Overhead Ratio Research Articles

A Mobile Opportunistic Routing Algorithm Based on Mobility Classification

The advancement of novel technologies has positioned mobile wireless networks as the central focus of computer technology research. The selection process for the subsequent relay node in message forwarding holds paramount importance and is deemed critical. Drawing inspiration from Social Mobility theory, this paper presents a pioneering node classification model that categorizes all nodes into three distinct types. Furthermore, we introduce the concept of Active Entropy, where the numerical value of Active Entropy serves as a metric for message forwarding. Consequently, the routing algorithm transforms into a limited set of strategies between nodes belonging to different types. This routing algorithm offers numerous advantages including enhanced delivery rate, reduced network overhead ratio and decreased transmission delay. We evaluate and compare the performance of our proposed algorithm with that of DFDL algorithm and Prophet algorithm on the ONE simulation platform. Experimental results demonstrate that our proposed algorithm achieves a delivery rate at least 15% higher compared to the other two algorithms while also reducing overhead ratio.

SDERP: Stable‐aware differential evolution‐based routing protocol for mobile wireless networks

SummaryA mobile ad hoc network (MANET) is a network with non‐wired communications and non‐centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real‐time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable‐aware differential evolution‐based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV‐EE, FT‐AORP, EE‐LB‐AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end‐to‐end delay reduced by up to 16%.

Multi-objective-derived efficient energy saving in multipath routing for mobile ad hoc networks with the modified aquila–firefly heuristic strategy

Mobile ad hoc networks (MANETs) suffer from a major problem with the energy drainage of network nodes, as the mobile nodes rely on batteries and do not have a permanent power supply. Therefore, a new multipath routing technique, using a novel hybrid heuristic algorithm, is designed to solve the diverse metrics of a multi-objective function, including energy consumption, distance, routing overhead ratio, throughput, end-to-end delay and packet delivery ratio. This approach implements a new hybrid algorithm termed the modified aquila–firefly heuristic strategy (MA-FHS), which integrates the aquila optimizer and the firefly algorithm. This derived fitness function is applied to achieve optimal paths in multipath routing with minimization of the multi-objective function. The simulation demonstrates that the developed routing protocols outperformed traditional protocols under several network constraints.

How High Is Too High? An Experimental Analysis of Donors’ Aversion to Nonprofit Overhead

Despite the abundance of literature related to nonprofit overhead, the following questions remain unclear: (a) How high is too high for individual donors when considering an organization’s overhead? (b) Is there a difference between nonprofit subsectors in individual donors’ aversion to nonprofit overhead? Moreover, (c) Does trust play a role in individual donors’ overhead aversion? This study used a survey experiment and randomly assigned participants to one of four overhead ratio conditions (5%, 20%, 35%, and 50%). We find that individuals’ donations to human service nonprofits substantially decrease when the overhead reaches 35%. In contrast, their donations to health care nonprofits do not decrease until the ratio reaches 50%. In addition, we find that donors lose trust in nonprofits when overhead costs are higher, leading to decreased donations. The findings contribute to the theoretical understanding of donors’ giving behavior, offering practical implications for promoting sustainable giving.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

The effect of manufacturing overhead on policies for allocating production capacity

This research explores how manufacturing overhead influences profit margins under different capacity allocation policies in a build-to-order context. By applying a multi-period Mixed-Integer-Programming model to assess industries with varying overhead structures, the study establishes a link between the levels of capacity allocation and profitability, underscoring the strategic significance of overhead costs in capacity management decisions. Our results indicate that industries with lower overhead costs benefit more significantly from increased capacity allocation, with up to a 25% improvement in profit, compared to a 5–10% improvement in higher overhead industries. Conversely, at full capacity demand rates, increased allocation levels detrimentally affect profit, particularly in industries with higher overhead ratios. The study utilises a 3-way ANOVA to confirm the significance of these findings, revealing that industry type, demand rates, and allocation levels are critical factors in optimising profit. This research highlights the need for a pragmatic approach to capacity allocation that aligns with industry characteristics and demand levels, steering away from revenue maximisation to a more holistic view of profitability that includes overhead cost management.

