Increases Packet Delivery Ratio Research Articles

Design of CSKAS-VANET model for stable clustering and authentication scheme using RBMA and signcryption

A public key infrastructure-enabled system authentication model is developed to provide essential security functions for Vehicular ad hoc networks (VANETs). An intelligent transportation system is provided by VANET, an emerging technology. Dedicated short-range communication is used to disseminate messages wirelessly. Communications may be hacked, and messages can be stolen or fabricated. Hence, authenticated communication is crucial in the VANET environment. Some parameters such as trust, authentication, privacy, and security are at high risk. This article suggests a VANET with secure authentication and trust-based clustering mechanisms to provide stable and secure communication. Initially, the Restricted Boltzmann Machine learning algorithm (RBMA) is used to select the cluster head, which depends upon trust, vehicle lifetime, and buffer level. Then, cluster members are formed, followed by grouping. Diffie–Hellman Hyperelliptic Curve Cryptography and cryptographic hash functions are used by signcryption for secure communication in VANET. Therefore, the essential component of the key agreement strategy that will give superior authentication is this signcryption mechanism. Over the medium access protocol layer, all of these security characteristics are updated. The proposed method of clustering signcryption key agreement scheme (CSKAS) approach reduces time complexity and increases packet delivery ratio which is vital in providing stable, secure communication.

IS3CQL : Intelligent sleep scheduling of small cells in high density 5G MIMO networks via incremental Deep Q learning optimizations

Effective management of small cell networks is crucial as the demand for fast, lowlatency communication grows as a result of the development of 5G and Massive Multiple Input Multiple Output (M-MIMO) technology. Small cell sleep cycle scheduling in highdensity networks frequently encounter issues as it tries to strike a compromise between energy efficiency, quality of service (QoS), and maintaining a high throughput. Additionally, these traditional methods ignore the complex spatial and temporal communication characteristics, resulting in subpar network performance. In order to overcome these difficulties, we suggest a brand-new intelligent sleep scheduling approach that makes use of both fundamental green computing ideas and incremental deep learning. In order to determine the best sleep schedules for tiny cells, our model takes into account both temporal and spatial communication factors, such as end-to-end delay, packet delivery ratio, throughput, and energy consumption of the nodes. The model adjusts the small cells’ sleep scheduling based on the ongoing spatial-temporal fluctuations in the network, providing the best possible use of resources. Additionally, its focus on green computing helps to lower energy consumption during extensive connections, making it an environmentally beneficial alternative for different scenarios. Under enormous MIMO situations, the results from our model show a significant improvement over the current approaches. These improvements include a notable improvement in communication consistency, a notable increase in throughput, a noticeable increase in packet delivery ratio, and a noticeable decrease in energy consumption and delay. In summary, our concept is a major step forward in the continuous development of intelligent, resilient, and effective 5G MIMO networks.

An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods.

Genetic Algorithm-Based Secure Routing Protocol for Wireless Sensor Networks

In a wireless sensor network (WSN), security threats are prevalent as a result of the network's distributed nature and resource limitations. This paper proposes a secure routing protocol for WSNs based on Genetic Algorithms (GA). The protocol utilizes GA to optimize secure route selection while considering network lifetime and energy efficiency. To showcase the efficiency of the suggested protocol, we present a case study, provide a detailed algorithmic representation, and evaluate its performance through extensive simulations. The results showcase the protocol's ability to enhance security and improve network performance, including increased packet delivery ratio, reduced energy consumption, and robustness against attacks. The proposed GA-based secure routing protocol offers a promising approach to address the unique security challenges of WSNs, contributing to the development of secure and efficient communication frameworks for various applications. Additionally, we highlight the significance of incorporating genetic algorithms into secure routing protocols as a means to optimize route selection in WSNs. By leveraging the evolutionary nature of genetic algorithms, our proposed protocol adapts to dynamic network conditions and effectively balances security requirements with energy efficiency and network lifetime considerations. The case study, algorithmic representation, and comprehensive simulation results validate the protocol's ability to enhance security while maintaining robust network performance. This study contributes to the advancement of secure routing in WSNs, offering a viable solution to mitigate security threats and ensure reliable communication in resource-constrained environments.

