Ad-hoc System Research Articles

Exploring VANET Security within the VEINS Framework: A Review

Vehicular Ad Hoc Systems (VANETs) are urgent components of future shrewd transportation frameworks, encouraging communication between vehicles and framework to move forward security, effectiveness, and comfort through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) intuitive. In any case, the broad selection of VANETs postures critical security challenges, counting potential malevolent assaults and security breaches, which debilitate the unwavering quality and reliability of vehicular communication systems. In reaction to these challenges, the VEINS Vehicles in Organize Recreation system has risen as an unmistakable apparatus for re enacting VANETs and assessing their security instruments. This systematic literature audit points to supplying a comprehensive investigation of investigative endeavours centring on the security angles of VANETs inside the VEINS framework. By synthesizing discoveries from existing considerations, this audit recognizes key security dangers, vulnerabilities, assault models, and countermeasures significant to VANETs re enacted utilizing VEINS. It sheds light on the current state of VANET security inside re enacted situations, highlighting crevices in information and giving experiences into future investigation directions. The discoveries of this audit contribute to a more profound understanding of VANET security challenges and offer profitable direction for analysts, professionals, and policymakers included in planning, creating, and sending secure VANET frameworks. Eventually, the point is to upgrade the security pose of VANETs and guarantee the astuteness and dependability of vehicular communication in future transportation systems.

Empowering Learning through Intelligent Data-Driven Systems

The evolution of educational systems is closely tied to technological advancements, particularly the emergence of machine learning. This technology offers a sophisticated system capable of predicting, explaining, and influencing behavior. Many efforts have aimed to integrate machine learning into education, focusing on specific cases using ad-hoc models. This paper introduces an intelligent educational system that relies on data-driven student models, aiming to surpass the limitations of these ad-hoc systems. The approach outlined in this endeavor adopts a comprehensive and methodical modeling methodology centered on machine learning techniques. By employing Long Short-Term Memory (LSTM), the proposed approach enables predictive student models based on historical educational data. The effectiveness of this method was tested through experimentation on an intelligent tutoring system using 5-fold cross-validation, revealing that the smart educational system achieved a remarkable 96% accuracy rate. Furthermore, a comparison between the importance scores of features with and without the student models demonstrated the practicality and effectiveness of the proposed methodology.

Making accessibility accessible: A flexible planning tool for enhanced urban analytics

Most existing accessibility analysis tools are ad-hoc systems with predetermined geographic coverage, spatial resolution, methodology and indicators. These features are often challenging to modify by end-users without collaboration with the original tool developers. This might partially explain why accessibility tools remain in use predominantly within the academic realm, resulting in a seemingly low uptake among transport and planning professionals. ADAPT (Automatic Dynamic Accessibility Planning Tool) was designed as the first step to bridge this gap by enhancing user empowerment. It enables users to supply their own analysis areas, choose the resolution, and focus on any specific indicator (e.g. residents' access to jobs, education and services; businesses' access to the labour pool or potential clients/customers). The goal is to provide a versatile tool, in which users can insert their available data and geography and measure and map accessibility, without requiring prior geospatial analysis expertise or reliance on specialised closed-system tools.

A FRAMEWORK FOR ANALYSIS AND DESIGN OF DYNAMIC AD HOC SOCIO-TECHNICAL SYSTEMS

AbstractAd-hoc systems are socio-technical systems emerging in response to dynamic problematic situations. These systems form when situation systems interact with respondent systems organized by human agents using elements available in the situation and assets brought in from outside the situation. The immediacy of formation, fast evolution, and short lifecycles of ad-hoc systems intertwine design, implementation, and operation activities in complex ways not addressed by current approaches to Systems Engineering focusing on more sedate environments. The proposed framework presents a language for classifying fundamental building blocks of ad-hoc systems – single-agent intervention, staging, readiness, and development systems. Further classification according to the physical location of agents and assets relative to situations generates 16 system classes on the 4x4 matrix of Ad-hoc Systems Gameboard, which is a helpful tool for managing the evolution of system portfolios. Combining the Gameboard with mapping the systems onto the PSI matrix reveals additional relationships and evolution patterns, opening up promising directions to address the challenge of designing, planning, and implementing interventions in complex situations.

Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network. It is achieved by changing the residual energy of each network node. Also, in this paper, various algorithms for optimal routing based on parameters like energy, distance, mobility, and the pheromone value are proposed. Moreover, an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters. The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest, which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad-hoc Cloud System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

Life cycle sustainability assessment of non-beverage bottles made of recycled High Density Polyethylene

The current plastic industry is associated with climate change, fossil fuel depletion and littering of plastic waste. To reduce these environmental impacts, companies and governmental bodies are increasingly adopting strategies based on the concept Circular Economy. However, circular decision-making is usually based on analyses that do not provide enough insights in every sustainability dimension, risking burden-shifting. In this study, environmental, economic and social life cycle assessment (LCA) techniques have been integrated into an overarching sustainability life cycle assessment (LCSA) to assess the impact of recycling High Density Polyethylene (HDPE) non-beverage bottles. The study assesses the impact in 11 environmental categories, the life cycle costs, and the social risks associated with the related economic sectors. An ad-hoc system expansion approach was developed to overcome the multifunctionality issue so commonly challenging in circular systems. The results indicate that using recycled HDPE leads to significant reductions in all the considered environmental categories. The economic analysis indicated that the material cost of recycled HDPE is slightly lower than for virgin HDPE, but the manufacturing costs are higher and highly dependent on the specific value chain. The social risks of recycling were found to be higher than for virgin plastic production, and mainly occurring outside the country where the recycling takes place (The Netherlands). Nevertheless, this analysis presents high uncertainty due to the heterogeneity in the recycling sector of the database. This study shows how the LCSA approach can be used to assess and compare the impacts and benefits of circular strategies and calls for further efforts to develop higher disaggregated social risk databases.

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data

Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline’s processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.

Modelling hydrogeological parameters to assess groundwater pollution and vulnerability in Kashan aquifer: Novel calibration-validation of multivariate statistical methods and human health risk considerations

Modeling and Investigation on pollution potential of aquifers is a matter of importance in terms of management, development and land-use allocation as well as quality monitoring, pollution prevention and groundwater protection. The purpose of this study is to calibrate and modeling the methods used for pollution potential assessment, in which the impact and apportionment of hydrogeological parameters on groundwater pollution of an aquifer located in Kashan is considered. To do so, Analytic Hierarchy Process (AHP) and fuzzy-statistical analysis methods are used for weighting, ranking and standardize the parameters based on research awards of experts and Ad-Hoc systems. This was performed in such a way that the level and importance of each class of classification parameters is considered equal to the final model, and is equivalent to the reclassified class of indices of groundwater quality and human health risk to nitrate pollution. After ranking and standardizing the parameters as well as final model by using fuzzy-statistical approach, the process of weighting the parameters is accomplished with aid of AHP and Factor Analysis-weighted Principal Component Analysis (FA-PCA) methods based on their apportion and impact on groundwater pollution in addition to their correlation with nitrate map. In addition to the correlation with the standardized nitrate concentration, techniques of the root mean square error (RMSE) and coefficient of variation (COV) are employed to validate the model. The results illustrated that parameters of net recharge, soil media, impact of vadose zone, hydraulic conductivity and aquifer media have created the highest apportion and impact on groundwater pollution. In addition, it was found that weight of these parameters for calibrated and validated model of GPPI (groundwater pollution potential index) is proved to be 8.5, 3.4, 3.3, 2.6, 2.2, for GPRI (groundwater pollution risk index) as the best model is 4.4, 3.7, 3.1, 2.9, 1.1, and it is 4.8 for the land use layer, respectively. Weighting procedure was conducted by FA-PCA approach and following considerations were used; R = 73, RMSE = 1.08 and COV = 20%. Moreover, based on these models with better calibration-validation than generic model, it was found high pollution potential in western margin, high pollution risk in the central parts to the western margin, while it was observed not to have that very high pollution potential and risk in Kashan aquifer.

Design o f Wireless Ad Hoc System u sing Adaptive Multi Hop Link State Optimal Routing Protocol

Now a days for the radio network communication multi-hop routing is used. This multi-hop routing technique covers larger coverage area. Therefore to reach at specific location data is transferred in form of packets from one node to other node. But for the transmission of radio signals over the large distance, large number of transreceivers are required and these transreceivers requires large power to operate. As a result, multi-hop routing can saves energy over separate routing. Therefore it is necessity to design a cost effective multi-hop routing technique for successful transmission of ratio packet data. In this paper a hop by hop adaptive link state optional routing (HALO) is explained. It is the first packet transmitting solution with hop by hop and link state routing, which reduces the cost of transporting data across a packet switch network[3]. The triple model is designed for multi hop packet routing. In this work each node of network iteratively and separately improves the small part of traffic bound. This algorithm finds the shortest path of specific location for every iteration and it is calculated by the marginal cost of the various links of network. The marginal link cost is used to calculate the shortest path between the node and the destination location. This marginal link cost is gathered from link state updates. The various networks changes are automatically identified by the adaptive method which is used in this paper. Due to this the exchange between the packets on wrong node is reduced over the overhead traffic. To validate these theoretical results the experimental evaluations and mathematical calculations are also reported in this work. Net beans java is the programmed use in this proposed project.

