Efficient management of limited resources, reduced data transmission latency, and improved overall network performance are critical in ground networks. This paper proposes a hierarchical sensing and offloading framework for intelligent transportation systems (ITS), where unmanned aerial vehicles (UAVs) act as mobile data aggregators to support ITS monitoring in areas beyond roadside unit (RSU) coverage, during RSU outages, or under severe ground network congestion. By enabling localized offloading, the framework ensures the timely delivery of critical ITS data. The proposed secrecy-aware approach enhances the resilience, efficiency, and integrity of ITS sensing and communications. Although UAVs offer advantages such as high mobility, on-demand deployment, and reliance on line-of-sight (LoS) communication channels, they remain vulnerable to security threats. To counter eavesdropping and jamming threats, the proposed method combines policy-gradient reinforcement learning with protective jamming and secrecy-aware transmission scheduling, enabling UAVs to adaptively adjust flight paths, transmit power, and time slot assignments. A multi-objective reward function is designed to jointly optimize secrecy rate, communication delay, and energy consumption under adversarial conditions. Extensive simulations confirm the proposed model’s effectiveness in enhancing communication security and operational efficiency across varying threat scenarios.

Traffic signal control (TSC) at urban intersections is crucial for optimizing vehicle traffic flow and ensuring pedestrian safety. Advances in Internet of Things (IoT) and Internet of Vehicles (IoV) technologies have significantly improved traffic monitoring. However, most existing TSC studies primarily focus on optimizing vehicular traffic flow metrics such as waiting time and queue length, often overlooking crucial factors like lane fairness, emergency vehicle priority, and pedestrian convenience and safety at crosswalks. This paper proposes a novel deep reinforcement learning (DRL)-based TSC framework that jointly optimizes vehicular and pedestrian requirements. Two algorithms are introduced to address these multi-objective goals: DFASD (Dynamic Feasible Action Set Derivation), which guarantees pedestrian crosswalk requirements by leveraging a set of feasible actions during action selection, and ACSCS (Adaptive Crosswalk State Combined System), which integrates crosswalk sub-states into the state representation. Simulation results demonstrate that the proposed methods outperform conventional DRL-based dynamic signal control and cycle-based approaches, achieving superior performance in both vehicle traffic flow and pedestrian crosswalk management. These results underscore the potential of the proposed framework to effectively balance vehicular and pedestrian needs, enhancing urban intersection management.

As the Internet of Things (IoT) reshapes modern transportation, there is a growing need for an intelligent navigation strategy that addresses the challenges of various moving objects (MOs) like ground vehicle, indoor robots, and unmanned aerial vehicles (UAVs), while also overcoming recent global positioning system (GPS) vulnerabilities. These vulnerabilities include signal unavailability in obstructed environments, geopolitical or institutional dependencies of GPS services, and risks of centralized system failures, such as single-node disruptions. To address these issues, we introduce UDMSU-distributed reinforcement learning (DRL), a real-time user-demand-based DRL algorithm for intersection navigation, incorporating a multishot update mechanism. Our decentralized, GPS-independent approach meets real-time user preferences, such as time, energy, and safety—while ensuring private navigation by restricting data exchange to localized road segment interactions with the intersection agent. The proposed multishot Q-table update (MSQU) mechanism enhances data efficiency by reusing Q-values across multiple Q-table updates, thereby accelerating learning convergence. Extensive experiments show that our method effectively adapts to dynamic environments with diverse user demands, while reducing computational complexity and memory usage compared to other methods.

Unmanned aerial vehicles (UAVs) integrated with wireless sensor networks (WSNs) on the ground have proven to be a reliable and robust solution to a variety of applications, such as wide-area environment monitoring, surveillance, and event tracking systems. However, determining the optimal movement paths of asynchronous UAVs to obtain the highest sensing information value while satisfying energy constraints in unknown environments remains a challenge. In this article, we propose a novel approach called spatial and temporal substate-based$Q$-learning (STSQL) to acquire time-varying sensing data using UAVs in an unknown environment. Our approach utilizes a$Q$-learning algorithm, a reinforcement learning technique, to perform work in a 3-D topographic area. A spatial substate is defined as a hexagonal area to ensure disjoined coverage of a UAV, while the temporal substate models the evolution of discrete sensing information based on elapsed time from previous data acquisition. We aim to find the best trajectory for UAVs that maximizes the accumulated value of sensing data while minimizing energy consumption. Hence, we design a multiobjective reward function. Comprehensive experiments demonstrate that the proposed STSQL method outperforms other methods in terms of convergence time and the amount of acquired sensing information.

