Connection Management Research Articles

A comprehensive framework to assess multi‐species landscape connectivity

Abstract Due to the central role of landscape connectivity in many ecological processes, evaluating and accounting for it has gained attention in both theoretical and applied ecological sciences. To address this challenge, researchers often use generic species to simplify multi‐species connectivity assessments. Yet, this approach tends to oversimplify movement behaviour, likely reducing realism and precision of connectivity model outputs. Also, the most widely used methods and theories for assessing landscape connectivity, namely circuit and network theories, have strong limitations. Finally, uncertainty or robustness estimates are rarely integrated in connectivity assessments. Here, we propose a versatile framework, which, instead of using arbitrary defined generic species, first identifies species groups based on species' environmental niches and morphological, biological, and ecological traits. Second, it combines circuit and network theories to take the best of the two methods to assess landscape connectivity for those groups, while integrating uncertainties in modelling choices. Specifically, ecological continuities (i.e. landscape elements contributing to connectivity) are calculated for these groups and used together with group dispersal capacities to derive network‐based connectivity metrics for conservation areas. We detailed our framework through a case study where we assess the connectivity of 1619 protected areas in metropolitan France for 193 vertebrate species. Our study revealed that both the protection of ecological continuities and the connectivity of protected areas for 11 mammal and 19 bird groups, respectively, were quite low, with variations among groups. Different protection types (i.e. national parks, reserves or prefectural orders) contributed unequally to the overall connectivity of group‐specific suitable habitats. Considering uncertainty propagation was crucial, as many connectivity metrics varied among repetitions. The proposed framework combines different connectivity tools to provide a more relevant and comprehensive assessment of landscape connectivity. It can be used to inform the decision‐making process for spatial planning, particularly in the context of connectivity conservation and management, or support theoretical studies to better understand the ecological role of landscape connectivity. Its flexibility allows easy application under various environmental conditions, including future scenarios.

The construction of international wetland urban ecological security pattern coupled with MSPA and ESF.

Creating an ecological security pattern is crucial for balancing the sustainable development of areas where human activities and the natural environment intersect. Using Hefei, an internationally recognized Wetland City, as a case study, we extracted ecological sources through ecological service function (ESF) analysis and morphological spatial pattern analysis (MSPA) core area connectivity analysis. Based on these ecological sources, we developed an ecological resistance surface system and identified ecological corridors and nodes using circuit theory. The findings are as follows: (1) Ecological source areas: The primary ecological sources in Hefei are located in the water bodies, forested areas, and scattered grasslands in the central and eastern parts of the city. This ecological source area covers 978.96 km2, which constitutes 8.55% of the city's total area. (2) Ecological corridors: Hefei contains 43 ecological corridors with a total length of 940.3km, averaging 21.87km each. These corridors are crucial for maintaining ecological connectivity and facilitating species movement. (3) Ecological nodes: There are 13 significant ecological nodes in Hefei, including 6 ecological obstacle points and 7 ecological pinpoints. These nodes play a vital role in supporting ecological processes and ensuring habitat connectivity. (4) Evaluation metrics: The α and β values for the source identification method that integrates MSPA with ecological service functions were 2.15 and 0.8, respectively, which are higher than those of the control group. Conversely, the γ-value was 0.18, lower than that of the control group. These results indicate that the combined ecological source extraction method provides significant advantages in terms of ecological corridor integrity, connectivity, and ecological flow management.

Large, but Dispersal-Limited Populations of the Marsh Fritillary Euphydryas aurinia Persist on Abandoned Military Training Areas Three Decades After the End of the Cold War.

Military training areas can host important biodiversity, due to the preservation of diverse, nutrient-poor historical cultural landscapes and an insect-friendly disturbance regime. In Europe, many training areas were abandoned after the end of the cold war in 1991 and the withdrawal of the Allied and Soviet forces. Many of these are now protected areas, and current management strategies vary from rewilding to active habitat management such as grazing or mowing. In a capture-release-recapture approach, marking 2418 individuals, we assessed the population size and movement patterns of the dry ecotype of the Marsh Fritillary Euphydryas aurinia Rottemburg 1775 on three former military training areas in Germany that varied in size and management (natural succession, mowing, and sheep-/goat grazing). Euphydryas aurinia is a rare and declining butterfly species listed in Annex II of the European Union Habitats Directive. Jolly-Seber models revealed a large population of ca. 19,000 individuals on the largest study site and a smaller population at a second site, whereas recapture rates were too low to predict the population size reliably at a third site. Population densities were 190-194 butterflies ha-1 at the unmanaged, large site and 56-71 butterflies ha-1 at a smaller site grazed with sheep. Thirty-nine percent of the recapture events occurred within the same 1-ha-study plot. The average minimum flight distance between the study plots was 313 m for males and 328 m for females. The maximum lifetime flight distance was 1237 m within 3 days. No dispersal was detected between study sites. Thirty years after cessation of the military use, the large former training site still held what likely is one of the largest populations of the species dry ecotype in Central Europe, including in areas where management ceased already in 1991. This suggests remarkable persistence of the species in areas without regular management, contrary to current opinion. However, regular flight distances seem not to be sufficient to connect the isolated habitat patches. It remains unknown how long the large population at the abandoned military area will persist without active habitat management. Careful, but active habitat management and restoration of habitat connectivity should thus be considered.

