Connectivity Constraints Research Articles

Automated Plant Irrigation System using Arduino and IoT Optimizing Water

Water scarcity and inefficient irrigation methods have long challenged agricultural productivity, especially in regions where every drop counts. In this study, we present an innovative automated plant irrigation system that leverages Arduino microcontrollers, IoT connectivity, and sensor networks to optimize water usage while enhancing crop yields. Traditional irrigation techniques often lead to either overwatering or underwatering, resulting in diminished crop health and wasted resources. Our approach addresses these challenges by integrating real-time soil moisture monitoring, local weather data, and machine learning algorithms to control water distribution precisely. The system architecture is built around an Arduino Uno, which serves as the central processing unit. It collects data from a network of sensors—including soil moisture sensors, flow meters, and optional weather modules—and processes this information to determine the optimal timing and quantity of water delivery. The decision-making process is further enhanced by intelligent algorithms that adapt to both immediate soil conditions and historical environmental trends. This automated setup not only reduces human error but also offers scalability for farms of various sizes. A significant feature is its remote monitoring capability, achieved through mobile and web interfaces that allow farmers to oversee and control the system from virtually anywhere. Field experiments were conducted over multiple growing seasons and under varied climatic conditions. Detailed analyses revealed that our automated system could reduce water consumption by up to 40% compared to conventional irrigation methods. In addition, the precision in water application contributed to an average increase of 20% in crop yields. These improvements were accompanied by a reduction in labor costs, with operational expenses dropping by roughly 15%. The system’s ability to automatically adjust watering schedules during unexpected weather changes further underscores its robustness and practical utility. A thorough investigation of the design, implementation, and performance of the system was undertaken. Several challenges were identified during the development phase, including sensor calibration issues, network connectivity constraints in rural areas, and the integration of diverse hardware components. Each challenge was addressed through a combination of hardware fine-tuning and software algorithm adjustments. The iterative design process not only refined the performance of the prototype but also provided valuable insights into the practical constraints of deploying advanced irrigation systems in real-world agricultural settings. In addition to presenting experimental results, this study discusses the broader implications of integrating IoT technology into agriculture. By providing real-time data analytics, the system empowers farmers to make informed decisions that are critical to sustainability and resource management. Moreover, the system’s modular design offers opportunities for future enhancements, such as incorporating renewable energy sources, expanding compatibility with different crop types, and leveraging advanced predictive algorithms. The potential for such systems to revolutionize agricultural practices—by conserving water, boosting crop productivity, and reducing costs—positions this research as a significant contribution to sustainable farming practices. This abstract sets the stage for a detailed exposition on the theoretical foundations, experimental methodology, and comprehensive evaluation of the system. The subsequent sections will delve into the research background, a review of relevant literature, the specifics of system implementation, detailed result analysis, and finally, a discussion on future research directions. Together, these elements form a robust narrative that underscores the transformative impact of technology-driven irrigation methods on modern agriculture..

A secure end-to-end communication framework for cooperative IoT networks using hybrid blockchain system

The Internet of Things (IoT) is a disruptive technology that underpins Industry 5.0 by integrating various service technologies to enable intelligent connectivity among smart objects. These technologies enhance the convergence of Information Technology (IT), Operational Technology (OT), Core Technology (CT), and Data Technology (DT) networks, improving automation and decision-making capabilities. While cloud computing has become a mainstream technology across multiple domains, it struggles to efficiently manage the massive volume of OT data generated by IoT devices due to high latency, data transfer costs, limited resilience, and insufficient context awareness. Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. However, IoT networks still face critical challenges, including connectivity, heterogeneity, scalability, interoperability, security, and real-time decision-making constraints. Security is a key challenge in IoT implementations, including secure data communication, IoT edge and fog device identity, end-to-end authentication, and secure storage. This paper presents an efficient blockchain-based framework that creates a secure end-to-end communication cooperative flow IoT network. The framework utilizes a hybrid blockchain network that collaborates to offer a collaborative flow of end-to-end secure communication from end devices to cloud storage. The fog servers will maintain a private blockchain as a next-generation public key infrastructure to identify and authenticate the IoT’s edge devices. The consortium blockchain will be maintained in the cloud and integrated with the permission blockchain system. This system ensures secure cloud storage, authorization, efficient key exchange, and remote protection (encryption) of all sensitive information. To improve the synchronization and block generation, reduce overhead, and ensure scalable IoT network operation, we proposed the threshold signature-based Proof of Stake and Validation (PoSV) consensus. Additionally, lightweight authentication protects resource-constrained IoT nodes using an aggregate signature, ensuring security and performance in real-time scenarios. The proposed system is implemented, and its performance is evaluated using key metrics such as cryptographic processing overhead, consensus efficiency, block acceptance time, and transaction delay. The findings show that threshold signature-based Proof of Stake and Validation (PoSV) consensus, reduces the computational burden of individual signature verification, which results in an optimized transaction latency of 80–150 ms, compared to the previous 100–200 ms without Non-PoSV. Additionally, aggregating multiple signatures from different authentication events reduces signing time by 1.98 ms compared to the individual signature time of 2.72 ms and the overhead of verifying multiple individual transactions is 2.87 ms is significantly reduced to1.46 ms along with authentication delay ranges between 95–180 ms. Hence, the proposed framework improves over existing approaches regarding linear computing complexity, increased cryptographic methods, and a more efficient consensus process.

