Application Layer Research Articles

Task Scheduling Algorithm for Power Minimization in Low-Cost Disaster Monitoring System: A Heuristic Approach

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments.

A smart monitoring and observation framework for aquaponics ecosystem

Aquaponics is a sustainable and efficient agricultural system that integrates aquaculture with agriculture. This system integrates Internet of Things (IoT) technology in aquaponics systems with the potential to change sustainable agriculture by combining farming of fish (aquaculture) and soilless cultivation of plants (hydroponics) into a single, efficient ecosystem. This paper explores the design and development of a smart aquaponics system that allows aquaponic farming at commercial scale feasible, leveraging IoT to continuously gather data through various sensors, monitor environmental conditions, and automate control processes. The proposed system architecture is based on a three-tier model, ensuring scalability and maintainability. The proposed system consists of a three-layer architecture: the Perception Layer for measuring different environmental factors and controlling them, the Network Layer for data transmission, and the Application Layer for data storage, manipulation, and user interaction. The key components include sensors for monitoring pH, humidity, temperature, sunlight, oxygen levels, and water proximity; devices such as lights, water pumps, fans, and oxygen pumps for environmental control; and an ESP8266 microcontroller for system management. The system's software is developed using C# DOT NET CORE 8.0, React, MySQL, C, and Arduino. The system allows users the ability to maintain optimal conditions for fish and plant growth, notify users of irregularities, and allow remote control through a web application, proving it to be an intelligent, flexible, low-cost, and stable solution for large-scale aquaponics.

Study of electrical properties of fullerene based all-small-molecule low donor organic devices with and without cathode buffer layers for photovoltaic application

This article examines electrical properties of fullerene (C70) based all-small-molecule organic photovoltaics with low donor concentration. Enhancements in the properties of devices are observed with the incorporation of lithium fluoride (LiF) and molybdenum trioxide (MoO3) as cathode buffer layers (CBLs). Samples are characterized using field emission-scanning electron microscopy (FESEM), optical absorption spectra, current-voltage (I-V) characteristics in dark and under illumination, and capacitance-frequency (C-ω) measurements. Device with single CBL (LiF) exhibits reduced photoabsorption leading to less saturation photocurrent density (Jsat) and lower maximum exciton generation rate (Gmax), due to non-uniform deposition of LiF in device. In contrast, device having two CBLs (LiF & MoO3) has enhanced the photoabsorption exhibiting high Jsat and Gmax attributed to uniform deposition of MoO3. Moreover, dielectric properties and AC conductivity of devices are also measured confirming the results and these are found to be dependent on the frequency and voltage.

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Optimal resource management for multi-access edge computing without using cross-layer communication

We consider a Multi-access Edge Computing (MEC) system with a set of users, a base station (BS) with an attached MEC server, and a cloud server. The users can serve the service requests locally or can offload them to the BS which in turn can serve a subset of the offloaded requests at the MEC and can forward the requests to the cloud server. The user devices and the MEC server can be dynamically configured to serve different classes of services. The service requests offloaded to the BS incur offloading costs and those forwarded to the cloud incur additional costs; the costs could represent service charges or delays. Aggregate cost minimization subject to stability warrants optimal service scheduling and offloading at the users and the MEC server, at their application layers, and optimal uplink packet scheduling at the users’ MAC layers. Classical back-pressure (BP) based solutions entail cross-layer message exchange, and hence are not viable. We propose virtual queue-based drift-plus-penalty algorithms that are throughput optimal, and achieve the optimal delay arbitrarily closely but do not require cross-layer communication. We first consider an MEC system without local computation, and subsequently, extend our framework to incorporate local computation also. We demonstrate that the proposed algorithms offer almost the same performance as BP based algorithms. These algorithms contain tuneable parameters that offer a trade off between queue lengths at the users and the BS and the offloading costs.

Passive isothermal film with self-switchable radiative cooling-driven water sorption layer for arid climate applications

Reducing substantial energy demand of active heating, ventilation, and air conditioning in arid climates is of paramount importance. Here, we develop a millimeter-scale passive isothermal film that maintains temperature near 25 °C without relying on energy consumption solely through natural phenomena. A radiative cooling unit comprising polydimethylsiloxane (PDMS) with diffraction grating and fused SiO2/Ti/Ag film facilitates radiative cooling during daylight hours. Net thermal energy is stored through water desorption of porous materials (latent cooling) and dissolution of ammonium nitrate in water (endothermic reaction). At nighttime, thermal emission is suppressed by the destructive interference of PDMS diffraction, and thermochemical reverse reaction occurs to sustain temperature in response to heat loss from the ambient environment. It reduces cooling and heating loads by approximately −1.1 and 0.3 kW m−2, respectively, throughout a full day. Our results indicate the potential of designing a passive system suitable for human habitation without an active system.

