Transmission Interval Research Articles

Exploration of transferable deep learning-aided radio frequency fingerprint identification systems

Radio frequency fingerprint identification (RFFI) shows great potential as a means for authenticating wireless devices. As RFFI can be addressed as a classification problem, deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance. RFFI is suitable for securing the legacy existing Internet of Things (IoT) networks since it does not require any modifications to the existing end-node hardware and communication protocols. However, most deep learning-based RFFI systems require the collection of a great number of labelled signals for training, which is time-consuming and not ideal, especially for the IoT end nodes that are already deployed and configured with long transmission intervals. Moreover, the long time required to train a neural network from scratch also limits rapid deployment on legacy IoT networks. To address the above issues, two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning. More specifically, they rely on fine-tuning and distance metric learning, respectively, and only require only a small amount of signals from the legacy IoT network. As the dataset used for transfer is small, we propose to apply augmentation in the transfer process to generate more training signals to improve performance. A LoRa-RFFI testbed consisting of 40 commercial-off-the-shelf (COTS) LoRa IoT devices and a software-defined radio (SDR) receiver is built to experimentally evaluate the proposed approaches. The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another IoT network with less than ten signals from each LoRa device. The classification accuracy is over 90%, and the augmentation technique can improve the accuracy by up to 20%.

An Anomaly Detection Method Based on Multiple LSTM-Autoencoder Models for In-Vehicle Network

The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system’s high precision with a 99% detection rate in identifying anomalies.

Towards Estimating Probability of Fish–Turbine Encounter: Using Drifters Equipped with Acoustic Tags to Verify the Efficacy of an Array of Acoustic Receivers

An area has been designated for demonstrating the utility of marine hydrokinetic turbines in Minas Passage, Bay of Fundy. Marine renewable energy may be useful for the transition from carbon-based energy sources, but there is concern for the safety of fish that might encounter turbines. Acoustic receivers that detect signals from acoustically tagged fish that pass through the tidal demonstration area and the detection efficiency of tag signals might be used to estimate the likelihood of fish encountering marine hydrokinetic turbines. The method requires that tagged fish passing through the development area will be reliably detected by a receiver array. The present research tests the reliability with which passing tags are detected by suspending tags beneath GPS-tracked drifters. Drifters carrying high residency Innovasea tags that transmitted every 2 s were usually detected by the receiver array even in fast currents during spring tides but pulse-position modulation tags were inadequate. Sometimes very few high residency tag signals were detected when fast tidal currents swept a drifter through the receiver array, so increasing the transmission interval degrades performance at the tidal energy development area. High residency tags suspended close to the sea surface were slightly less likely to be detected if they passed by during calm conditions. Previously measured detection efficiencies were found to slightly overestimate the chances of a high residency tag carried by a drifter being detected as it passed by a receiver. This works elucidates the effectiveness with which acoustically tagged fish are detected in fast, highly turbulent tidal currents and informs the application of detection efficiency measurements to calculate the probability that fish encounter a marine hydrokinetic turbine.

Ecology and space in the COVID-19 epidemic diffusion: a multifactorial analysis of Italy’s provinces

ABSTRACT This study aimed to identify the ecological and spatial correlates of the COVID-19 secondary transmission mechanisms. The dataset comprised cross-province daily COVID-19 cases recorded in Italy from 25 February to 26 May 2020. Several physical and socio-economic variables and a broad set of spatial matrices were employed to explain epidemic spreading. First, the serial transmission interval was identified through autoregressive models. Second, the impact of the ecological and spatial variables on the epidemic’s secondary transmission was analysed by means of ordinary least squares and spatial regression models. The study found that, against widespread belief, the epidemic diffusion was higher in the more affluent and developed territorial units. As for the spatial component, a secondary transmission spatial spillover emerged. Contiguity and distance but also mobility fluxes proved to be significant in this transmission. This study’s contribution is methodological and empirical. As for methodology, it shows that spatial models can be used not only to identify epidemic localisation but also to predict epidemic transmission, provided the problem of the multidirectional nature of spatial dependence is adequately dealt with. Moreover, this study shows that no satisfactory understanding of the epidemic would be achieved without considering both ecological and spatial determinants. As for the empirical aspect, it was found that the main contribution to epidemic spreading came from the anthropologic context rather than the physical one. In particular, direct and indirect indicators of mobility, transport and social interaction emerged as crucial for the epidemic transmission.