The rapid growth of the Internet of Vehicles (IoV) has generated significant interest in routing techniques for vehicular ad hoc networks (VANETs) in both academic and industrial communities. To address the complexity of urban environments and dynamic vehicle mobility, we propose a hierarchical Q-learning-based routing algorithm with grouped roadside unit (RSU) for VANETs. RSUs are grouped, and a Q-vector containing group information is exchanged through vehicle-to-everything (V2X) communications. Q-vector-based road-segment (QVRS) control messages are periodically broadcasted to refresh the V2X evaluation metric, which considers vehicle positions, velocities, directions, and communication conditions. To adapt to the nonstationary vehicular environment, a multi-agent reinforcement learning (RL) algorithm is performed on RSUs at each intersection to achieve distributed learning and local decisions. The hierarchical Q-learning algorithm trains group Q-table and local Q-table individually for reaching destinations on each RSU. The optimal data routing behavior is conducted with two separate Q-tables by utilizing the integrated V2X metric as the reward function. Simulation results demonstrate that our proposed method reduces broadcasting overhead, prolongs path lifetime and maintains a high packet delivery ratio and low average end-to-end delay. The incorporation of group design in our method accelerates the learning process, which facilitates more efficient communication in VANETs.

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this article, we propose a novel asynchronous UAV path planning mechanism for multiobjective UAV operation. In a 3-D sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid cells within the coverage area. We define a multipurpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experimental results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.

The conventional transmitted reference (TR) technique in ultra wide-band (UWB) communications has provided a springboard for a lot of studies, and a TR pulse cluster (TRPC) structure was proved to have robust performance. Most of TR and TRPC related studies hitherto are based on the assumption that the delay lines (DLs) in their schemes are ideal. However, the group delay ripple (GDR) of nonideal DLs triggered by practical imperfect factors at implementation causes an unexpected distortion in both TR and TRPC UWB systems, and there are few studies so far. In this article, we first analyze the impact of the GDR of nonideal DLs on the performance of TR and TRPC UWB systems. Then a new design solution for both TR and TRPC transceivers is proposed, where the GDR information of the nonideal DL at the receiver side is introduced into transmitter side. The performance of the new design solution is analyzed and simulation results prove the robustness and efficiency of this new design method in reducing the impaction caused by nonideal DLs. Moreover, the proposed design solution can also greatly relieve the high design requirement of DLs in both the transmitter and receiver side and therefore offer valuable insight for the practical transceiver design in both TR and TRPC related UWB systems.

In order to evacuate people safely and quickly in indoor disaster environments, it is necessary to estimate the current location of the individuals, detect disaster situations, predict disaster propagation, derive optimal individual escape paths, and implement a user-friendly and intuitive guidance system. In this study, we propose a machine-learning-based indoor augmented reality (AR) navigation and emergency evacuation system that can guide an optimal escape path for individual users. To detect emergency events and deliver sensing data, an Internet of Things (IoT)-enabled ad hoc network is considered. To deliver the sensing data safely and reliably to the server, we present a hybrid reinforcement-learning-based routing algorithm that combines direct and indirect$Q$-learning methods. Prediction of disaster propagation at multiple time scales is important to prevent dangerous situations. We propose a simple disaster area prediction method that ensembles elementary component gradient boosting machine models. User location is estimated by a deep neural network using the received signal strength from beacon nodes. To derive the optimum evacuation path for each individual, we propose a novel model-based$Q$-learning method, in which we consider the building structural model and disaster context information. The performance of the proposed system is experimentally evaluated for various disaster scenarios.

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called$\alpha $-sum and$\alpha $-product of maximum and minimum eigenvalues ($\alpha $-SMME,$\alpha $-PMME) are further designed, in which$\alpha $is a weight coefficient. Furthermore, the theoretical performance analysis of the$\alpha $-SMME and$\alpha $-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

In this paper we propose a novel out-of-band spectrum sensing and dynamic channel management scheme for frequency hopping-based cognitive radio ad-hoc networks. At the beginning of each channel hopping time, member nodes perform spectrum sensing of the next hopping channel. Based on the proposed collision free primary detection notification, the member nodes determine whether they execute a hopping time extension of the current channel or not. When the primary detected hopping channel is re-idled, the hopping pattern recovery procedure is performed. In this paper we evaluate the performance of the proposed dynamic sensing and hopping channel extension mechanism for the various wireless network conditions. As a result, the proposed method can increase channel utilization and provide reliable channel management operation.