Evaluation of Handoff Drop Call Rate in GSM Network in Delta State with Dynamic Cut off Channel Allocation Scheme

This study aims at evaluating Handoff drop call rate in GSM Network using dynamic Cut off Channel Allocation Scheme by evaluating different Mobile Network Operators, MNOs, and comparing with Nigerian Communication Commission (NCC)’s Key Performance Indicator (KPI). A hybrid handoff algorithm was developed to reduce drop calls, analyze the performance of the hybridized handoff algorithm; and compare the simulated results of the hybridized algorithm with that of the existing Mobile network operators, MNOs. The current handoff failure and drop call rates of the best mobile communication network in Nigeria was evaluated and was discovered that the existing Hand off failure rate HFR per cell was 5% and the drop call rate DCR was 10%. These were above the NCC maximum target of 2%. These defects account for the unacceptable drop call rates experienced and thus negatively impact on the quality of service QoS. An optimal solution was provided by developing an enhanced and highly sensitive handoff algorithm from two different algorithms - the Entire Channel Allocation, and the Fixed Channel Allocation - while putting the Modified Reserved Channel Scheme with Guard Channel, and the Fuzzy handoff initiation method into consideration. A software known as Netbeans 7.2 was interfaced with Octave 5.1.0 and used to simulate the developed algorithm. The programme trial was made to run repeatedly for 1200s. Our simulation results indicated HFR of 3% as against 5% from that of the existing network, and DCR of 6% as against the 10% from the network. In further comparing the existing DCR with that obtained from our simulation, an average of 40% dropped calls reduction was realized. This implied that the algorithm reduced drop calls thereby enhancing connectivity and mobility management through the minimization of handoff failures.

Collaborative IoT learning with secure peer-to-peer federated approach

Federated Learning (FL) has emerged as a powerful model for training collaborative machine learning (ML) models while maintaining the privacy of participants’ data. However, traditional FL methods can exhibit limitations such as increased communication overhead, vulnerability to poisoning attacks, and reliance on a central server, which can impede their practicality in certain IoT applications. In such cases, the necessity of a central server to oversee the learning process may be infeasible, particularly in situations with limited connectivity and energy management. To address these challenges, peer-to-peer FL (P2PFL) offers an alternative approach, providing greater adaptability by enabling participants to collaboratively train their own models alongside their peers. This paper introduces an original framework that combines P2PFL and Homomorphic Encryption (HE), enabling secure computations on encrypted data. Furthermore, a defense approach against poisoning attacks based on cosine similarity is introduced These techniques enable users to collectively learn while preserving data privacy and accounting for ideal energy optimization. The proposed approach has demonstrated superior performance metrics in terms of accuracy, F-scores, and loss when compared to other similar approaches. Furthermore, it offers robust privacy and security measures, leading to an enhanced security level, with improvements ranging from 5.5% to 14.6%. Moreover, we demonstrate that the proposed approach effectively reduces the communication overhead. The proposed approach resulted in impressive reductions in communication overhead ranging from 63.8% to 79.6%. The implementation of these security models was cumbersome, but we have made the code publicly available for your reference 11https://github.com/Artifitialleap-MBZUAI/Collaborative-Learning-with-Secure-Peer-to-Peer-Federated-Approach..

A ranking framework for the selection of IoT cloud platforms using hybrid multi-attribute decision-making method

PurposeThe Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.Design/methodology/approachThe optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.FindingsA case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.Originality/valueThe proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.

Ultra reliable load-aware connection management (LACM) algorithm in WIA-FA systems

Ultra reliable load-aware connection management (LACM) algorithm in WIA-FA systems

Clinical Outcomes, Predictors, and Surgical Management of Infracardiac Total Anomalous Pulmonary Venous Connection.

