Search Results (80)

Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heterogeneous Team Orienteering Problem (LSH-TOP) within operational time limits due to the coupled complexity of task allocation and route planning. A Hierarchical Deep Reinforcement Learning framework decomposes this high-dimensional combinatorial problem into tractable sub-problems. An upper-level policy, guided by Monte Carlo Tree Search (MCTS), partitions the global target set to balance fleet workload distribution, whereas a lower-level Transformer-based model constructs near-optimal trajectories for individual agents. A Curriculum-Integrated Alternating Cooperative Training (C-ACT) protocol resolves the convergence difficulties associated with sparse feasible solutions in constrained environments. This protocol incorporates a dynamic constraint annealing strategy and a virtual agent buffer to progressively shape the solution space from relaxed to strictly constrained formulations. Experiments conducted on real-world geographic data demonstrate the proposed approach consistently outperforms all baselines across scales of 80 to 300 targets, improving over the strongest competitor by 0.63–8.51% and over conventional heuristics by up to 53.27% in objective value. Results indicate a task completion rate of 27.5% at the 300-target scale (versus 25.1% for the strongest baseline MCTS + OR) and balanced workload distribution, validating framework adaptability to complex emergency response scenarios. Full article

Recyclers worldwide face a common bottleneck: incoming mixed plastic bales are chemically opaque, yet the choice between mechanical recycling, chemical recycling, and energy recovery hinges on contaminant levels that cannot be judged by visual inspection alone. This study develops and validates a tiered analytical decision-support framework that translates standard laboratory measurements into explicit, actionable go/no-go routing criteria for any mixed polyolefin waste stream. The framework is organized into three successive analytical tiers of increasing specificity: Tier 1 uses FTIR and DSC for rapid polymer identification and thermal subclass confirmation; Tier 2 applies TGA/DTG for thermal stability assessment and filler quantification; and Tier 3 deploys ICP-OES, WD-XRF, CIC, and TG–MS for targeted heavy metal, halogen, and evolved gas profiling, triggered only when Tier 1/2 flags are raised. This staged logic minimizes unnecessary testing while ensuring that contaminant-relevant information is captured where it matters. The framework is demonstrated on nine blind mixed plastic waste streams (P1–P9) supplied by an industrial recycling facility without prior disclosure of polymer identity, filler content, or additive history—conditions that replicate the uncertainty encountered at any sorting plant globally. Application of the tiered protocol identified dominant polymers (HDPE, LDPE, PP), quantified inorganic fillers (CaCO₃ up to ~38 wt%), and detected hazardous contaminants, including chlorine (up to ~1900 ppm), lead, chromium, and titanium, enabling each stream to be assigned to a specific recycling route with defined contaminant thresholds. Because the method relies exclusively on commercially available, vendor-independent instrumentation and follows a reproducible, rule-based decision logic, it is directly transferable to recycling facilities in any geographic context without site-specific calibration. The proposed framework thus provides a practical, scalable decision-support tool for feedstock-level quality control under emerging regulations such as the UNEP Global Plastics Treaty. Full article

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O), suffer from steady-state oscillations and slow convergence under rapidly varying environmental conditions, leading to suboptimal energy extraction and unnecessary switching activity. To address these limitations, we propose a predictive control strategy in which the DC–DC converter control signal is adaptively updated only when significant deviations are detected between measured and model-predicted voltage and current values. The approach leverages power-of-two quantized Artificial Neural Networks ($2^{Q}ANNs$), enabling highly accurate inference with extreme weight quantization (2–3 bits) while remaining suitable for MPPT. A dataset-driven evaluation using year-long climatic records from geographically distinct locations indicates annual energy yields of up to 99.90% of the ideal maximum under the adopted modeling assumptions. Under the adopted fixed-condition evaluation protocol, compared with conventional P&O implementations, the proposed method requires 20–40× fewer internal control updates to approach the same efficiency region. Additionally, a robustness experiment with perturbed voltage and current measurements further shows that the recurrent $2^{Q}ANN$ controllers remain above 98% aggregated efficiency even under the strongest tested sensing-noise condition, without retraining. Finally, post-place-and-route FPGA implementation estimates on a highly resource-constrained device indicate that the resulting architecture supports low-resource edge-oriented implementation. Full article

