Packet Success Rate Research Articles

Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.

UNJUK KERJA KOMUNIKASI NIRKABEL HALF-DUPLEX ANTARKENDARAAN MEMANFAATKAN NRF24L01

This study investigates the efficacy of the affordable NRF24L01 wireless communication module in half-duplex mode for vehicle-to-vehicle (V2V) communication. The prototype integrated the NRF24L01 module with an ESP32 microcontroller and an OLED display. The study assesses the delay and packet success rate to uncover the module's attributes under half-duplex situations. This not only surpasses the mere collecting of data, but also emphasizes the practical limitations and strengths of the module. The project aims to achieve two main objectives: firstly, to gather empirical data that may be used to make well-informed decisions in the design of NRF24L01 applications, and secondly, to contribute to the broader examination of wireless communications protocols. It improves comprehension of the impact of half-duplex operation on the performance of the NRF24L01 module. By revealing the mechanics of half-duplex communication, it enhances the efficiency of NRF24L01 usage and facilitates the development of a better protocol.

Reliability Evaluation of a Wireless Sensor Network in Terms of Network Delay and Transmission Probability for IoT Applications

Reliability is a crucial performance metric for wireless sensor networks (WSNs) because it ensures network functionality even with degraded sensor nodes and wireless connections. Due to the limited availability of power resources associated with the sensor nodes, their efficacy is reduced. In addition, parameters such as the number of neighbours, packet success rate, packet size, and node capacity have a significant impact on how sensor nodes function. Similarly, inter-node distance and signal-to-noise ratio (SNR) have the greatest influence on the functioning of wireless communications in WSNs. Before evaluating the reliability of a WSN, therefore, the node and link reliability must be computed. This paper evaluates the reliability of sensor nodes by taking into account all of the parameters previously mentioned. In addition, the lifetime of each sensor node is estimated in terms of the fractional energy consumed for each wake-up operation and the transmission of packets, respectively. Using the inter-node distance and SNR values, each wireless link’s reliability is determined. Following this, a proposed algorithm evaluates the reliability of WSN by first enumerating all minimal paths between the sensor node and the base station and then converting these minimal paths into their sum-of-disjoint-product terms. Making use of a suitable example network, every step of the proposed method is illustrated. For evaluating the reliability of WSN, the proposed method is simulated in different environments. In addition, the influence of parameters such as inter-node distance, SNR, number of neighbours, packet success ratio, and time on the reliability values of WSN is portrayed and analysed.

Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning.

This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q -Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q -learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.

Wireless‐SpaceWire bridge for intrasatellite transmissions

SummaryThe scope of this paper is to present the proof‐of‐concept and functional verification of a Wireless‐SpaceWire bridge (High‐Throughput Wireless‐SpaceWire Bridge for Intra‐Satellite Transmissions [HiSAT] bridge) designed to replace the wired SpaceWire (SpW) connections for intraspacecraft communications. To provide proper data handling and conversion, the proposed solution implements two main components: (1) the SpW Converter, which provides the SpW interface, and (2) the Wireless Converter, which provides the multiantenna radio frequency (RF) front‐end. High‐end research infrastructure is used in the solution implementation. STAR‐Dundee SpW products emulate real spacecraft instrumentation and implement the SpW links and interfaces. Xilinx FPGA ZCU102 boards are used for the implementation of the hardware/software communication stack of the SpW Converter. A comprehensive National Instruments USRP Software Defined Radio platform is used to implement the Wireless Converter. End‐to‐end laboratory tests are run to evaluate the performance of the proposed solution in terms of average end‐to‐end delay, average data rate, and packet success rate and to assess the technology readiness. The results demonstrate that the HiSAT bridge is TRL4 and that the technological approach (i.e., using FPGAs and OFDM transmissions) can successfully replace an on‐board intraspacecraft SpW link.