A New Miniaturized Acoustic Transmitter for Marine Animal Tracking

Most marine renewable energy (MRE) technologies are still in their infancy. Many uncertainties remain regarding MRE systems’ impacts on the marine environment and species where they are deployed, which is one of the major hurdles for these systems to be widely adopted at a commercial scale. Currently, the primary technologies to monitor marine animals’ behavior around existing or potential MRE deployment sites are optical and acoustic imaging. However, these methods cannot identify individual animals and thus are less useful in identifying protected species and studying their behavior. Implantable acoustic transmitters with unique identification codes could serve as a complementary technology. To minimize potential bias introduced to the study results, the implanted transmitters must be sufficiently small and light to not affect the animals’ behavior while still having acceptable signal strengths and service life. Recently, we developed a new miniaturized acoustic transmitter that offers a significantly improved signal strength and service life (140 days [projected value] at a 5-s transmission interval) in a smaller and lighter package (0.45 g in the air), compared to the existing technologies. The detection range and detection efficiency of the new transmitter were tested in an actual marine environment and demonstrated a detection range of up to 330 m, a 65% improvement over the existing commercial counterparts operating at similar frequencies. Here, we report the design principles, performance, and manufacturing procedure of the transmitter. Theoretical estimates of the detection range, the actual range, and detection efficiency results from the field testing are presented.

Improved time-varying reproduction numbers using the generation interval for COVID-19.

Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of , which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.

Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection

Airborne radar plays a crucial role in detecting aerial targets, but its detection capability can be significantly affected by strong ground clutter when operating in down-looking mode. The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable. In addition to target detection, accurate extraction of target parameters is also of great importance. Due to the high-speed movement of the airborne platform, targets may exhibit range-Doppler ambiguity, necessitating the use of appropriate methods to resolve the range-Doppler ambiguity. Aiming at the problem of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection, this paper conducts a study on the detection of airborne radar down-looking targets based on orthogonal signals combined with pulse repetition frequency (PRF) staggered transmission. An optimization method is employed to design multiple sets of PRF values, aiming to reduce the radar blind zone. The range-Doppler two-dimensional ambiguity of targets is resolved using multiple sets of PRF, and coherent processing is performed on the corresponding echoes using the transmission intervals, maximizing the utilization of radar transmission energy. This approach enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets. The superiority of the proposed approach is demonstrated through simulation experiments.

Station Grouping Mechanism using Machine Learning Approach for IEEE 802.11ah

IEEE 802.11ah, also known as Wi-Fi HaLow, improves the scalability of the Internet of Things (IoT) by connecting a large number of low-power devices. IEEE 802.11ah has developed a novel feature called Restricted Access Window (RAW) that aims to address one of the main problems of the IoT: excessive channel contention in large sensor networks. All stations can access the channel from the same group when using RAW, which allows the Access Point (AP) to split stations into different groups. Existing station grouping schemes support only the homogeneous traffic scenarios, having the same data transmission interval, packet size, and Modulation and Coding Scheme (MCS). In this paper, we highlight two contributions to solving this problem. The first is a Machine Learning (ML) based model that predicts RAW configuration parameters and performance under specific network conditions. It predicts accurately and is very quick to train. Second, a technique for a channel access mechanism that uses an ML model to determine the optimal RAW station grouping configuration for dynamic traffic conditions and heterogeneous stations. We compare our ML-based model with the existing state-of-the-art solutions. We calculated the performance of our proposed model and compared it to traditional 802.11ah and the Traffic Adaptive RAW Optimization Algorithm (TAROA). The results show that our proposed model outperforms a significant improvement in terms of throughput, packet received ratio, delay, and execution time.

Quadratic filtering for non-Gaussian stochastic parameter systems with buffer-aided strategy

This paper is concerned with the quadratic filtering problem for a class of linear discrete time-varying systems with non-Gaussian stochastic parameters and intermittent measurements. In order to improve the filtering performance, a buffer-aided strategy is used to store the historical measurement data which is sent to the filter via a shared communication channel at each transmission instant. The main objective of this paper is to design a quadratic filter to estimate the states of the considered non-Gaussian stochastic systems by using the buffer-aided strategy. Specifically, an augmented system is constructed by combining the original system states and their second-order Kronecker powers. As such, the considered quadratic filtering problem amounts to the Kalman filtering problem for the augmented system. The filtering error covariance (FEC) conditioned on the transmission intervals is firstly derived recursively and then is minimized by choosing the suitable filtering gains. Moreover, sufficient conditions are deduced to guarantee the boundedness of both the FEC and the conditional FEC. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed filtering method.

