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The Internet of Things (IoT) is a pair of technologies which can connect everything from everyday objects to more complex connected devices. The IoT model is continues to increases end-users owned devices. Adoption of smart devices for the IoT, including sensors are growing rapidly. Therefore, storing and processing the amount of data generated on local platforms is becoming increasingly difficult. The scalability of Cloud Computing (CC) offers solutions for this issue. CC makes resources available to users at low cost. However, platform-independent techniques for collecting sensor data and sending it to the cloud are not widely available. This framework represents a cloud-based platform for observing, collecting, and processing data from smart devices. The analysis presents that the detected requirements are data gathering and processing. Hence this analysis achieves best results in terms of security, accuracy, cost and processing time.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

Copyright of Belügyi Szemle / Academic Journal of Internal Affairs is the property of Ministry of Interior of Hungary and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cognitive Radio Sensor Network (CRSN) provides effective utilization of spectrum, using dynamic spectrum allocation that incorporates multi-hop clustering and routing algorithms for energy-efficient data delivery. The existing clustering and routing methods modeled for Cognitive Radio Sensor Network, typically operates with the assumption of flawless spectrum sensing, disregarding incorrect alarms and miss detection techniques. These could lead to transmission failures, Primary User collisions, or restricted spectrum usage. An Adaptive Spectrum Sensing Multi-Hop Grey wolf optimization Algorithm for Primary User Emulation Attack (ASSMGA-EA) has been modeled to mitigate the effect of poor spectrum sensing which affects the sensor network performance when a Primary User Emulation Attack is present and dynamically adapts sensing approaches in response to changing network conditions and attack situations. Nodes are elected as Cluster Heads based on residual energy, high spectrum sensing capability, and accessible channel detection level functions. Accuracy-based, idle channel detection promotes effective intra-cluster and inter-cluster data transmission. Energy consumption due to control overhead is minimized by regulating cluster formation and Cluster Head selection. The malicious users also possess a spectrum sensing capability which makes spectrum access even more difficult. By distinguishing between the Primary User and Primary User Emulation Attack signals, the proposed method improves detecting accuracy while lowering the likelihood of sensing errors, and enhances throughput, and energy efficiency more than cooperative and hybrid sensing techniques. The simulations demonstrate that the proposed algorithm has clear advantages over the current clustering and routing algorithms for CRSN in terms of extending the network’s lifespan, efficient data gathering, higher residual energy, and improving the capacity of the network.

Elderly care becomes more and more important as elderly populations increase. Many solutions are proposed for monitoring elderly activities of daily living (ADL) or healthcare related data. There are two aspects to be tackled to enable monitoring for elderly care. On the one hand, the up-to-date ADL sensors and biosensors greatly facilitate collection of various ADL and healthcare related data. On the other hand, data captured by sensors need to be gathered from residences of elderly people to local community center for monitoring and analysis. Therefore, gathering sensor data plays a significant role for building elderly care systems or architectures. Most current elderly care systems or architectures are developed based on reliable network infrastructures. However, such presumption is less practical in network deficient environments such as geographically depopulated rural and remote areas. In this paper, we propose a data gathering system model for elderly care based on autonomous device-to-device (D2D) communications, which is self-contained and independent from other network infrastructures. The proposed system model doesn’t need central control; thus, it can be simply deployed in network deficient environments or for users without access to existing networks. The idea behind is to use ad hoc D2D links by introducing moving devices. Procedures of data gathering using D2D are proposed and configured by modifying the two-way untargeted discovery protocols of IEEE Std 802.15.8. To examine the feasibility of the proposed system model, a prototype D2D system is developed using off-the-shelf radio modules and ADL sensors. We examine the efficacy of the prototype system through field trials in a mountainous village. Our obtained results confirm the usefulness of the proposed D2D solution for gathering ADL sensor data for elderly care in network deficient environments. Examples of using the gathered ADL data to detect anomalous events are presented.

This proposed system introduces a dynamic coupon generation mechanism empowered by machine learning algorithms, particularly reinforcement learning, to enhance people's engagement and optimize coupon distribution in such a way that the coupon for the subsidy is given to farmers fairly by prioritizing those in need. This process involves key stages, including data gathering, preprocessing, feature enhancement, and autonomous decision-making logic development, taking into account diverse factors such as farmer segments and budget constraints. The system interacts with HTML, PHP, CSS, and JavaScript, with Python supporting the implementation. This paper aims to continuously adapt the model to meet evolving customer behaviors and preferences. This technological approach is essential for optimizing coupons effectively and staying responsive to real-time customer interactions and preferences. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was

