Node Capacity Research Articles

Flood Prediction System Using IOT & Artificial Neural Network

Floods pose significant challenges as one of nature's most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface.

An adaptive transmission strategy based on cloud computing in IoV architecture

Because of recent developments in wireless communication, sensor technology, and computing technology, researchers have recently shown a significant amount of interest in the Internet of Vehicles (IoV), which has become feasible as a result of these improvements. Because of the distinctive characteristics of IoV, such as the varied compute and communication capacities of network nodes, it is difficult to process jobs that are time-sensitive. The purpose of this study is to investigate the ways in which cloud computing may collaborate with the IoV to make the processing of time-sensitive procedures easier. We propose a vehicle design that makes advantage of cloud computing as a means of accomplishing this goal. Increasing the proportion of time-sensitive jobs that are ultimately completed was the motivation behind the development of the offloading model that we devised. Taking this into perspective, we present an adaptive task offloading and transmission method. Taking into account the ever-changing requirements and constraints on the available resources, this algorithm dynamically organizes all of the tasks into separate cloud link lists on the cloud. Following that, the tasks contained within each list are distributed in a cooperative manner to a number of different nodes, with the characteristics of those nodes being taken into consideration. Following the presentation of the simulation model, we carried out an experimental investigation into the effectiveness of the model that was proposed. It is abundantly evident that the proposed model is effective, as indicated by the findings.

A Forward–Backward Simheuristic for the Stochastic Capacitated Dispersion Problem

In an effort to balance the distribution of services across a given territory, dispersion and diversity models typically aim to maximize the minimum distance between any pair of facilities. Specifically, in the capacitated dispersion problem (CDP), each facility has an associated capacity or level of service, and the objective is to select a set of facilities so that the minimum distance between any pair of them (dispersion) is maximized, while ensuring a user-defined level of service. This problem can be formulated as a linear integer model, where the sum of the capacities of the selected facilities must match or exceed the total demand in the network. Real-life applications often necessitate considering the levels of uncertainty affecting the capacity of the nodes. Failure to account for this uncertainty could lead to low-quality or infeasible solutions in practical scenarios. However, research addressing the stochastic version of the CDP is scarce. This paper introduces two models for the CDP with stochastic capacities, incorporating soft constraints and penalty costs for violating the total capacity constraint. The first model includes a probabilistic constraint to ensure the required level of service with a certain probability, while the second model introduces a soft constraint with penalty costs for violations. To solve both variants of the model, a forward–backward simheuristic algorithm is proposed. Our approach combines a metaheuristic algorithm with Monte Carlo simulation, enabling the efficient handling of the random behavior of node capacities and obtaining reliable solutions regardless of their probability distribution.

Analysis of land system resilience: Static and dynamic perspectives

Land system face multiple threats due to the long-term effects of climate change, global geopolitical and economic. Our research simulates the static and dynamic resilience of land systems under increasing socioeconomic pressures and frequent disturbances from natural disasters. We use an improved load-capacity model to simulate the cascading failure effects of agricultural land systems. The study finds that parameters change after deliberate attacks much earlier than random attacks that cause network collapse. The core nodes in the trade structure carry most of the functions, and such regions and land types should be focused on. Cascading failures make the network more vulnerable due to the transmissibility of pressure. Static attacks can cause network collapse when more than 30% of nodes are attacked, while dynamic attacks only need to attack 10% of nodes. According to parameter optimization, the node capacity should be maximized while reducing the gap in the initial load of the nodes, thereby weakening the cascading effect. To ensure the reliability of the results, the article conduct robustness tests. Finally, in order to develop sustainable land systems, the article propose that it is necessary to assess resilience, identify key elements, effectively monitor land systems, and develop sound land protection plans.

Estimating the Railway Network Capacity Utilization with Mixed Train Routes and Stopping Patterns: A Multiobjective Optimization Approach

