Packet Reception Rate Research Articles

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

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

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’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.

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).

Optimal Design of LEMoNet for Environmental Monitoring of Data Centers

Half of the electrical power in today’s data centers (DCs) is consumed by cooling units, but much is wasted due to over-cooled or under-utilized servers. Environmental monitoring using Data Center Wireless Sensor Networks (DCWSNs) plays a central role in detecting and mitigating hotspots or over-cooling conditions. These networks often include thousands of sensors and require a reliable and power-efficient communication protocol. The Low Energy Monitoring Network (LEMoNet) is a two-tier DCWSN protocol that features Bluetooth Low Energy (BLE) for sensor communication in the first tier and leverages multi-gateway packet receptions in its second tier. This research studies the performance of LEMoNet in different DC topologies through the development of an analytical model. The model solves a multi-objective optimization problem to calculate two design parameters. The first parameter is the optimal number of gateways and an optimal location for each one. The second parameter is an optimal transition (TX) power for each sensor, subject to the application requirements. Evaluation results show that the optimal design includes one gateway on every other row. Also, the model reduces 65% of the sensors’ power consumption via TX power optimization while Packet Reception Rate (PRR) is kept up at the level of 95%.

Evaluating LoRaWAN Connectivity in a Marine Scenario

The growing need for interoperability among the different oceanic monitoring systems to deliver services able to answer the requirements of stakeholders and end-users led to the development of a low-cost machine-to-machine communication system able to guarantee data reliability over marine paths. In this framework, an experimental evaluation of the performance of long-range (LoRa) technology in a fully operational marine scenario has been proposed. In-situ tests were carried out exploiting the availability of (i) a passenger vessel and (ii) a research vessel operating in the Ligurian basin (North-Western Mediterranean Sea) both hosting end-nodes, and (iii) gateways positioned on mountains and hills in the inland areas. Packet loss ratio, packet reception rate, received signal strength indicator, signal to noise, and expected signal power ratio were chosen as metrics in line of sight and not the line of sight conditions. The reliability of Long Range Wide Area Network (LoRaWAN) transmission over the sea has been demonstrated up to more than 110 km in a free space scenario and for more than 20 km in a coastal urban environment.

Efficient RSU Selection Scheme for Fog-Based Vehicular Software-Defined Network

Efficient traffic control center (TCC) management requires roadside units (RSUs) to frequently update vehicles with traffic messages (TMs). RSUs are costly to road operators in terms of deployment and maintenance. Therefore, vehicular users expect the TCC to deliver high-quality and rapid responses to query requests on TMs through RSUs. This paper presents a novel RSU selection scheme based on a software-defined network (SDN) for traffic message transmission. In the proposed scheme, the RSU selection is determined by comparing the utility function calculated at the SDN controller. The SDN controller discovers the network connectivity topology through a cluster approach in which a cluster head (CH) is elected to report the network topology to the SDN controller. Furthermore, the SDN controller relies on the RSU selection manager to load existing RSU deployment techniques. In addition, a distance vector hop localization algorithm (DV-hop) is used to locate hidden RSUs to control signaling between the SDN controller and local controllers at RSUs. The proposed scheme achieves minimum latency and appropriate RSU management within a higher packet reception rate, which improves the selection of RSUs and traffic message delivery.

Efficient Link Scheduling Based on Estimated Number of Packets in Queue on Industrial Wireless Sensor Networks

The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less).

Improved optical camera communication systems using a freeform lens.

Optical camera communication (OCC) systems, which utilize image sensors embedded in commercial-off-the-shelf devices to detect time and spatial variations in light intensity for enabling data communications, have stirred up researchers' interest. Compared to a direct OCC system whose maximum data rate is strongly determined by the LED source size, a reflected OCC system can break that limitation since the camera captures the light rays reflecting off an observation plane (e.g., a wall) instead of those light rays directly emanated from the light source. However, the low signal-to-noise ratio caused by the non-uniform irradiance distribution produced by LED luminaire on the observation plane in current reflected OCC systems cannot be avoided, hence low complexity and accurate demodulation are hard to achieve. In this paper, we present a FreeOCC system, which employs a dedicatedly tailored freeform lens to precisely control the propagation of modulated light. A desired uniform rectangular illumination is produced on the observation plane by the freeform lens, yielding a uniform grayscale distribution within the received frame captured by the camera in the proposed FreeOCC system. Then, the received signal can be easily demodulated with high accuracy by a simple thresholding scheme. A prototype of the FreeOCC system demonstrates the high performance of the proposed system, and two pulse amplitude modulation schemes (4-order and 8-order) are performed. By using the freeform lens, the packet reception rate is increased by 35% and 32%, respectively; the bit error rate is decreased by 72% and 59%, respectively, at a transmission frequency of 5 kHz. The results clearly show that the FreeOCC system outperforms the common reflected OCC system.

Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes

Wireless body area networks (WBANs) present unique challenges due to their specific characteristics of mobility and over-the-body radio propagation. A huge amount of factors—both internal and external to the network—affect WBAN channel conditions, so a reliable and comprehensive theoretical model of these communications is unfeasible and impractical in real scenarios. Thus, an empirical performance analysis of several WBAN channels is presented in this work, based on the receiver signal strength indicator (RSSI) and packet reception rate (PRR) metrics. Four different static and dynamic activities have been characterized: standing, sitting, cycling and walking. This analysis confirms the theoretical notions of path attenuation due to body parts obstructing the signal path, while serving as a benchmark for the design of future algorithms. The experiments have been carried out with real hardware nodes with wireless interfaces in three ISM bands: 433 MHz, 868 MHz and 2.4 GHz, evaluating the effect of the transmit power and node placement for different subjects. In all scenarios, the PRR metric reaches its maximum of 100% for both sub-GHz bands. Finally, our study concludes that the RSSI metric is sufficient to exploit the periodicity of dynamic activities, without the need for any extra hardware resources.

A robust guaranteed time slots allotment scheme for real-time and reliable communication in WBANs

The ever-increasing aging population and expenditure related to healthcare, consequently demand the requirement for a state-of-the-art healthcare system. Wireless body area networks (WBANs) are the potential contender for this requirement. The IEEE 802.15.4 standard is considered a suitable protocol for WBANs and its beacon-enabled mode supports real-time traffic through guaranteed time slots (GTS) feature. Nevertheless, it suffers from reliability and timeliness guarantees. To address this issue, this work advocates a robust guaranteed time slots allotment scheme (RGAS) that consists of two phases, viz.: 1) determination of the severity of sensor data using fuzzy inference system; 2) dynamic allocation of available GTS slots to the contending nodes. Through simulation results, we demonstrate that the proposed RGAS significantly outperforms the existing GTS mechanism specified in the IEEE 802.15.4 in terms of various performance metrics such as packets transmission, packet reception rate, packet loss rate, and effective utilisation of contention free period.

A robust guaranteed time slots allotment scheme for real-time and reliable communication in WBANs

The ever-increasing aging population and expenditure related to healthcare, consequently demand the requirement for a state-of-the-art healthcare system. Wireless body area networks (WBANs) are the potential contender for this requirement. The IEEE 802.15.4 standard is considered a suitable protocol for WBANs and its beacon-enabled mode supports real-time traffic through guaranteed time slots (GTS) feature. Nevertheless, it suffers from reliability and timeliness guarantees. To address this issue, this work advocates a robust guaranteed time slots allotment scheme (RGAS) that consists of two phases, viz.: 1) determination of the severity of sensor data using fuzzy inference system; 2) dynamic allocation of available GTS slots to the contending nodes. Through simulation results, we demonstrate that the proposed RGAS significantly outperforms the existing GTS mechanism specified in the IEEE 802.15.4 in terms of various performance metrics such as packets transmission, packet reception rate, packet loss rate, and effective utilisation of contention free period.

A Link Quality Estimation Method Based on Improved Weighted Extreme Learning Machine

The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols. Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability. The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient. According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality. The link quality grade is taken as a metric of the link quality estimation. Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor. A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed. In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM. Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean.

LoRaWAN Energy Optimization with Security Consideration

Long Range Wide Area Network (LoRaWAN) is an emerging wireless technology that is expected to be widely deployed and implemented in several applications, especially with the promising widespread use of the Internet of Things (IoT) and its potential applications within the Fifth Generation (5G) communication technology. LoRaWAN consists of a number of nodes that monitors and senses the environment to collect specific data, and then sends the collected data to a remote monitoring device for further processing and decision-making. Energy consumption and security assurance are two vital factors needed to be optimized to ensure an efficient and reliable network operation and performance. To achieve that, each of LoRaWAN nodes can be configured by five transmission parameters, which are the spreading factor, carrier frequency, bandwidth, coding rate and transmission power. Choosing the best values of these parameters leads to enhancing the network deployment. In this paper, we shed the light to the security aspect in LoRaWAN network. Then, we introduced an algorithm that depends on the reinforcement learning approach to enable each node in the network to choose the best values of spreading factor and transmission power such that it leads to a lower energy consumption and higher packets’ delivery rate. The results of the simulation experiments of our proposed technique showed a valuable increase in the packet reception rate at the gateway and a significant decrease in the total consumed energy at the end nodes compared with the most related work in literature

Impact of the Generation Interval on the Performance of Sidelink C-V2X Autonomous Mode

Cellular-vehicle-to-everything (C-V2X) is gaining an increasing interest among the technologies under consideration for future connected and automated vehicles, especially for sidelink LTE-V2X Mode 4 and sidelink 5G-V2X Mode 2, where nodes autonomously perform resource allocations and transmissions, without relying on any infrastructure. In these cases, the allocation process has been designed based on the assumption of periodic packet generation constrained to a few possible allocation periods. Nevertheless, even assuming that the awareness messages are continuously exchanged, the packet generation might not always be exactly periodical or might come with a periodicity not constrained to the expected values, as for example with the Cooperative Intelligent Transport Systems (C-ITS) standard defined by the European Telecommunications Standards Institute (ETSI). The non-ideal periodicity of packet generation, if not properly taken into account, can significantly impact the operation and performance of C-V2X when vehicles autonomously select their radio resources. This paper provides an analysis of how a misalignment between packet generation and resource allocation affects the system performance and provides insights into how to design the parameter settings to improve the performance. A case study is provided, focusing on LTE-V2X Mode 4 and the cooperative awareness message (CAM) generation in agreement with the the ETSI specifications. Results show that the performance generally improves if the smallest allocation interval is adopted and if the reservation is maintained even when no packets are ready for transmission. It is also shown how the parameter controlling the maximum latency in LTE-V2X, jointly with the misalignment between the allocation and generation intervals, can further affect the packet reception rate.