A Novel Resource-Aware Distributed Cooperative Decision-Making Mechanism for Connected Automated Vehicles

<div>This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as example objectives. RAD-VEGA then cooperatively solves this MOOP, taking into account the availability of AV’s onboard resources and the application layer characteristics of today’s ITS communication tools. The performance of the proposed mechanism is evaluated by solving the ZDT1 test problem and studying pareto-frontier solutions to the true front over time. The scalability of the proposed solution is estimated to be 305 CAVs, considering a communication bandwidth of 6 MB/s. Additionally, cooperative AV planning scenario examples are simulated, and the effectiveness of the proposed mechanism is demonstrated by comparing final and initial solutions after solving the MOOP using RAD-VEGA.</div>

Character-Level Text Generation for Shakespearean Style with LSTMs

This paper presents a pioneering approach to text generation employing Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) architecture, inspired by the rich and timeless prose of William Shakespeare. The motivation stems from the enduring allure of Shakespearean language, which has captivated audiences across centuries, and the challenge of replicating itsintricate style using modern computational techniques. Our research contributes a novel methodology that leverages the capabilities of RNN LSTM networks to emulate the linguistic nuances of Shakespeare with remarkable fidelity. The paper begins by providing a comprehensive overview of RNN LSTM networks, highlighting their suitability for sequential data processing tasks and their ability to capture long-rangedependencies. A review of related work in the field sets the stage for our proposed approach, shedding light on recent advancements and methodologies employed in text generation using similar techniques. We formulate the problem by defining the mathematical framework, optimization objectives, and evaluation metrics for our proposed model. The architecture consists of three layers: the data layer for preprocessing input text data, the intelligence layer comprising multiple LSTM units for capturing different aspects of Shakespearean language, and the application layer for generating output text based on learned representations. Experimental results demonstrate the effectiveness of our approach, with evaluations conducted on a corpus of Shakespearean texts.In conclusion, our research presents a significant advancement in the field of natural language generation, opening new avenues for exploring the intersection of literature and artificial intelligence.

Organic Heterostructures with Dendrimer Based Mixed Layer for Electronic Applications.

Recently, much research has focused on the search for new mixed donor-acceptor layers for applications in organic electronics. Organic heterostructures with layers based on the generation 1 poly(propylene thiophenoimine) (G1PPT) dendrimer, N,N'-diisopropylnaphthalene diimide (MNDI), and a combination of the two were prepared and their electrical properties were investigated. Single layers of G1PPT and MNDI and a mixed layer (G1PPT:MNDI) were obtained via spin coating on quartz glass, silicon, and glass/ITO substrates, using chloroform as a solvent. The absorption mechanism was investigated, the degree of disorder was estimated, and the emission properties of the layers were highlighted using spectroscopic methods (UV-Vis transmission and photoluminescence). The effects of the concentration and surface topographical particularities on the properties of the layers were analyzed via atomic force microscopy. All of the heterostructures realized with ITO and Au electrodes showed good conduction, with currents of the order of mA. Additionally, the heterostructure with a mixed layer exhibited asymmetry in the current-voltage curve between forward and reverse polarization in the lower range of the applied voltages, which was more significant at increased concentrations and could be correlated with rectifier diode behavior. Consequently, the mixed-layer generation 1 poly(propylene thiophenoimine) dendrimer with N,N'-diisopropylnaphthalene diimide can be considered promising for electronic applications.

