Search Results (226)

Multi-hop routing over low-power wide-area networks (LPWANs) has emerged as a promising technology for extending network coverage. However, existing protocols face high transmission disruption risks due to factors such as dynamic topology driven by stochastic events, dynamic link quality, and coverage holes induced by imbalanced energy consumption. To address this issue, we propose a failure risk-aware deep Q-network-based multi-hop routing (FRDR) protocol, aiming to reduce transmission disruption probability. First, we design a power regulation mechanism (PRM) that works in conjunction with pre-selection rules to optimize end-device node (EN) activations and candidate relay selection. Second, we introduce the concept of routing failure risk value (RFRV) to quantify the potential failure risk posed by each candidate next-hop EN, which correlates with its neighborhood state characteristics (i.e., the number of neighbors, the residual energy level, and link quality). Third, a deep Q-network (DQN)-based routing decision mechanism is proposed, where a multi-objective reward function incorporating RFRV, residual energy, distance to the gateway, and transmission hops is utilized to determine the optimal next-hop. Simulation results demonstrate that FRDR outperforms existing protocols in terms of packet delivery rate and network lifetime while maintaining comparable transmission delay. Full article

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

Recently, electromagnetic wireless power transfer (WPT) has emerged as a promising technology for supplying power to multiple terminals. Previous studies have devised packet transmission methods, commonly used in telecommunication, for power analysis. This study develops a simulator that calculates the received power by integrating a power–packet conversion method, based on previous research. The simulator incorporates several scheduling functions to facilitate the investigation of the efficiency of the power-feeding methods. This study analyzes the efficacy of a first-come–first-served (FCFS) method, a round-robin (RR) method, and a multilevel feedback queue (MFQ) scheme for wireless power transfer, all of which were devised based on existing scheduling methods used in operating systems. Simulation results show that, although the FCFS method is simple, it may lead to battery depletion due to delayed power supply, particularly in terminals with lower initial battery levels. The RR method improves fairness by allocating the power supply in time slices; however, its performance is sensitive to the slice duration. The MFQ method, which incorporates a promotion mechanism based on battery status and power demand, exhibits higher adaptability, achieving efficient and balanced power distribution even when terminals differ in distance from the transmitter or in power consumption. These evaluations were conducted using a proposed power–packet conversion method that discretizes continuous power into packet units, allowing for the application of communication network-inspired scheduling and control techniques. The capacity to construct such models enables the simulator to analyze the flow and distribution of power, predict potential issues that may arise in real systems in advance, and devise optimal control methodologies. Moreover, the model can be employed to enhance the efficiency of power management systems and construct smart grids, and it is anticipated to be utilized for the integration of power and communication systems. Full article

A two-stage fusion filtering positioning algorithm based on prediction residuals and gain adaptation is proposed to address the problems of disturbance and modeling errors in the application of distance-based positioning algorithms in wireless sensor networks, as well as inaccurate initial filtering values leading to large estimation errors or even divergence. Firstly, based on parameterization methods, a pseudo linear equation is constructed from the time of arrival (TOA) and multipath delay. The weighted least squares (WLS) method is applied to obtain the initial value of target position resolution, and its performance approaches the Cramér–Rao lower bound (CRLB). Secondly, the exact position of the target is obtained using the reconstructed Gaussian white noise statistics and the Kalman filtering algorithm. The simulation results show that compared with other initial positioning algorithms, the average positioning accuracy of the proposed algorithm is improved by 28.57%, and it has a better filtering performance. Full article

High battery swapping demand from electric taxis and drivers’ subjective station selection often leads to congestion and the uneven utilization of battery swapping stations (BSSs). Efficient vehicle guidance is essential for improving the operational performance of electric taxis. In this study, we have developed a vehicle-to-station guidance model that considers dynamic demand and diverse driver response-time preferences. We have proposed two decision-making strategies for BSS recommendations. The first is a real-time optimization method that uses a greedy algorithm to provide immediate guidance. The second is a delayed optimization framework that performs batch scheduling under high demand. It integrates a genetic algorithm with KD-tree search to handle dynamic demand insertion. A case study based on Beijing’s Fourth Ring Road network was conducted to evaluate the strategies under four driver preference scenarios. The results show clear differences in vehicle waiting times. A balanced consideration of travel distance, waiting time, and cost can effectively reduce delays for drivers and improve station utilization. This research provides a practical optimization approach for real-time vehicle guidance in battery swapping systems. Full article