This study sought to find the risk factors of postoperative pulmonary venous obstruction (PVO), PVO-related reintervention, and postoperative mortality, determine the relationship between pulmonary venous confluence (PVC) morphology and the clinical outcomes, and provide the basis for the selection of the optimal surgical strategy for infracardiac total anomalous pulmonary venous connection (TAPVC). From December 2009 to December 2023, 101 patients with infracardiac TAPVC undergoing surgical repair in our institution were included [Conventional surgery (CS) group, n = 73; Sutureless technique (ST) group, n = 28]. The Kaplan-Meier curve was used to demonstrate the survival estimates. The Cox proportional hazard model was used to identify risk factors for primary endpoints. There were 8 in-hospital deaths and 4 late deaths. The survival rates at 1, 5, and 10years were 88.8%, 87.6%, and 87.6%, respectively, in the whole cohort. Multivariable analysis showed that prolonged cardiopulmonary bypass (CBP) time was associated with overall mortality and Y-shaped PVC has a trend for a higher mortality rate. Postoperative PVO occurred in 27 patients. Freedom from postoperative PVO at 1, 5, and 10years were 81.8%, 69.9%, and 69.9%, respectively. Y-shaped PVC and preoperative PVO were incremental risk factors for PVO, but ST repair was a protective factor. Sixteen patients underwent PVO-related reintervention. Y-shaped PVC was the only independent risk factors for PVO-related reintervention while ST repair was a protective factor. Sutureless technique can achieve satisfactory outcomes for infracardiac TAPVC repair. Compared with CS repair, ST repair is significantly associated with decreased postoperative PVO and PVO-related reintervention. Y-shaped PVC is associated with a higher risk of PVO, PVO-related reintervention, and mortality, respectively. Patients with preoperative PVO are more likely to develop postoperative PVO.

RPL*: An Explainable AI-based routing protocol for Internet of Mobile Things

The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.

Client Connection System with (CRM) Solution

Centralizing Client Connection System is the main goal of the CRA system. Organizations can store and manage comprehensive client data, such as contact details, purchase history, and interaction logs, by using a centralized database. This will enable better service delivery and relationship management by enabling a more structured and accessible approach to consumer data.Efficient management of client connections is essential for business success in today's competitive industry. A comprehensive software program called the Customer Resource Administration (CRA) system was created to improve how businesses handle and use customer data. In order to boost sales, enhance customer satisfaction, and promote enduring client loyalty, this project intends to create a solid customer relationship management system that incorporates analytics, communication tracking, and customer data management

Enhancing reconnaissance security: a 2-tier deception-driven model approach (2TDDSM)

The emergence of network security has revolutionized the way educational institutions operate, providing advanced connectivity, enhanced communication, and efficient management of resources. However, with the increasing dependence on interconnected systems, institutions and organizations became vulnerable targets for cyber threats. To address these security challenges, a two-tier deception-driven model specifically designed to for the initial phase of attacks in reconnaissance period where the adversaries is to gather information of the targets. Defending threats in this phase can provide active and proactive defense allowing the administrator to identify potential attackers and understanding their methods, motivation and potential target assets. The model's layered approach creates a resilient defense mechanism that aligns with the advanced deception techniques which aims to misguide potential threats attempting to gather intelligence within the network.

Pengaruh struktur GCG terhadap manajemen laba dengan koneksi politik sebagai variabel moderasi

This research aimed to determine the influence of good corporate governance structure on earnings management with political connection as a moderating variable. The independent variables were managerial ownership, institutional ownership, board size, and independent commissioners. Earnings management was the dependent variable. The moderating variable was political connection. The research population was non-financial services companies listed on the Indonesia Stock Exchange 2018-2022. The research sample was 562 firm-years. The research results showed that institutional ownership and independent commissioners had a negative influence on earnings management, while managerial ownership and board size had no influence on earnings management. Political connection weakened the negative influence of institutional ownership on earnings management. Political connection was not proven to strengthen the positive influence of managerial ownership on earnings management. Political connection was not proven to weaken the negative influence of board size and independent commissioners on earnings management. Public interest statements This research can increase knowledge regarding GCG structures, political connection and earnings management. The results of this research can also be used as input for regulators in implementing GCG to prevent earnings management.

Surgical anastomosis of vertical vein to left atrial appendage: A case report of technical aspects

Management of partial anomalous pulmonary venous connection (PAPVC) differs significantly between the defect on the left side, which is adjacent to left atrial appendage, and that on the right one adjacent to the interatrial septum. Here, we report two patients operated at our center. The first case is an adult diagnosed with the left-sided PAPVC draining to an innominate vein through a left-sided vertical vein, and with a large ostium secundum atrial septal defect. The second case is a neonate with mixed-type total anomalous pulmonary venous connection with all right pulmonary veins opening into the coronary sinus, and all the left pulmonary veins connecting to the innominate vein through a left-sided vertical vein. Both patients experienced severe pulmonary arterial hypertension, necessitating surgical treatment involving disconnection of the vertical vein from its junction, or, ligation of the vertical vein-innominate vein junction, coupled with its redirection to the left atrial appendage. We employed different surgical procedures on these patients, given the distinct age difference, intending to anastomose their respective vertical veins to the adjacent left atrial appendage. In this report, the technical aspects of anastomosing vertical vein to left atrial appendage, to avoid pulmonary venous obstruction in these two patients, are discussed.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

