Search Results (52)

This paper investigates a wireless ad hoc network node registration and access system based on a hybrid Frame Slotted ALOHA and Time Division Multiple Access mechanism for Unmanned Aerial Vehicle (UAV) charging station cluster management scenarios. Based on combinatorial mathematics and the inclusion–exclusion principle, the exact conditional probability distribution of the number of successfully accessed nodes within a single frame is derived, and a closed-form analytical expression for the total system access delay as a function of node population is established, revealing the existence of an optimal operating point. Building upon this foundation, a Dynamic Access Probability (DAP) mechanism is introduced, which adaptively regulates the number of nodes participating in contention per frame, effectively suppressing collisions and significantly reducing the overall system access delay. To address the difficulty of obtaining the real-time node count in practical systems, a Probability Model-based Maximum Likelihood Estimation algorithm is further proposed, which takes the number of successfully accessed nodes observable in each frame as input to infer the current number of unaccessed nodes. Simulation results demonstrate that the PMLE algorithm achieves superior performance in both estimation accuracy and stability, effectively supporting the accurate operation of the DAP mechanism. 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

This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding to time-slot scheduling and power allocation. The sub-objective corresponding to time-slot scheduling is addressed by constructing a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) learning. In this model, the agent learns the time slot allocation policy with the goal of increasing the number of successfully transmitted links while reducing the collision. For the sub-objective corresponding to power allocation, another MDP model is developed, solved by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, in which each underwater transmission node acts as an independent agent. The MADDPG approach enables the system to improve channel capacity under energy limitation, which maximizes the total capacity of successfully transmitted links. In terms of model execution, the DQN adopts a centralized training and time slot allocation, while MADDPG uses a centralized training and distributed execution to select the transmission power by each node. Simulation results show that the proposed joint optimization algorithm demonstrates better performance in the number of successfully transmitted links and channel capacity compared to TDMA, Slotted ALOHA, and other algorithms. 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

Passive radio frequency identification (RFID) sensing systems integrate wireless energy transfer with information identification. However, conventional passive RFID systems still face three key challenges in practical applications: low RF energy harvesting efficiency, high power consumption of sensor loads, and high complexity of tag anti-collision algorithms. To address these issues, this paper proposes a hardware–software co-optimized RFID sensor system. For hardware, low threshold RF Schottky diodes are selected, and an input inductor is introduced into the voltage multiplier rectifier to boost the signal amplitude, thereby enhancing the radio frequency to direct current (RF-DC) energy conversion efficiency. In terms of loading, a low-power management strategy is implemented for the power supply and control logic of the sensor node to minimize the overall system energy consumption. For algorithmic implementation, a Dual-Threshold Stepped Dynamic Frame Slotted ALOHA (DTS-DFSA) anti-collision algorithm is proposed, which adaptively adjusts the frame length based on the observed collision ratio, eliminating the need for complex tag population estimation. The algorithm features low computational complexity and is well suited for resource constrained embedded platforms. Through simulation validation, we compare the conversion efficiency of the RF energy harvesting circuit before and after improvement, the current of the sensor load in active and idle states, and the performance of the proposed algorithm against the low-complexity DFSA (LC-DFSA). The results show that the maximum conversion efficiency of the improved RF energy harvesting circuit has increased from 60.56% to 68.69%; specifically, the sensor load current drastically drops from approximately 2.0 $\mathrm{m}\mathrm{A}$ in the active state to around 74 $\mathsf{\mu }\mathrm{A}$ in the idle state, validating the efficacy of the proposed power gating strategy, and the proposed DTS-DFSA algorithm outperforms existing low-complexity schemes in both identification efficiency and computational complexity. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

This paper analyzes a discrete-time single-server retrial queue with preemptive service, Bernoulli arrivals, and adaptive retrial times, tailored to telecommunications systems. In call centers, the model captures caller retries and priority interruptions, while in cellular networks, it represents user channel access attempts with preemption for emergency calls. Using a Markov chain framework, we derive the stationary distribution, establish a stability condition, and compute performance metrics, including the mean number of retrying callers or users and orbit size probabilities. The model incorporates a novel retrial time adaptation probability, reflecting dynamic retry behaviors in telecommunications. Numerical results demonstrate the impact of arrival rates, preemption probabilities, and retrial adaptations on system performance, offering insights for optimizing call center staffing and cellular network protocols. Applications to slotted ALOHA and TDMA systems highlight the model’s practical relevance. Full article