A Novel Inter-Symbol Interference Model and Weighted Sum Detection for Diffusion-Based Molecular Communication Systems

Inter-symbol interference (ISI) may result in substantial performance degradation in molecular communication systems. In this paper, we propose a more accurate and realistic ISI model compared to the literature, by quantifying the ISI in a three-dimensional fluid environment with a spherical receiver. Moreover, due to the propagation delay in a fluid environment, a large number of the molecules absorbed earlier in a transmission interval are due to the previous transmissions. Therefore, we find the optimal delay time, that the receiver should wait at the beginning of each time interval before counting the absorbed molecules, to reduce the effect of the ISI. Further, and to enhance the performance of the system, we adopt a detection approach based on multiple molecular observations at the receiver, and introduce a weighted sum detector, in which the transmission interval is divided into a number of sub-intervals. We analytically derive the weights, assigned to different sub-intervals, that minimize the bit error rate (BER). Simulations, based on the presented approaches, show the impact of the transmitter-receiver distance, the reaction rate, and the diffusion constant of the environment on the BER performance. We also show that using a weighted sum detector significantly improves the BER performance.

An Edge Device Framework in SEMAR IoT Application Server Platform

Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems.

Energy redistribution assisted charging of WRSNS with multiple mobile chargers having multiple base stations

The utilization of mobile chargers (MCs) for charging the nodes in wireless rechargeable sensor networks (WRSNs) has been a significant research subject. However, existing works mainly focused on planning the moving paths of MCs whereas neglecting opportunities to enhance charging efficiency through the exploitation of energy redistribution (ERD) processes among nodes and the broadcast nature of radio signals. To improve charging efficiency by exploiting these opportunities, we investigate the underlying energy redistribution assisted charge scheduling (ERACS) problem. This problem aims to identify a charging schedule that meets the nodes’ energy demands while minimizing energy loss and time span in WRSNs with multiple MCs and multiple base stations. After demonstrating the NP-hardness of the ERACS problem, we propose an energy loss minimization charge scheduling (ELMCS) algorithm to solve the problem. The ELMCS algorithm first constructs and solves a linear programming problem to obtain a time-length list that leads to minimum transmission energy loss. It then constructs the moving path set of MCs using a heuristic MCs moving path planning strategy that we propose in this paper to minimize moving energy loss. Additionally, after generating an MC energy transmission list that describes the MCs’ energy transmission time intervals, the ELMCS algorithm minimizes the time span by capitalizing on concurrent energy transmission opportunities while ensuring optimal energy loss. Finally, we compare our ELMCS algorithm with typical algorithms, and the simulation results demonstrate that our algorithm is markedly superior.

Heterogeneous Architecture for DSRC/LTE Vehicular Communication Networks Based on the ITS Reference Architecture with Fuzzy Logic for Decision-Making

Methods to integrate different access technologies such as heterogeneous communication architecture remains a challenge. Among a variety of V2X-supporting access technologies, Dedicated Short Range Communication (DSRC) and cellular communications, e.g., Long Term Evolution (LTE) is promising reliable and efficient vehicular communications. DSRC has been designed to allow for direct low-latency communications among different vehicles (i.e., V2V) and between vehicles and roadside units (RSU). On the other hand, DSRC suffers from link quality degradation with the presence of buildings and vehicles, especially in urban areas, where channel collisions become serious when vehicle density is high. LTE networks can provide wide area coverage and are favorable to bandwidth-greedy applications, which require high data rates and reliability. Considering the relatively high end-to-end delay for message transmission due to the long transmission time interval (TTI) current LTE networks have drawbacks regarding latency to support highfrequency safety-related information exchange among vehicles in local areas. Combining LTE and DSRC approaches as a heterogeneous solution is essential to fast introduce V2X services for future automated driving. By this intelligent Vertical Handover (VHO) algorithms are needed to ensure seamless connectivity of vehicles to the best network at a particular point in time. For this paper, we propose a 3- input fuzzy-logic-based VHO scheme for Heterogeneous DSRC/LTE Vehicular Communication Networks. The simulation results in this paper show that the proposed 3- input fuzzy-logic model can be able to make decisions and select the best network to connect to base on the network with the highest HF value. The 3-input fuzzy-logic model was also tested in a vehicular highway scenario simulation. The proposed model shows optimized communication results in terms of the mean end-to-end delay against that of the literature.

