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In Wireless Sensor Networks (WSNs), sensor nodes are generally placed in different geographical locations. The sensor nodes collect data from environment and they react based on the received query requests. It is a great challenge for sensor nodes to send data periodically to the sink node in a conflict-free way. For this, authors present a Conflict-free Periodic Data Aggregation (CPDA) technique in WSNs. CPDA also works in heterogeneous environment where some of the sensor nodes in a network suffer from less amount of energy. The authors have also presented a mathematical model for the proposed data aggregation technique. The simulation results indicate that CPDA technique works well as compared with the existing approaches. The simulation results of CPDA technique are validated through NS-2 simulations.

Vehicular sensing is advocated to perform data collection by exploiting a plethora of vehicular on-board sensors; meanwhile, with the merging of vehicular sensing and fog computing, the deployed road side units (RSUs) can act as fog nodes to collect and share vehicular sensory data at the network edge. However, there are still several problems in terms of the secure and reliable sharing of sensory data in vehicular fog. To resolve these issues, in this paper, we present an efficient, privacy-preserving and verifiable sensory data collection and sharing scheme with a permissioned blockchain in vehicular fog. During the data collection phase, by combining the homomorphic 2-DNF (Disjunctive Normal Form) cryptosystem and an identity-based signcryption scheme, our proposed scheme achieves the secure and verifiable computation of the average and variance of the collected vehicular sensory data. Meanwhile, to achieve efficient and reliable data sharing, we exploit a permissioned blockchain to maintain an immutable and tamper-proof record of the derived sensory data. Security analysis demonstrates the security properties of the proposed scheme, in terms of location privacy preservation, verifiability and immutability. Performance evaluations are conducted to validate the efficiency of the proposed scheme, i.e., improvements in computation and communication efficiency in comparison with a scheme without exploiting blockchain. This work was supported in part by a Grant 2018YFB1800800, a Grant 2018B030338001, a Grant NSFC61629101, a Grant ZDSYS201707251409055, and a Grant 2017ZT07X152.

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can be aggregated without loss of information on the Poisson parameter, which remains true if the Poisson assumption is relaxed towards quasi-Poisson. However, in biodosimetry in particular, but also beyond, the question of how the dispersion estimates for quasi-Poisson models behave under data aggregation have received little attention. Indeed, for real data sets featuring unexplained heterogeneities, dispersion estimates can increase strongly after aggregation, an effect which we will demonstrate and quantify explicitly for some scenarios. The increase in dispersion estimates implies an inflation of the parameter standard errors, which, however, by comparison with random effect models, can be shown to serve a corrective purpose. The phenomena are illustrated by γ-H2AX foci data as used for instance in radiation biodosimetry for the calibration of dose-response curves.

With the advent of cloud computing and Internet of Things and delegation of data collection and aggregation to third parties, the results of the computations should be verified. In distributed models, there are multiple sources. Each source creates authenticators for the values and sends them to the aggregator. The aggregator combines the authenticated values and creates a verification object for verifying the computation/aggregation results. In this paper, we propose two constructions for verifying the results of countable and window-based countable functions. These constructions are useful for aggregate functions such as median, max/min, top-k/first-k, and range queries, where the distribution of values is not visible for sources but is visible to the aggregator. The proposed constructions are secure based on the RSA problem in the random oracle model and have the correctness and succinctness properties. Experimental results show that the communication and computation costs of the constructions are acceptable in practice and the proposed solution can be employed for real-world applications.

One of the key problems in developing and integrating expert systems for medical research is the problem of data aggregation. Most of the times, general information about the patient and data about undergone research procedures exist as part of several disconnected information systems, each using its own schema for presenting and storing information. The paper proposes a solution to aggregate research and patient data in medical establishments using formal projections mechanism, which allows to unify data extraction from separate data sources. Graph-based patient and research record representation is introduced, which allows to support and optimize complex queries for single patient and for a set of historical data from single research. Proposed representation mechanism is also shown to be effective for centralized processing using various data mining and intelligent analysis techniques.

