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Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

In the realm of vehicular ad hoc networks (VANETs), data aggregation plays a pivotal role in bringing together data from multiple vehicles for further processing and sharing. Erroneous data feedback can significantly impact vehicle operations, control, and overall safety, necessitating the assurance of security in vehicular data aggregation. Addressing the security risks and challenges inherent in data aggregation within VANETs, this paper introduces a blockchain-based scheme for secure and anonymous data aggregation. The proposed scheme integrates cloud computing with blockchain technology, presenting a novel blockchain-based data aggregation system that robustly supports efficient and secure data collection in VANETs. Leveraging key escrow resilience mechanisms, the solution ensures the security of system keys, preventing the security problems caused by keys generated by third parties alone in the past. Furthermore, through secondary data aggregation, fine-grained data aggregation is achieved, providing effective support for cloud services in VANETs. The effectiveness of the proposed scheme is confirmed through security analysis and performance evaluations, demonstrating superior computational and communication efficiency compared existing alternatives.

Abstract The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.

Under the general trend of the rapid development of smart grids, data security and privacy are facing serious challenges; protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention. In this study, we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart meters. First, we use an improved homomorphic encryption method to realize the encryption aggregation of users’ data. Second, we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one another. Finally, the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy mechanism. Even if an attacker has enough background knowledge, the security of the electricity information of one another can be ensured.

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model.

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

The Internet of Things and Quantum Computing raise concerns, as Quantum IoT defines security that exploits quantum security management in IoT. The security of IoT is a significant concern for ensuring secure communications that must be appropriately protected to address key distribution challenges and ensure high security during data transmission. Therefore, in the critical context of IoT environments, secure data aggregation can provide access privileges for accessing network services. "Most data aggregation schemes achieve high computational efficiency; however, the cryptography mechanism faces challenges in finding a solution for the expected security desecration, especially with the advent of quantum computers utilizing public-key cryptosystems despite these limitations. In this paper, the Secure Data Aggregation using Quantum Key Management scheme, named SDA-QKM, employs public-key encryption to enhance the security level of data aggregation. The proposed system introduces traceability and stability checks for the keys to detect adversaries during the data aggregation process, providing efficient security and reducing authentication costs. Here the performance has been evaluated by comparing it with existing competing schemes in terms of data aggregation. The results demonstrate that SDA-QKM offers a robust security analysis against various threats, protecting privacy, authentication, and computation efficiency at a lower computational cost and communication overhead than existing systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed. [ABSTRACT FROM AUTHOR]

Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths. [ABSTRACT FROM AUTHOR]

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources. [ABSTRACT FROM AUTHOR]

This paper introduces a method that combines the Kmeans clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system’s effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches. [ABSTRACT FROM AUTHOR]

Nearly one hundred types of control charts have been proposed that incorporate ranked-set sampling (RSS) methods. The performance of these charts has been evaluated with comparisons to existing charts based on simple random sampling. The reduction of the standard error in estimating the parameter being monitored with RSS leads to uniformly better average run length performance. We show, however, that these performance comparisons can be very misleading once the sampling strategy over time is considered more carefully with the benefits of RSS being considerably overstated. We consider the most basic RSS method when monitoring the mean of the process, but the approach can be applied to evaluate other RSS monitoring methods. [ABSTRACT FROM AUTHOR]

The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, thresholdbased fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently. [ABSTRACT FROM AUTHOR]

The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the semantic data aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a cryptomining detection system (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS. [ABSTRACT FROM AUTHOR]

Summary: A fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods. [ABSTRACT FROM AUTHOR]

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through webbased tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences. [ABSTRACT FROM AUTHOR]

This article applies analytical approaches to map illegal psychostimulant (cocaine and methamphetamine) trafficking networks in the US using purity-adjusted price data from the System to Retrieve Information from Drug Evidence. We use two assumptions to build the network: (i) the purity-adjusted price is lower at the origin than at the destination and (ii) price perturbations are transmitted from origin to destination. We then adopt a two-step analytical approach: we formulate the data aggregation problem as an optimization problem, then construct an inferred network of connected states and examine its properties. We find, first, that the inferred cocaine network created from the optimally aggregated dataset explains 46% of the anecdotal evidence, compared with 28.4% for an over-aggregated and 14.5% for an under-aggregated dataset. Second, our network reveals a number of phenomena, some aligning with what is known and some previously unobserved. To demonstrate the applicability of our method, we compare our cocaine data analysis results with parallel analysis of methamphetamine data. These results likewise align with prior knowledge, but also present new insights. Our findings show that an optimally aggregated dataset can provide a more accurate picture of an illicit drug network than can suboptimally aggregated data. [ABSTRACT FROM AUTHOR]

