Packet Reception Research Articles

An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks

Nautical wireless ad hoc networks are becoming increasingly popular in oceans due to their easy deployment and self-curing capability. They may alternate frequently between connected mobile ad hoc networks and partitioned opportunistic networks due to mobility in large spaces. Traditional mobile ad hoc network routing is used to find the shortest route for connected networks. However, for opportunistic networks, routing schemes with a broadcast nature mainly exploit the reduction in message duplication and the local relaying technologies described in the literature, which may lead to unnecessary resource waste and low packet delivery ratios. To solve the problem, we propose an efficient opportunistic routing scheme based on prediction for nautical wireless ad hoc networks. The scheme first develops an effective candidate intermediate region to recognize the unavailability of some apparently qualified intermediate nodes, and then takes into account the packet reception ratio between nodes and relay advancement prediction, to improve packet delivery. The proposed scheme achieves performance improvements regarding packet loss ratio and throughput with a tolerable latency increase, compared to other schemes.

Routing in LoRa for smart cities: A gossip study

Wireless communications for IoT in smart cities may be affected by Non-Line-of-Sight (NLOS) propagation conditions due to obstacles such as buildings, trees, and others. LoRaWAN technology transmits information through several kilometers in one hop, from a source node to a gateway. However, packet reception decreases when the link has NLOS conditions and there are no alternative routes for packet delivery. This paper presents a LoRa implementation of MACGSP6, a Gossip-based routing algorithm, to improve communications in a LoRaWAN network where all nodes have NLOS conditions. The paper compares the packet delivery ratio of LoRaWAN and MACGSP6. Results show that packet delivery to the gateway increased from 0% with LoRaWAN to a minimum of 53% with the routing algorithm. Additionally, most packets were delivered using the shortest path available, even when the algorithm does not explicitly compute the routes, decreasing the delay associated with this process. Thus, the proposed solution is useful for IoT applications in smart cities, such as smart metering and structural health monitoring, where one relay node can forward the information of many other nodes.

Performance analysis of cognitive radio‐assisted clustering car‐following V2X communication system

SummaryIn this paper, we study high spectrum efficiency transmission in vehicle to everything (V2X) system to allow the roadside unit (RSU) broadcasts its safety information to vehicles which belong to a dedicated group. However, low latency and high reliability are tightly required in related applications of V2X; we explore advantages of cognitive radio (CR) and non‐orthogonal multiple access (NOMA) to form system, namely, CR NOMA‐V2X. Such considered system provides transmissions from the RSU to vehicles with respect to lower access latency and to enhance the packet reception probability. In the proposed scheme, the vehicles need channel state information (CSI) for signal decoding order, but we face with imperfect CSI occurs in practical scenarios. To evaluate such degraded system performance of the involved CR NOMA‐V2X, we introduce the approximate closed‐form expressions of outage probability. Numerical results indicate the validness of our derivations.

LoRa Signal Synchronization and Detection at Extremely Low Signal-to-Noise Ratios

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

Fast Finite Frame Length IRSA Optimization Based on Bayesian Optimization

Simplified packet reception models such as collision model are usually adopted for optimizing irregular repetition slotted ALOHA (IRSA), which may lead to performance penalty due to its poor accuracy. In this letter, we consider the problem of online IRSA optimization in the finite frame length regime using a more accurate reception model. In order to allow a fast optimization of IRSA, we present a new method based on Bayesian optimization with Gaussian processes. Our method finds the optimal user degree distribution minimizing packet loss rate (PLR) in an iterative way. At each iteration, a surrogate is built to model the unknown IRSA PLR performance function using Gaussian process (GP) regression, and then an acquisition function defined from the surrogate is utilized to choose the next degree distribution, whose PLR is evaluated by the more accurate model. The proposed method is able to infer the PLR of an untested degree distribution, thus converging quickly within only tens of iterations. Simulation results show that IRSA schemes optimized using our method can achieve lower PLR compared with those optimized based on the collision model.