Offline System for 2D Indoor Navigation Utilizing Advanced Data Structures

This study introduces a robust offline system for 2D indoor navigation, developed to address common challenges such as complex layouts and connectivity constraints in diverse environments. The system leverages advanced spatial modeling techniques to optimize pathfinding and resource efficiency. Utilizing a structured development process, the proposed solution integrates lightweight data structures and modular components to minimize computational load and enhance scalability. Experimental validation involved a comparative approach: traditional navigation methods were assessed against the proposed system, focusing on usability, search efficiency, and user satisfaction. The results demonstrate that the offline system significantly improves navigation performance and user experience, particularly in environments with limited connectivity. By providing intuitive navigation tools and seamless offline operation, the system enhances accessibility for users in educational and other complex settings. Future work aims to extend this approach to incorporate additional features, such as dynamic adaptability and expanded application in sectors like healthcare and public services.

RISK FACTORS FOR POOR HEALTH OUTCOMES IN REMOTE AND DIGITALLY DISCONNECTED COMMUNITIES

Background: Remote and digitally disconnected communities experience significant health disparities due to limited access to healthcare, poor socioeconomic conditions, and lack of digital resources. These challenges contribute to higher rates of preventable diseases and delays in medical intervention. Objective: This study aimed to identify the risk factors associated with poor health outcomes in remote communities of Pakistan and assess the role of healthcare accessibility, digital connectivity, and socioeconomic constraints in worsening these disparities. Methods: A cross-sectional study was conducted from April 2024 to October 2024 in five rural areas of Khyber and Sindh, Pakistan, including Chitral, Upper Dir, Ghotki, Tharparkar, and Umerkot. A sample of 420 participants was selected using stratified random sampling. Data were collected through structured interviews, anthropometric measurements, and standardized health assessment tools. Statistical analysis was performed using SPSS version 26, employing chi-square tests and logistic regression to identify significant predictors of poor health outcomes. Ethical approval was obtained, and informed consent was secured from all participants. Results: The prevalence of hypertension, diabetes, malnutrition, anemia, and respiratory diseases was 35.7%, 28.6%, 42.9%, 33.3%, and 31.0%, respectively. Limited healthcare access was a major concern, with 45.2% residing over 10 km from a health facility, 59.5% lacking digital health resources, and 40.5% unable to afford medications. Digital connectivity remained low, with only 33.3% having internet access and 21.4% utilizing digital health services. Conclusion: Health disparities in remote communities are driven by financial constraints, inadequate healthcare facilities, and poor digital access. Strengthening healthcare infrastructure and digital inclusion strategies can mitigate these inequalities and improve health outcomes.

A scalable energy internet approach for hop regulated peer-to-peer power trading with connectivity and preference constraints

A scalable energy internet approach for hop regulated peer-to-peer power trading with connectivity and preference constraints

Maximizing UAV network communication capacity: joint optimization of network topology and timeslot resource allocation

This paper studies a UAV network modeled as a globally-connected, undirected graph, accounting for UAVs’ physical and operational constraints. Due to limited communication capabilities, each UAV can only connect with a limited number of other nodes and cannot transmit and receive simultaneously. Time is divided into fixed-length slots to manage communication states. To maximize network capacity, this study focuses on jointly optimizing UAV topology control and timeslot allocation under connectivity constraints, formulated as a linear 0–1 integer programming problem. Using bipartite graph theory, the maximum network capacity is shown to correspond to the maximum weight of a restricted bipartite graph, setting an upper bound. By introducing communication coding, a model is constructed to achieve this maximum capacity. Numerical examples validate the model's effectiveness and computational complexity.