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) and driverless cars which are equipped with communication capabilities, advanced sensing, and Intelligent Control Systems (ICS), aim to modernize the transportation system. It increases user satisfaction by enhancing personal safety, reducing infrastructure costs, decreasing environmental interruption, and saving time for passengers. On the other hand, in emergency cases when AVs require maintenance, their generated sensitive information (e.g., AV location, low brake fluid amount of an AV) should be shared with Road Side Units (RSUs) and other vehicles to address their problems and provide quality services. Despite its appealing benefits, sensitive data sharing carries security and privacy issues that trigger serious risks like unintentional physical accidents. If the privacy of the AV is breached and its sensitive data is unintentionally disclosed during data transmission, adversaries can misuse them and cause artificial accidents. Current studies in this area lack efficiency and cost-effectiveness. To fill this gap and reduce the number of potential accidents, this article proposes a new Context-Aware Privacy-Preserving method for Autonomous Driving (CAPPAD). In particular, the Software-Defined Networking (SDN) paradigm is employed to bring flexibility to AVs' privacy management while its SDN controller runs a novel algorithm for privacy preservation. Depending on whether the data generated is sensitive or not and whether there is an emergency, the AV applies Differential Privacy (DP) or Data Aggregation (DA) as its privacy-preserving method. Finally, extensive simulations are performed through MININET-WIFI to show the performance of CAPPAD in terms of privacy-preserving degree, computational cost overhead, computational complexity overhead, and latency. We also compare it with other relevant well-known studies to show its superior effectiveness. [ABSTRACT FROM AUTHOR]

The development of an energy-efficient wireless sensor network is a difficult problem since batteries are used to energize the sensor nodes. In certain circumstances, charging a battery is extremely difficult or even impossible. If the heterogeneity of sensor nodes is not correctly used, it can result in unequal energy consumption and lowering network performance. By combining power control and data aggregation, clustering has the ability to reduce energy consumption and extend network life. Many routing methods have been suggested for network optimization, with a major focus on energy efficiency, network longevity, and clustering processes. We proposed the Adaptive Active Sleep Energy Efficient Method (AASEEM) for Wireless Sensor Networks (WSNs), which takes into account network heterogeneity. We examine and improve some difficulties including network stability and cluster head selection procedure. The principle of providing a detailed pairing among sensor nodes is used to maximize energy usage. The results of the simulations show that the suggested method improves network performance significantly and it might be a beneficial technique for WSNs. [ABSTRACT FROM AUTHOR]

This paper shows how the capture and circulation of data about social lives are enabled through digitalisation and market logics and practices. Drawing on Australia's new Consumer Data Right, a state-led initiative that creates access rights to personal data, we distinguish between market promises and the translation of market models in actually existing markets and regulatory frameworks. 'Life's work' is brought to market through promises to fix the problems of essential service markets by harnessing data. We argue that the Consumer Data Right is underpinned by a more ambitious vision to create future markets that transcend individual sectors through aggregation across the economy. These visions are silent on how the data, which cannot be owned and therefore cannot be commoditised, is capitalised. We show the Consumer Data Right's discursive, administrative, regulatory and technical aspects through which the previously hard-to-penetrate spaces of the home and everyday life become enrolled in circuits of value, both present and future. This involves technical standard setting by state agencies for accreditation, consent and approval processes; discourses of trust and calculative devices to promote consumer control; and weak de-identification and deletion requirements that grant data an afterlife beyond the original agreed use. This paper calls for greater attention to the enabling role of the state in digital markets as a counterbalance to the focus on the state's regulatory and constraining role. We argue for a more staged approach to market-making analysis to show how the state lays the market foundations that can then be deepened through practices of intermediation and capitalisation by private firms. [ABSTRACT FROM AUTHOR]