Characterization of MoS2:Nb sputtered thin films. An application as hole transport layer in Cu2ZnSnS4/Si tandem solar cells

MoS2:Nb films deposited by radio-frequency magnetron sputtering are investigated in view of their application in infrared (IR)-transparent contacts for tandem photovoltaic devices.This material is already known to give a good electrical contact with p-type chalcogenide semiconductors, which are typically grown onto opaque molybdenum metallic contacts and a MoS2 layer spontaneously forms at the back interface during the on-top semiconductor growth. Our study explores a different approach, which involves the direct growth of IR-transparent MoS2:Nb films via sputtering inside complete photovoltaic devices, like Cu2ZnSnS4 (CZTS)-based single junction solar cells and CZTS/Si tandem devices. Films deposited at different sputtering pressures are compared by analysing their microstructure, morphology, chemical composition, optical and electrical properties. The effects of post-deposition sulfurization treatments are also investigated. We find that MoS2:Nb films deposited at around 0.1 Pa exhibit compactness but show a notable sulfur deficit ([S]/[Mo]≈1.4), a significant sub-bandgap optical absorptance and lack of crystallinity. Increasing the Ar pressure to 1 Pa raises the [S]/[Mo] ratio to 2.2, yielding crystalline films with good IR-transparency, although with a porous morphology. Despite 0.5 wt% Nb-doping of the sputtering target, the as-deposited films demonstrate n-type conductivity likely due to uncontrolled impurities and intrinsic defects. Ultraviolet Photoemission Spectroscopy measurements suggest that films’ work function higher than 5 eV can be obtained with a post-deposition sulfurization, making these materials suitable as Hole Transport Layer in photovoltaic applications. A similar increase in work function is expected in the CZTS/MoS2 junctions, since the sputtered MoS2:Nb films undergo a sulfurization process needed to obtain the overlying polycrystalline CZTS absorber.CZTS solar cells produced with sputtered MoS2 and Transparent Conductive Oxides contacts on glass substrates, despite plagued by severe adhesion problems, show the potentiality to give efficiencies comparable to reference devices with standard Mo back contact. Fabrication of CZTS/Si tandem devices on textured silicon bottom cells yields a maximum efficiency of 4.4 %, primarily hindered by the low quality of the CZTS film on textured substrates. Nonetheless, optoelectronic characterizations based on both spectrophotometric and quantum efficiency measurements confirm a good IR transparency of the MoS2-based intermediate contacts and the desired electrical behaviour.

Platinum Layers for Applications from Hydrogen to Connectors

Platinum Layers for Applications from Hydrogen to Connectors

Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell.

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

A novel lightweight multi-factor authentication scheme for MQTT-based IoT applications

The present authentication solutions employed in the Internet of Things (IoT) are either inadequate or computationally intensive, given the resource-constrained nature of IoT devices. This challenges the researchers to devise efficient solutions to embed an important security tenet like authentication. In IoT, the most popular machine-to-machine communication protocol used at the application layer is Message Queuing Telemetry Transport (MQTT). However, the MQTT protocol inherently lacks security-related functions, like authentication, authorization, confidentiality, access control, and data integrity, which is unacceptable for IoT-driven mission-critical applications when connected over public networks. In such a situation, the security is hardened by employing a transport layer security protocol like TLS, which entails significant computational overheads. This paper presents a novel scheme to enhance MQTT security by providing a lightweight multi-factor authentication scheme based on Elliptical curve cryptography. The proposed scheme uses a low-cost signature and a fuzzy extractor to correct errors in imprinted biometrics in noisy environments. This scheme attains mutual authentication, generates a securely agreed-upon session key for secret communication, and guarantees perfect forward secrecy. Furthermore, the rigorous informal security analysis shows the proposed scheme resists cryptographic attacks, including known session critical attacks. Furthermore, an empirical study has been carried out to assess the effectiveness of the proposed scheme in the Cooja simulated environment.

Large-scale hydropower dispatching system based on cloud platform and its key technologies

In the past decade, the rapid expansion of hydropower capacity in China brings challenges to hydropower dispatching in data management and operation scheduling. In data management, the scale of hydropower operation data increases explosively, presenting typical big data features. In operation scheduling, the optimization of hydropower system operations is a high-dimension, nonconvex, and nonlinear problem. Conventional optimization methods encounter the “dimensionality disaster”, and cannot solve the scheduling problem efficiently. To deal with these challenges, by analyzing the data characteristics and operation scheduling requirements, this paper develops a large-scale hydropower dispatching system based on the cloud platform. The dispatching system consists of four layers: data access layer, cloud platform layer, data sharing layer, and application layer, which has good scalability and flexibility in data management and operation scheduling. In the application layer, key technologies based on the big data and cloud computing resources of the dispatching system are proposed to carry out the optimal operation of hydropower systems, including distributed parallel dynamic programming (DPDP) for long-/medium-term scheduling and data mining-based method (DMM) for short-term scheduling. Finally, the dispatching system is applied to the operation of hydropower systems in southwest China. Results indicate that: 1) The developed dispatching system is capable of processing 18 million data records per day, with a peak processing speed of 10,000 data requests per second, fulfilling the requirements for hydropower dispatching. 2) In long-term generation scheduling, under varying levels of runoff and plant scales, DPDP has demonstrated a remarkable reduction in computing time compared to dynamic programming, obtaining the same optimal solution with over 90 % less computational time. 3) In short-term peak shaving operation, on typical dry and wet days, the computing time of DMM is 7.2 s and 7.5 s, respectively, far less than the time of mixed-integer linear programming, and the maximum residual load obtained by DMM is lower than historical observations, indicating its effectiveness.