Cross-Tier Interference Mitigation in 3D HetNets for LTE and Wi-Fi Access

Due to the unprecedented rate of transformation in the field of wireless communication industry, there is a need to prioritise the coverage, network power and throughput as preconditions. In Heterogeneous Networks (HetNets) the low power node inclusion like Femto and Pico cells creates a network of Multi-Tier (M-Tier) which is regarded as the most significant strategy for enhancing the coverage, throughput, 4G Long Term Evolution (LTE) ability. This work mainly focuses on M-Tier 3D Heterogeneous Networks Energy Efficiency (EE) based Carrier Aggregation (CA) scheme for streaming real-time huge data like images. At first, M-Tier 3D HetNets scheme was made for investigating Signal to Noise Interference Ratio (SNIR) on assessing the collective Pico-tier and Femto-tier interference. Next, the scheme of channel allocation is scrutinised so as to estimate throughput from the multiple tiers. Additionally, with the use of CA technique, the problem of energy efficiency for M-Tier 3D Heterogeneous Network (HetNet) in relation to energy metrics and throughput was evaluated with the use of LTE and Wireless Fidelity (Wi-Fi) coexistence. The simulation is carried out in a MATLAB setting, and the outcomes reveal a huge impact on EE. The simulation is carried in terms of EE, transmission time, throughput, packet success rate, convergence probability, and coverage region. The analysis from simulation shows that on improving the output of the device, interference among small cell base stations is reduced on increasing EE. The outcomes attained aid in the effective creation of M-Tier 3D HetNets for enhancing EE by employing Multi-Stream Carrier Aggregation (MSCA) in HetNets.

DRLLA: Deep Reinforcement Learning for Link Adaptation

Link adaptation (LA) matches transmission parameters to conditions on the radio link, and therefore plays a major role in telecommunications. Improving LA is within the requirements for next-generation mobile telecommunication systems, and by refining link adaptation, a higher channel efficiency can be achieved (i.e., an increased data rate thanks to lower required bandwidth). Furthermore, by replacing traditional LA algorithms, radio transmission systems can better adapt themselves to a dynamic environment. There are several drawbacks to current state-of-the-art approaches, including predefined and static decision boundaries or relying on a single, low-dimensional metric. Nowadays, a broadly used approach to handle a variety of related input variables is a neural network (NN). NNs are able to make use of multiple inputs, and when combined with reinforcement learning (RL), the so-called deep reinforcement learning (DRL) approach emerges. Using DRL, more complex parameter relationships can be considered in order to recommend the modulation and coding scheme (MCS) used in LA. Hence, this work examines the potential of DRL and includes experiments on different channels. The main contribution of this work lies in using DRL algorithms for LA, optimized for throughput based on a subcarrier observation matrix and a packet success rate feedback system. We apply Natural Actor-Critic (NAC) and Proximal Policy Optimization (PPO) algorithms on simulated channels with a subsequent feasibility study on a prerecorded real-world channel. Empirical results produced by experiments on the examined channels hint that Deep Reinforcement Learning for Link Adaptation (DRLLA) offers good performance indicated by a promising data rate on the additive white Gaussian noise (AWGN) channel, the non-line-of-sight (NLOS) channel, and a prerecorded real-world channel. No matter the channel impairment, the agent is able to respond to changing signal-to-interference-plus-noise-ratio (SINR) levels, as exhibited by expected changes in the effective data rate.

Performance of Narrow Band Wide Area Networks with Gateway Diversity.

This paper quantifies the coverage area of Low-Power Wide-Area Networks (LPWAN) for Packet Success Rates (PSR) above 85%, where acceptable Quality of Service (QoS) can be achieved. The network consists of battery-operated end-nodes (ENs) and multiple stationary gateways (GWs). We consider asynchronous communication that uses ALOHA-based random channel access. Each transmission from the ENs can be received by multiple GWs. Such spatial diversity results in favorable Signal-to-Noise ratios (SNR). The LoRa modulation is assumed and its specific features, such as IQ inversion, further contribute to decreasing the impact of interference. An increase in the GW density improves network performance, which allows support for a larger density of end-nodes as well as increasing the coverage area. Our simulation results show that a suburban area of up to 1.44 km2 can be covered with five GWs with up to fifty end-nodes with a PSR greater than 86%.

A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.