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

SMART: Secured and Mobility Aware Routing Technique for Opportunistic IoT Network in Smart Cities

Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.

AstroSA: An astronomical observation scheduler assessment framework in python

Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (AstroSA), implemented as a Python package. The AstroSA offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, AstroSA includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.

The Nonprofit Starvation Cycle: The Extent of Overhead Ratios’ Manipulation, Distrust, and Ramifications

While little evidence supports the notion that financially responsible nonprofits must maintain low overhead ratios, the persistent preference for reduced overhead costs endures. Our study explores (a) the extent of underreporting behaviors, (b) the level of trust (or distrust) that nonprofit leaders have in overhead ratio reports, and (c) the motivations perceived by managers that drive nonprofits to adjust their overhead ratios and the resulting consequences. Experiment results from the “item sum double-list technique” (ISDLT) reveal that nonprofit managers may artificially lower their overhead ratios by approximately 10 percentage points, a range spanning from 7 to 16 percentage points. This adjustment aims to enhance their competitiveness in the funding market. Our vignette-based experiment uncovers significant trust issues related to reported low overhead ratios, potentially indicating accounting manipulation within the field. Complemented by open-ended survey responses from nonprofit managers, our research offers valuable insights into this domain.

A Bayesian approach using spatiotemporal features for suitable next hop selection in opportunistic networks

SummaryOpportunistic network (OppNet) belongs to the category of Mobile Ad‐hoc Networks (MANETs), a kind of Delay Tolerant Network (DTN), where the wireless nodes are completely mobile and the data transmission routes are dynamic. The major challenge in developing a routing model for such a network is the unpredictable nature of the movement of the nodes. In this paper, a spatiotemporal prediction model based on human mobility pattern is proposed using Bayesian posterior probability (BPPR) where several clusters are identified within the network and the day and time duration of nodes visiting those clusters are recorded. The Bayesian posterior probability is then used to determine the probability of the neighbor node visiting the destination's cluster. If the calculated probability for that node is higher than a specified threshold, the packet will be forwarded. A comparison of the results obtained on simulation is made with benchmark models—Epidemic, Prophet, HBPR, EDR, NexT, and EBC, to name a few, where it is found that on average the proposed model outperforms the other models in terms of delivery probability, hop count and number of messages dropped by around 23.89%, 24.8%, 24.4%, 37%, 11%, and 42% respectively, with varying number of nodes, TTL, message generation interval, and buffer size. Similar improvements have been observed in terms of the other two metrics. In terms of overhead ratio, the proposed model outperforms Epidemic, Prophet, HBPR, NexT, and EBC. However, as the number of nodes and TTL are varied, BPPR performs better than NexT by around 9% and 12%, respectively, in terms of average latency.

A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability

The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, and sustainable networking solutions has never been more pressing. Opportunistic networks, characterized by intermittent connectivity and dynamic network conditions, present unique challenges that necessitate innovative approaches for optimal performance and sustainability. This paper introduces a groundbreaking paradigm that integrates the principles of cybersecurity with opportunistic networks. At its core, this study presents a novel routing protocol meticulously designed to significantly outperform existing solutions concerning key metrics such as delivery probability, overhead ratio, and communication delay. Leveraging cybersecurity’s inherent strengths, our protocol not only fortifies the network’s security posture but also provides a foundation for enhancing efficiency and sustainability in opportunistic networks. The overarching goal of this paper is to address the inherent limitations of conventional opportunistic network protocols. By proposing an innovative routing protocol, we aim to optimize data delivery, minimize overhead, and reduce communication latency. These objectives are crucial for ensuring seamless and timely information exchange, especially in scenarios where traditional networking infrastructures fall short. By large-scale simulations, the new model proves its effectiveness in the different scenarios, especially in terms of message delivery probability, while ensuring reasonable overhead and latency.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National Institute of Science and Technology). The analytical results illustrate the importance of the proposed framework by achieving better security performance and outperforming the other existing models. Lastly, the proposed framework has more potential than the other existing frameworks and finds its place in real-time healthcare systems, but it needs to be improvised for real-time datasets.