In this paper, we consider the design problem of an unknown-input observer for distributed network systems under the existence of communication delays. In the proposed method, each node estimates all states and calculates inputs from its own estimate. It is assumed that the controller of each node is given by an observer-based controller. When calculating each node, the input values of the other nodes cannot be utilized. Therefore, each node calculates alternative inputs instead of the unknown inputs of the other nodes. The alternative inputs are generated by own estimate based on the feedback controller of the other nodes given by the assumption. Each node utilizes these values instead of the unknown inputs when calculating the estimation and delay compensation. The stability of the estimation error of the proposed observer is proven by a Lyapunov-Krasovskii functional. The stability condition is given by a linear matrix inequality (LMI). Finally, the result of a numerical simulation is shown to verify the effectiveness of the proposed method.

Objective Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool. This work aims to describe the initiative and illustrate its potential utility through several use cases.Methods Cosmos is designed to scale rapidly by leveraging preexisting agreements, clinical health information exchange networks, and data standards. Data are stored centrally as a limited dataset, but the customer facing query tool limits results to prevent patient reidentification.Results In 2 years, Cosmos grew to contain EHR data of more than 60 million patients. We present practical examples illustrating how Cosmos could further efforts in chronic disease surveillance (asthma and obesity), syndromic surveillance (seasonal influenza and the 2019 novel coronavirus), immunization adherence and adverse event reporting (human papilloma virus and measles, mumps, rubella, and varicella vaccination), and health services research (antibiotic usage for upper respiratory infection).Discussion A low barrier of entry for Cosmos allows for the rapid accumulation of multi-institutional and mostly de-duplicated EHR data to power research and quality improvement queries characteristic of learning healthcare systems. Limitations are being vendor-specific, an “all or none” contribution model, and the lack of control over queries run on an institution's healthcare data.Conclusion Cosmos provides a model for within-vendor data standardization and aggregation and a steppingstone for broader intervendor interoperability.

Finding a more efficient use of energy is an important problem that needs attention. Compared with the traditional power grid, a smart grid can monitor users’ electricity situation and electricity consumption instantly. However, it involves many problems of deploying network equipment. Consequently, it is vital to promote smart grids by collecting data from smart meters efficiently and keeping costs low. In this article, we propose a two-stage method of data collection for smart grids. The main contribution of this paper is to lower the number of data aggregation points (DAPs) so that the cost can be reduced. By using the K-means method, an entire smart grid can be divided into many smaller parts. In addition, the needs of transmitting and receiving data in the entire smart grid can be met by installing the least number of DAPs. Finally, the simulations show that the proposed two-stage method of data collection can use fewer DAPs to collect data than other methods which use one-stage methods, so the proposed scheme is more cost-effective.

Multivariate networks comprising several compositional and structural variables can be represented as multigraphs by various forms of aggregations based on vertex attributes. We propose a framework to perform exploratory and confirmatory multiplexity analysis of aggregated multigraphs in order to find relevant associations between vertex and edge attributes. The exploration is performed by comparing frequencies of the different edges within and between aggregated vertex categories, while the confirmatory analysis is performed using derived complexity or multiplexity statistics under different random multigraph models. These statistics are defined by the distribution of edge multiplicities and provide information on the covariation and dependencies of different edges given vertex attributes. The presented approach highlights the need to further analyse and model structural dependencies with respect to edge entrainment. We illustrate the approach by applying it on a well known multivariate network dataset which has previously been analysed in the context of multiplexity.

Industrial Internet of Everything (IIoE), as the deep integration of industry 6.0, the Internet of Things (IoT) and 6G mobile communication technology, pave the way for intelligent industry, enabling industrial optimization and automation. To ensure the high quality of services (QoS) in IIoE, tremendous real-time information generated by the pervasive smart things needs to be aggregated and processed quickly and reliably. However, a large-scale disaster could damage the entire communication network and cut off data aggregation such that Qos is compromised. In this paper, an Intelligent NIB based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IIoE. Specifically, IDAS first applies both iterative cubature kalman filter and radial basis function neural network to predict the data collection rates of survived infrastructures. Then, an energy efficient task distribution mechanism is design. Next, a deep reinforcement learning method is developed for the car-carrying NIB route design to perform corresponding task. Eventually, all data are aggregated toward the rescue headquarter by NIB deployment based on Fermat tree constructions. The theoretical analysis and simulations indicate that IDAS is not only energy efficient for after disaster scenarios but requires the least NIB consumption while compared with contemporary strategies.