A Review on Various Applications of Reputation Based Trust Management

<p class="0abstract">The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system. </p>

Trust-Based Secure Network For Detection Of Attacks (Wormhole And Black Hole) Due To Malicious Nodes In Ad Hoc Wireless Sensor Network

Recent researches about in alternate points of view and industrialized situated applications have comprehensively used Wireless Sensor Network (WSN). At present, WSN have achieved the authority consideration for giving the protected systems, which is free from the assailants and vindictive hubs. The remote sensor systems security can be liable to dangers by assaulters. It requires the sensor to sporadically detect and transmit touchy information to the base station for information transmitting generally through multi bounce way. As, it is exceptionally fundamental to plan the safe Ad Hoc system to identify the savvy assailants productively. Here in this paper we configuration trust based secure system for discovery of assaults (wormhole and dark opening) because of the nearness of noxious hubs (TSDAMN) plan of Ad Hoc remote sensor organize. For the upgrade of system security Heterogeneous Cluster Based Secure (HCBS) directing convention has been utilized. The test results indicates security enhanced system with the parameters like throughput, limited end-end delay, parcel transmission rate, vitality preservation rate, assault discovery rate.

An Ad Hoc Movement Monitoring Algorithm for Indoor Tracking During Examinations

Academic dishonesty includes cheating in examinations tests and any academic assignments, plagiarism, fabrication of information or citations, facilitation of acts of academic studies by others, unauthorized possession of examination materials, and tampering with the academic work of other researchers or scholars. These acts can be done before, during, and after the actual examination. This paper proposes a holistic algorithm for setting up an ad-hoc monitoring system to track candidates who leave the examination hall during the examination for either toilet breaks, medical breaks, or otherwise. Since most examinations are held indoors it is especially difficult to use existing GPS and GPRS based methods. We submit a proposal to deploy an indoor monitoring system based on the RSS of carefully placed receivers as they communicate with active tags held by the candidates. This paper outlines an algorithm that is used to place the receivers in such a way as to avoid any blind spots which may be utilized as cheating spots. The algorithm was deployed into a testbed system which was 100% foul proof. The proposed algorithm can be used to set up the deployment of the monitoring system in any environment where a clearly defined path is identified.

Visual Interestingness Prediction: A Benchmark Framework and Literature Review

In this paper, we report on the creation of a publicly available, common evaluation framework for image and video visual interestingness prediction. We propose a robust data set, the Interestingness10k, with 9831 images and more than 4 h of video, interestigness scores determined based on more than 1M pair-wise annotations of 800 trusted annotators, some pre-computed multi-modal descriptors, and 192 system output results as baselines. The data were validated extensively during the 2016–2017 MediaEval benchmark campaigns. We provide an in-depth analysis of the crucial components of visual interestingness prediction algorithms by reviewing the capabilities and the evolution of the MediaEval benchmark systems, as well as of prominent systems from the literature. We discuss overall trends, influence of the employed features and techniques, generalization capabilities and the reliability of results. We also discuss the possibility of going beyond state-of-the-art performance via an automatic, ad-hoc system fusion, and propose a deep MLP-based architecture that outperforms the current state-of-the-art systems by a large margin. Finally, we provide the most important lessons learned and insights gained.

Optimal Adaptive Data Dissemination Protocol for VANET Road Safety Using Optimal Congestion Control Algorithm

Introduction: VANET is a mobile ad-hoc system (MANET) that concerns vehicle-to-vehicle and vehicle-to-infrastructure communication. Unique characteristics such as higher node speeds and restricted road topology extricate VANET from other MANETs. Messages between vehicles and roadside infrastructure in VANET is over short to medium range wireless technologies. Dedicated Short Range Communications (DSRC) is a RF technology-based standard that has been designed exclusively for automotive communications. Like most MANETs, data are broadcasted in VANETs through the exchange of messages between the nodes. The limited road topology unlike MANETs, executes a directional way to the message flow. It becomes important that the data to be transmitted in the most effective ways with less delay, due to higher node speeds and unbalanced connectivity among the nodes. Hence, propagating data to the intended node or region of interest is a unique problem in VANETs and requires incorporating effective techniques to disseminate data. Data broadcasted from a vehicle will be received by all the nodes within the broadcast range. The difficulty of data dissemination is hence related to propagating data, not within but beyond the transmission range of the sender. An intuitively simple way to disseminate data beyond the transmission range is through flooding. In flooding each node which receives the message would simply rebroadcast the message without any regard to its current position or any other factors. Thus, data are propagated beyond the transmission range when a node at the border of the broadcast range rebroadcasts the message. For effective broadcasting, each vehicle upon receiving a message would make a decision to rebroadcast the message depending on whether or not it is the farthest node in the transmission range. Thus, the decision-making ability of each vehicle on participating in the message propagation is dependent on its awareness of vehicles around it and determines the overall effectiveness of the technique used in disseminating data. Objective: To identify an optimal cluster head based on effective parameters to reduce Control Overhead messages in a collision based traffic network. Methods: The proposed system consists of two processes namely candidate selection and control OH message reduction. The candidate selection process is carried out by Chaotic Fish Swarm Optimization (CFSO) algorithm which consists of cluster formation and Cluster Head (CH) selection. The control OH messages are reduced by a Predictor based Decision Making (PDM) algorithm. Results: Based on the performance metrics such as success rate, redundant rate, collision rate, number of control OH messages, data propagation distance, data propagation time and data dissemination efficiency, the proposed system is evaluated. The results shows that the proposed system performs well than the existing system. Conclusion: In this paper, we have suggested an Optimal Adaptive Data Dissemination Protocol (OAddP) for VANET road safety. The proposed OAddP mechanism uses the Chaotic Fish Swarm Optimization (CFSO) algorithm to perform the clustering and uses a Predictor based Decision Making (PDM) algorithm for control overhead messages reduction.

