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.

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.

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.

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.

Unsegmented network coding was incorporated into opportunistic routing to improve inefficient schedule of segmented network coding due to delayed feedback. However, most unsegmented network coding schemes fail to constrain the size of decoding window. Large decoding window not only challenges limited computational ability and decoding memory in practical environments, but also introduces large decoding delay, which is undesirable in delay sensitive applications. In this paper, we prove that the possibility of unacceptably large decoding window is innegligible, and verify the necessity of handling decoding window. To solve the issue, we argue that the number of unknown packets injected into the network must be strictly constrained at the source. Based on the idea, we improve unsegmented network coding scheme for opportunistic routing. Simulation results shows that the solution is robust to losses. In addition, to achieve optimal throughput, the redundancy factor should be selected larger than the reciprocal of end-to-end delivery ratio.

The main challenge in DTNs is how to deal with path uncertainty in achieving a reliable routing scheme. All Erasure coding based routing algorithms make the assumption that the underlying path probabilities are known previously and remain constant, which is unpractical. On the other hand, the overall behavior of path probability tends to be stable with the increasing number of paths, which can be used to increase the stability of Erasure coding based schemes. Bearing this in mind, we present Inter-Coding: Inter-Coding is designed to fully combine the reliability of erasure coding, and the stability of interleaving to cope with uncertainties. We evaluate our approach in terms of delivery ratio under different level of uncertainty as well as different interleaving policy, and validate that Inter-Coding offers reliable and stable performance even the path uncertainty and dynamic is high.