Real-Time Status Updates in Wireless HARQ With Imperfect Feedback Channel

We study the impact of the erroneous wireless control feedback channel on the Age of Information (AoI) performance. We consider a point-to-point communication setup employing packet combining strategies to transmit status update packets over an erroneous wireless data channel. The sender receives the positive acknowledgment (ACK) or negative acknowledgment (NACK) of packet reception over an error-prone wireless feedback channel. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt an asymmetric signal detection model to control the detection accuracy of ACK and NACK signals. We then compute the explicit expressions for the average AoIs under preemptive and non-preemptive service management policies. We show the optimum parameter design for the control channel model in order to minimize the average AoI. The numerical results validate the analysis and provide detailed perspectives on the optimal signal detection setup minimizing the average AoI, and the possible trade-off between AoI and resource utilization. Generally, the analysis for a preemption setting illustrates that a better protection for the NACK messages compared to the ACK messages can preserve the minimum AoI performance. Especially, under a high noisy feedback channel setup, we show that the viable solution minimizing the average AoI is a blind transmission mechanism at the cost of increasing unnecessary utilization of the channel resources. Moreover, the analysis for a non-preemptive policy reveals the dependence of the optimal feedback signal detection design on the status packet generation rate at the sensor. Such a dependency makes the feedback signal detection approach to provide a more reliable ACK detection compared to NACK messages under the condition of more frequent packet arrival, whereas the opposite holds under the condition of less frequent packet arrival.

Boat Detection and Tracking Using RSSI Signal in A Device-Free Environment

Abstract: This project presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So, it raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receiver sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method. Keywords: RSSI Signal, Detection and tracking

Game-Theoretic Power and Rate Control in IEEE 802.11p Wireless Networks

Optimization of the transmission power and rate allocation is a significant problem in wireless networks with mobile nodes. Due to mobility, the vehicles establishing wireless networks may exhibit severe fluctuations of their link quality, affecting their connection reliability and throughput. In Vehicular Ad-hoc Networks (VANETS), the IEEE 802.11p standard provides a practical metric for the Packet Reception Ratio (PRR), which is related with the transmission power and rate. Finding a global strategy for optimizing PRR for all mobile nodes can be treated as a potential game where each vehicle is considered as a selfish player, aiming to maximise its transmission reliability while rate constraints are satisfied. To this end, we propose a game-theoretic approach that converges to a Nash equilibrium. The main contributions of this work include: (i) identification of the best case equilibrium, for two cases of interference: diminished or kept stable, and (ii) verification of the equilibrium optimality, by showing that the value of stability is 1. Moreover, numerical results exhibiting the ease of the utility function calculation are provided, especially after an SINR level, whereby the utility function is concave and can be solved efficiently in polynomial time.

Automated Agricultural Management Systems Using Smart Based Technology

This research focuses on the design and implementation of an Internet of Things (IoT) smart-based system for the agriculture industry, especially chilli plantations. Every year, chilli farmers experience huge losses due to pest infestation and a lack of technology to precisely manage their farms. To solve several problems, the IoT system was designed to continuously monitor pest detection status, temperature, humidity index, and soil moisture level simultaneously. Besides these, the IoT system is also designed for long-distance remote water, pesticide, and fertiliser pumps. An IoT platform was applied in this project to provide a monitoring dashboard and switches to empower several pumps with long-distance remotes using the LoRa communication systems in a wide coverage range of chilli farms. Three experiments were designed to determine the range of the LoRa network. All experiments and data collection were done on the campus of Universiti Teknikal Malaysia Melaka (UTeM) and the Melaka area. All experiments were analysed based on the results collected, such as packet reception rate (PRR), signal-to-noise ratio (SNR), and received signal strength indicator (RSSI). Based on the experiment results and conclusion, the LoRa communication system is suitable for applying to chilli farms in order to monitor all the elements such as soil moisture and pest detection status. The experiment also concludes that this LoRa network can be worked effectively up to cover a range of 260m with the elevation of the gateway. Keywords—Management systems; Internet of Things; crop field; LoRa; agriculture industries; precision engineering

LSTM-Characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-Assisted Sensor Network

Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSNs). Due to UAV’s maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor. This article proposes a new deep reinforcement learning-based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on deep deterministic policy gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices. To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to the existing deep reinforcement learning solutions.

Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.

Opportunistic Routing for Opto-Acoustic Internet of Underwater Things

Internet of Underwater Things (IoUT) is a technological revolution that could mark a new era for scientific, industrial, and military underwater applications. To mitigate the hostile underwater channel characteristics, this article considers a multimodal underwater network that hybridizes acoustic and optical wireless communications to achieve an ubiquitous control and high-speed low-latency networking performance, respectively. Since underwater optical wireless communications (UOWCs) suffer from limited range, it requires effective multihop routing solutions. In this regard, we propose a sector-based opportunistic routing (SectOR) protocol. Unlike the traditional unicast routing (TUR) techniques, which send packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in TUR. Contingent upon the performance characterization of a single-hop link, we obtain a variety of local and global metrics to evaluate the fitness of a candidate set (CS) and develop candidate prioritization techniques for various OR metrics. Since rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula> error and range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula> beamwidth tradeoffs yield different CS diversities, we develop a candidate filtering and searching algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that the SectOR protocol can perform even better than optimal unicast routing protocols in well-connected underwater networks.