The accurate detection and identification of collision states in industrial robot environments is a critically important and challenging task. Deep learning-based methods have been widely applied to collision detection; however, these methods primarily rely on dynamic models and dynamic threshold settings, which are subject to modeling errors and threshold adjustment latency. To address this issue, we propose MomentumNet-CD, a novel collision detection method for industrial robots that leverages backpropagation (BP) neural networks. MomentumNet-CD extracts collision state features through a momentum observer and constructs an observation model using Mahalanobis distance. These features are then processed by an optimized three-layer BP neural network for accurate collision identification. The network is trained using a modified Levenberg–Marquardt algorithm by introducing regularization terms and continuous probability outputs. Furthermore, we developed a comprehensive acquisition system based on the Q8-USB data acquisition card and the QUARC 2.7 real-time control environment. The system integrates key hardware components including a MR-J2S-70A servo driver, ATI six-dimensional force/torque (F/T) sensor, and ISO-U2-P1-F8 isolation transmitter, and the corresponding software module is developed through MATLAB/Simulink R2022b, which achieves the high-frequency real-time acquisition of critical robot joint states. The experimental results show that the MomentumNet-CD method achieves an overall accuracy of 93.65% under five different speed conditions, and the detection delay is only 12.16 ms. Compared with the existing methods, the method shows obvious advantages in terms of the accuracy and response speed of collision detection. Full article

Communications in delay-tolerant networking (DTN) environments like deep space face significant challenges due to immense distances and the intermittent nature of links. Overcoming these issues requires moving beyond the assumptions of immediacy and reliability that underpin traditional terrestrial Internet Protocol (IP) networks. Historically, deep-space networks have relied on custom architectures or protocols like the Bundle Protocol (BP) to address these challenges; however, such solutions impose the constraint that nodes must implement the chosen protocol for proper operation, thereby not providing interoperability with standard IP-based nodes. This paper proposes an alternative approach, leveraging innovations in IP version 6 (IPv6) and Internet Control Message Protocol version 6 (ICMPv6) to integrate delay-tolerant features directly at Layer 3. By embedding these functionalities within the existing IPv6 framework, the proposed IP-compliant solution enhances interoperability, with terrestrial networks enabling DTN nodes to seamlessly communicate with compliant IPv6 nodes. This study provides a detailed comparison of the capabilities of IPv6 and BP version 7, highlighting gaps and opportunities. Based on this analysis, a node architecture is designed to implement the necessary functionalities for DTN, paving the way for more seamless integration of deep-space and terrestrial networks while reducing complexity and improving scalability. Full article

In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent years. Although multi-system GNSS fusion positioning can alleviate the problem of insufficient satellite visibility, the existing methods are difficult to effectively cope with the challenges of multi-source noise coupling and inter-system error differences unique to high orbit. In this paper, we propose an adaptive GNSS positioning optimization framework for a high-orbit environment, which improves the orbiting reliability under complex signal conditions through dynamic weight allocation and a multi-system cooperative strategy. Different from the traditional weighting model, this method innovatively constructs a two-layer optimization mechanism: (1) Based on BP neural network, it evaluates the noise characteristics of pseudo-range observations in real time and realizes the adaptive suppression of receiver thermal noise, ionospheric delay, etc.; (2) it introduces Helmert variance component estimation to optimize the weighting ratio of GPS, GLONASS, BeiDou, and Galileo and reduces the impact of signal heterogeneity on the positioning solution of the multi-system. Simulation results show that the new method reduces the root-mean-square error of positioning by 32.8% compared with the traditional algorithm to 97.72 m in typical high-orbit scenarios and significantly improves the accuracy loss caused by the defective satellite geometrical configurations under multi-system synergy. Full article

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments. Full article

This research proposes a hybrid path planning framework for intelligent inland waterway Unmanned Surface Vehicles (USVs), which integrates the enhanced BIT* (Batch Informed Trees) algorithm with the TD3 (Twin Delayed Deep Deterministic Policy Gradient) deep reinforcement learning method. To address the limitations of traditional path planning algorithms in dynamic environments, the proposed BIT*+TD3 model leverages the BIT* algorithm to generate initial paths in static environments through elliptical informed sampling and heuristic search. Simultaneously, it utilizes the TD3 algorithm to dynamically optimize these paths through twin Critic networks and delayed policy updates. This research designs a novel reward mechanism aimed at minimizing turning angles, smoothing speed transitions, and shortening path lengths. Furthermore, it incorporates a hydrodynamics-based energy consumption model and multi-threaded parallel computation to enhance computational efficiency. Experimental validation demonstrates that, compared to traditional methods, this model exhibits significant improvements in obstacle avoidance success rate, safe distance maintenance, convergence speed, and smoothness. By bridging sampling-based planning methods with deep reinforcement learning methods, this research advances autonomous navigation technology and provides a scalable and energy-efficient solution for maritime applications. Full article

Low Earth Orbit (LEO) satellite networks are promising for satellite-based cloud platforms. Due to frequent link switching and long transmission distances in LEO satellite networks, applying the TCP/IP architecture introduces challenges such as packet loss and significant transmission delays. These issues can trigger excessive retransmissions, leading to link congestion and increased data acquisition delay. Deploying Named Data Networking (NDN) with connectionless communication and link-switching tolerance can help address these problems. However, the existing congestion control methods in NDN lack support for congestion avoidance, lossless forwarding, and tiered traffic scheduling, which are crucial for achieving low-delay operations in satellite-based cloud platforms. In this paper, we propose a Congestion Control method with Lossless Forwarding (CCLF). Addressing the time-varying nature of satellite networks, CCLF implements zero packet loss forwarding by monitoring output queues, aggregating packets, and prioritizing packet scheduling. This approach overcomes traditional end-to-end bottleneck bandwidth limitations, enhances network throughput, and achieves low-delay forwarding for critical Data packets. Compared with the Practical Congestion Control Scheme (PCON), the CCLF method achieves lossless forwarding at the network layer, reduces the average flow completion time by up to 41%, and increases bandwidth utilization by up to 57%. Full article