Transmission Characteristics and Inactivated Vaccine Effectiveness Against Transmission of SARS-CoV-2 Omicron BA.5 Variants in Urumqi, China

In 2022, Omicron variants circulated globally, and Urumqi, China, experienced a COVID-19 outbreak seeded by Omicron BA.5 variants, resulting in the highest number of infections in the city's record before the exit of the zero COVID-19 strategy. Little was known about the characteristics of Omicron variants in mainland China. To evaluate transmission characteristics of Omicron BA.5 variants and the effectiveness of inactivated vaccine (mainly BBIBP-CorV) against their transmission. This cohort study was conducted using data from an Omicron-seeded COVID-19 outbreak in Urumqi from August 7 to September 7, 2022. Participants included all individuals with confirmed SARS-CoV-2 infections and their close contacts identified between August 7 and September 7, 2022 in Urumqi. A booster dose was compared vs 2 doses (reference level) of inactivated vaccine and risk factors were evaluated. Demographic characteristics, timeline records from exposure to laboratory testing outcomes, contact tracing history, and contact setting were obtained. The mean and variance of the key time-to-event intervals of transmission were estimated for individuals with known information. Transmission risks and contact patterns were assessed under different disease-control measures and in different contact settings. The effectiveness of inactivated vaccine against the transmission of Omicron BA.5 was estimated using multivariate logistic regression models. Among 1139 individuals diagnosed with COVID-19 (630 females [55.3%]; mean [SD] age, 37.4 [19.9] years) and 51 323 close contacts who tested negative for COVID-19 (26 299 females [51.2%]; mean [SD] age, 38.4 [16.0] years), the means of generation interval, viral shedding period, and incubation period were estimated at 2.8 days (95% credible interval [CrI], 2.4-3.5 days), 6.7 days (95% CrI, 6.4-7.1 days), and 5.7 days (95% CrI, 4.8-6.6 days), respectively. Despite contact tracing, intensive control measures, and high vaccine coverage (980 individuals with infections [86.0%] received ≥2 doses of vaccine), high transmission risks were found in household settings (secondary attack rate, 14.7%; 95% CrI, 13.0%-16.5%) and younger (aged 0-15 years; secondary attack rate, 2.5%; 95% CrI, 1.9%-3.1%) and older age (aged >65 years; secondary attack rate, 2.2%; 95% CrI, 1.5%-3.0%) groups. Vaccine effectiveness against BA.5 variant transmission for the booster-dose vs 2 doses was 28.9% (95% CrI, 7.7%-45.2%) and 48.5% (95% CrI, 23.9%-61.4%) for 15-90 days after booster dose. No protective outcome was detected beyond 90 days after the booster dose. This cohort study revealed key transmission characteristics of SARS-CoV-2 as they evolved, as well as vaccine effectiveness against variants. These findings suggest the importance of continuously evaluating vaccine effectiveness against emerging SARS-CoV-2 variants.

Two-Tier 360-Degree Video Delivery Control in Multiuser Immersive Communications Systems

In the immersive communications systems, video information is stringently delivered for extended reality applications. Since users may demand various immersive experience and have different levels of view prediction accuracy, the two-tier 360-degree video delivery frame structure with dynamic transmission time interval duration needs to be designed appropriately. To maximize the system's quality of experience, two new frame structures and power control are proposed. Meanwhile, the synchronous and asynchronous cases with different computational complexities are addressed. The results demonstrate the effectiveness of the proposed design and indicate that flexible frame structure is beneficial for multiuser immersive communications.

Batteryless NB-IoT prototype for bidirectional communication powered by ambient light

IoT use-cases aim to provide high-quality services where IoT infrastructures are always linked with the Internet. They require large scale deployment of small devices acting as sensors and actuators in different places. These devices need to be energy efficient and run sustainably. The maintenance of such large scale devices is difficult if they are powered by batteries that need to be replaced after some time. Moreover, disposing of these batteries also pollutes the soil and contaminates groundwater. Narrowband IoT (NB-IoT) is one of the long-range technologies that supports connectivity even in deep indoor closed areas such as underground parking lots, elevators, and tunnels. Therefore, in this article, the suitability of an NB-IoT device or User Equipment (UE) to be powered without any batteries to enable a long-term sustainable maintenance-free IoT network is demonstrated. We develop a prototype using a small photovoltaic solar panel for indoor light harvesting using sunlight or an artificial light bulb to power the NB-IoT UE. Due to the unpredictability of energy harvesting, the batteryless UE can experience frequent switches between the ON and OFF states. Therefore, we evaluate the performance of the solution where the UE communicates either uni- (uplink only) or bi-directionally (uplink and downlink) for different capacitor sizes at different artificial light intensities. We also evaluate the solution under dynamic harvesting power using natural light during daytime. The experimental results conclude that the batteryless NB-IoT UE supports a transmission interval of 60 s without experiencing any restarts when harvesting 6 mW or more, which can be harvested by placing a 6 W artificial light bulb at a distance of 60 cm or less.