Fuzzy Logic Based WSN with High Packet Success Rate and Security

Considering the evidence that conditions accept a considerable place in each structure, owing to limited assets available at each sensor center, it is a difficult problem. Vitality safety is the primary concern in many remote sensor hub implementations. This is critical as the improvement in the device's lifetime depends primarily on restricting the usage of vitality in sensor hubs. The rationing and modification of vitality usage are of the most serious value in this context. In a remote sensor arrangement, the fundamental test is to schedule measurements for the least use of vitality. These classification frameworks are used to frame the classes in the structure and help efficiently use the strength that burdens out the network's lifespan. Besides, the degree of the center was taken into account in this work considering the measurement of cluster span as an improvement to the existing methods. The crucial piece of leeway of this suggested approach on affair clustering using fuzzy logic can increase the system's lifespan by reducing the problem area problem word.

Energy Auditing and Broken Path Identification for Routing in Large-Scale Mobile Networks Using Machine Learning

In mobile computing, all nodes are movable nodes, which causes many problems for transmitting data packets in a sequence manner; since the mobile nodes are connected with each other, during movement, nodes make the connection fail or damaged. This kind of link damage is caused by nodes that travel out of range from the network limit and also affect the packet success rate. This reduces the network lifetime and detection efficiency and increases the communication overhead. Every mobile node in mobile computing is an unstable node, causing numerous problems for broadcasting data packets in a series method. When the mobile nodes are connected to each other, relay nodes cause the link to break or else sustain damage. This type of connection failure is brought on by nodes that leave the network’s permitted range, which also lowers the packet success rate. The link failure cannot be recovered by the multipath routing algorithm. As a result, the communication overhead is increased while the network lifetime and detection effectiveness are reduced. Then, the novel energy routing (NER) method that has been proposed is employed to support the energetic routing path across the middle nodes. It is challenging to locate the failed channels and carry on with the successful packet transfer. The master node selection algorithm is intended to identify the best relaying node, fault-free packet transmission process among the network structure’s relaying nodes. The master node is efficiently chosen in this manner. The master node, also known as the energy-based important node, is employed in the mobile network to carry out error-free packet transmission procedures. The other nodes are lower energy nodes that do not participate in packet forwarding, and this algorithm only detects the higher energy successful nodes. This lengthens the network’s lifespan, improves detection effectiveness, and lowers communication overhead. The energy-based heavy node is also known as the master node, which is used to perform a problem-free communication process in the mobile network. This algorithm only accepts the higher energy successful node; the remaining nodes are lower energy nodes which do not perform the communication process. This increases the network lifetime and detection efficiency and reduces the communication overhead. The performance metrics for the proposed system is evaluated by end to end delay, communication overhead, throughput, detection efficiency, network lifetime, and packet drop rate.

Grant-Free Coexistence of Critical and Noncritical IoT Services in Two-Hop Satellite and Terrestrial Networks

Terrestrial and satellite communication networks often rely on two-hop wireless architectures with an access channel followed by backhaul links. Examples include cloud-radio access networks (C-RAN) and low-Earth orbit (LEO) satellite systems. Furthermore, communication services characterized by the coexistence of heterogeneous requirements are emerging as key use cases. This article studies the performance of critical service (CS) and non-CS (NCS) for Internet-of-Things (IoT) systems sharing a grant-free channel consisting of radio access and backhaul segments. On the radio access segment, IoT devices send packets to a set of noncooperative access points (APs) using slotted ALOHA (SA). The APs then forward correctly received messages to a base station over a shared wireless backhaul segment adopting SA. We study first a simplified erasure channel model, which is well suited for satellite applications. Then, in order to account for terrestrial scenarios, the impact of fading is considered. Among the main conclusions, we show that orthogonal interservice resource allocation is generally preferred for NCS devices, while nonorthogonal protocols can improve the throughput and packet success rate of CS devices for both terrestrial and satellite scenarios.

Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks

In recent years, unmanned aerial vehicles (UAVs) have become the trend for different types of research and applications. UAVs can accomplish some technical and risky tasks while still being safe, mobile, and inexpensive to operate. However, UAVs need flying ad-hoc networks (FANET) to operate in inaccessible or infrastructure-less areas. Subsequently, in many military and civil applications, the UAVs are connected ad hoc. FANET-based UAV systems have been developed for search and rescue, wildlife surveys, real-time monitoring, and delivery services. Maintaining the reliability and connectivity among UAV nodes in FANET becomes challenging because of the UAV movement, environmental conditions, energy efficiency, etc. Energy-aware routing protocols have become essential for developing advanced and effective FANETs. This paper presents a proposed Cross-Layer and Energy-Aware Ad-hoc On-demand Distance Vector (CLEA-AODV) routing protocol for improving FANET performance. The CLEA-AODV protocol is mainly divided into three sections: routing with AODV protocol, Glow Swarm Optimization (GSO)-based Cluster Head Selection, and Cooperative Medium Access Control (MAC). The cross-layer approach is implemented on the network layer and the data layer. The major parameters considered to evaluate the performance of the FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) delay, and packet drop ratio (PDR). The Network Simulator version 2 (NS2) is used to implement the CLEA-AODV protocol and evaluate the network performance. The results are compared with the standard AODV, Self-Organization Clustering-GSO (SOC-GSO), and Energy Efficient Neuro-Fuzzy Cluster-based Topology Construction with Meta-Heuristic Route Planning (EENFC-MRP) protocols. The results show that the CLEA-AODV surpasses these protocols in terms of PSR, TP, E2E delay, and PDR.

Developing capacity sharing strategy for vehicular networks with integrated use of licensed and unlicensed spectrum

A widely deployed cellular network, supported by direct connections, can offer a promising solution that supports new services with strict requirements on access availability, reliability, and end-to-end (E2E) latency. The communications between vehicles can be made using different radio interfaces: One for cellular communication (i.e., cellular communication over the cellular network based on uplink (UL)/downlink (DL) connections) and the other for direct communication (i.e., D2D-based direct communications between vehicles which allows vehicular users (V-UEs) to communicate directly with others). Common cellular systems with licensed spectrum backed by direct communication using unlicensed spectrum can ensure high quality of service requirements for new intelligent transportation systems (ITS) services, increase network capacity and reduce overall delays. However, selecting a convenient radio interface and allocating radio resources to users according to the quality of service (QoS) requirements becomes a challenge. In this regard, let’s introduce a new radio resource allocation strategy to determine when it’s appropriate to establish the communication between the vehicles over a cellular network using licensed spectrum resources or D2D-based direct connections over unlicensed spectrum sharing with Wi-Fi. The proposed strategy aims at meeting the quality of service requirements of users, including reducing the possibility of exceeding the maximum delay restrictions and enhancing network capacity utilization in order to avoid service interruption. The proposed solution is evaluated by highlighting different conditions for the considered scenario, and it is demonstrated that the proposed strategy improves network performance in terms of transmitted data rate, packet success rate, latency, and resource usage

Toward Automatic Subsea Operations Using Real-Time Underwater Optical Wireless Sensor Networks

Automatic subsea operations using real-time underwater optical wireless sensor networks are mooted as a new paradigm of underwater Internet of Things in this paper. To this end, we develop an underwater optical wireless sensor network prototype called AquaE-net, which consists of an optical hub and two sensor nodes with temperature, salinity, conductivity, and pH sensing capabilities. Clock synchronization is realized in AquaE-net, which paves the way for future underwater positioning and navigation. Moreover, real-time and bi-directional network communication with 100% packet success rates has been realized by AquaE-net in free space and turbid water in the harbor of the Red Sea at King Abdullah University of Science and Technology. This is the first step toward future effective marine environmental monitoring and marine resources exploration.

SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks

ABSTRACT A consumer in Information Centric Network (ICN) generates an Interest packet by specifying the name of the required content. As the network emphasizes on content retrieval without much bothering about who serves it (a cache location or actual producer), every Content Router (CR) either provides the requested content back to the requester (if exists in its cache) or forwards the Interest packet to the nearest CR. While forwarding an Interest packet, the ICN routing by default does not provide any mechanism to predict the probable location of the content searched. However, having a predictive model before forwarding may significantly improve content retrieval performance. In this paper, a machine learning (ML) algorithm, particularly a Support Vector Machine (SVM) is used to forecast the success of the Interest packet. A CR can then send an Interest packet in the outgoing interface which is forecasted successful. The objective is to maximize the success rate which in turn minimizes content search time and maximizes throughput. The dataset used in is generated from a simulation topology designed in ndnSim comprising 10 K data points having 10 features. The linear, RBF and the polynomial kernel (with degree 3) are used to analyze the dataset. The polynomial kernel shows the best behavior with 98% accuracy. A comparative retrieval time with and without ML demonstrates around 10% improvement with SVM enable forwarding compared to normal ICN forwarding.