An improved deep reinforcement learning routing technique for collision-free VANET

Vehicular Adhoc Networks (VANETs) is an emerging field that employs a wireless local area network (WLAN) characterized by an ad-hoc topology. Vehicular Ad Hoc Networks (VANETs) comprise diverse entities that are integrated to establish effective communication among themselves and with other associated services. Vehicular Ad Hoc Networks (VANETs) commonly encounter a range of obstacles, such as routing complexities and excessive control overhead. Nevertheless, the majority of these attempts were unsuccessful in delivering an integrated approach to address the challenges related to both routing and minimizing control overheads. The present study introduces an Improved Deep Reinforcement Learning (IDRL) approach for routing, with the aim of reducing the augmented control overhead. The IDRL routing technique that has been proposed aims to optimize the routing path while simultaneously reducing the convergence time in the context of dynamic vehicle density. The IDRL effectively monitors, analyzes, and predicts routing behavior by leveraging transmission capacity and vehicle data. As a result, the reduction of transmission delay is achieved by utilizing adjacent vehicles for the transportation of packets through Vehicle-to-Infrastructure (V2I) communication. The simulation outcomes were executed to assess the resilience and scalability of the model in delivering efficient routing and mitigating the amplified overheads concurrently. The method under consideration demonstrates a high level of efficacy in transmitting messages that are safeguarded through the utilization of vehicle-to-infrastructure (V2I) communication. The simulation results indicate that the IDRL routing approach, as proposed, presents a decrease in latency, an increase in packet delivery ratio, and an improvement in data reliability in comparison to other routing techniques currently available.

Performance Improvement of AODV in Wireless Networks using Reinforcement Learning Algorithms

This paper investigates the application of reinforcement learning (RL) techniques to enhance the performance of the Ad hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad hoc networks (MANETs). MANETs are self-configuring networks consisting of mobile nodes that communicate without the need for a centralized infrastructure. AODV is a widely used routing protocol in MANETs due to its reactive nature, which reduces overhead and conserves energy. This research explores three popular Reinforcement Learning algorithms: SARSA, Q-Learning and Deep Q-Network (DQN) to optimize the AODV protocol's routing decisions. The RL agents are trained to learn the optimal routing paths by interacting with the network environment, considering factors such as link quality, node mobility, and traffic load. The experiments are conducted using network simulators to evaluate the performance improvements achieved by the proposed RL-based enhancements. The results demonstrate significant enhancements in various performance metrics, including reduced end-to-end delay, increased packet delivery ratio, and improved throughput. Furthermore, the RL-based approaches exhibit adaptability to dynamic network conditions, ensuring efficient routing even in highly mobile and unpredictable MANET scenarios. This study offers valuable insights into harnessing RL techniques for improving the efficiency and reliability of routing protocols in mobile ad hoc networks.

Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy.

AI-enabled cluster head selection through modified density based clustering in Aeronautical Ad Hoc Networks

Offering in-flight connectivity has emerged as an essential demand for everyday flights. Such aircraft’s high speed and dynamic characteristics make this task challenging, particularly in distant areas with no ground-to-air link. Aeronautical Ad Hoc Network (AANET), a network of commercial airplanes with air-to-air connectivity, is a viable solution to this problem. However, the instability of air-to-air links results in poor performance of such ad hoc networks. A cluster-based topology formation is an initial step to boost the performance and connectivity in such a network. Selecting a well-connected cluster head is the next stage in enhancing connection and stability. This study presents a new cluster head selection technique for AANETs that calculates the neighbor nodes within a given distance of each node and selects the node with the most connections as the new cluster head. A gateway node is chosen to facilitate connections with other clusters. According to simulations, the proposed method increases packet delivery ratio by 3%, end-to-end delay by 9%, and throughput by up to 10%. In addition, the proposed method reduces cluster head replacements by 17% and increases cluster head longevity by 8%.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently available resources among terminals required to address the service requirements of the users. These three categories improve performance metrics by varying transmission rates and simulation time. For solving that problem, the proposed work is divided into Queue Management (QM), Admission Control (AC) and RA. For effective QM, this paper develops a QM model for elastic (EL) and inelastic (IEL) Traffic Flows. This research paper presents an AC mechanism for multiple TF for effective AC. This work presents a Resource Allocation Using Tokens (RAUT) for various priority TF for effective RA. Here, nodes have three cycles which are: Non-Critical Section (NCS), Entry Section (ES) and Critical Section (CS). When a node requires any resources, it sends Resource Request Message (RRM) to the ES. Elastic and inelastic TF priority is determined using Fuzzy Logic (FL). The token holder selects the node from the inelastic queue with high priority for allocating the resources. Using Network Simulator-2 (NS-2), simulations demonstrate that the proposed design increases Packet Delivery Ratio (PDR), decrease Packet Loss Ratio (PLR), minimise the Fairness and reduce the EED.