The Guardian Node Slow DoS Detection Model for Real-Time Application in IoT Networks.

The pernicious impact of malicious Slow DoS (Denial of Service) attacks on the application layer and web-based Open Systems Interconnection model services like Hypertext Transfer Protocol (HTTP) has given impetus to a range of novel detection strategies, many of which use machine learning (ML) for computationally intensive full packet capture and post-event processing. In contrast, existing detection mechanisms, such as those found in various approaches including ML, artificial intelligence, and neural networks neither facilitate real-time detection nor consider the computational overhead within resource-constrained Internet of Things (IoT) networks. Slow DoS attacks are notoriously difficult to reliably identify, as they masquerade as legitimate application layer traffic, often resembling nodes with slow or intermittent connectivity. This means they often evade detection mechanisms because they appear as genuine node activity, which increases the likelihood of mistakenly being granted access by intrusion-detection systems. The original contribution of this paper is an innovative Guardian Node (GN) Slow DoS detection model, which analyses the two key network attributes of packet length and packet delta time in real time within a live IoT network. By designing the GN to operate within a narrow window of packet length and delta time values, accurate detection of all three main Slow DoS variants is achieved, even under the stealthiest malicious attack conditions. A unique feature of the GN model is its ability to reliably discriminate Slow DoS attack traffic from both genuine and slow nodes experiencing high latency or poor connectivity. A rigorous critical evaluation has consistently validated high, real-time detection accuracies of more than 98% for the GN model across a range of demanding traffic profiles. This performance is analogous to existing ML approaches, whilst being significantly more resource efficient, with computational and storage overheads being over 96% lower than full packet capture techniques, so it represents a very attractive alternative for deployment in resource-scarce IoT environments.

Research on jujube storage and preservation system based on Internet of Things (IoT)

With the surge in Xinjiang jujube production, there has been a concurrent increase in storage losses, posing an ongoing challenge for farmers to preserve the freshness of the jujubes. To tackle this issue, a novel storage and freshness control system for jujubes, integrating Internet of Things technology, has been developed. The control system is centered around a programmable logic controller (PLC) and utilizes an HMI as the interface for human–computer interaction. It is structured with distinct layers, including equipment, transportation, and application layers. By integrating with the Internet of Things cloud platform, data transmission between the device layer and the cloud server is ensured through the use of the TCP/IP protocol over a 4G network. A mobile app enables remote control functionality for monitoring and managing the storage and preservation environment. Experimental results demonstrate the system's stable performance, enabling remote monitoring and convenient operation by management personnel during the jujube storage and preservation process. This contributes to enhancing automation levels in jujube storage, extending its storage cycle while maintaining quality standards, thus providing valuable insights into intelligent fruit and vegetable storage practices in Xinjiang.

The Application of the Internet of Things in Intelligent Logistics Management

Starting from the application of the Internet of Things in intelligent logistics management, this paper first analyzes the main characteristics of intelligent logistics, including information, networking and intelligence, and then expounds the important value of the Internet of Things in intelligent logistics management, such as improving logistics efficiency, reducing logistics cost, optimizing resource allocation and so on. On this basis, the paper puts forward the key points of the application of the Internet of Things in intelligent logistics management, including the construction of the Internet of Things perception layer, building the Internet of Things network layer, improving the application layer of the Internet of Things, strengthening data analysis and mining, and paying attention to the security protection of the Internet of Things for reference.