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks.

An Approach for Intersection Prevention in Transmission Channel Using Correlation Matrix and Tree Construction Model in V2X Framework

Typically, the delivery of information in vehicular ad hoc networks (VANETs) is regarded as a demanding one because of high mobility and invariable topological difference. One major concern in VANET framework is the occurrence of simulated intersection at which the packet reception probability become complicated for predicting and the model turn out to be complex highly. So as to overcome this dispute, a new framework is introduced which in turn prevents the intersection among the transmission path. Initially, the system model is initialized and the availability of channel is estimated. Based on the strength and size of data to be transmitted, the availability of channel is computed. After that, the best forwarding zone computation is made for minimizing the redundant data packets flow. To check the priority of packet and occurrence of packet collision, tree construction based data strength transmission is employed at last, the prevention of intersection or collision between the transmission channel is checked by means of correlation matrix based intersection prevention approach by monitoring the neighbor node. Then, the data packets were forwarded in an efficient manner without any intersection between the frames. The performance analysis is estimated in terms of network lifetime, packet delivery ratio, packet collision; inter node collision, throughput, and end-to-end delay.

Smart Biomedical Sensor Network for Multi-patient Cardiac Arrhythmia Monitoring

Energy consumption has been a major bottleneck in wireless sensor network applications. This paper presents wireless interconnected smart biomedical sensors for cardiac arrhythmia detection and remote monitoring of a group of in-house static patients arranged in a regular grid matrix. In node continuous detection of arrhythmias is performed using an autoencoder with a decision tree classifier from a single-channel ECG data. The accumulated arrhythmic episodes over some time in each node are delivered through a residual energy based intelligent routing to a mobile sink, which uploads them to a cloud-based server for remote access by an expert physician. The energy consumption of the node and network is minimized in three stages. First, an event-triggered data routing strategy allows intermittent transmission instead of continuous one. Second, the arrhythmic episodes are delivered to a mobile sink (a caregiver) based on its location sensing to minimize the number of hops in a routing cone. Third, an adaptive power level adjustment at the destination node is done in accordance with mobility of the sink. The scheme was hardware implemented with 15 sensor nodes utilizing ARM-based standalone controller in a 52.68 sqm floor area with a latency of 1.69 ms per byte and energy consumption of 0.338 mJ per byte, with a packet reception accuracy of 97.8% over 4000 sessions of transmission. The blind test accuracy of arrhythmia detection over nearly 20442 cardiac beats from MIT BIH arrhythmia dataset containing NSR, L, R and V beats was 99.48% with a beat abnormality detection latency of 16 ms.

Interference-Aware Transmission Scheduling for Internet of Vehicles

Next-generation Intelligent Transportation Systems (ITS) use wirelessly connected vehicles for safety and non-safety applications such as autonomous driving, cooperative awareness, route guidance and multimedia transmissions. This network known as the Internet of Vehicles (IoVs) suffers from many challenges such as collisions due to hidden terminals, and interference from simultaneously transmitting vehicles. Moreover, the packet reception ratio of transmissions between vehicles is significantly reduced at high vehicle densities and severe fading scenarios. As safety applications require periodic broadcast of safety messages from each vehicle to all other vehicles in the neighborhood, the development of an efficient medium access technique is a key challenge. In this paper, we propose an interference-aware medium access protocol for vehicular networks that allocate time slots to the vehicles such that interference due to hidden nodes is minimized. We first provide an analytical model to compute the interference range of a vehicle in the presence of simultaneously transmitting vehicles. We also propose a scheduling algorithm that uses vehicle GPS positions and reuse the same time slot only at a distance greater than the interference range. Simulation results show that the proposed technique improves the packet reception ratio by 70% as compared to the standard IEEE 802.11p based CSMA/CA protocol.