With the exponential growth of the data generated by Internet of Things (IoT) devices, computation offloading becomes a promising method to alleviate the computation burden of local IoT device and improve processing latency. In order to address the bottleneck problem of limited resources in IoT device more efficiently and provide security guarantee in data processing and forwarding process, in this paper, we propose a privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks. Specifically, a fog-assisted three-layer security computing architecture is developed to counteract security threats and enable the aggregation operation can be performed in ciphertext. Meanwhile, a momentum gradient descent based energy-efficient offloading decision algorithm is developed to minimize the total energy consumption of computation tasks, which can achieve the optimal value with fast convergence rate. Finally, the security and performance evaluations reveal that the developed data aggregation offloading scheme is a secure data processing scheme and achieves significant performance advantage in energy consumption. For example, the total energy consumption can be reduced by an average of 23.1% compared with benchmark PGCO solution.

In recent years, due to the rapid development of information techniques and network technologies, more and more medical documents have been replaced by electronic files for sharing and transmitting in real time. However, medical data transmitted over public communication channels may suffer from security attacks and privacy threats. Blockchain technology has been gotten many attentions in different areas due to its unique properties such as anonymity, verifiability, immutability and decentralization. In order to secure patient privacy and provide more personal healthcare services, in this paper, we propose a data aggregation scheme based on Blockchain technology for medical environments. Moreover, in order to implement remote medical monitoring, we design a group authentication mechanism for multiple authorized users (such as patient, doctors, caregivers, family and friends) to freely access patient's personal health records. The authorized group members in a group will agree on a group session key and use it to protect patient's sensitive information. In case of a new member joins the medical group or an old member leaves the medical group, the group session key needs to be updated at any time. Finally, the electronic medical system will become more secure, reliable and useful by our proposed scheme.

Feature analysis of weak nonlinear signals from geographic spatiotemporal data has received increasing attention. Most existing signal processing methods cannot effectively perform comprehensive feature analysis because of the multiple dimensions and unbalance of spatiotemporal data. We developed a divide–aggregate–explore method for the feature analysis of spatiotemporal data. In our method, strategies for dividing different dimensions are defined for multidimensional analysis, and the tensor–block structure is adopted to reorganize the original data and distinguish differences in dimensions. Then, information‐based data aggregation is used to weaken the impact of dimensional unbalance. Case studies based on climatic reanalysis field data released by the National Oceanic and Atmospheric Administration showed that the proposed method can effectively extract weak propagation signals such as the El Niño–Southern Oscillation and El Niño–Southern Oscillation Modoki. Our method can also reveal more detailed evolutionary characteristics of complex coupling systems in different dimensions compared with classical feature detection methods such as principal component analysis and tensor decomposition.

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.

BackgroundData quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues.ObjectivesTo explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task.MethodsWe started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods’ results and corresponding outcome data (data that indicated the data’s suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without.ResultsOur self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods’ results.ConclusionsOur data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods’ results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

BackgroundAssessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.ObjectivesTo present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.MethodsWe describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool—openCQA—that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.ResultsApplying the method on the study’s dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.ConclusionsThe main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the “best” final prediction. When experts’ performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts’ estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

The Office of the National Coordinator for Health Information Technology estimates that 96% of all U.S. hospitals use a basic electronic health record, but only 62% are able to exchange health information with outside providers. Barriers to information exchange across EHR systems challenge data aggregation and analysis that hospitals need to evaluate healthcare quality and safety. A growing number of hospital systems are partnering with third-party companies to provide these services. In exchange, companies reserve the rights to sell the aggregated data and analyses produced therefrom, often without the knowledge of patients from whom the data were sourced. Such partnerships fall in a regulatory grey area and raise new ethical questions about whether health, consumer, or health and consumer privacy protections apply. The current opinion probes this question in the context of consumer privacy reform in California. It analyzes protections for health information recently expanded under the California Consumer Privacy Act (“CA Privacy Act”) in 2020 and compares them to protections outlined in the Health Information Portability and Accountability Act (“Federal Privacy Rule”). Four perspectives are considered in this ethical analysis: 1) standards of data deidentification; 2) rights of patients and consumers in relation to their health information; 3) entities covered by the CA Privacy Act; 4) scope and complementarity of federal and state regulations. The opinion concludes that the CCPA is limited in its application when health information is processed by a third-party data aggregation company that is contractually designated as a business associate; when health information is deidentified; and when hospital data are sourced from publicly owned and operated hospitals. Lastly, the opinion offers practical recommendations for facilitating parity between state and federal health data privacy laws and for how a more equitable distribution of informational risks and benefits from the sale of aggregated hospital data could be fostered and presents ways both for-profit and nonprofit hospitals can sustain patient trust when negotiating partnerships with third-party data aggregation companies.