An Angular 3D Path Selection Protocol in Wireless Sensor Networks

AbstractGeographical routing is an area of interest for wireless sensor networks because of its scalability and local decision making capability. Usually, geographical routing protocols rely on greedy approach and suffer from the void node problem (VNP). This paper presents an angular three-dimensional routing protocol (A3DR) for wireless sensor networks. In A3DR, the next hop is selected from nodes available in solid angle decided dynamically based on network density,i.e., larger solid angle for sparse networks while a smaller angle for dense one. The delay based contention mechanism has been proposed to select a suitable forwarding node among nodes lying in a particular solid angle. A3DR has a provision to adaptively tune the solid angle to resolve the void node problem. Further, the proposed protocol A3DR will take care of traffic congestion problem suffered by beaconless routing protocol. The A3DR tackle the concave void problem by allowing conditional backtracking to find another suitable path and prevent looping. Additionally, A3DR address overhear problem by utilizing the concept of request-to-forward and clear-to-forward for the nodes lying outside the solid angle 60°. The performance of proposed A3DR is compared with existing protocols by implementing it on the INET framework under OMNET++ simulator. The simulation results indicate that A3DR achieves a high packet reception rate while maximizing the residual energy and minimizing traffic overhead.

Implementation and Performance Evaluation for DSRC-Based Vehicular Communication System

Traffic accidents and congestion are the most common on-road problems that might be anywhere and anytime. Recently, to deal with such problems, well-coordinated wireless communication system between vehicles, road side units (RSU), drones, etc., namely vehicle-to-everything (V2X) communication system, has been considered as a key technology enabling on-road human safety and convenience. One of the well-known building blocks of the V2X communication system is Wi-Fi based Dedicated Short Range Communication (DSRC) which performs stably, owing to a long year of study, simplicity and capability of distributed operation. In spite of promising opportunities brought by DSRC, its elaborate performance evaluation must be done under real-world scenarios, in advance to the actual use, since its performance is directly related to human and vehicle safety. In this context, this article presents field test results of DSRC-based V2X communication system we implemented. As a result of the line-of-sight (LoS) test, the distance represented by 90% or more of packet reception rate (PRR) was 720 meters at 5 dBm, and 1,035 meters at 11 dBm. In Non-LoS (NLOS) shadowing test, the distance represented by 90% or more of PRR was 175 meters at 5 dBm, and 520 meters at 11 dBm. And in NLoS intersection test, the distance represented by 90% or more of PRR was 320 meters at 5 dBm, and 515 meters at 11 dBm. Meanwhile, the distance for PRR 90% was up to 520 meters at urban environment, 1,219 meters on highway, and 1,700 meters inside a tunnel.

Providing interoperability in Bluetooth mesh with an improved provisioning protocol

Different versions of Bluetooth Low Energy (BLE) have been standardized with point-to-point and broadcast topologies. Recently Bluetooth mesh specification provides BLE with mesh topology, highly demanded by Internet of Things (IoT) and numerous applications such as smart cities or Industry 4.0. Bluetooth mesh emerges as one of the most promising low-power IoT standards, enabling many-to-many device communications and optimising the deployment of large-scale device networks. Our research focuses on addressing the significant real-life problem of interoperability, the integration of devices with different topologies and versions of BLE to permit mesh transmissions. This integration will permit to increase the coverage area, to diminish important drawbacks in communication and power consumption and to save money by reducing the number of new devices. We have implemented and evaluated the Bluetooth mesh and the provisioning procedure, which provides network devices with all the necessary data to communicate with the rest of devices in the mesh network. Moreover, the Lightweight Provisioning, our proposal to improve the provisioning procedure, is presented. Our implementation allows Bluetooth non-mesh devices to be provisioned and to take part in a Bluetooth mesh network, making possible to continue using current devices. Performance was evaluated by a set of experiments across two steps: First, the provisioning time was measured for different configurations, including our proposal. Second, we measured the end-to-end delay and the packet reception rate in the lowest compatible BLE version. This work demonstrates real interoperability among devices with different BLE versions, a low end-to-end delay in a small and medium-scale mesh networks and a high packet reception rate for advertising intervals greater than 100 ms.

Human Detection through RSSI Processing with Packet Dropout in Wireless Sensor Network

This paper 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 this paper firstly 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 intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive 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.

Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks.

Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.