Analysis of Learning Techniques for Performance Prediction in Mobile Adhoc Networks

Current advancements in cellular technologies and computing have provided the basis for the unparalleled exponential development of mobile networking and software availability and quality combined with multiple systems or network software. Using wireless technologies and mobile ad-hoc networks, such systems and technology interact and collect information. To achieve the Quality of Service (QoS) criteria, the growing concern in wireless network performance and the availability of mobile users would support a significant rise in wireless applications. Predicting the mobility of wireless users and systems performs an important role in the effective strategic decision making of wireless network bandwidth service providers. Furthermore, related to the defect-proneness, self-organization, and mobility aspect of such networks, new architecture problems occur. This paper proposes to predict and simulate the mobility of specific nodes on a mobile ad-hoc network, gradient boosting devices defined for the system will help. The proposed model not just to outperform previous mobility prediction models using simulated and real-world mobility instances, but provides better predictive accuracy by an enormous margin. The accuracy obtained helps the suggested mobility indicator in Mobile Adhoc Networks to increase the average level of performance.

An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents.

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers’ suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a “classical” machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the “Avila Bible”. This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems.

Design of intelligent surveillance system based on wireless ad-hoc network under special conditions

For oil fields, shooting target range, military forbidden zones and other special complex scenarios, it is not only difficult to deploy communication infrastructure because of its remote location and difficult environment, but also difficult to transmit surveillance video images to command stations in real time through centralized communication networks. Furthermore, there is a waste of bandwidth resources and the use of wireless ad hoc networks to transmit surveillance video. Therefore, we aim at designing and implementing a real-time intelligent surveillance system based on wireless ad-hoc networks in special scenes in this project. The system is supposed to capture the video frames of the monitored area and perform automatic intrusion detection. Only when an intrusion is detected, the video frame is encoded and transmitted together with the alarm message to the command center. An improved Yolov2 algorithm to achieve real time object detection on a computationally limited platform was proposed first. A wireless ad-hoc communication system was designed and implemented on the basic of the Tactical Targeting Network Technology (TTNT) Data Link to transmit surveillance video stream, control signaling and alarm message. Then the extensive experiments were conducted to evaluate the system in terms of detection accuracy and communication performance. The experiments results demonstrate the effectiveness and accuracy of the proposed system, the size of the model is only 7.6% of Yolov2 and the detection speed is increased 4 times. This design transforms long-time data transmission into burst data transmission, thus saving bandwidth resources and improving bit error rate of 96.9%. It is of great significance for the future application of artificial intelligence and machine learning in the field of communication

Investigation of the influence of product variety on inventories in hospitals

Abstract The literature on product variety provides great insights into various businesses. However, little research has been conducted on product variety in the healthcare industry. This study aims to explore the influence of product variety on inventory in hospitals. Since most hospitals are known to replenish products using a homegrown ad-hoc system, a model is developed for exploring all possible product combinations and substitutions. This article presents the behaviour of product substitution, which may be either one-to-one or many-to-one for both sterile and non-sterile products, in the hospital with cost factors. It discusses the product variety reduction and its corresponding cost impacts. The data on a hospital inventory over the course of six years has been procured from a hospital in Norway. Based on the results, the hospital could have a potential product variety reduction of approximately 11% and cost savings from the spending of approximately NOK 3.6 million. Reducing the variety of products in hospital inventories proves to be an approach to reducing costs. The model developed for the research is universal in nature and could be used in other fields, such as retail, marketing etc.

RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.