Technology driven Smart Agriculture is transforming modern day farming practices through digital tools and real-time data analysis. It monitors plant health in both soil and soilless cultivation, promoting environmentally responsible practices by utilizing integrated sensors and precision methods. Despite the significant potential benefits, challenges persist in ensuring data security in the smart agriculture frameworks. The existing research has explored various mechanisms to cater these issues, yet these solutions susceptible to known attacks i.e., Confidentiality, Authentication, Privacy preservation. Notably, the available schemes in open literature face performance deficiencies due to an overreliance on Rivest-Shamir-Adleman (RSA), bilinear pairing, and elliptic curve (EC). To comprehensively address these challenges, this study presents a novel approach via employing homomorphic Signcryption based on Hyper-Elliptic Curves (HEC). Our HEC based Signcryption technique enhances security with lower computational cost and communication overhead as compared to the existing techniques. Furthermore, our solution provides the best performance in terms of efficiency, addressing the privacy preservation issues with conformance to the (Confidentiality, Integrity, and Availability) CIA triad. For proof of concept and correctness, we have validated the results in Scyther tool, with the assurance that proposed scheme is correct, verified, and resistant against known set of attacks. The proposed scheme achieves a remarkable reduction in both computational and communication costs around 95.08 % and 89.28 % respectively, enhancing practicality and suitability for real-world implementation. Thus, making it a suitable candidate for employing in smart agriculture environments, encompassing both soil and soilless cultivation scenarios. This research contributes a robust, secure, cost effective, and efficient solution to the challenges posed by the evolving landscape of agriculture, paving the way for enhanced data protection and system resilience in smart agriculture systems.

Data aggregation involves the integration of relevant data generated across platforms and devices, leveraging the potential value of sensory data. However, in addition to security and efficiency, which are the basic requirements for data aggregation involving private data, how to achieve fault tolerance and interference of aggregation in real computing networks is imminent and is the main contribution of this paper. In this paper, we propose a secure aggregation framework involving multiple servers based on coding theory, which is not only robust to clients dropping out and tolerant to partial server withdrawal but also resistant to malicious computation by servers and forgery attacks by adversaries. In particular, the proposed protocol employs the Chinese Residual Theorem (CRT) to encode private data and constructs Lagrange interpolation polynomials to perform aggregation, which achieves lightweight privacy preservation while achieving robust, verifiable and secure aggregation goals.

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

Abstract For over‐the‐air computation (AirComp) systems, energy‐constrained sensors become a bottleneck to improve the performances. To address this issue, in this letter, a full‐duplex (FD) wireless powered AirComp system with self‐energy recycling is proposed, where all the nodes operate in the FD mode and the sensors can harvest energy from the hybrid access point (HAP)' signal and it's own transmit signal by self‐energy recycling. A joint beamforming design problem is formulated and an effective iterative algorithm is proposed to solve the non‐convex optimization problem, aiming to minimize the computation mean square error at the HAP under the transmit power constraints of the HAP and sensors. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Wireless body area networks ( $WBAN$ ) are essential components of intelligent healthcare monitoring techniques. Especially, when the number and datatype in $WBAN$ increases. In $WBAN$ , secure multidimensional data aggregation received a lot of attention. However, the related schemes consume more computational and communication overhead to encrypt/decrypt the multidimensional health reports. In this paper, a blockchain-assisted scalable and secure multidimensional data aggregation scheme is introduced for fog-based $WBAN$ . The multidimensional health data are efficiently generated, encrypted, and decrypted by using the Paillier cryptosystem. Further, the batch verification method is used to achieve efficient authentication. The proposed system offers significant security attributes with less computation and communication overhead in comparison with competing systems. Further, it supports statistical analyses such as summation and variance to analyze the received health report.

In vehicular ad hoc networks (VANETs), data aggregation is pivotal as it consolidates data from multiple vehicles for further analysis. Malicious users may launch attacks during the aggregation process to threaten the security and privacy of vehicles. Therefore, it is essential to ensure the security of vehicle data aggregation in vehicular networks. In order to deal with the security risks and challenges related to data aggregation in VANETs, this paper proposes a secure and efficient multi-dimensional perception data aggregation solution. The proposed solution integrates cloud computing with blockchain, presenting a blockchain-based data aggregation system for vehicular networks, enabling efficient and secure data collection and analysis tasks. This solution utilizes an enhanced Paillier cryptosystem to protect location privacy when aggregating sensor data. Additionally, it constructs multi-dimensional sensor data from different locations. A central control centre can fully recover and analyze the aggregated data results. The security analysis has demonstrated the security and effectiveness of the solution, while the performance evaluation has verified that the solution incurs low computational and communication overheads.