SOLUSI ALTERNATIF PENYAMPAIAN INFORMASI DENGAN TOLERANSI WAKTU TUNDA UNTUK PENGGUNA LAYANAN KOMUNIKASI DATA PADA PEDESTRIAN NETWORK

This research aims to evaluate the performance of routing patterns in opportunistic networks for transmitting time-tolerant information, such as product prices and store coordinates in the Kuta, Badung area by using pedestrians as intermediaries and recipients of information. Two basic routing patterns, epidemic and spray and wait, are simulated using The ONE Simulator into two scenarios including a scenario of increasing average pedestrian density and a scenario of increasing buffer size. The simulation results show that in the scenario of increasing the average pedestrian density, the epidemic routing has a higher delivery probability and lower average latency than the spray and wait routing, but the spray and wait routing has a lower overhead ratio value. In the scenario of increasing buffer size, the spray and wait routing has a higher delivery probability and lower average latency at smaller buffer sizes. Meanwhile, the epidemic routing pattern produces a higher delivery probability and lower average latency at a larger buffer size, although with a difference that is not too significant.

CR-5: Resource optimization opportunistic network routing algorithm based on node dynamic attributes

The opportunistic network routing algorithms are the core technology of intermittent communication of mobile nodes in opportunistic networks. However, the existing researches on opportunistic network routing algorithms mainly focus on how to improve the message delivery ratio and reduce the route overhead ratio, and often neglect managing energy consumption and cache usage of node. This results in problems such as communication chain easy to interruption and congestion control instability in opportunity networks. To solve the above problems, this paper proposes CR-5: resource optimization opportunistic network routing algorithm based on node dynamic attributes. CR-5 comprehensively considers node dynamic attributes such as historical encounter information, resource availability, and node social input degree, defines node communication relay degree as the basis for selecting relay nodes, and realizes node energy balance and cache optimization. CR-5 tends to choose relay nodes with strong communication ability, which solves the problem of communication chain easy to interruption to some extent. CR-5 also enhances the resource management and message processing ability of nodes through energy balance and cache optimization, reduces the phenomenon that some nodes process too many messages, which solves the problem of congestion control instability to some extent. Simulation results show that in opportunistic networks with low node initial cache, the CR-5 algorithm greatly improves the message delivery ratio and the node average remaining energy, and significantly reduces the route overhead ratio.

Advancements in Delay-Tolerant Networking: The SPWEBR Protocol for Superior Routing Efficiency and Performance

In the rapidly growing field of mobile ad hoc networking, having ideal routing protocols is essential to maintaining communication. We provide a thorough simulation examination of many well-known routing protocols, including Source Spray and Wait, Binary Spray and Wait, Encounter-Based Routing (EBR), MaxPro, Direct Delivery, Epidemic, and First Contact, among others. In light of this, we are pleased to present and thoroughly assess Spray and Wait with Encounter Based Routing (SPWEBR), a unique protocol. Initial analyses show that SPWEBR is a strong competitor, attaining a pleasing equilibrium in the parameters of Delivery Ratio, Overhead Ratio, and Message Drop. Above all, SPWEBR shows a benefit: it can outperform some existing protocols by as much as 50% in Delivery Ratio, approximately 60% in Reduced Overhead Ratio, and with less Message Drops in particular node event intervals. These outcomes highlight its exceptional effectiveness and possible relevance. This comprehensive comparison analysis demonstrates SPWEBR's supremacy in the current mobile networking environment and its potential for future advancements. In light of these results, we suggest conducting additional study on SPWEBR in order to fully utilize its potential and flexibility to a variety of networking settings.