Seizure clusters (SCs) are an acute and transient increase in seizure frequency relative to an individual patient’s baseline and are associated with an increased risk of injury, morbidity, and potentially mortality if not promptly and adequately treated. Despite their clinical importance, the management of SCs remains highly heterogeneous, primarily due to the absence of a universally accepted definition, which is determined also by the wide variability in seizure semiology and baseline individual burden;, as well as by differences in care settings. Outpatient treatment relies largely on caregivers’ ability to recognize SCs and administer rescue medication, whereas inpatient management may also involve invasive routes of administration. We conducted a literature review identifying 32 original articles addressing the treatment of SCs. The analysis focused on definitions, efficacy outcomes, and adverse events across three clinical scenarios: outpatient, Emergency Department (EDs) and Epilepsy Monitoring Units. The results show that in the outpatient setting, the available evidence suggests that diazepam nasal spray (DZP-NS), midazolam nasal spray (MDZ-NS), and oral lorazepam (LZP) solution may demonstrate comparable efficacy and safety. However, comparisons are limited by heterogeneity in studies’ designs, patient populations and outcome definitions, as well as by the absence of head-to-head trials. Moreover, geographic differences in drug availability (e.g., USA vs. Europe) limit the development of universally applicable treatment protocols. Consequently, the off-label use of oral benzodiazepines, including clobazam, clonazepam, and lorazepam, remains common when oral therapy is feasible, despite limited evidence. The implementation of a patient-specific Acute Seizure Action Plan (ASAP) incorporating an individualized SC definition is recommended. In contrast, inpatient management shows greater consensus, largely reflecting first-line treatment paradigms for status epilepticus. These include prompt intravenous benzodiazepine administration, followed by the intravenous loading of antiseizure medications such as brivaracetam or lacosamide in cases of seizure recurrence. In ED settings, “empirical” definitions of SCs (i.e., more than three seizures within 24 h) may facilitate timely intervention. Full article

Achieving autonomous and reliable unmanned aerial vehicle (UAV) swarm applications requires a flexible and efficient communication network structure. Unfortunately, the high-speed movement of UAVs leads to drastic changes in wireless links and topology structures, posing significant challenges to reliable data transmissions. Geographic routing protocols exhibit better adaptability to highly dynamic network topologies and have garnered extensive attention in UAV networks. However, existing works did not effectively address the impact of factors such as link state fluctuations and routing holes on the performance of these protocols. To this end, by considering future evolution of link states, this paper proposes a reinforcement-learning-based geographic routing protocol (Evo-QGeo) and introduces a new routing hole bypass method. Thanks to the evaluation of future evolution of link states and the multihop optimization capability of reinforcement learning, the end-to-end packet reception rate of Evo-QGeo is improved by up to 11.81~44.61% compared to existing ones. Meanwhile, the energy consumption is reduced by up to 36.94~74.47%, the latency is reduced by up to 21.63~38.68%, and the end-to-end expected transmission count is reduced by up to19.60~26.10%. This makes Evo-QGeo more suitable for highly dynamic UAV networks. Full article

