Search Results (260)

As RFID systems become increasingly widespread, the limitations imposed by tag collisions on system performance are becoming more evident. Parallel decoding has attracted significant attention due to its ability to improve channel utilization and throughput. However, existing schemes often perform poorly when decoding weak signals. Several challenges remain, including the assumption of ideal channel conditions, difficulty in detecting tag state transitions, and the complexity of state cluster formations in the In-phase and Quadrature (IQ) domain. To address the above issues, this paper first experimentally verifies the ability of middle state points to segment tag states, and proposes a time-window-based pre-processing method to improve the density of state clusters in the IQ domain. Second, by leveraging the high vertical resolution of the reader, we propose an ideal magnitude calculation method and a matching strategy for combined state clusters under weak signal conditions. Finally, we propose MagMap, a parallel decoding scheme based on middle state points and magnitude extraction. Experimental results demonstrate that, under weak signal conditions, MagMap reduces the decoding BER (Bit Error Ratio) of received packets by more than 60% compared to the state-of-the-art. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

The rapid proliferation of low-altitude unmanned aerial vehicle (UAV) applications has made autonomous identification technology critical for flight safety and collaborative operations. In this paper, we propose and systematically analyze an autonomous identification scheme based on Bluetooth Low Energy (BLE) technology. We formulate a comprehensive system model that integrates link budget, packet collision, identification success probability, and power consumption. By incorporating safety interval constraints and a three-channel integrated reception probability, we employ an exhaustive search algorithm to optimize monitoring strategy parameters, thereby achieving an optimal trade-off between the Recognition Success Rate (RSR) and power consumption. Simulation results indicate that, at a PHY 1 Mbps rate, the optimal monitoring strategy theoretically approaches the Target Level of Safety (TLS) requirements for civil UAVs under the defined model assumptions, with a power consumption of 19.24 mW and an Average First Identification Delay (AFID) of 105 ms. Furthermore, simulation analysis verifies the scheme’s feasibility under dynamic topology, interference, and multi-UAV scenarios, providing a solid theoretical and technical reference for the practical implementation of autonomous UAV identification. Full article

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem $\left(N_h\right)$ left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios. Full article

Vehicle ad hoc networks (VANETs) can help prevent traffic accidents through wireless communication; however, most studies are based on simulations or static evaluations. This research paper presents the design, implementation, and experimental evaluation of a prototype early-warning system for vehicle proximity based on VANETs using ESP-NOW. The prototype utilizes five ESP32-CAM nodes equipped with MaxSonar sensors installed in vehicles and an RSU unit with a Raspberry Pi for vehicle-to-infrastructure (V2I) communication. Field tests were conducted in Quito, Ecuador, at speeds ranging from 10 to 70 km/h, measuring latency, packet loss, and received signal strength (RSSI). The results show average latencies of 9.9 ms at 10 km/h and 114.5 ms at 70 km/h, with packet loss rates of 2% and 60%, respectively. Statistical analysis reveals 95% confidence intervals for latency ranging from ±0.98 ms to ±6.90 ms, while obstacles introduce marginal attenuation (p = 0.051) with significant dispersion (σ = 5.85 dB). The Doppler shift is negligible (155.6 Hz), but the channel coherence time (2.7 ms) explains the observed degradation. Models were obtained that relate speed to latency (R² = 0.994) and packet loss (R² = 0.991). The prototype is viable for early collision warning at urban speeds (up to 60 km/h), outperforming human reaction time (1.5 s). Full article

Dense Long Range (LoRa) networks suffer from packet loss when many end devices contend for the same unlicensed channel. Channel activity detection (CAD) can miss weak or cross-spreading-factor (cross-SF) transmissions, while a uniform carrier sense multiple access with collision avoidance (CSMA/CA) backoff rule ignores the different time-on-air (ToA) costs of SF7–SF12 packets. To address these two coupled problems, this paper proposes an interference-limit-aware CSMA protocol (ILA-CSMA). ILA-CSMA first combines CAD with an instantaneous received signal strength indicator (RSSI) test derived from the residual interference tolerance of the selected spreading factor, and then scales the contention window according to normalized ToA. The protocol is implemented in the Framework for LoRa (FLoRa), an OMNeT++-based LoRa network simulator, and is evaluated for networks with 100–2000 nodes. Compared with Pure ALOHA, Slotted ALOHA, standard CSMA/CA, and two ablation variants, ILA-CSMA improves dense-network access by jointly reducing hidden collisions and airtime imbalance. In the 2000-node case, it increases the packet delivery ratio (PDR) by about 20 percentage points relative to standard CSMA/CA, keeps the Jain fairness index (JFI) above the 0.85 reference line, reduces the energy consumed per successful packet to 22% of the standard CSMA/CA value, and reduces conditional average packet delay from 18.5 s to 8.2 s. These results show that interference-aware sensing and ToA-aware backoff can improve large-scale LoRa access under the evaluated simulation conditions. Full article

Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing network, the system can interact with the user by sending notifications when necessary, based on the environmental conditions and user activities detected by the wireless sensors, and make corresponding adjustments to or control the environment. The advancement and widespread adoption of the internet have enabled the development of this technology. Wireless sensors are widely used in product positioning and environmental monitoring management, making the management of complex products more accurate. The Monitor and Control System (MCS), which combines network cameras and wireless sensors with neural network technology and fuzzy control systems, improves the existing positioning method and enhances positioning accuracy. Product management, which comprises comprehensive digital services and is facing serious staff shortages, has turned to digital payment to reduce labor costs. This experiment was simulated using Network Simulator 2 (NS2). In the sensing system part, the application of a ZigBee network and its status were explored, and interference was analyzed. Information on network interference simulations and their impact on normal services was compiled for network management purposes. Using NS2 network simulation, this study utilizes ZigBee with different neuron nodes and different training times to find the best network model, compares various queuing mechanisms and functions as a network interference intrusion detection system, and explores its node defense capabilities in cases of interference. Node Density: Node density is typically determined by the number of nodes in the simulation area and the size of the scene. Low Density: Sparse node distribution, prone to network partitioning, is suitable for testing latency-tolerant networks (DTNs) or route discovery capabilities. High Density: It entails dense node distribution, severe signal interference, and packet collisions. It is suitable for testing MAC layer collision prevention mechanisms (such as CSMA/CA) and the scalability of outing protocols. Configuration Method: the “set Dest” tool is used in a Tcl script to generate a mobile scene file, defining the number of nodes, range (X, Y), and time to be more significant in product management. Full article

Differential ionization in ${\mathrm{C}}^{6+}+$He collisions is investigated using the single- and two-center wave-packet convergent close-coupling (WP-CCC) method for projectile energies of 1–6 MeV/u. We present three types of singly differential cross-sections (SDCSs) as functions of the ejection angle, ejection energy, and projectile scattering angle. The two-center framework incorporates couplings across all channels as well as electron correlations. Overall, both the single- and two-center WP-CCC results agree well with existing experimental and theoretical data (apart from the first Born ones) for the SDCS as a function of electron energy and the SDCS as a function of ejection angle, laying a foundation for investigation of doubly and fully differential ionization cross-sections. The cross-sections differential in the projectile scattering angle are presented for the first time. Full article

Low-Power Wide-Area Networks (LPWANs) like LoRaWAN enable IoT applications with low-power and long-range characteristics. While LoRaWAN class B mode is server-initiated downlink communication-oriented, its uplink communication, especially in mobile scenarios, remains underexplored. This paper proposes two novel systems, Edge-based SmartSync and Gateway-based SmartSync, aiming to enhance uplink by leveraging class B synchronization. Edge-based SmartSync enables end devices to dynamically adjust the Spreading Factor (SF) based on real-time Received Signal Strength Indicator (RSSI) from beacons, achieving a significant improvement in terms of packet delivery and energy consumption. Gateway-based SmartSync ensures the fair distribution of end devices across a lower SF to further enhance the efficiency of the system. The beacon is reengineered to convey sensitivity limits to end devices. The systems were implemented in the OMNeT++ simulator over a 25 km² area with 100–1000 mobile devices and evaluated against a baseline using metrics like the Packet Delivery Ratio, collisions, and energy consumption. The obtained results show that both systems are capable of improving the delivery ratio by over 40% and reducing collisions by 80% compared to the baseline, with energy savings exceeding 35%. Proposed systems offer cost-effective, adaptable solutions, paving the way for more reliable IoT deployments. Full article

