Transmission Success Rate Research Articles

Learning-Based Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO Systems

Intelligent reflecting surface (IRS) is amenable to assisting cell-free wireless networks because of its low cost and high spectrum efficiency. However, solving the IRS optimization problem usually requires high computational complexity. Therefore, we propose a novel multi-agent reinforcement learning (MARL) based joint optimization scheme to improve the system performance. First, we model the MARL framework to portray the dynamic wireless communication environment and interactive agent entities, which interprets the relationship between reinforcement learning and communication strategies. Moreover, in an IRS-aided cell-free massive MIMO system, entries are collaborated by the CPU for joint optimization, and the actions are continuous. Thus we propose a multi-agent deep deterministic policy gradient scheme for jointly optimizing the user's power control and IRS's passive beamforming (MADDPG-JPA-PB). Via centralized training and decentralized execution, the system gradually converges into an optimized policy associated with the maximization of the success transmission rate. Simulation results verify that the performance of the proposed scheme outperforms various independent deep deterministic policy gradient (DDPG) schemes and traditional optimization methods.

Handover-Aware Downlink Beamforming Design for LEO Multibeam Satellite Communications

Because of their capability of providing seamless connectivity, low-Earth-orbit (LEO) satellites will play a vital role in integrated satellite–terrestrial paradigms. However, high-speed satellite motion brings frequent handovers, during which redundant transmission and even loss of data easily occur. In this letter, we propose a handover-aware downlink beamforming design. To mitigate the impact of inter-satellite handover, we reduce the blocklength of coverage-edge users, aiming at completing transmission within the remaining visible time (RVT). Hence, the capacity of these users should be evaluated in the finite-block length (FBL) regime. After obtaining a surrogate of the FBL capacity function with a low-complexity method, we formulate the beamforming optimization model as a semidefinite programming (SDP) problem and solve it with CVX. The simulation results show that the proposed scheme can reach a 99.35% transmission success rate before inter-satellite handover.

Development and Verification of an IoT-enabled Air Quality Monitoring System Based on Petri Nets

An internet of things (IoT) system for monitoring environmental parameters on campus has been successfully developed in this study. Various sensors are adopted in front of the IoT system for detecting air pollutants. The architecture of LongRange in Class C uses low-power and long-distance transmission technologies, which has been set up on the large campus so that the terminal equipment can reach a balance between downlink latency and battery life, making it the best communication protocol. In addition, this monitoring system uses the Petri net software tool to build a correct IoT platform based on the fundamental design process and to demonstrate the feasibility of the IoT system through simulation-based verification. Finally, the experimental results indicate that the IoT system for monitoring the environmental parameters on campus achieves the goal of an acceptable data transmission success rate of more than 95%. Thus, it can facilitate the air quality trends for policy making as well as the hazardous prediction and prevention.

Follow-up of implantable cardiac electronic devices during the Covid-19 pandemic: organizational model and results of a single center experience

Abstract Funding Acknowledgements Type of funding sources: None. Introduction During the SARS-CoV-2 COVID-19 pandemic, the global health system has had to review important processes involved in daily routines such as outpatient activities within the hospital, including follow-up visits of implantable cardiac electronic devices (CIEDs), pacemakers (PMs) and implantable defibrillators (ICDs) carried out in office. The aim of this study was to prospectively evaluate the success rate of remote transmissions of all devices followed by the CIED control clinic and verify the adopted organizational models. Methods All patients with remote monitoring system were considered in the study if automatic algorithm such as autocapture and autosensing were activated and well-functioning. Between January 2021 and November 2022, they were completely followed by remote monitoring and called for unscheduled in-office visit only because of remote yellow or red alarms. Patients were divided into two groups: Manual Transmission System (MTS) and Automatic Transmission System (ATS), based on the available technology. Organizational models shown in figures 1-2, were followed for the two groups. The technology of the ATS group, in addition to ensuring a daily transmission of any yellow or red alarms, allowed a control of missed communication at least every 14 days. The MTS group provided a manual transmission every 6 months. The ATS group was controlled at least once in the year, even if no alarms occurred. Results At the end of 2022 a total of 1193 patients were followed remotely by our center. Of these, 238 from the MTS group and 955 from the ATS group. 3314 remote transmission required medical intervention, which generated 182 unscheduled outpatient visits, mainly due to lack of transmission for prolonged periods, failure of the autocapture test and newly onset atrial fibrillation. 3496 transmissions were generated and 120 transmissions were missed; the overall success rate of transmissions with the organizational model adopted was 96.6% (connectivity index). On average, the monthly rate of unconnected manual devices was 5.1% while 3.5% for automatic devices. 2-year mortality was 12.7%. Conclusions By adopting an appropriate organizational model, it is possible to maintain high transmission success rates. The available technology allows you to change hospital outpatient habits in the cardiology field for CIED checks and move to a virtual clinic safely. The automatic transmission technology allows a more frequent control of patients with CIED, the impact on the patients' health must be assessed.