Autonomous intersection management (AIM) is gaining increasing attention due to its crucial role in ensuring safety and efficiency. Various methods have been proposed in the literature to address the AIM problem, including traffic light optimization, connected vehicle optimization based on vehicle-to-anything (V2X) communication technology, and multi-agent autonomous systems. However, each of these approaches has its own limitations, such as parking delays, communication latency, or the lack of guaranteed collision avoidance. This paper presents a novel approach to AIM using adaptive control barrier functions (aCBFs). The proposed aCBF first estimates the power transmission efficiency and incorporates it into the CBF design to ensure collision-free operation. Compared to existing methods, the aCBF approach offers several advantages. Firstly, it eliminates parking delays caused by traffic light systems. Secondly, it can be deployed in intersections with limited network coverage, unlike IoT-based solutions that rely heavily on connectivity. Thirdly, it ensures guaranteed collision-free agent movement at intersections. Specifically, our method guarantees that the minimum distance between agents remains no less than the safe distance at all times, significantly enhancing safety. Furthermore, compared to the TriPField algorithm, our approach achieves a 95% success rate in collision avoidance, demonstrating reliability in autonomous intersection management. The effectiveness of the proposed aCBF-based AIM algorithm has been validated through simulations and experiments with multiple autonomous agent-like robots. Full article

Wireless rechargeable sensor networks (WRSNs) address the energy scarcity problem in wireless sensor networks by introducing mobile chargers (MCs) to recharge energy-hungry sensor nodes. Scheduling MCs to charge the recharge nodes is the primary focus of the energy replenishment scheme in WRSNs. The performance of the energy replenishment scheme is significantly influenced by the energy level of each node, which is depends on the data collection scheme employed by the network. Consequently, integrating energy replenishment and data collection has become a new concern in WRSN research. However, the MCs’ workload and travel time increase when data collection and energy replenishment are performed simultaneously, leading to an increase in both the node’s charging delay and data collection delay. In this work, our goal is to reduce the delays in data collection and node charging by proposing a new joint energy replenishment and data collection approach. In the proposed approach, certain nodes in the network are selected as data storage nodes to temporarily store all the collected data based on their geographical locations. A special class of MCs, called MCDs (mobile charger and data collectors), is then assigned the responsibility of charging these data storage nodes and collecting the data stored. Afterwards, the task of recharging the remaining network nodes falls to another type of MC. By combining the capabilities of two distinct MC types, the workload and the travel distance of MCs are reduced. When compared to the conventional joint algorithms, the simulation results demonstrate that the proposed approach successfully decreases the delay it takes to gather data and recharge nodes. Full article

Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the ‘$C_1$’ and ‘$C_2$’ RT packets, and the average throughput of RT is 10.4% better when compared with NRT. Full article

Optoelectronic oscillators (OEOs) have emerged as indispensable tools for generating low-phase-noise microwave and millimeter-wave signals, which are critical for a variety of high-performance applications. These include radar systems, satellite links, electronic warfare, and advanced instrumentation. The ability of OEOs to produce signals with exceptionally low phase noise makes them ideal for scenarios demanding high signal purity and stability. In radar systems, low-phase-noise signals enhance target detection accuracy and resolution, while, in communication networks, such signals enable higher data throughput and improved signal integrity over extended distances. Furthermore, OEOs play a pivotal role in precision instrumentation, where even minor noise can compromise the performance of sensitive equipment. This review examines the progress in OEO technology, transitioning from classical designs relying on long optical fiber delay lines to modern integrated systems that leverage photonic integration for compact, efficient, and tunable solutions. Key advancements, including classical setups, hybrid designs, and integrated configurations, are discussed, with a focus on their performance improvements in phase noise, side-mode suppression ratio (SMSR), and frequency tunability. A 20-GHz oscillation with an SMSR as high as 70 dB has been achieved using a classical dual-loop configuration. A 9.867-GHz frequency with a phase noise of −142.5 dBc/Hz @ 10 kHz offset has also been generated in a parity–time-symmetric OEO. Additionally, integrated OEOs based on silicon photonic microring resonators have achieved an ultra-wideband tunable frequency from 3 GHz to 42.5 GHz, with phase noise as low as −93 dBc/Hz at a 10 kHz offset. The challenges in achieving fully integrated OEOs, particularly concerning the stability and phase noise at higher frequencies, are also explored. This paper provides a comprehensive overview of the state of the art in OEO technology, highlighting future directions and potential applications. Full article