Autonomous Underwater Vehicle: 5G Network Design and Simulation Based on Mimetic Technique Control System

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay and higher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memetic algorithm-based AUV monitoring system for the underwater environment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime.

AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication

ABSTRACT VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput.

A Novel and Enhanced Routing Protocol for Large Scale Disruption Tolerant Mobile Ad hoc Networks

Because of the lack of fixed infrastructures, the existence of open media and diverse network topologies, internetworking networks and mobile ad hoc networks (MANET’s), the design of MANET protocols is complicated. In this paper, we propose an evolutionary trust mechanism imitating cognitive processes that uses sensitive information to avoid routing. Moreover, we propose an Enhanced Self-organizing Cooperation and Trust based (ESCT) Protocol, where the mobile nodes share self-reliance and interpret information from a cognitive point of view. Each node develops its information dynamically to eradicate malicious entities. The most attractive attribute of the proposed ESCT protocol, even if domestic attackers know how it operates, is to prevent infringements. In this paper, the efficiency of the proposed ESCT protocol is assessed for different routing disturbances and varying number of attackers. The results of a simulation show that, the proposed ESCT protocol supports diverse network platforms and provides an efficient routing method for MANET routers. The proposed ESCT protocol displays increased throughput, reduction in end-to-end delay and increase in packet delivery ratio when compared to the peers that were taken for comparison.

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

Self-Adaptive Efficient Dynamic Multi-Hop Clustering (SA-EDMC) Approach for Improving VANET’s Performance

Vehicular ad hoc networks (VANETs) are mainly used in the intelligent transportation field for smart applications. VANETS received maximum concentration among the researchers in academic research. Providing traffic safety at the time of high mobility in the network is the major issue. Also, it consists of few general issues namely self-organization, link failure and speedy variation in topology. In order to address this issue, we developed a novel approach namely, Self-Adaptive Efficient Dynamic Multi-Hop Clustering (SA-EDMC) approach which is designed to reduce the delay and energy consumption in the VANETs that leads to increase the efficiency and overall Quality of Service (QoS) of the network. This approach is composed into four parts (1) network construction, (2) prime cluster head selection, (3) dynamic prime CH selection and (4) gateway creation. Network simulator is utilized to demonstrate the behavior of our SA-EDMC approach. The simulation evaluation exhibits that our proposed work achieved high packet delivery ratio, low delay and high throughput when compared with earlier research outputs like MCA-V2I and QMM-VANET. The comparative analysis expressed that performance of the proposed SA-EDMC is ~5% increase in packet delivery ratio, produced 300 to 500 kbps extra throughput compared with the earlier methods and delay is comparatively low.

Hierarchical and On-Demand Attack Defence Framework for IoT Devices

Internet of Things (IoT) devices are lightweight such that they generally possess low battery power. Hence, the chances of battery exhaustion and flooding attacks are more. In order to perform attack response actions against various attacks, this paper proposes Hierarchical and On-Demand Attack Defence Framework (HOAD) for IoT security. In this framework, primary (PC) and secondary controller (SC) nodes are deployed in the network along with the IoT devices. The SC scan be moved on-demand by the PC. The response agent at PC will first establish a new route via the SCs by excluding the intruders and the suspected nodes. Then, it will resend the stored packets to their destination via the newly established route. The proposed HOAD framework is implemented in NS2 and compared with the MECshield framework. Simulation results show that the HOAD has reduced end-to-end delay, increased packet delivery ratio, and increased residual energy.