Experimental Assessment of Common Crucial Factors That Affect LoRaWAN Performance on Suburban and Rural Area Deployments.

LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and energy efficiency need to be overcome. The scope of this study is to review the limitations of the LoRaWAN protocol in order to summarize and assess the crucial factors that affect communication performance, related to data rate allocation, bidirectional traffic and radio spectrum utilization. Based on the literature, these factors correspond mostly to configurable payload transmission parameters, including transmission interval, data rate allocation, requirement for acknowledgements and retransmission. In this work, with simulation experiments, we find that collision occurrences greatly affect channel occupancy. In particular, it was evaluated that collision occurrence is increasingly affected by transmission intervals, which have the most significant negative impact on packet delivery rate (PDR). We then validated that clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, can improve network scalability. This can assure distribution of the total transmission time to end nodes with respect to application-related QoS requirements. Following this clustering approach, we achieved a PDR greater than 0.90 in a simulation setting with 6000 end nodes in a 10 km coverage.

Modeling and Experiments of a Wireless Power Transfer System Considering Scenarios from In-Wheel-Motor Applications

This paper presents the design and modeling procedure of a wireless power transfer (WPT) system applied to In-wheel-motor (IWM). The system is designed to transmit over 10 kW of power following the physical constraints faced by the IWM applications. The issues of coil misalignment and load change are discussed as particular scenarios in IWM. The finite element model is built for circular, rectangular, and double-D coils, finding that the rectangular coil has the best performance considering the transmission interval and misalignment resistance. The circuit design procedure is presented, and the analysis of the influence of load and mutual inductance change on the WPT system is addressed. Finally, the performance of the design is verified with experiments on a full-scale prototype. It is proved that the WPT system successfully transmits 10 kW of power with a DC–DC efficiency of over 90% under a transmission interval of 140 mm. The output voltage is stable under 40 mm coil misalignment scenarios and over 50% load change.

Reverse average dwell time constraints enable arbitrary maximum allowable transmission intervals

When designing networked control systems (NCS), stability guarantees are typically derived based on the maximum possible time between successive transmissions. A bound on this time is called the maximum allowable transmission interval (MATI). In order to minimize network usage, one is often interested in the maximum value of the MATI for which stability can still be guaranteed for the NCS. However, in many practical scenarios, the worst case transmission behavior occurs only seldom, rendering an analysis based solely on the MATI unnecessarily conservative. It was recently shown in the literature that considering information about the average allowable transmission interval in the form of a reverse average dwell time (RADT) can be used to increase the maximum value of the MATI for which stability can be guaranteed. Using the same assumptions, we show that stability guarantees can even be obtained for any arbitrary given value of the MATI as long as the RADT is small enough.

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

The 6TiSCH (IPv6 over IEEE802.15.4e time-slotted channel hopping mode) wireless sensor network architecture utilizes control packets to construct network formation. These control packets are essential for establishing communication links between nodes and configuring network settings. The trickle timer algorithm is utilized to broadcast the DIO control packet. DIO carries information about the available parent nodes, which is then used to form the routing tree. Sensors transmit control packets in one cell on each TSCH slotframe, called the minimal cell. This leads to the problem that RPL trickle timer algorithm encounters congestion in DIO control packet transmission with other control messages, particularly in dense networks. Moreover, high traffic transmission also leads to high queue usage, which then drops the DIO control packet. Failed DIO transmission can increase network formation time and energy consumption. To address this issue, we propose Q-Trickle, an adaptive trickle timer algorithm based on Q-learning that determines the optimal policy for transmitting or suppressing DIO based on minimal cell and transmission queue conditions. Q-Trickle adaptively selects a redundancy constant value and transmission interval that promotes fair transmission distribution and considers network condition. Additionally, a control scheme over minimal cell transmission is formulated to lower transmission congestion and faster synchronization. The proposed methods were assessed using simulation and actual testing on the FIT IoT-LAB testbed. The results indicated that Q-Trickle performed better than the benchmark methods. Q-Trickle decreases joining time, energy consumption, and number of failed DIO compared with the original algorithm by -13%, -11%, and -43%, respectively.