Recurrent-Neural-Network-Based Anti-Jamming Framework for Defense Against Multiple Jamming Policies

Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this paper proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers’ occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNN)s. The performance of the proposed anti-jamming methods is evaluated by calculating the users’ successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on deep Q-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and a high STR and ER are maintained. Moreover, when 70% of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves a STR and ER greater than 75% and 80%, respectively. These values reach 90% when 30% of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered jamming scenarios.

A multi-species evaluation of digital wildlife monitoring using the Sigfox IoT network

Bio-telemetry from small tags attached to animals is one of the principal methods for studying the ecology and behaviour of wildlife. The field has constantly evolved over the last 80 years as technological improvement enabled a diversity of sensors to be integrated into the tags (e.g., GPS, accelerometers, etc.). However, retrieving data from tags on free-ranging animals remains a challenge since satellite and GSM networks are relatively expensive and or power hungry. Recently a new class of low-power communication networks have been developed and deployed worldwide to connect the internet of things (IoT). Here, we evaluated one of these, the Sigfox IoT network, for the potential as a real-time multi-sensor data retrieval and tag commanding system for studying fauna across a diversity of species and ecosystems. We tracked 312 individuals across 30 species (from 25 g bats to 3 t elephants) with seven different device concepts, resulting in more than 177,742 successful transmissions. We found a maximum line of sight communication distance of 280 km (on a flying cape vulture [Gyps coprotheres]), which sets a new documented record for animal-borne digital data transmission using terrestrial infrastructure. The average transmission success rate amounted to 68.3% (SD 22.1) on flying species and 54.1% (SD 27.4) on terrestrial species. In addition to GPS data, we also collected and transmitted data products from accelerometers, barometers, and thermometers. Further, we assessed the performance of Sigfox Atlas Native, a low-power method for positional estimates based on radio signal strengths and found a median accuracy of 12.89 km (MAD 5.17) on animals. We found that robust real-time communication (median message delay of 1.49 s), the extremely small size of the tags (starting at 1.28 g without GPS), and the low power demands (as low as 5.8 µAh per transmitted byte) unlock new possibilities for ecological data collection and global animal observation.

A Sub-flow Adaptive Multipath Routing Algorithm for Data Centre Network

In response to the fast and intensive traffic changes and the presence of redundant links in the network topology in data centre networks, multipath routing has become the dominant approach. Existing multipath routing algorithms are still inadequate in terms of adapting to dynamic changes in the network state quickly and the cooperation of path selection and sub-flow assignment. Therefore, this paper proposes a sub-flow adaptive multipath routing algorithm for data centre networks (SAMP). The deep deterministic policy gradient (DDPG) algorithm is introduced. DDPG combines deep learning (DL) and reinforcement learning (RL) to implement different network state changes quickly, especially in the process of topology, to achieve dynamic migration of the optimised decisions that have been learned. An adaptive multipath routing model for subflows is established to accomplish collaborative scheduling of path selection and subflow assignment based on the real-time state of the network. The experimental results show that the algorithm can be well adapted to the data centre network, and when compared with several traditional methods, it reduces the latency by an average of 31.3%, improves the task transmission success rate by an average of 14.9%, and increases the throughput by 37.1%.

Study on optimization of communication network for multi-unmanned aerial vehicles

The unmanned aerial vehicles (UAV) base station is most widely used as the emergency communication in response to emergencies. However, the performance improvement is in great demand at communication network. To improve the performance between the UAVs and the users, a novel method is proposed based on k-means++ algorithm and antenna adjusting. The established simulation platform and models are credible and reliable according to the very excellent approximation capability and the strong correlation between the simulation results and the Shannon’s formula. The transmission success rate for the three conditions (using only k-means++, antenna pointing optimization and antenna downtilt angle optimization) dramatically increases with an increase in k and all reach the maximum when k is equal to 5. The fan throughput of the proposed method is almost two times than that of the other two conditions after the k value exceeds 5. Meanwhile, the edge users’ rate and average user rate of the proposed method are all markedly increased. Overall, a novel method is provided for optimizing the performance between multi-UAVs and users, and the performance communication network for multi-UAVs is optimized by using the proposed method in this paper.

Emergence of Bipartite Flocking Behavior for Cucker-Smale Model in Presence of Lossy Links

This work investigates the issue of discrete-time bipartite flocking control for Cucker-Smale model subject to cooperation-competition interations and lossy links. The data packets loss from the controller to the actuator are simulated as a Bernoulli distribution process. The cooperation and competition among individuals are described as a signed digraph. Based on the products convergence approach of infinite sub-stochastic matrices, a algebraic condition which is related to initial states, the topological structure and the successful information transmission rate is established, so as to achieve bipartite flocking. At last, the theoretical result is exemplied through numerical simulations.