SOLUSI ALTERNATIF PENYAMPAIAN INFORMASI DENGAN TOLERANSI WAKTU TUNDA UNTUK PENGGUNA LAYANAN KOMUNIKASI DATA PADA VEHICULAR NETWORK

Data demand from personal mobile devices over cellular networks is rapidly increasing. For time-delay tolerant data, the use of paid technology is inefficient. As an alternative for time-delay tolerant data delivery, opportunistic networks are emerging as an effective infrastructure-less communication model. Before it can be used as an alternative solution, it is necessary to analyze the performance of using opportunistic networks, especially the basic routing patterns used. This research analyzes the performance of routing patterns in opportunistic networks by utilizing inter-vehicle networks as mobile nodes in Denpasar City. Two basic routing patterns, namely epidemic and spray and wait, are simulated using The ONE Simulator into two scenarios including a scenario of increasing the average vehicle and a scenario of increasing the buffer size. The simulation results show that in the inter-vehicle network utilization, epidemic routing pattern is superior to spray and wait in terms of delivery probability and average latency, but when assessing in terms of overhead ratio, spray and wait routing pattern can provide lower results. This shows that the epidemic routing pattern is suitable for systems that do not care about high resource utilization.

Implementation of Enhanced Spray Routing Protocol for VDTN On Surabaya Smart City Scenario

The application of smart-city, which promises better city management in helping to improve people's quality of life, is still inhibited due to the high cost of infrastructure investment. In several Smart Cities, it takes at least $ 30 - 40 billion to convert a conventional town into a smart city, including for data collection infrastructure. Alternatively, low-power wide-area networks (LPWANs) could be considered, but they need more bandwidth to serve data transmission in a smart city. Vehicle Delay Tolerant Network (VDTN) is one part of DTN that employs vehicles as a communication infrastructure that allows communication in challenging conditions and could make it an alternative network for Data Collection in a Smart City. This paper proposes a Surabaya Smart City scenario with VDTN as data collection. The scenario consists of 40 wireless sensors and 50 to 200 vehicles (car and bus) with five Road Side Units that forward data from the sensor to the monitoring server. Furthermore, to increase the VDTN performance, we improve our proposed routing protocol, Spray and Hop Distance (SNHD), with two sprays method (Adaptive and Simple) and data collection support from multiple sources and destinations. The evaluation was carried out using a simulation-based comparison with an increase in the number of vehicles to determine the impact of vehicle density on data collection performance in terms of delivery probability, latency average, and overhead ratio. Based on the simulation results, the simple spray method in SNHD and A-SNHD outperformed the well-known VDTN routing protocol, i.e., Epidemic and Spray and Wait. Furthermore, when the number of cars increases from 50 to 200, the performance of VDTN does not increase significantly as the density of the network increases. It means that VDTN only requires a small number of vehicles to be used as a low-cost alternative network for smart cities.
 

A social relationship-based energy efficient routing scheme for Opportunistic Internet of Things

Opportunistic Internet of Things (OppIoT) combines Opportunistic Networks and the Internet of Things to provide opportunistic communication between IoT devices and human-created communities in order to improve network connectivity, reliability, and longevity. This is significant because it introduces a new approach to connect and communicate smart devices in situations where there is no end-to-end connectivity. Due to the non-stable existence of connectivity between nodes, building a scalable, flexible, interoperable, and energy efficient routing protocol is a difficult issue. This study presents a new routing protocol for the Opportunistic Internet of Things called Social relationship based Energy Efficient Routing (SEER), which bases routing decisions on the forwarding feasibility degree, residual energy, and buffer capacity of nodes. SEER surpasses EBR and EDR in terms of delivery probability, hop count, and overhead ratio, according to simulation results utilizing the Opportunistic Network Environment (The ONE) simulator.