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.

F2SO: An Energy Efficient Cluster Based Routing Protocol Using Fuzzy Firebug Swarm Optimization Algorithm in WSN

AbstractThe most important design concern associated with wireless sensor networks is energy efficiency that mostly deals with routing and clustering techniques. The major aim of this study is to provide energy efficient cluster routing protocols for wireless sensor network. Initially, we used a Honey Badger Algorithm for cluster heads selection. Honey Badger Algorithm is used to choose the distance to the base station, residual energy, distance to its neighbors, node degree node and centrality with the optimal cluster head among all sensors. The routing between base stations and cluster heads is performed through Fuzzy Firebug Swarm Optimization algorithm. Hence, the fuzzy decision module automatically adjusts the search behavior of firebug swarm optimization. Fuzzy rules are used with the node degree, residual energy and distance to the base station to address the firebug swarm optimization algorithm’s constraints during routing. Finally, cluster maintenance is done which is the major step to eliminate the failure nodes. This process assists to improve the network lifetime during data transmission by eliminating the faulty nodes and establish an efficient path between the source node and the base station. Wide experimentation is accomplished to assess the efficiency of the proposed protocol using different evaluation measures. The proposed methodology offers improved end-to-end delay, increased packet delivery ratio, higher throughput, low packet drop ratio and minimizes energy consumption when compared with the existing wireless sensor network routing protocols.

MSE-RPL: Mobility Support Enhancement in RPL for IoT Mobile Applications

Due to the expansion of IoT applications which causes the generation of a massive amount of data, data routing is one of the most important challenges in these networks. The Routing Protocol for Low-power and Lossy Networks (RPL) was developed to cope with the Low-power and lossy network constraints, which play a significant role in IoT networks. Although most IoT applications involve mobility and topology change that makes mobility support a substantial need to prevent disconnection of nodes and data loss, the RPL is designed for static networks. This article proposes a mobility support method called MSE as an extension of RPL. The MSE supports mobility of all nodes except the root node, and it provides a seamless connection during the mobility. It also manages a situation when a physical obstacle settles between two paired nodes in a dynamic environment. To this end, it uses a dynamic trickle timer with two different ranges, a neighbor link quality table, a function to select the best parent in case of mobility, confidence, critical zones, and a blacklist. Simulations in multiple scenarios indicate that MSE, despite causing a slight increase in signaling cost and power consumption, significantly reduces hand-off delay, increases Packet Delivery Ratio, reduces the number of lost data packets, and outperforms both RPL as a reactive and mRPL as a proactive protocol regarding mobility.

New RPL Protocol for IoT Applications

The Objective Function (OF) can be used by the Routing protocol for low power lossy networks (RPL) to construct a Destination Oriented Directed Acyclic Graph (DODAG) based on routing metrics. The standard OFs suffer from long hops when selecting the route, which may cause consume the node's energy faster. In this paper, we suggest an improvement of RPL OF that considers three metrics. The results show that the proposed protocol increases network lifetime by reducing energy consumption, increasing efficiency, increasing Packet Delivery Ratio (PDR), and decreasing packet loss ratio. In terms of PDR, packet loss ratio, and average power consumption, the best performance of the proposed protocol is shown in the network with 70 nodes and when the transmission range is 50m. Compared with the MRHOF, the proposed protocol increased the PDR by 58.425%, decreased the packet loss ratio by 0.21765, and decreased the total power consumption by 181.815mW. In terms of the average Expected Transmission Count (ETX) the best performance of the proposed protocol is shown in the network with 60 nodes and the transmission range is 40m. The proposed protocol reduced the average ETX by 49 compared to the MRHOF.