the efficiency of neighbor discovery. Unlike former researches on single beam directional antenna, double beam directional antenna is introduced to our system. However, the direction of the antenna has been limited to two exclusive ranges due to the structure of double-face phased array radar. Obviously, the performance of the system which is in half duplex working mode can be better than the same one using single beam antenna. Thus we compare our system with the one using single beam antenna which is in full duplex mode. The experiential results show that the performance of neighbor discovery using double-face phased array radar in half duplex working mode is about 5 times better than the one using single beam directional antenna in full duplex working mode with the same algorithm.

Radar self-communication makes full use of the radar hardware resource and spectrum to fulfill the communication. As MIMO radar has become the new generation radar technology to be actively studied, MIMO radar self-communication and its capability are plausible to be investigated. The baseband models of MIMO radar and MIMO radar self-communication are set up, and radar mutual information is used to evaluate the radar performance. The optimal MIMO radar waveforms are derived to avoid the situation that the radar performance is degraded when the baseband waveform is modulated by the communication symbols. The channel capacity of radar self-communication is investigated under three situations, that is, without considering the radar performance, without channel state knowledge, and on condition that the optimal radar mutual information being guaranteed. Simulations show that the channel capacity of radar self-communication endures no loss in the high SNR region.

We consider a multiuser scenario with a center node for data fusion, where simultaneously transmitting is required in real time and with low probability of interception. We established a novel multiple access scheme, named matrix division multiple access (MDMA), based on OFDM technology. Precoded with different matrices, the signal from different end nodes can be distinguished due to the signal space division. At the receiver, the zero-forcing (ZF) and minimum mean square error (MMSE) decoding schemes are developed. Additionally, we analyze the sum capacity of the MDMA system. Simulations show that the MDMA scheme achieves the better performance compared to the OFDMA.

The large spectrum sidelobes of orthogonal frequency division multiplexing (OFDM) signal lead to the interference with the licensed user working on the adjacent band in spectrum pooling. The power spectral leakage of discrete Fourier transformation (DFT)-based OFDM systems can be divided into two parts, in-band–out-of-subband (IBOSB) spectral leakage and out-of-band (OOB) spectral leakage. A correlative coding technique is proposed to suppress the IBOSB leakage and redistribute the OOB leakage of DFT-based OFDM signal through the subcarrier coding in the frequency domain with appropriate weighting. The correlation is introduced between adjacent subcarriers and mutual cancellation of the spectrum sidelobes reshapes the spectrum of DFT-based OFDM signal. The optimum weighting factor is obtained through optimisation. Analytical results and simulation show that the proposed coding scheme significantly suppresses the IBOSB leakage in contrast with the common OFDM system and introduces least interference with the licensed user. Furthermore, the carrier–interference ratio (CIR) of OFDM system with correlative coding increases by 3 dB. At the same time, maximum likelihood sequence detection (MLSD) can mostly compensate the bit error rate (BER) degradation caused by error propagation at the expense of introducing a little computation complexity.

A Flow Driven MAC Protocol(FD-MAC) is a slot based MAC protocol which is designed for long distance wireless multihop transmission. With the flow-driven resource reservation mechanism, FD-MAC protocol is especially suitable to be used for wireless ad hoc network with dynamic traffic pattern. We argue that the Flow Classification Threshold(FCTs) of the protocol are the key parameters to the performance of the protocol,which optimal values are not yet been fully investigated. Then the value of the parameters are analyzed theoretically, and the optimal values are given in turn. Simulation result validates our analysis, best performance of the protocol would be anticipated when optimal FCT values are chosen.

In the scenario of dynamic spectrum access application for cognitive radio (CR), the spectrum-sidelobe problem of OFDM (orthogonal frequency division multiplexing) must be considered. A precoding ahead of IDFT (inverse discrete Fourier transformation) is presented to suppress the in-band-out-of-subband (IBOSB) radiation. Its design is based on generalized eigenvalue problem. In order to improve its performance for practical application, one column of the precoding matrix can be inverted to reduce the signal peak-to-average-power-ratio (PAPR) to a lower level. At the receiver, iterative soft detection with interference cancelation is adopted to detect the precoded data and further employs the frequency diversity. Both methods introduce some additional transceiver complexity compared with pure precoding, whereas simulations show that joint design not only provides improved PAPR statistics, but also achieves the markedly gain of Bit-Error-Rate (BER) performance over multipath fading channel.

In spectrum pooling applications, the spectral leakage of DFT-based OFDM systems should be divided into two parts, in-band-out-of-subband (IBOSB) leakage and out-of- band (OOB) radiation. A scheme is proposed to suppress the IBOSB leakage and redistribute OOB radiation through the frequency domain correlative coding with appropriate weighting. It introduces the correlation between adjacent subcarriers, and the DFT-based spectrum is reshaped through mutually cancelation of the sidelobes. The optimum weighting factor is given. Analysis and simulation show that the proposed scheme significantly suppresses IBOSB spectral leakage compared to conventional OFDM. At the same time, carrier-interference ratio (CIR) is increased and maximum likelihood sequence detection (MLSD) can mostly compensate the BER degradation caused by error propagation at the expense of a little computation complexity.