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. Full article

Radio Frequency Identification (RFID) is a key component in automatic systems that address challenges in environment monitoring. However, tag collision continues to be an essential challenge in such applications due to high-density RFID deployments. This paper addresses the issue of RFID tag collision in large-scale intensive tags, particularly in industrial membrane contamination monitoring systems, and improves the system performance by minimizing collision rates through an innovative collision-avoiding algorithm. This research improved the Predictive Framed Slotted ALOHA–Collision Tracking Tree (PRFSCT) algorithm by cooperating probabilistic and deterministic methods through dynamic frame length adjustment and multi-branch tree processes. After simulation and validation in MATLAB R2023a, we performed a hardware test with the RFM3200 and UHFReader18 passive tags. The method’s efficiency is evaluated through collision slot reduction, delay minimization, and enhanced throughput. PRFSCT significantly reduces collision slots when the number of tags to identify is the same for PRFSCT, Framed Slotted ALOHA (FSA), and Collision Tracking Tree (CTT); the PRFSCT method needs the fewest time slots. When identifying more than 200 tags, PRFSCT has 225 collision slots for 500 tags compared to FSA and CTT, which have approximately 715 and 883 for 500 tags, respectively. It demonstrates exceptional stability and adaptability under increased density needs while improving tag reading at distances. Full article

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered. Full article

The Internet of Things (IoT) is a fast-growing field that has found a variety of applications, such as smart agriculture and industrial processing. In these applications, it is important for nodes to maximize the amount of useful information transmitted over a limited channel. This work seeks to improve the performance of low-powered sensor networks by developing an architecture that leverages existing techniques such as lossy compression and different queuing strategies in order to minimize their drawbacks and meet the performance needs of backend applications. The Age of Information (AoI) provides a useful metric for quantifying Quality of Service (QoS) in low-powered sensor networks and provides a method for measuring the freshness of data in the network. In this paper, we investigate QoS requirements and the effects of lossy compression and queue strategies on AoI. Furthermore, two important use cases for low-powered IoT sensor networks are studied, namely, real-time feedback control and image classification. The results highlight the relative importance of QoS metrics for applications with different needs. To this end, we introduce a QoS-aware architecture to optimize network performance for the QoS requirements of the studied applications. The proposed network architecture was tested with a mixture of application traffic settings and was shown to greatly improve network QoS compared to commonly used transmission architectures such as Slotted ALOHA. Full article

In this manuscript, we present in detail the design and implementation of the hardware and software to produce a standalone wireless sensor node, called SensorQ system, for the detection of a toxic chemical agent. The proposed wireless sensor node prototype is composed of a micro-controller unit (MCU), a radio frequency (RF) transceiver, a dual-band antenna, a rechargeable battery, a voltage regulator, and four integrated sensing devices, all of them integrated in a package with final dimensions and weight of 200 × 80 × 60 mm and 0.422 kg, respectively. The proposed SensorQ prototype operates using the Long-Range (LoRa) wireless communication protocol at 2.4 GHz, with a sensor head implemented on a hetero-core fiber optic structure supporting the surface plasmon resonance (SPR) phenomenon with a sensing section (L = 10 mm) coated with titanium/gold/titanium and a chemically sensitive material (zinc oxide) for the detection of Di-Methyl Methyl Phosphonate (DMMP) vapor in the air, a simulant of the toxic nerve agent Sarin. The transmitted spectra with respect to different concentrations of DMMP vapor in the air were recorded, and then the transmitted power for these concentrations was calculated at a wavelength of 750 nm. The experimental results indicate the feasibility of detecting DMMP vapor in air using the proposed optical sensor head, with DMMP concentrations in the air of 10, 150, and 150 ppm in this proof of concept. We expect that the sensor and wireless sensor node presented herein are promising candidates for integration into a wireless sensor network (WSN) for chemical warfare agent (CWA) detection and contaminated site monitoring without exposure of armed forces. Full article

In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, a packet awaiting transmission from the queue experiences delays. Thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. To understand the trade-off between reliability and delays that might lead to packet drops due to deadline-constrained bursty traffic with retransmissions, the scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Towards this end, analysis of the system is performed by means of discrete-time Markov chains. Two scenarios are studied: (i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and (ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probabilities and number of retransmissions is conducted. We are able to determine numerically the optimal probability of transmissions and the number of retransmissions, given the packet arrival rate and the packet deadline. Furthermore, we highlight the impact of transmit probability and the number of retransmissions on the average drop rate and throughput. Full article

The capture effect is a frequently observed phenomenon in vehicular ad hoc networks (VANETs) communication. When conflicts arise during time slot access, failure to access does not necessarily occur; instead, successful access may still be achieved. The capture effect can enhance the likelihood of multiple access and improve communication efficiency. The security of VANETs communication is undoubtedly the primary concern. One crucial approach to enhance security involves the design of an efficient and reliable medium access control (MAC) protocol. Taking into account both aspects, we propose a novel framed slotted Aloha (FSA) MAC protocol model. Firstly, we derive the closed-form expression for the capture probability in the Rician fading channel in this paper. Subsequently, we analyze how the number of vehicles and time slots influence the success probability of vehicle access channels as well as examine the impact of the capture effect on this success probability. Then, under constraints regarding vehicle access channel success probability, we derive optimal values for slot numbers, access times, and transmission power while proposing a comprehensive implementation method to ensure high access channel success probabilities. We verify both theoretical derivations and proposed methods through simulation experiments. Full article