Against the dual backdrop of rapid population aging and legacy neighborhood renewal, morphologically planning-locked legacy neighborhoods in high-density cities face persistent imbalances in outdoor activity spaces that undermine aging-in-place participation and health equity. This study advances a Spatial Pathology framework. Using nine representative communities in Longtan Subdistrict, Dongcheng District, Beijing, we develop a GIS-assisted spatial audit, a systematic behavioral observation protocol with temporal-intensity metrics, and a validated perception instrument. These tools form a closed evidentiary loop with explicit indicator definitions, formulas, and decision thresholds, alongside a reproducible analytic and visualization pipeline. Tri-dimensional baselines revealed substantial inter-community disparities: Spatial Quality Index (SQI) ranged from 43.3 to 77.0; activity intensity varied from 1.5 to 15.7 persons/100 m²·hour; and overall satisfaction scores spanned 3.88–4.49. It quantifies and identifies three core paradoxes in outdoor activity spaces within this context: (1) the Functional Failure Paradox with FFI exceeding +0.5 and ELR surpassing 60% in dormant communities; (2) the Value Misalignment Paradox where Facilities & Equipment showed the strongest satisfaction impact (β = 0.344) yet the largest unmet-need gap (VQGI > +8); (3) the Practice–Perception Decoupling Paradox evidenced by a negative correlation (r = −0.38) between usage intensity and satisfaction. These paradoxes reveal the spatial roots of planning-locked legacy neighborhoods—compound mechanisms of planning inertia, decision–demand information gaps, and elderly adaptability masking environmental deficits. We translate the diagnosis into typology-specific prescriptions—reactivating dormant spaces via “route–node–plane” continuity and proximal micro-spaces; decongesting peak periods through elastic zoning and equipment redistribution; and precision calibration of facilities and walking loops—implemented through co-creation and light-touch stewardship. This provides evidence-based, precision-targeted intervention pathways for micro-renewal of aging neighborhoods, supporting localized implementation of UN Sustainable Development Goals (SDG 11 Sustainable Cities; SDG 10 Reduced Inequalities). This methodological framework is transferable to other high-density aging cities, offering theoretical scaffolding and empirical reference for multi-source geographic data-driven urban spatial analysis and equity-oriented age-friendly retrofitting. Full article

Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol designed to handle the challenges of high mobility, dynamic topology, and variable vehicle density in urban environments. The proposed protocol integrates context-aware routing, dynamic clustering, and geographic forwarding to enhance performance under diverse traffic conditions. Simulation results demonstrate that ACAVR achieves higher throughput, improved packet delivery ratio, lower end-to-end delay, and reduced routing overhead compared to existing routing schemes. The proposed ACAVR outperforms benchmark protocols such as DyTE, RGoV, and CAEL, improving PDR by 12–18%, reducing delay by 10–15%, and increasing throughput by 15–22%. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR). Full article

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm. Full article

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators. Full article

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network’s route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments. Full article

Non-geographic routing protocols are inefficient when applied to large-scale mobile networks composed of hundreds of nodes. On the other hand, geographic routing protocols have the disadvantage of needing a location sensor. The goal is to address the challenges of efficient content retrieval and routing scalability in NDN-based networks by leveraging the benefits of both NDN and Bloom Filter technologies. In this article we propose a routing protocol for mobile networks, which is scalable to networks composed of hundreds of nodes. The protocol does not require any localization equipment and is adapted for devices with limited memory and/or processing resources. This goal is achieved through the use of Bloom Filters to efficiently store and spread topological information. In the methodology followed, nodes do not forward messages with topological information to other nodes. To make the process efficient, each node aggregates the topological information it receives from its direct neighbors with its own and only the result of this operation is transmitted to the remaining nodes. Several simulations were carried out in the Qualnet network simulator in order to validate the algorithm proposed by the Hybrid Routing Algorithm with NDNs (HRAN). The obtained results were compared with other non-geographic protocols for mobile networks. HRAN seems to be a routing protocol designed for MANETs, utilizing Bloom Filters to manage topological information. A Bloom Filter is a data structure used to test whether an element is a member of a set. It uses a bit array and multiple hash functions to determine if an element is present in the set. This type of data structure allows storing a large amount of binary information in an efficient way, reducing the resources required by the routing protocol. Full article

Reducing transmission traffic delay is one of the most important issues that need to be considered for routing protocols, especially in the case of multimedia applications over vehicular ad hoc networks (VANET). To this end, we propose an extension of the FzGR (fuzzy geographical routing protocol), named MNH-FGR (multi-next-hops fuzzy geographical routing protocol). MNH-FGR is a multipath protocol that gains great extensibility by employing different link metrics and weight functions. To schedule multimedia traffic among multiple heterogeneous links, MNH-FGR integrates the weighted round-robin (WRR) scheduling algorithm, where the link weights, needed for scheduling, are computed using the multi-constrained QoS metric provided by the FzGR. The main goal is to ensure the stability of the network and the continuity of data flow during transmission. Simulation experiments with NS-2 are presented in order to validate our proposal. Additionally, we present a neural network algorithm to analyze and optimize the performance of routing protocols. The results show that MNH-FGR could satisfy critical multimedia applications with high on-time constraints. Also, the DNN model used can provide insights about which features had an impact on protocol performance. Full article