Abstract In wireless networks, when a mobile roaming station decides to initiate a handover, it should scan multiple channels operated by neighboring base stations (BSs) (or access points (APs)) in order to find an appropriate target base station before the actual handover. In some wireless networks, the active base station is able to provide a list of channels operated by neighboring base stations. However, some of these candidate channels may not be accessible to the mobile station (MS); nonetheless, the MS scans the candidate channels consecutively. For this reason, it may take a relatively long time for the MS to select an adequate target base station channel. This process can degrade the quality of service (QoS) during handovers. To shorten the scanning latency efficiently, in this paper we propose a cooperative channel scanning method whereby groups of MSs scan candidate channels using a dispersive schedule. They then share the scanning results amongst themselves, which results in a fast handover channel decision. To apply the proposed method to a real network environment, we present a group scanning architecture and detailed application scenarios appropriate for IEEE 802.16e worldwide interoperability for microwave access (WiMAX) networks. Numerical analyses and simulation results show that our proposed method achieves a shorter target channel scanning latency. Our method is thus more efficient in terms of scanning time and channel selection accuracy. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT Referring to most media access control (MAC) protocols in low rate personal area networks (LR‐WPANs), there is no hidden node collision avoidance mechanism utilized. Quantitative analysis in this paper based on the IEEE 802.15.4 specification shows a high probability of the continuous hidden node collisions (CHNCs), which seriously decreases network throughput. Based on this observation, we propose a cost‐efficient recovery mechanism to achieve fast self‐healing when LR‐WPANs suffer CHNCs, while introducing no overhead when there are no collisions. Simulation results demonstrate the efficiency of our proposed protocol in terms of network throughput and power saving. Copyright © 2011 John Wiley & Sons, Ltd.

In cognitive radio networks, secondary users conduct local sensing to use underutilized spectrum bands. However, every secondary user's local sensing usually gives rise to much sensing overhead. Moreover, a globally synchronized quiet period cannot be determined in a decentralized network. In this paper, we propose a zone-based distributed sensing scheme, in which multiple secondary users alternate in local sensing and share the results with others. To do that, we define a sensing zone in which one's sensing results are sharable with others. Moreover, a secondary user can determine its quiet period schedule efficiently while performing the proposed sensing. Numerical analyses and computer simulations show that our proposed sensing achieves the significantly reduced sensing overhead as well as the improved channel access opportunity.

Recently, the cognitive radio network with the underlay transmission mode which can enhance the spectrum reutilization is gathering more and more attentions. With the development of research on cognitive radio (CR) underlay mode, transmission power control the determination of the interference range of the CR communication pair is becoming an essential research issue. In this paper, we propose an adaptive neighbor coordinated interference avoidance power control scheme with collision free reporting protocol which can reduce the non-determinacy of the interference estimation in cognitive radio ad hoc networks. It provides the detection and protection for incumbent systems around the CR communication pair. Simulation results show that the proposed power control scheme can greatly reduce interference to the neighbor incumbent devices. A higher number of neighbor nodes leads to better protection of incumbent devices.

Abstract Supporting seamless handovers between different wireless networks is a challenging issue. One of the most important aspects of a seamless handover is finding a target network and point of attachment (PoA). This is achieved by performing a so‐called channel scanning. In most handovers, such as between universal mobile telecommunications system (UMTS), wireless local area network (WLAN), and worldwide interoperability for microwave access (WiMAX), channel scanning causes severe service disruptions with the current PoA and degrades the quality of service (QoS) during the handover. In this paper, a new architecture for QoS supported scanning that can be generalized to different wireless networks is proposed. It employs two techniques. The first is for determining a policy‐based order for the channel scanning sequence. With this technique, depending on the network costs and user requirements, the policy engine determines the channel scanning order for different network types and sets up a scanning sequence of PoAs for a given network type. This policy‐based scanning order provides a faster discovery of the target PoA that meets the QoS demands of the user. The second technique consists of a QoS supported dynamic scanning algorithm where the scanning frequency and duration are determined based on the user QOS requirements. Most importantly, the scanning duration is scheduled to guarantee the user QoS requirements while the scan progresses. Simulation results show that the proposed mechanism achieves relatively short service disruptions and provides the desired quality to users during the scanning period. Copyright © 2009 John Wiley & Sons, Ltd.