A Beam Search Framework for Quantum Circuit Mapping.

In the era of noisy intermediate-scale quantum (NISQ) computing, the limited connectivity between qubits is one of the common physical limitations faced by current quantum computing devices. Quantum circuit mapping methods transform quantum circuits into equivalent circuits that satisfy physical connectivity constraints by remapping logical qubits, making them executable. The optimization problem of quantum circuit mapping has NP-hard computational complexity, and existing heuristic mapping algorithms still have significant potential for optimization in terms of the number of quantum gates generated. To reduce the number of SWAP gates inserted during mapping, the solution space of the mapping problem is represented as a tree structure, and the mapping process is equivalent to traversing this tree structure. To effectively and efficiently complete the search process, a beam search framework (BSF) is proposed for solving quantum circuit mapping. By iteratively selecting, expanding, and making decisions, high-quality target circuits are generated. Experimental results show that this method can significantly reduce the number of inserted SWAP gates on medium to large circuits, achieving an average reduction of 44% compared to baseline methods, and is applicable to circuits of various sizes and complexities.

Enumerating Minimal Vertex Covers and Dominating Sets with Capacity and/or Connectivity Constraints

In this paper, we consider the minimal vertex cover and minimal dominating sets with capacity and/or connectivity constraint enumeration problems. We develop polynomial-delay enumeration algorithms for these problems on bounded-degree graphs. For the case of minimal connected vertex covers, our algorithms run in polynomial delay, even on the class of d-claw free graphs. This result is extended for bounded-degree graphs and outputs in quasi-polynomial time on general graphs. To complement these algorithmic results, we show that the minimal connected vertex cover, minimal connected dominating set, and minimal capacitated vertex cover enumeration problems in 2-degenerated bipartite graphs are at least as hard as enumerating minimal transversals in hypergraphs.

Self-supervised learning of scale-invariant neural representations of space and time.

Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning. We found that a network based on autoencoders can, given a particular inputs and connectivity constraints, produce scale-invariant time cells. When the animal's velocity modulates the decay rate of the leaky integrators, the same network gives rise to scale-invariant place cells. Importantly, this is not the case when velocity is fed as a direct input to the leaky integrators, implying that weight modulation by velocity might be critical for developing scale-invariant spatial receptive fields. Finally, we demonstrated that after training, scale-invariant place cells emerge in environments larger than those used during training. Taken together, these findings bring us closer to understanding the emergence of neurons with bell-shaped tuning curves in the hippocampus and highlight the critical role of velocity modulation in the formation of scale-invariant place cells.

CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.

Recurrent neural networks (RNNs) have emerged as a prominent tool for modeling cortical function, and yet their conventional architecture is lacking in physiological and anatomical fidelity. In particular, these models often fail to incorporate two crucial biological constraints: i) Dale's law, i.e., sign constraints that preserve the "type" of projections from individual neurons, and ii) Structured connectivity motifs, i.e., highly sparse yet defined connections amongst various neuronal populations. Both constraints are known to impair learning performance in artificial neural networks, especially when trained to perform complicated tasks; but as modern experimental methodologies allow us to record from diverse neuronal populations spanning multiple brain regions, using RNN models to study neuronal interactions without incorporating these fundamental biological properties raises questions regarding the validity of the insights gleaned from them. To address these concerns, our work develops methods that let us train RNNs which respect Dale's law whilst simultaneously maintaining a specific sparse connectivity pattern across the entire network. We provide mathematical grounding and guarantees for our approaches incorporating both types of constraints, and show empirically that our models match the performance of RNNs trained without any constraints. Finally, we demonstrate the utility of our methods for inferring multi-regional interactions by training RNN models of the cortical network to reconstruct 2-photon calcium imaging data during visual behaviour in mice, whilst enforcing data-driven, cell-type specific connectivity constraints between various neuronal populations spread across multiple cortical layers and brain areas. In doing so, we find that the interactions inferred by our model corroborate experimental findings in agreement with the theory of predictive coding, thus validating the applicability of our methods.