Railway capacity estimation problem is typically defined as estimating the maximum number of trains that can be operated in a railway section within a given time interval. However, trains with different speeds, routes, and stopping patterns in a railway network will likely compete for the limited capacity of network nodes and sections. As these trains may provide different services, it is ambiguous to simply indicate the network capacity by a scalar number of trains. To comprehensively estimate and interpret the railway capacity considering the capacity competition between heterogeneous trains, we propose a multiobjective perspective for the capacity estimation problem to enrich the capacity theory while handling the competition among trains with different routes and stopping patterns. Based on a time-space network timetable saturation model, we extend the multiobjective capacity estimation approach to the detailed timetable level by optimizing the saturated timetable under capacity estimation objectives with respect to different routes and stopping patterns. With the ε-constraint method, we can obtain the Pareto front of saturated timetables, i.e., a set of nondominated optimized timetables that no more candidate train can be additionally scheduled. The result is a more comprehensive capacity representation than a single absolute scalar number. A case study is conducted on a combined high-speed and intercity network of Zhengzhou Railway group in China. An extensive set of Pareto-optimal saturated timetables describing the effects on the capacity of the railway network is obtained. The results can help infrastructure managers select saturated timetables as the capacity utilization reference by considering the trade-off between time indexes from passengers’ and operators’ perspectives.

Two-stage Inhibition Effects of Burkholderia sp. Y4 Application on Cadmium Uptake and Transport in Wheat

In order to evaluate the feasibility of using Burkholderia sp. Y4 as a cadmium （Cd）-reducing bacterial agent in contaminated wheat fields， the changes in the rhizosphere soil microbial community and Cd available state， as well as the content and transport characteristics of Cd in the wheat root， basal node， internode， and grain under the treatment of strain Y4 were tested using microbial high-throughput sequencing， step-by-step extraction， subcellular distribution， and occurrence analyses. The results showed that root application of strain Y4 significantly reduced the root and grain Cd content of wheat by 7.7% and 30.3%， respectively， compared with that in the control treatment. The Cd content and Cd transfer factor results in wheat vegetative organs showed that strain Y4 reduced the Cd transfer factor from basal node to internode by 79.3%， and Cd content in the wheat internode stem also decreased by 50.9%. The study of Cd occurrence morphology showed that strain Y4 treatment increased the proportion of residual Cd in roots and basal ganglia， decreased the contents of inorganic and water-soluble Cd in roots， and increased the content of residual Cd in basal ganglia. Further examination of the subcellular distribution of Cd showed that the Cd content in root cell walls and basal ganglia cell fluid increased by 21.3% and 98.2%， respectively， indicating that the Cd fixation ability of root cell walls and basal ganglia cell fluid was improved by the strain Y4 treatment. In the rhizosphere soil， it was found that the microbial community structure was changed by strain Y4 application. Under the Y4 treatment， the relative abundance of Burkholderia increased from 9.6% to 11.5%， whereas that of Acidobacteriota decreased. Additionally， the relative abundance of Gemmatimonadales， Pseudomonadales， and Chitinophagales were also increased by strain Y4 treatment. At the same time， the application of strain Y4 increased the pH value of rhizosphere soil by 8.3%. The contents of exchangeable Cd， carbonate-bound Cd， and iron-manganese oxide-bound Cd in the soil decreased by 44.4%， 21.7%， and 15.9%， respectively， whereas the proportion of residual Cd reached 53.6%. Root application of strain Y4 increased the contents of nitrate nitrogen and ammonium nitrogen in the soil by 22.0% and 21.4%， respectively， and the contents of alkaline nitrogen also increased to a certain extent. In conclusion， the root application of strain Y4 not only improved soil nitrogen availability but also inhibited Cd transport and accumulation from contaminated soil to wheat grains in a "two-stage" manner by reducing Cd availability in rhizosphere soil and improving Cd interception and fixation capacity of wheat roots and basal nodes. Therefore， Burkholderia Y4 has application potential as a Cd-reducing and growth-promoting agent in wheat.

A Material Allocation Model for Public Health Emergency under a Multimodal Transportation Network by Considering the Demand Priority and Psychological Pain

In a public health emergency, residents urgently require a large number of rescue materials for treatment or protection. These rescue materials are usually located far from the emergency area. The government must organize rescue materials transportation by selecting suitable transport modes. Thus, we propose a material allocation model for public health emergencies under a multimodal transportation network to determine the best rescue material supply route. In this model, we set the demand priorities according to the emergency degrees to decide the transportation sequence. Meanwhile, we introduce the psychological pain cost brought by the rescue material shortage into the proposed model to trade off the priority and fairness of demand. Having compared it to the research literature, this is the first study that considers multiple categories of materials, absolute pain costs, relative pain costs and demand priority under multimodal transportation. The research problem is formulated into an integer programming model, and we develop a modified genetic algorithm to solve it. A set of numerical examples are conducted to test the performance of the proposed algorithm, and to investigate features and applications of the proposed model. The results indicate that the modified genetic algorithm performs better in the calculation examples at different scales. For small-scale instances, the algorithm produces consistent results with Gurobi. As the instance size increases, Gurobi fails to find the optimal solution within 1800 s, while this algorithm is able to find the optimal solution within an acceptable time frame. Additionally, when dealing with large-scale instances, the algorithm exhibits a significant advantage in terms of runtime. Sensitivity analysis of key factors indicate that (1) Adjusting the relative pain cost coefficient can make the best trade-off between fairness, economy and timeliness; (2) Compared with a single mode of transport, multimodal transport can reduce the psychological pain cost and the logistics cost; (3) Improving the loading and unloading capacity of nodes can reduce the delivery time of materials and the psychological pain cost of residents, but the influence of other factors and cost-effectiveness need to be considered.