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network (GAN). The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm, which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates. The results show that the GDNN obtains an increased rate of successful transmission by taking optimal actions to act as a defense mechanism to mislead the jammer, where the jammer makes high misclassification errors on transmission dynamics.

Memory-Occupied Routing Algorithms for Quantum Relay Networks

Quantum transmission experiments have shown that the successful transmission rate of entangled quanta in optical fibers decreases exponentially. Although current quantum networks deploy quantum relays to establish long-distance connections, the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next hop. However, most of the existing quantum network models prefer to consider the parameters of the physical layer, which ignore the influence factors of the network layer. In this paper, we propose a meshy quantum network model based on quantum teleportation, which considers both network layer and physical layer parameters. The proposed model can reflect the realistic transmission characteristics and morphological characteristics of the quantum relay network. Then, we study the network throughput of different routing algorithms with the same given parameters when multiple source-destination pairs are interconnected simultaneously. To solve the challenges of routing competition caused by the simultaneous transmission, we present greedy memory-occupied algorithm Q-GMOA and random memory-occupied algorithm Q-RMOA. The proposed meshy quantum network model and the memory-occupied routing algorithms can improve the utilization rate of resources and the transmission performance of the quantum network. And the evaluation results indicate that the proposed methods embrace a higher transmission rate than the previous methods with repeater occupation.

Research on Remote Online Firmware Upgrade System for Embedded Devices

<p>To effectively reduce the cost of firmware upgrade and maintenance of embedded devices, according to the requirement of a remote firmware update of embedded devices, this paper proposes a remote online firmware upgrade technology based on ZigBee. Combined with the requirements of remote online firmware upgrade application of tower-mounted amplifier, this paper analyzes and studies the system requirements, ZigBee module selection and design, software design and implementation (communication protocol design of host and slave program, breakpoint continuation function), fault analysis and other aspects. Designed experiments and tested the ZigBee connection success rate under the device startup state and normal operation state, the relationship between transmission firmware size and transmission success rate, system CPU resource consumption, transmission distance, transmission rate, the success rate, data reuse rate and acceleration ratio of breakpoint continuation function, and other performance indicators. The experimental results show that the remote online firmware upgrade technology based on ZigBee for embedded devices can better meet the system’s performance requirements in all aspects and improve the data reuse rate and acceleration ratio in the process of data transmission. The experimental results show that this scheme has the advantages of simplicity, low cost, high reliability, and broad application prospects.</p> <p> </p>

Reliable design of communication link for power emergency vehicle mobile station based on Beidou satellite

In order to establish a reliable power emergency communication network and speed up the efficiency of power rescue, a vehicle-mounted reliable communication link based on Beidou short message protocol is designed. Aiming at the unreliability of Beidou short message communication and the limitation of communication capacity, the enhanced data handshake mechanism is adopted to enhance the reliability of data transmission, and the multi-card cooperative transmission mechanism is adopted to improve the efficiency of data transmission. The example results show that the transmission success rate of the proposed reliable communication link is significantly higher than that of the traditional method, which can improve the reliability of mobile station data transmission and provide communication guarantee for power recovery.

Adaptive Emergency Call Service for Disaster Management

Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims’ devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER.

Secure routing protocol based on dynamic reputation and load balancing in wireless mesh networks

Aiming at the characteristics of dynamic topology change and vulnerability of wireless mesh networks (WMN), a secure routing protocol based on dynamic reputation and load balance (SRP-DRLB) is proposed to improve the routing security and load balance. By dynamically predicting of the reputation and traffic of the node in WMN, SRP-DRLB adopts edge weight strategy and node load idleness to avoid the competitive access of a large number of data packets to nodes with high reputation. SRP-DRLB dynamically adjusts routes according to the real-time status of network nodes’ reputation and traffic, reducing the impact of malicious nodes on the network and the overload of high-reputation nodes, thus improving the identification rate of malicious nodes and node load balancing. Simulation results show that SRP-DRLB can effectively ensure the security of data transmission, and it has the advantages of improving the identification rate of malicious nodes, the success rate of packet transmission and avoiding network congestion.