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

Channel congestion has been an open challenge for vehicular networks due to the limited resource of communication channels. Explosion of channel access requests from a massive number of transmitter vehicles can exhaust bandwidth and then degrade transmission quality. The rapid drop of messages (because of the high bit error rate in the transmission congestion condition) can threaten the safety of connected vehicles. Maintaining congestion-free communications is then essential to improve the reliability for vehicular networks, including Cellular-V2X (C-V2X)-based cooperative intelligent transport systems and road-safety applications. In this work, we present a novel intelligent transmission control model, namely DEEPCUT, to automatically adjust the message broadcasting rate of a transmitter vehicle. DEEPCUT works based on a Double Deep Q-learning Networks with Prioritized Experience Relay framework. DEEPCUT encourages the transmitter vehicle to (1) reduce its broadcasting rate if the vehicle is maintaining a safe distance from its neighbors and (2) increase the rate if the vehicle is approaching the others at a high-risk distance, all done by using reward/punish strategies. The evaluation results show that DEEPCUT can cut up 16% redundant data while increasing 22% packet reception rate compared with baseline models, particularly in crowd vehicular communications. Our risk-based transmission control can be an excellent complement to address the congestion when the channel cannot satisfy every vehicle&#x2019;s resource requests. At best, the risk assessment-based approach in our congestion control method can provide a novel material to enhance Decentralized Congestion Control (DCC) for 5G V2X sidelink in the coming specifications.

A Packet Delay Assessment Model in the Data Link Layer of the LTE

The issues of modeling and evaluating the characteristics of the LTE data link layer functioning are considered. Transmitting packets in the data link layer are represented by a probabilistic-temporal graph consisting of two subgraphs. The first subgraph describes the operation of the HARQ protocol, and the second subgraph describes the operation of the ARQ protocol. The first subgraph is nested within the second subgraph. The probabilities of correct reception, non-error detection, and retransmission of packets in the MAC and RLC layers and generating functions of the packet service time based on the HARQ and ARQ protocols are determined. With the help of generating functions, the average value, variance, and coefficient of variation of the packet service time are determined. To calculate the average packet delay time in the LTE data link layer, the type of queuing system is selected, taking into account the coefficient of variation of the packet service time. The analysis of packets' delay time in the network's data link layer is carried out for different values of the intensity of packet arrival and the probabilities of a bit error in the physical layer of the network. For the sustainable functioning of the data link layer of the network, the limit values of the intensity of the arrival of packets are determined for a given probability of a bit error in the physical layer of the network.

IMPROVED ROUTING PROTOCOLS BASED ON RPL FOR FULL IOT-WSN STACK

The Internet Engineering Task Force (IETF) standardized several protocols such as Constraint Application Protocol (CoAP) to run over WSN-IoT constrained devices. IPv6 is used to transmit packets over IEEE802.15.4 radio link called 6LoWPAN. The routing protocol for low power and lossy (RPL) network enable connectivity of WSN over IoT. Nodes in RPL optimized its path using objective function (OF), which depends on different node/link metrics. In this paper, the performance of IoT-WSN stack consisting of CoAP, 6LoWPAN and two proposed protocols based on RPL are evaluated. The two proposed protocols depend on a new OF based on link reliability and energy metric. The first is a modification of recently introduced protocol called Energy Threshold RPL (ETRPL) protocol. ETRPL uses the remaining energy of the preferred parent node as a metric. The other protocol used a new metric that combines energy consumption with ETX for all paths to the root called EERPL. The protocols are tested with full IoT-WSN stack and implemented using Cooja simulator. The results showed that ETRPL and EERPL performed better than standard RPL in terms of the energy consumption, average time delay, packet reception ratio, throughput and the number of dead nodes.

LiteNap: Downclocking LoRa Reception

This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked ‘light sleep’ mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing and cause ambiguity in symbol demodulation. LiteNap addresses the problem by leveraging an empirical observation that the hardware of LoRa radio can cause phase jitters on modulated chirps, which result in frequency leakage in the time domain. The timing information of phase jitters and frequency leakages can serve as physical fingerprints to uniquely identify modulated chirps. We propose a scheme to reliably extract the fingerprints from under-sampled chirps and resolve ambiguities in symbol demodulation. We update the reception pipeline of LoRa radio to enable reliable packet detection and decoding when operating in downclocked mode. We implement LiteNap on a software defined radio platform and conduct trace-driven evaluation to validate the proposed strategies. Experiment results show that LiteNap can downclock LoRa receiver to sub-Nyquist rates for energy savings ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., 1/8 of Nyquist rate), without substantially affecting packet reception performance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., >95% packet reception rate).