Toward Reliable DNN-Based Task Partitioning and Offloading in Vehicular Edge Computing

Modern vehicles have become typical consumer electronics with the development of sensing, transmission, and computation technologies. The emerging intelligent transportation systems (ITSs) yield lots of deep neural network (DNN) based tasks, requiring intensive computation. In view of this, this paper makes the first exploration of accelerating the processing of DNN-based tasks while maintaining decent system reliability in vehicular edge computing (VEC) via task partitioning and offloading. Specifically, we present a specific scenario, where vehicles partition and offload their tasks to nearby vehicles and roadside infrastructures, while they may fail to receive results due to unstable vehicular communications. Then, we model the task delay by considering task properties and node capacities. On this basis, the Partitioning and Offloading Problem (POP) is formulated as a bi-objective optimization problem, to maximize both the acceleration ratio and service ratio of DNN-based tasks in VEC. Further, we propose a Distributed Partitioning and Offloading Solution (DPOS), where a delay-priority-oriented offloading strategy is designed to help edge nodes make offloading decisions, and a stacking-based partitioning strategy is designed to assist client vehicles to make partitioning decisions. Finally, we give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution.

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

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

Allocation of transportation capacity for complex natural gas pipeline network under fair opening

China has formed a national natural gas pipeline network, and reasonable and accurate pipeline capacity allocation scheme has important significance for improving the utilization rate of pipeline network, improving the income of pipeline network operators and ensuring the gas consumption of downstream users, which poses greater challenges to the formulation of pipeline capacity allocation rules and mechanisms. This paper takes the natural gas pipeline network system as the research object, and studies the rules and mechanisms of pipeline capacity allocation. A capacity allocation model of natural gas pipeline network is proposed with the net present value of pipeline network operator's income as the goal, considering the gas consumption characteristics of downstream users, the dynamic change of gas source node capacity, pipeline hydraulic characteristics, pipeline flow direction, compressor and other constraints. Based on the simulation of the actual pipeline network by the proposed model, seven allocation rules and one mechanism are proposed. Specifically, the user priority of non-interruptible type, the pipeline path adjustable type and the gas source node adjustable type is higher than the corresponding comparison type. In the pipeline capacity allocation mechanism, the amount compensation mechanism for pipeline capacity transactions is as follows: When the pipeline capacity transaction occurs between users, the transaction information is submitted to the pipeline network operator. The pipeline network operator proposes the corresponding amount compensation according to the transaction information, and the transaction parties negotiate the proportion of the amount compensation and submit it to the pipeline network operator. In addition, it is recommended that pipeline network operators vigorously develop LNG receiving stations and downstream interruptible and compressible users in the future.

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

Vulnerability Analysis of China‐Europe Railway Express Network Based on Improved Nonlinear Load‐Capacity Model

The China‐Europe Railway Express (C‐ER Express) provides a transcontinental rail container service between China and Europe. As most C‐ER Expresses are affected by frequent natural disasters and public health incidents, it faces the increasing risk of network vulnerability. When previous studies investigated the evolution of network vulnerability through local information, they often overlook the complexity of the network’s multidimensional characteristics. The nonlinear load‐capacity (NLC) model proposed in this paper integrates local and global information of the network. This approach enables a detailed investigation into how condition thresholds and different types of nodes influence network vulnerability. Firstly, a feature matrix is constructed for C‐ER Express based on the topological measures, freight information, and external environment scores. Then, the autoencoder is used to extract the low‐dimensional dense information, and the DBSCAN is used to classify C‐ER Express into distinct clusters. Secondly, The NLC model integrates feature coefficient to describe the initial capacity of nodes. Subsequently, the failure load is redistributed proportionally to neighboring nodes and remaining normal nodes based on time‐varying load and initial capacity of nodes. Finally, the improved NLC model is applied to the C‐ER Express under different simulation scenarios. Simulation results show that a reasonable condition threshold can mitigate the impact of small‐scale node failures on the network. The DBSCAN attack strategy can effectively identify the node types and prevent the network from chain reactions brought by different types of node failures. This research study is expected to provide some reference value for relevant research about vulnerability analysis of the C‐ER Express network.

ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints

In this research, we present a variant of the Ant Colony Optimization (ACO) algorithm tailored for optimizing routing within a limited-capacity environment for a swarm of drones. Our primary objective is to minimize task completion time while ensuring that individual node capacity constraints are never breached. Our algorithm leverages both pheromone trails and heuristic information to guide drone movements intelligently, and it dynamically updates the pheromone trail based on solution quality. This research underscores the importance of ideal routing algorithms in drone swarm systems and showcases ACO's effectiveness in optimizing such challenges. We conducted a comprehensive performance comparison against the existing methods like Christofides algorithm, Simulated Annealing, K-Opt and Lin-Kernighan Heuristic and the Brute-force method. Results indicate that our algorithm significantly improves time efficiency and is feasible for real-time environments. Furthermore, we highlight the potential for substantial enhancements in the system by deploying suitable hardware, such as quadcopters with specific capabilities.

Vulnerability analysis of public transit systems from the perspective of the traffic situation

The critical to sustainable urban development that a robust bus transit system is constructed to alleviate urban congestion problems. Unfortunately, disruptions in bus transit systems can lead to dysfunction and threaten sustainable development. This paper proposed a vulnerability analysis model for constructing complementary urban bus networks (UBNs), which considers the actual capacity of the bus network at the speed of transport roads. In addition, the UBNs aimed analysis method is presented from three specific aspects: (a) utilizing the stochastic user equilibrium model and different traffic situations to measure the UBNs of passenger flow distribution according to multi-source data; (b) introducing the node capacity and structural, functional vulnerability analysis model by combining real traffic flows and the complex network theory; (c) analysing the vulnerability characteristics of UBNs cascading failures from different passenger flow distributions and increasing capacity parameters, and comparing with those under different attack methods. Then, the new vulnerability analysis model is applied to the Harbin trunk bus. Considering different attack scenarios, the UBNs show strong vulnerability in the case of high congestion. Compared with other deliberate attacks, the bus network based on the attack mode of the maximum intensity site is more vulnerable at different times. These insights could be used to inform a vulnerability-based spatiotemporal design of bus transit networks.

Assessing the resilience of complex ecological spatial networks using a cascading failure model

The resilience of ecological networks is an important guarantee for maintaining regional ecological stability. However, current research on the resilience of ecological networks does not take into account the dynamic processes of the network. This study is based on complex network theory, takes the southern foothills of the Qilian Mountains in China as the research area, and uses the shortest path method to construct an ecological network with 104 nodes and 306 edges. It then uses the cascading failure model to simulate the dynamic response process of the ecological network under different attack strategies, and uses this to evaluate the resilience of the complex ecological space. The research results show that The topological structure of the ecological space network in the southern foothills of the Qilian Mountains has the characteristics of fewer central high-degree nodes and more peripheral low-degree nodes; When the initial load intensity difference of each ecological node in the network is small, the attacked low-degree nodes make the decline in network resilience more obvious. When the initial load intensity difference of each ecological node in the network is large, the attacked high degree nodes make the decline in network resilience more obvious. The study suggests that maintaining the load of high degree nodes and increasing the capacity of low degree nodes are the keys to maintaining network stability. The results provide a new perspective for optimising ecological network configuration and protecting key areas.

Improved Security-Based Model for Heterogeneous Wireless Sensor Networks Using the Dispense Incline Drop Algorithm