Mine Internet of Things (MIoT) is of great significance in mine production monitoring and disaster prediction. However, the MIoT is easily affected by mine accidents in data transmission. Accidents often lead to the damage of IoT nodes (IoTN). The surviving IoTNs are limited by low quantity and energy, so it is difficult to complete the task of collecting and transmitting a large number of monitoring data in the roadway. In order to ensure the reliable and energy-efficient data communication of MIoT after disaster, an unmanned aerial vehicle (UAV)-assisted clustered MIoT communication system architecture is established. Based on this, an UAV-assisted data gathering method based on clustering and A* search is proposed. Firstly, the energy consumption of IoTNs and the path length of UAV are considered to construct the objective function. The optimal K is determined by plotting the relationship between the variance distance from the node to the cluster center and the path length of UAV data gathering and different K values. Then the K-means algorithm is used to divide all IoTNs into K clusters. Next, by considering the data gathering energy consumption of UAV and IoTNs, the path planning problem of UAV is established as an optimization problem to minimize the overall energy consumption of MIoT system, and an improved A* search algorithm for UAV data collection path planning is proposed. In this algorithm, the starting point of UAV and all clustering information are input into A * network by using the pointer network. A group of sorted cluster heads output by A * network is the flight path of UAV. Simulation results show that compared with the flat-based UAV data gathering method, the proposed data gathering method significantly reduces the energy consumption of UAV; Compared with two clustered-based UAV data acquisition methods, the proposed method effectively reduces the average and total energy consumption of IoTNs. Therefore, the proposed UAV-assisted data gathering method improves the energy consumption of the MIoT system after disaster, prolongs the network lifetime, and plays an important role in improving the reliability of the MIoT data gathering system after disaster.

Abstract Drug development is a complex, resource intensive and long process in any disease area, and developing medicines to treat rare diseases presents even more challenges due to the small patient populations, often limited disease knowledge, heterogeneous clinical manifestations, and disease progression. However, common to all drug development programs is the need to gather as much information as possible on both the disease and the patients’ needs ahead of the development path definition. Here, we propose a checklist named START, a tool that provides an overview of the key pillars to be considered when starting an orphan drug development: STakeholder mapping, Available information on the disease, Resources, and Target patient value profile. This tool helps to build solid foundations of a successful patient-centered medicines development program and guides different types of developers through a set of questions to ask for guidance through the starting phase of a rare disease therapeutic pathway.

Can trust norms within the African moral system support data gathering for Generative AI (GenAI) development in African society? Recent developments in the field of large language models, such as GenAI, including models like ChatGPT and Midjourney, have identified a common issue with these GenAI models known as “AI hallucination,” which involves the presentation of misinformation as facts along with its potential downside of facilitating public distrust in AI performance. In the African context, this paper frames unsupportive data-gathering norms as a contributory factor to issues such as AI hallucination and investigates the following claims. First, this paper explores the claim that knowledge in the African context exists in both esoteric and exoteric forms, incorporating such diverse knowledge as data could imply that a GenAI tailored for Africa may have unlimited accessibility across all contexts. Second, this paper acknowledges the formidable challenge of amassing a substantial volume of data, which encompasses esoteric information, requisite for the development of a GenAI model, positing that the establishment of a foundational framework for data collection, rooted in trust norms that is culturally resonant, has the potential to engender trust dynamics between data providers and collectors. Lastly, this paper recommends that trust norms in the African context require recalibration to align with contemporary social progress, while preserving their core values, to accommodate innovative data-gathering methodologies for a GenAI tailored to the African setting. This paper contributes to how trust culture within the African context, particularly in the domain of GenAI for African society, propels the development of Afro-AI technologies.

The intelligent collaboration of Unmanned Aerial Vehicles (UAV) and Wireless Sensor Networks (WSN) can bring about increased data collection efficiency and overall network performance in numerous applications. Despite the success of different studies, UAV-WSN collaboration still faces many open challenges including trajectory planning, data collection and obstacle avoidance strategies. In this paper, we propose an Enhanced Evolutionary Scheme for Obstacle-Aware Data Gathering in UAV-assisted WSNs (OaD-UAV). Different from the existing studies, we include different factors that influence the energy efficiency and delay in WSN data gathering to define an efficient UAV trajectory. We examine the UAV path feasibility and introduce different evolutionary operations to derive efficient path that considers both the path length and UAV angle changes in an obstacle-present environment. An improved heuristic evolutionary approach based on genetic algorithm is designed to determine the best trajectory for the UAV to hover and collect data from the selected rendezvous points. The results reveal the effectiveness of the OaD-UAV scheme against the other related approaches in terms of path cost and data collection efficiency, energy consumption and network lifetime. [ABSTRACT FROM AUTHOR]