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

Multi-hop networks’ performance strongly depends on relay node placement, which affects delay, throughput, and coverage. This work introduces a dual-layer protocol combining Slotted ALOHA for node-to-relay communication and TDMA for relay-to-gateway transmission. Using a Java-based simulator, we evaluate three relay placement strategies—random, square grid, and hexagonal grid—considering metrics such as delay, throughput, packet collisions, and coverage. Results show that the hexagonal grid offers superior performance, reducing collisions, minimizing delay, and expanding coverage. A fallback mechanism for out-of-range nodes and sensitivity analysis of different backoff values are also included. The study quantifies the benefits of structured relay placement for LoRaWAN and wireless sensor networks, while also identifying challenges for realistic deployments. These findings provide guidelines for designing scalable and reliable IoT networks and highlight directions for future work involving irregular placements and dynamic routing. The simulation results are intended to provide comparative, trend-based insights under conservative modeling assumptions, rather than absolute performance predictions. Full article

Broadcast-based flooding in wireless ad hoc networks is subject to the broadcast storm problem, characterized by excessive transmissions, collisions, and link losses. While randomized network coding (RNC) enhances resilience against packet losses, efficient buffer management and adaptive transmission strategies are essential. This paper proposes novel buffering mechanisms and adaptive coding strategies to improve data unit reception rates in RNC-based broadcast flooding. Our buffering mechanism combines Last-In-First-Out (LIFO) and Least Recently Used (LRU) discard policies. When buffers are full, it prioritizes the discarding of stale, incomplete buffers based on elapsed time since the last coded block arrival, thereby overcoming First-In-First-Out (FIFO) limitations that prematurely discard buffers before sufficient coded blocks have accumulated. Our adaptive coding dynamically adjusts transmitted coded packets based on data unit duplication rates without inter-node coordination, reducing blocks during high duplication and increasing them under difficult reception conditions. Simulation experiments using OMNeT++ and INET framework for Vehicular Ad Hoc Networks demonstrate that LIFO+LRU buffering significantly increases the received data units and prevents redundant reception, while adaptive coding further improves reception rates under challenging conditions. Full article

Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments. Full article

Low Power Wide Area Networks (LPWAN) have emerged as essential connectivity solutions for the Internet of Things (IoT), addressing requirements for long range, energy efficient communication that traditional wireless technologies cannot meet. With LPWAN connections projected to grow at 26% compound annual growth rate until 2027, understanding real-world performance is crucial for technology selection. This review examines four leading LPWAN technologies—LoRaWAN, Sigfox, Narrowband IoT (NB-IoT), and LTE-M. This review analyzes 20 peer reviewed studies from 2015–2025 reporting real-world deployment metrics across power consumption, range, data rate, scalability, availability, and security. Across these studies, practical performance diverges from vendor specifications. In the cited rural and urban LoRaWAN deployments LoRaWAN achieves 2+ year battery life and 11 km rural range but suffers collision limitations above 1000 devices per gateway. Sigfox demonstrates exceptional range (280 km record) with minimal power consumption but remains constrained by 12 byte payloads and security vulnerabilities. NB-IoT provides robust performance with 96–100% packet delivery ratios at −127 dBm on the tested commercial networks, and supports tens of thousands devices per cell, though mobility increases energy consumption. In the cited trials LTE-M offers highest throughput and sub 200 ms latency but fails beyond −113 dBm where NB-IoT maintains connectivity. NB-IoT emerges optimal for large scale stationary deployments, while LTE-M suits high throughput mobile applications. Full article

In intelligent meter reading and other applications, power line communication can use relay technology to solve the problem of cross-station or long-distance reliable communication. This study investigates the combined impact of the physical and Media Access Control (MAC) layers on power line relay communication system performance. To this end, cross-layer modeling, optimization, and simulation analysis integrating both layers are conducted. Based on the CSMA algorithm of IEEE 1901 protocol, a cross-layer performance analysis model of two-hop relay power line communication system is established considering the influence of non-ideal channel transmission at physical layer and competitive access at MAC layer on system performance. In order to reduce the high collision probability caused by two competitions of packets in the above scheme, an improved two-hop transmission algorithm based on CSMA-TDMA is proposed. The cross-layer performance of the system under different single-hop and two-hop schemes is compared, and the mechanism of how parameters such as the MAC layer and the physical layer affect the cross-layer performance of the power line communication system is analyzed. And the optimal power allocation factor is obtained by using the sequential quadratic programming method for the joint system throughput and packet loss rate optimization model with the two-hop power constraint. Simulation results show that the two-hop transmission scheme based on CSMA-TDMA can avoid the second-hop competition and backoff process, and has better performance in terms of throughput, packet loss rate, and delay. Full article