Numerous real-time applications rely on heterogeneous wireless sensor networks (HWSNs), including the Internet of Vehicles (IoV), Internet of Sensors (IoS), medical applications, and so on. Due to the restricted computational capacity, memory, and connectivity of wireless nodes, it is essential to establish authentication techniques that consume less energy, yet guarantee strong security. It takes a lot of time and energy to construct trust models between sensor devices that can recognize and differentiate between benign and malicious attackers, which, in turn, reduces the detection rate and increases overhead. This article suggests a hybrid forewarn approach based on improved security to attain this degree of safety in HWSN communication called the Improved Security-based hybrid forewarn model (ISBHF) and the Dispense Incline Drop Algorithm. It is recommended to offer a trust score and evaluations for messages and knowledge transfer to improve detection capabilities against attackers targeting essential data transmission. To further minimize packet loss, a reliability secure measure is created, and a weighted averaging strategy is implemented to ensure the robustness and veracity of the secure scores. Furthermore, empirical evidence from measurements and simulations will demonstrate that unsecured paths of communication are key to identifying fraudulent nodes that are causing an outsized fraction of network traffic or data breaches. The suggested protocol is superior to the current approach in terms of efficiency and security, making it ideal for use in HWSN networks by decreasing communication overhead, increasing detection performance, and other parameters such as packet delivery ratio and connectivity ratio.

Securing smart home against sinkhole attack using weight‐based IDS placement strategy

AbstractExtensive use of the Internet of Things (IoT) in smart homes makes users' lives easy and comfortable. Yet, these resource‐constrained devices are prone to manifold security attacks. The sinkhole attack is one of the most destructive attacks that disrupt smart home operations, causing user dissatisfaction. Existing intrusion detection systems (IDS) cannot handle sinkhole attacks competently as they (i) do not consider the node capacity for being an IDS agent, leading to a low attack detection ratio, (ii) do not examine the sinkhole node's role when mitigating attacks, causing remaining network disconnection with the root node and (iii) do not consider replacing energy‐exhausted IDS nodes, causing connectivity loss of partial network with the root. This paper addresses these shortcomings and adequately presents a mechanism to handle sinkhole attacks. A formulation for assigning weights to network nodes based on their resources is proposed here. An IDS placement strategy is introduced to place IDS agents on particular resourceful nodes that extend network lifetime and enhance attack detection capability. We present a novel attack detection and mitigation strategy by ensuring network connectivity. The proposed mechanism achieves 95% attack detection accuracy and reduces false negative rates by 25% and energy consumption reasonably compared to the state‐of‐the‐art.

Simulation based QoS aware dynamic caching scheme for heterogeneous content requests in vehicular edge computing

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.

-Memory Computing Based Reliable and High Speed Schmitt trigger 10T SRAM cell design

Static random access memories (SRAM) are useful building blocks in various applications, including cache memories, integrated data storage systems, and microprocessors. The von Neumann bottleneck difficulties are solved by in-memory computing. It eliminates unnecessary frequent data transfer between memory and processing units simultaneously. In this research, the replica-based 10T SRAM design for in-memory computing (IMC) is designed by adapting the word line control scheme in 14nm CMOS technology. In order to achieve high reading and writing capability, the Schmitt trigger inverter was used for energy-saving and stable use. To speed up the writing process of the design, a single transistor is inserted between the cross-coupled inverters. In addition, to increase the node capacity, the voltage boosting circuitry is emphasized. The adaptive word line control scheme was utilized by integrating the replica column based circuit. The Replica approach regulates signal flow through the core by using a dummy column and a dummy row in RAM. To demonstrate the viability of the suggested design, the simulated outcomes are contrasted with those of existing designs. The various performance metrics examined are Read Static Noise Margin (RSNM), Write (WSNM), Hold (HSNM), Read Access Delay (RAD), Write Access Delay (WAD), Read performance and Write performance the varying supply voltage is evaluated.

Cooperative Computing Offloading Scheme via Artificial Neural Networks for Underwater Sensor Networks

Aiming at the problem of being unable to meet some high computing power, high-precision applications due to the limited capacity of underwater sensor nodes, and the difficulty of low computation power, in this paper, we introduce the edge servers, known as base stations for underwater sensor nodes, and propose a scheme to process the computational tasks based on coalition game theory. This scheme provides functions such as cooperation among different base stations within the coalition, the smart division of tasks, and efficient computational offloading. In order to reduce the complexity of the algorithm, the artificial neural network model is introduced into the method. Each task is divided into sub-parts and fed to an artificial neural network for training, testing, and validation. In addition, the scheme delivers the computed task from base stations back to sink nodes via a shortened path to enhance the service reliability. Due to the mobility of the base station in the ocean, our proposed scheme takes into account the dynamic environment at the same time. The simulation results show that, compared with the existing state-of-the-art methods, the success rate of our proposed approach improves by 30% compared with the Greedy method. The total service time of our proposed approach decreases by 12.6% compared with the Greedy method and 31.2% compared with the Always-Migrate method.