On Two-Handed Planar Assembly Partitioning with Connectivity Constraints

On Two-Handed Planar Assembly Partitioning with Connectivity Constraints

The role of river connectivity in the distribution of fish in an anthropized watershed.

The role of river connectivity in the distribution of fish in an anthropized watershed.

On how to avoid the formation of hinges in compliant mechanisms using connectivity constraints

On how to avoid the formation of hinges in compliant mechanisms using connectivity constraints

Linear-time quasi-static stability detection for modular self-reconfigurable robots

We address the problem of detecting potential instability in the planned motion of modular self-reconfigurable robots. Previous research primarily focused on determining the system’s unique physical state but overlooked the mutual compensation effects of connection constraints. We introduce a linear-time quasi-static stability detection method for modular self-reconfigurable robots. The internal connections, non-connected contacts, and environmental contacts are considered, and the problem is modeled as a second-order cone program problem, whose solving time linearly increases with the number of modules. We aim to determine the critical stable state of the system and that is achieved by finding the required minimum characteristic connection strength. By analyzing the critical stable state, we can assess the system’s stability and identify potential broken connection points. Furthermore, the internal stability margin is defined to evaluate the configuration’s stability level. The suspension and object manipulation configurations are first demonstrated in simulation to analyze the effectiveness of the proposed algorithm. Subsequently, a series of physical experiments based on FreeSN were carried out. The calculated stable motion ranges of manipulator configurations are highly consistent with the actual sampling boundaries. Moreover, the proposed algorithm successfully predicts stability and identifies broken connections in diverse configurations, encompassing quadruped and closed-chain configurations on both even and uneven terrains. The load experiment further demonstrates that the impacts from unmodeled factors and input errors under normal conditions can be on a small scale. By combining the proposed detection method and stability margin, we open the door to realizing real-time motion planning on modular self-reconfigurable robots.

NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment

NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment

Quantum Fourier Transform Using Dynamic Circuits.

In dynamic quantum circuits, classical information from midcircuit measurements is fed forward during circuit execution. This emerging capability of quantum computers confers numerous advantages that can enable more efficient and powerful protocols by drastically reducing the resource requirements for certain core algorithmic primitives. In particular, in the case of the n-qubit quantum Fourier transform followed immediately by measurement, the scaling of resource requirements is reduced from O(n^{2}) two-qubit gates in an all-to-all connectivity in the standard unitary formulation to O(n) midcircuit measurements in its dynamic counterpart without any connectivity constraints. Here, we demonstrate the advantage of dynamic quantum circuits for the quantum Fourier transform on IBM's superconducting quantum hardware with certified process fidelities of >50% on up to 16 qubits and >1% on up to 37 qubits, exceeding previous reports across all quantum computing platforms. These results are enabled by our contribution of an efficient method for certifying the process fidelity, as well as of a dynamical decoupling protocol for error suppression during midcircuit measurements and feed forward within a dynamic quantum circuit that we call "feed-forward-compensated dynamical decoupling." Our results demonstrate the advantages of leveraging dynamic circuits in optimizing the compilation of quantum algorithms.

Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

Abstract Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. This complete connectivity is crucial for manufacturability and structure-fluid interaction applications (e.g., fluid-filled lattices). In this study, we propose a generative graph neural network-based framework for designing the porous metamaterial units with the constraint of complete connectivity. First, we propose a graph-based metamaterial unit generation approach to generate porous metamaterial samples with complete connectivity in both solid and pore phases. Second, we establish and evaluate three distinct variational graph autoencoder (VGAE)-based generative models to assess their effectiveness in generating an accurate latent space representation of metamaterial structures. By choosing the model with the highest reconstruction accuracy, the property-driven design search is conducted to obtain novel metamaterial unit designs with the targeted properties. A case study on designing liquid-filled metamaterials for thermal conductivity properties is carried out. The effectiveness of the proposed graph neural network-based design framework is evaluated by comparing the performances of the obtained designs with those of known designs in the metamaterial database. Merits and shortcomings of the proposed framework are also discussed.

Prototype-based contrastive substructure identification for molecular property prediction.

Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https://github.com/VRPharmer/POSIT.

Multi-view clustering with adaptive anchor and bipartite graph learning

Multi-view clustering with adaptive anchor and bipartite graph learning

Tailored Functionally Graded Materials design and concurrent topology optimization with implicit fields

Tailored Functionally Graded Materials design and concurrent topology optimization with implicit fields