The exponential growth in wireless services has resulted in an overly crowded spectrum. The current state of spectrum allocation indicates that almost all usable frequencies have already been occupied. This makes one pessimistic about the feasibility of integrating emerging wireless services such as large-scale sensor networks into the existing communication infrastructure. Cognitive radio is the emerging dynamic spectrum access technology to achieve open spectrum sharing flexibly and efficiently. It is an intelligent wireless communication system that is aware of its radio environment and is capable of adapting its operation to statistical variations of the radio frequency. Ad-hoc networks in terms of cognitive radio rely on a common control channel (CCC) for operation. Control signals are used to enable cooperation communicate through a common control channel. However, common control channel may not be always available in an open spectrum allocation scheme due to interference and coexistence with primary systems (PS) of the spectrum. In this paper, we propose a novel common control channel selection protocol (DCP-CCC) in a distributed way based on appearance patterns of PS and connectivity among nodes. Using simulation results, we evaluate the performance of the proposed CCC selection scheme.

Abstract Effective and timely link‐layer trigger mechanisms can significantly influence the handover performance. The handover process will not perform the correct decision and execution unless adequate and timely link‐layer trigger information is delivered. In this paper, a predictive link trigger mechanism for seamless horizontal and vertical handovers in heterogeneous wireless networks is proposed. Unlike previous link trigger algorithms based on pre‐defined signal level thresholds, the link layer triggers in this study are adaptively and timely fired in accordance with the network conditions. Firstly, the time required to perform a handover is estimated based on the neighboring network conditions. Secondly, the time to trigger a Link_Going_Down to initiate a handover is determined using a least mean square linear prediction in which the prediction interval (kh) is dynamically determined based on the estimated handover time. An upper bound for the packet loss rate during a handover is derived for a Gaussian shadowing channel. A manner in which this approach can be applied to IEEE 802.21 is shown in media independent handover scenarios. Simulation results of the proposed predictive link triggering mechanism show that it provides a timely proactive handover. The packet loss rate observed in a Gaussian shadowing channel remains low during a handover. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract The MAC protocol for a cognitive radio network should allow access to unused spectrum holes without (or with minimal) interference to incumbent system devices. To achieve this main goal, in this paper a distributed cognitive radio MAC (DCR‐MAC) protocol is proposed for wirelessad hocnetworks that provides for the detection and protection of incumbent systems around the communication pair. DCR‐MAC operates over a separate common control channel and multiple data channels; hence, it is able to deal with dynamics of resource availability effectively in cognitive networks. A new type of hidden node problem is introduced that focuses on possible signal collisions between incumbent devices and cognitive radioad hocdevices. To this end, a simple and efficient sensing information exchange mechanism between neighbor nodes with little overhead is proposed. In DCR‐MAC, eachad hocnode maintains a channel status table with explicit and implicit channel sensing methods. Before a data transmission, to select an optimal data channel, a reactive neighbor information exchange is carried out. Simulation results show that the proposed distributed cognitive radio MAC protocol can greatly reduce interference to the neighbor incumbent devices. A higher number of neighbor nodes leads to better protection of incumbent devices. Copyright © 2008 John Wiley & Sons, Ltd.

Channel scanning is an important aspect of seamless handovers since it is required in order to find a target point of attachment (PoA). In the IEEE 802.11 WLAN, scanning of other channels causes service disruptions with the current AP so that the provided quality of service (QoS) will be degraded seriously during the handoff. In this paper, we propose a QoS supported dynamic channel scanning algorithm. The scanning period is scheduled to guarantee the user’s QoS requirements while the scan progresses. The simulation results show that the proposed mechanism reduces service disruptions and provides the desired quality of service to users during the scanning period.

In this paper, we propose a wireless differentiated service packet scheduling (WDSPS) algorithm that can provide reliable and fair services in the differentiated wireless Internet service networks. The proposed scheduling algorithm solves the HOL (head of line) blocking problems of class queue occurred in wireless network, supports the differentiated service for each class that is defined in differentiated service networks, and makes possible the gradual and efficient service compensation not only among classes but also among flows to prevent the monopoly of one class or one flow. Through simulations, we show that our proposed WDSPS scheduling algorithm can provide the required QoS differentiation between classes and enhance the service throughput in various wireless network conditions.