DFT study of the electronic, optical and elastic properties of Cu2-xAgxMgSnS4 (x=0, 0.5, 1, 1.5, 2) alloys using TB-mBJ+U potential

In this study, we have conducted first-principles computations on the promising photovoltaic materials Cu2-xAgxMgSnS4 (x = 0, 0.5, 1, 1.5, 2). To determine the electronic characteristics of Cu2-xAgxMgSnS4(x = 0, 0.5, 1, 1.5, 2) in kesterite and stannite phases, we have used the Wien2k code based on density functional theory with the generalised gradient approximation (GGA) and modified Becke-Johnson (TB-mBJ) exchange potential. The results obtained by TB-mBJ likewise fall short of the experimental findings, like GGA and LDA. To get a comparable bandgap value with the experimental results, we have employed the TB-mBJ potential coupled with the Hubbard U correction (TB-mBJ + U), resulting in a more accurate representation of the pd hybridization. Electronic structure calculation reveals Cu2-xAgxMgSnS4(x = 0, 0.5, 1, 1.5, 2) materials as direct bandgap semiconductors, with band gaps ranging from 1.477 eV to 2.38 eV. The computed elastic constants confirm the mechanical stability of all materials. Additionally, we explored optical properties essential for their potential as absorber layers in photovoltaic applications, such as the imaginary and real components of the dielectric function, absorption coefficient, refractive index, reflectivity, and extinction coefficient. These predicted parameters provide valuable insights for researchers, facilitating further investigations into the photovoltaic potential of Cu2-xAgxMgSnS4 materials.

Does digitalization always benefit cultural, sports, and tourism enterprises quality? Unveiling the inverted U-shaped relationship from a resource and capability perspective

The impact of digitalization on the high-quality development of “technology-sparse” but “labor-intensive” cultural, sports, and tourism enterprises may not follow a linear path. Therefore, there is an urgent need to explore the mechanism by which digitalization contributes to the integrated high-quality development of enterprises from the perspective of resources and capabilities. The majority of studies focus on the theoretical realm, while challenges in acquiring crucial data indicators constrain the advancement of empirical inquiries. This study creates a lexicon of digitized features encompassing strategic, technical, and application layers within cultural, sports, and tourism enterprises. The MD&A texts from annual reports were processed using the TF-IDF algorithm, which is based on machine learning, to assess the level of digitization. Additionally, integration development was incorporated into the evaluation system for high-quality development of cultural, sports, and tourism enterprises via social network analysis. In terms of theoretical contributions, enterprise search and reorganization capabilities were included within the scope of core enterprise capabilities. Furthermore, the study elucidated the roles of human capital and enterprise innovation in the interplay between digitalization and high-quality development, drawing upon the resource-based view and core capabilities theory. Finally, we empirically examined panel data from 116 listed cultural, sports, and tourism companies in Chinese A-share markets from 2010 to 2020. The findings reveal the following: (1) There is an inverted U-shaped association between digitalization and high-quality development, initially promoting then hindering progress, with a persistent pattern over time and robustness against instrumental variable regression and endogeneity tests. Additionally, the majority of cultural, sports, and tourism enterprises fall within a moderate range, indicating a positive impact on high-quality development. (2) Human capital and enterprise innovation are mediators of the inverted U-shaped relationship between digitalization and high-quality development. (3) The influence of digitalization on high-quality development significantly varies depending on the enterprise’s geographical location, life cycle, and risk tolerance. The study reveals the digitalization journey within cultural, sports, and tourism enterprises, offering theoretical insights for fostering collaborative high-quality development within the industry amid digitalization.

SuperSIM: a comprehensive benchmarking framework for neural networks using superconductor Josephson devices

Abstract This paper introduces SuperSIM, a benchmarking framework tailored for neural networks using superconducting Josephson devices, specifically focusing on Adiabatic Quantum Flux Parametron (AQFP) based Processing-in-Memory (PIM) architectures. Our framework offers in-depth architecture-level simulations and performance assessments to enhance AQFP PIM chip development. It supports single and multi-bit PIM designs, various AQFP memory cell types, and diverse clocking methods. Additionally, it integrates circuit-level models for precise energy, delay, and area measurements, ensuring accurate performance evaluation. The framework includes application, device, and architectural layers for versatile configurations and cycle-accurate energy, latency, and area simulations. Experiments validate our framework, with case studies on algorithm and architecture-level features, examining data precision, crossbar size, operating frequency and clocking scheme impacts on computational accuracy, energy use, overall latency and hardware cost.