FGOR: Flow-Guided Opportunistic Routing for Intrabody Nanonetworks

The advancement of nano communication has opened the door for the development of intrabody medical application services. Flow-guided nano-communication networks have gained major attraction in recent years as an effective solution for intrabody sensing and actuation. This article builds a three-layer vertical network structure for intrabody nanonetworks, i.e., nano nodes, nano routers, and gateway, where data packets generated by nano nodes are relayed to the gateway through nano routers or other nodes. However, how to guarantee the data transmission through the way of multiple hops in such a scenario is an unsolved challenge. In order to improve the throughput and reduce the energy consumption of intrabody nanonetworks in a single-flow environment where the nano devices are restricted, a flow-guided opportunistic routing (FGOR) protocol is proposed. In FGOR, a relative position (RP) model is proposed to formulate the criterion for candidate relay selection (CRS) and enable the nodes’ direction awareness to the gateway. Moreover, the CRS criterion is redesigned through a mobility gradient (MG) model further derived from the RP model. The candidate nodes are prioritized based on node ID, available energy, and RP information of nodes to perform backoff forwarding for decreasing transmission redundancy. Simulation results show that the RP model improves the throughput and significantly extends the lifecycle of intrabody nanonetwork by reducing the energy consumption. Compared with the RP model, the MG model performs better in terms of delay and successful transmission rate, especially within the circulation environment of intrabody.

PSXIII-7 Field Testing of Lora-wan Sensors for Real-Time Tracking and Biosensing of Brangus and Raramuri Criollo Cattle Grazing a Small Pasture

Abstract The study’s objective was to investigate the use of Internet of Things (IoT) biosensors using Long Range Wide Area Network (LoRa-WAN) communication to compare the foraging behavior of two desert adapted cattle breeds. Two groups of mature Brangus (BA) and Raramuri Criollo (RC) cows were introduced into a small irrigated pasture (38ha) at the New Mexico State University’s Clayton Livestock Research Center during Dec-Jan, 2021 (41-days). The real-time position and activity count data (15- and 1-minute interval, respectively) were retrieved continuously from 4 BA and 5 RC cows collared with commercially available trackers (Abeeway Industrial-trackers). Response variables derived from GPS records included daily distance traveled (Dist) and daily area explored (Area). Response variables derived from accelerometers included daily percentage motion (%Act) and cumulative activity data (CumAct). All variables were grouped into six consecutive weekly periods and analyzed by a repeated measures mixed model that included the fixed effects of BREED, WEEK, and the BREED*WEEK interaction in a completely randomized design. A significant (P< 0.01) BREED*WEEK interaction was detected on all GPS-derived variables, with RC exhibiting a trend to explore larger areas than BA (P=0.06). For the accelerometer-derived variables, the BREED*WEEK interaction was only significant (P=0.01) for CumAct. Overall, BA spent a larger percentage of the day active (%Act; BA= 76.74 vs. RC= 73.16; P=0.02), while investing a significantly greater overall activity (CumAct; BA= 8816.63 vs. RC= 6311.31; P=0.03) compared to RC cows. The insensitivity of GPS-derived variables is hypothesized to be the result of aggregating errors associated with less frequent GPS location measurements coupled with position errors (~5m) and a low success rate of data packet transmission (~40%). The spatial scale of the study is hypothesized to have influenced the foraging behavior differences that were observed between the breeds, and the better performance of the accelerometer vs. GPS-derived variables.

Performance Analysis of Distance-Based D2D Matching Mechanism

The traditional cellular architecture where devices connect to their service base station (BS) may cause poor performance especially for edge users. Device-to-device (D2D) communication enables nearby user as a relay to help BS forward information, thereby improving the network coverage and quality of service (QoS) of edge users. This paper proposes a distance-based D2D matching mechanism for general cellular networks, where a relay user who successfully connects to its targeted BS can transmit data to its closest user for D2D communication. A link of BS to D2D pair contains two sublinks, which occur at different time phases in each cell. Assuming a nonsynchronous system, we consider that there exists cross-layer interference for D2D links. Based on the techniques of stochastic geometry, we develop the performance of coverage probability and ergodic rate of the D2D network. A key intermediate step in this analysis is the derivation of the interference expressions for D2D links caused by BSs and cochannel D2D users. Then, we derive the meta distribution of the signal-to-interference ratio (SIR) to capture the performance changes of individual links. Simulation results demonstrate that our matching mechanism based on the appropriate time resource allocation favors the edge users with a higher probability of successful communication and transmission rate.

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm.

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Farming Performance can be Estimated using Blockchain Technology

On a wireless blockchain-based network, a sustainable smart farm with noise signal or noise signal with the ratio (SIR/SNR) to be employed as the optimal relay has already been addressed. During the uninterrupted selection of the optimal relay performance, the transmission success rate (TRS), overall communication throughput (OCT), power splitting relaying (PSR), and time switching relay (TSR) are also output. As the number of potential relay nodes increases, the performance of TRS, OCT, PSR, and TSR all improve. Numerical simulations were used to verify the validity of the theoretical values' strength.