Measuring ecological connectivity with ecological distance and dynamic resistant kernels

ContextSpecies-agnostic connectivity models are often used to inform management over broad spatial scales. The four main approaches to species-agnostic models parameterize resistance to movement based on naturalness, structural features, climate, or geodiversity variables. Though all four of these factors simultaneously affect species movement and flow of ecological processes, they are rarely combined.ObjectivesWe built upon an approach that uses all four of these factors to model current and future ecological connectivity for the Crown of the Continent Ecoregion, in Canada and the USA.MethodsWe estimated resistance for each pixel on the landscape based on multivariate ecological distances to surrounding pixels. We then modeled connectivity with resistant kernels at different scales, and dynamically in response to future climates from 2020 to 2080.ResultsAcross the study area, we found median connectivity values decreased by 17–50% from 2020 to 2080 depending on the scale, with broader scales experiencing greater losses in connectivity. Though often considered natural conduits for movement, stream and valley bottoms generally lost connectivity through time. Wilderness areas had significantly higher connectivity values than unprotected lands for all time steps and scales, indicating their importance for maintaining future connectivity of ecological processes.ConclusionsWe offer an updated approach for species-agnostic connectivity modeling that combines naturalness, structural features, and topo-climatic layers while considering multiple scales of ecological processes over a large spatial extent and dynamism through time. This approach can be applied to other landscapes to produce products for short- and long-term management of connectivity and ecological resilience.

Development of a convolutional neural network to accurately detect land use and land cover

The detection and modeling of Land Use and Land Cover (LULC) play pivotal roles in natural resource management, environmental modeling and assessment, and ecological connectivity management. However, addressing LULCC detection and modeling constitutes a complex data-driven process. In the present study, a Convolutional Neural Network (CNN) is employed due to its great potential in image classification. The development of these tools applies the deep learning method. A methodology has been developed that classifies the set of land uses in a natural area of special protection. This study area covers the Sierra del Cando (Galicia, northwest Spain), considered by the European Union as a Site of Community Interest and integrated in the Natura 2000 Network. The results of the CNN model developed show an accuracy of 91 % on training dataset and 88 % on test dataset. In addition, the model was tested on images of the study area, both from Sentinel-2 and PNOA. Despite some confusion especially in the residential class due to the characteristics in this area, CNNs prove to be a powerful classification tool.•Classifications based on a CNN model•LULC are classified into 10 different classes•Training and test accuracy are 91 % and 88 %, respectively

Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime.

Synergy in Action: Integrating Environmental Monitoring, Energy Efficiency, and IoT for Safer Shared Buildings

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, the integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces is relevant. The outcomes of the study reveal the delicate nature of all the involved components, which need to be carefully developed in an integrated manner.

When digital money meets relational surveillance: overseeing and reshaping people’s ordinary lives, emotions, and social relations in small towns and villages through Alipay

This study situates Alipay, China’s biggest financial platform, at the convergence of a communication medium that conveys symbolic messages, including the state’s political campaigns of the Chinese Dream and shared meanings of positive emotions, and a cultural technology that aims to improve, train, and monitor Chinese citizen subjects through a set of surveillance practices. I coin the term relational surveillance to broaden our understanding of how surveillance practices incorporate emotional differentials and power dynamics inherent in economic interactions, and how the characteristics of these interactions matter for the ways through which surveillance practices affect people’s social lives in small towns and villages. I suggest that everyday surveillance practices serve to establish, maintain, and reconfigure social relationships among users as borrowers, financial platforms as lenders, and local governors as facilitators and regulators. These practices involve the management of emotional connections toward economic well-being and the ritualization of self-monitoring, self-tracking, and self-disclosure as a special form of bookkeeping and record-keeping.

Building a Full Stack Solution for Seamless Device Connectivity and Data Management

This paper involves developing a full-stack IoT application with a cloud server. The project encompasses defining the project scope, selecting suitable hardware, developing firmware/software for IoT devices, setting up a cloud server with IoT services, configuring data ingestion and storage, building a backend application for data processing, creating a frontend UI/dashboard, establishing bidirectional communication, conducting testing, deploying the applications, and implementing continuous monitoring and optimization. By following these steps, the project aims to create a comprehensive IoT solution that collects, analyzes, and visualizes data from connected devices, enabling remote monitoring and control.