Priority and Traffic-Aware Contention-Based Medium Access Control Scheme for Multievent Wireless Sensor Networks

The requirement of high quality of service (QoS) in multi-priority industrial and domestic sensor networks poses new challenges to the increasing adoption of Internet of Things (IoT). In Multi-event Wireless Sensor Networks (MWSNs), nodes generate different types of data packets i.e., urgent (high priority) or normal (low priority), with different traffic proportions. High priority packets require an assurance of faster transmission and higher reliability in the network. In the literature, the existing medium access control (MAC) protocols for MWSNs have limited consideration in supporting data priority with different traffic proportions. Therefore, this paper proposes an energy efficient MAC scheme that incorporates multi-priority of data packets with dynamic traffic proportion, called PriTraCon-MAC. PriTraCon-MAC supports multi-events by considering four different priority levels of data packets and uses a novel approach that adjusts the contention window adaptively. Due to that, Request-To-Send frame of higher priority data can be sent earlier in the contention window, resulting in the corresponding faster acceptance by the receiver. Furthermore, mathematical delay analysis with different priority traffic proportions has also been undertaken. In addition, PriTraCon-MAC has been implemented in OMNET++ Castalia and its performance has been evaluated in terms of packet delay, reliability, and energy consumption, and compared with the existing Timeout Multi-priority based-MAC (TMPQ-MAC) under various network conditions. The simulation results demonstrate that PriTraCon-MAC offers lower average delay and achieves significantly higher packet success rate, while reducing energy consumption by up to 80% when compared with TMPQ-MAC protocol.

Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites

Software-defined radio (SDR) devices have made a massive contribution to communication systems by reducing the cost and development time for radio frequency (RF) designs. SDRs opened the gate to programmers and enabled them to increase the capabilities of these easily manipulated systems. The next step is to upgrade the reconfigurability into adaptability, which is the focus of this paper. This research contributes to improving SDR-based systems by designing an adaptable packet communication transmitter and receiver that can utilize the communication window of CubeSats and small satellites. According to the feedback from the receiver, the transmitter modifies the characteristics of the signal. Theoretically, the system can adopt many modes, but for simplicity and to prove the concept, here, the changes are limited to three data rates of the Gaussian minimum shift keying (GMSK) modulation scheme, i.e., 2400 bps GMSK, 4800 bps GMSK and 9600 bps GMSK, which are the most popular in amateur small satellites. The system program was developed using GNU Radio Companion (GRC) software and Python scripts. With the help of GRC software, the design was simulated and its behavior in simulated conditions observed. The transmitter packetizes the data into AX.25 packets and transmits them in patches. Between these patches, it sends signaling packets. The patch size is preselected. Alternatively, the receiver extracts the data and saves it in a dedicated file. It directly replies with a feedback message whenever it gets the signaling packets. Based on the content of the feedback message, the characteristics of the transmitted signal are altered. The packet rate and the actual useful data rate are measured and compared with the selected data rate, and the packet success rate of the system operating at a fixed data rate is also measured while simulating channel noise to achieve the desired Signal-to-Noise Ratio (SNR).

Shortest Propagation Delay-Based Relay Selection for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) have recently attracted considerable attention because of the numerous underwater applications. The propagation delay, however, is the most challenging factor for successful communication in UASNs. This article presents a cooperative communication scheme for reliable transmission in UASNs. To cope with packet collision caused due to propagation delay, we propose an adaptive control packet collision avoidance (ACP-CA) scheme to avoid collision of control packets during the communication channel reservation phase (CCRP). Moreover, to reduce a collision between data packets, we propose a relay selection scheme, called the shortest propagation delay-based relay selection (SPD-RS), to select the best relay node (R) for re-transmission in the data transmission phase (DTP). We evaluate the proposed scheme performance in terms of network throughput, average packet success rate, average packet drop rate, and latency. Compared to opportunistic cooperative diversity (OCD) and conventional medium access protocol (CMAP), the proposed scheme performs remarkably well by improving the network throughput and packet success rate, reducing the packet drop rate, and minimizing the latency.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.