The main driver of industry 4.0 is data and information for realizing resilient manufacturing systems through data-based decision-making. This requires the data flow from different machinery and its processing. Therefore, a methodical survey on the logical and technical requirements for enabling an efficient data flow can be beneficial since it fastens the process of establishing the data flow and gives production and software engineers a common understanding of the data's meaning. Hence, data, related metadata like the unit or the frequency of recordings, and technical information, e.g., the machine's interface or the communication protocol, are necessary. Based on the classical value stream mapping (VSM) as a lean management tool, the first approach for a methodical gathering of data, value stream mapping 4.0, was established in 2016. Since VSM 4.0 provides technical properties for specifying the data flow, some shortcomings were identified, particularly related to technical information. This contribution introduces the VSM 4.0 plus that solved the identified shortcomings by adding new properties. Compared to VSM 4.0, VSM 4.0 plus offers detailed specifications of data points, includes technical information, e.g., used communication protocols, adds information regarding data processing, and enhances the storage and usage dimension by providing more details. Through the added properties, VSM 4.0 plus allows domain and software engineers more efficient collaboration since all required information for establishing the data flow and its processing are united in one methodology. To verify the practicability, the VSM 4.0 plus was applied within several manufacturing-related use cases. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks are one of the most popular and promising research domains, with applications spreading across a wide range of fields. Due to the sensor nodes' limited battery and storage capacity, keeping the network up for a longer period of time is challenging. One solution to this problem is to mount wireless data collection and wireless energy transfer devices on a single mobile vehicle (MV). In this article, one or multiple MVs together visit all the sensor nodes utilizing a reduced number of anchor points. The anchor points are grouped into clusters/regions. Each group is assigned an MV for collecting the data and charging the corresponding sensor nodes. Our objectives are to maximize data collection per unit energy and the energy utilization efficiency of the MVs. The comparative data gathering and charging tour of the MVs are computed using the DGCS, T S P - A C O , and P E - D F A algorithms. The simulation results show that our proposed DGCS algorithm has outperformed the others. The analysis of variance (ANOVA) test validates the results. [ABSTRACT FROM AUTHOR]

Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path. [ABSTRACT FROM AUTHOR]

Ensuring the law’s ability to protect the environment is a major challenge. Nevertheless, the lack of adequate methodologies and tools is a hindrance to understanding “where, when and how” the law protects the oceans. Although the use of indicators in the analysis of complex phenomena is now widespread in many disciplines, it is an emerging approach in law, especially in environmental law. The methodology presented here operationalizes the use of indicators to measure the role of law in the protection of the oceans. Using the example of the application of this method to the analysis of plastic bag regulations in seven Atlantic countries, we demonstrate that this contribution of law to the development of digital humanities improves the readability of law outside of legal spheres, and deepens the understanding of the strengths and weaknesses of current law in its ability to protect the oceans.

Drug development is a complex, resource intensive and long process in any disease area, and developing medicines to treat rare diseases presents even more challenges due to the small patient populations, often limited disease knowledge, heterogeneous clinical manifestations, and disease progression. However, common to all drug development programs is the need to gather as much information as possible on both the disease and the patients' needs ahead of the development path definition. Here, we propose a checklist named START, a tool that provides an overview of the key pillars to be considered when starting an orphan drug development: STakeholder mapping, Available information on the disease, Resources, and Target patient value profile. This tool helps to build solid foundations of a successful patient-centered medicines development program and guides different types of developers through a set of questions to ask for guidance through the starting phase of a rare disease therapeutic pathway. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given the proliferation of sensors and actuators, evaluating the network performance of current and forthcoming city-wide Internet of Things (IoT) applications is a challenging task. To overcome this challenge, we created a large-scale simulator called PIoT over the years that can assess the performance of multiple millions of mobile/static IoT devices using a 4G/5G cellular infrastructure. In PIoT, different Key Performance Indicators (KPIs) are defined and collected to produce data to evaluate applications and network performance. PIoT is an on-going academic simulator project, and its most recent version is accessible to the public through the user interface found on https://www.piotsimulation.com without any installation requirements. It uses a realistic database that contains the real locations and features of Base Stations (BSs) and the real locations of IoT user equipment devices. The interface can be used by operators and researchers to understand network behavior when deploying new applications or to gather data to feed artificial intelligence and machine learning algorithms. The objective of this paper is to present a detailed description of the PIoT modeling architecture, as well as some use cases to help potential users understand the type of capabilities that are available when using the simulator. Limitations and comparisons with other popular engines are also included in the paper.

Abstract By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network.

Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III. [ABSTRACT FROM AUTHOR]

Our main objective focuses on the different design principles for applications that can receive and store various data from smart devices, e.g., smart bands, smart watches, and smart homes, and could gain more information about its users to some extent to their health. We would like to collect this information, store it, then create understandable diagrams and show them to the user in a modern, responsive environment. We placed great emphasis on the underlying architecture, which holds the necessary features (scalability and extensibility). We have followed the design process's standards, recommendations, and protocols. The result is an application framework that fulfills the duty of an XXI. century smart solutions application that could monitor people's health and even help them live healthier lives by avoiding chronic disorders, e.g., obesity and/or diabetes. We focused on predicting and classifying COVID-19 disease according to the collected information and research. [ABSTRACT FROM AUTHOR]

This paper proposes a realistic agent-based framework for crowd simulations that can encompass the input phase, the simulation process phase, and the output evaluation phase. In order to achieve this gathering, the three types of real-world data (physical, mental and visual) need to be considered. However, existing research has not used all the three data types to develop an agent-based framework since current data gathering methods are unable to collect all the three types. This paper introduces anew hybrid data gathering approach using a combination of virtual reality and questionnaires to gather all three data types. The data collected are incorporated into the simulation model to provide realism and flexibility. The performance of the framework is evaluated and benchmarked to prove the robustness and effectiveness of our framework. Various types of settings (self-set parameters and random parameters) are simulated to demonstrate that the framework can produce real-world like simulation. [ABSTRACT FROM AUTHOR]

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collection methods focus on maximizing data acquisition or reducing network energy consumption, they do not take into account the actual sensor nodes' coverage of the region of interest (ROI). To the best of our knowledge, most research on data collection focuses on path planning for the mobile collector in a static environment. During the lifetime of the network, coverage holes may appear due to node energy depletion. We propose a coverage aware topology adaptive path planning algorithm (CAPP) for path planning for WSNs where all sensor nodes are coverage aware and respond by moving to better locations to improve coverage of the network and compensate for the failed nodes. First, the path planning algorithm determines the number of Stop Points (SPs) where it will stop to gather data. Then, Particle Swarm Optimization is used to find the best location for these SPs. Finally, the shortest path through these SPs is determined by Ant Colony Optimization. Through extensive simulation, we show that CAPP performs efficiently in data collection while also allowing the nodes to move for coverage hole repair. The result shows improvement in area coverage and reduced delay in data collection, with no increase in energy consumption. [ABSTRACT FROM AUTHOR]

The expanding use and advancement of database extraction technology seriously threaten the safety of people's private details. Privacy-preserving database miners (PPDM), a new area of data analysis, have recently received a lot of attention. The fundamental tenet of PPDM will be to alter the content, so that database extraction techniques are being carried out successfully without endangering the confidentiality of delicate facts in the database. In reality, an unintentional revelation of delicate details will even occur during file collection, content publication, and details (that is, content gathering reviews) delivery. However, the majority of existing research on PPDM is focused on determining how to minimize the security threat caused by content extraction processes. In such an essay, we consider the security concerns raised by data gathering from a larger angle and look into several defense mechanisms for private communications. Specifically, we classify consumers that participate in database extraction systems into four separate categories: data providers, data collectors, data miners, as well as policymakers. To break down massive content platforms into four consecutive modules—data creation, data processing, data retention, and data analytics—we provide an organized methodology. Such four parts form a big database revenue loop.

In a low-cost wireless sensor network data collection is the fundamental source of energy dissipation. In such a scenario, compressive data gathering has emerged as a powerful tool to minimise the energy consumption. Compressive data gathering reduces energy dissipation by minimising the amount of transmitted data. In this work, compressive sensing based data collection and link scheduling have been jointly studied in a disconnected network by considering a physical interference model. The network being disconnected, mobile collectors have been employed in the network for data collection. In compressive sensing, only a subset of the sensors are activated which sends the compressed data to the mobile collectors which then recover the data for all the sensors. The objective of this work is to reduce both the end-to-end latency and the number of transmissions for data collection. As the joint problem is NP-Hard, heuristic approaches have been proposed for both tree construction and link scheduling. Simulation results have been performed to show the effectiveness of the proposed algorithm when compared with some existing algorithms. [ABSTRACT FROM AUTHOR]

Past studies reveal the benefits of using Mobile Sink in Wireless Sensor Networks to bring about increased data collection efficiency and overall network performance in numerous applications. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. In this paper, we propose a Modified Sparrow Search Algorithm-based Mobile Sink Path Planning for WSNs (MSSPP) to create shorter travel route for MS and minimize data gathering latency. The proposed method helps in improving the performance of basic SSA by enhancing the quality of initial sparrow population, population diversity and search ability through modified strategies and is adaptive to node failure scenarios. In the first phase, we introduce a modified Sparrow Search-based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, an ACO-based path planning algorithm is utilized to determine the shortest tour through the RPs. The results reveal the effectiveness of MSSPP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.