The American Association of State Highway and Transportation Officials’ Highway Safety Manual (HSM) includes a collection of safety performance functions (SPFs) or statistical models to estimate the expected crash frequency of roadway segments, intersections, and interchanges. These models are applied in several steps of the safety management process, including to screen the road network for opportunities to improve safety and to evaluate the performance of safety countermeasure deployments. The SPFs in the HSM are generally estimated using negative binomial regression modeling. In some instances, they are estimated using annual crash frequency and site-specific (e.g., traffic volume) data, while in other instances they are estimated using aggregate crash frequency and site-specific data. This paper explores the differences that result from estimating SPFs using aggregate versus disaggregate data using the same methods as those used to estimate the SPFs in the HSM. A synthetic dataset was first used to conduct these comparisons – these data were generated in a manner that is consistent with the properties of the negative binomial distribution. Then, an observational dataset from Pennsylvania was used to compare the SPFs from both aggregate and disaggregate data. The results show that SPFs estimated using the panel (disaggregate) data and aggregated data provide similar model coefficients, although some differences may sometimes arise. However, the overdispersion parameter obtained using each dataset can differ significantly. These differences result in systematic biases in calculations of expected crash frequency when Empirical Bayes adjustments are applied, which – as the paper demonstrates – could lead to different outcomes in a network screening exercise. Overall, these results reveal that aggregating crash data might result in biased SPF outputs and lead to inconsistent Empirical Bayes adjustments.

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Centralized biodiversity data aggregation is too often failing societal needs due to pervasive and systemic data quality deficiencies. We argue for a novel approach that embodies the spirit of the Web (“small pieces loosely joined”) through the decentralized coordination of data across scientific languages and communities. The upfront cost of decentralization can be offset by the long-term benefit of achieving sustained expert engagement, higher-quality data products, and ultimately more societal impact for biodiversity data. Our decentralized approach encourages the emergence and evolution of multiple self-identifying communities of practice that are regionally, taxonomically, or institutionally localized. Each community is empowered to control the social and informational design and versioning of their local data infrastructures and signals. With no single aggregator to exert centralized control over biodiversity data, decentralization generates loosely connected networks of mid-level aggregators. Global coordination is nevertheless feasible through automatable data sharing agreements that enable efficient propagation and translation of biodiversity data across communities. The decentralized model also poses novel integration challenges, among which the explicit and continuous articulation of conflicting systematic classifications and phylogenies remain the most challenging. We discuss the development of available solutions, challenges, and outline next steps: the global effort of coordination should focus on developing shared languages for data signal translation, as opposed to homogenizing the data signal itself.

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user&rsquo, s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

BackgroundIntegrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.ObjectiveThe architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.MethodsWe reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.ResultsDespite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).ConclusionsIDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

Data aggregation, understood as the process of gathering and combining data in order to prepare datasets that might be useful for specific business or other purposes, is not per se forbidden. However, some forms of it can be considered anticompetitive. In the Decision B6-22/16 of the German Federal Cartel Office (Bundeskartellamt) data aggregation, which included the collection of data from sources outside of Facebook’s social network (from Facebook-owned services such as WhatsApp and Instagram and from third party websites or mobile applications) and their combination with the information connected with a particular Facebook user account without that user’s consent, constituted an abuse of Facebook’s dominant position on the German market for social networks. The Bundeskartellamt found that the processing of user’s personal data by Facebook has, to some extent, been carried out in a way which infringed GDPR provisions. In the same decision, the Bundeskartellamt also identified the exclusionary nature of Facebook’s anticompetitive behaviour. According to the Bundeskartellamt, the illegal data aggregation formed a barrier to entry for Facebook’s competitors which, through compliance with data protection standards, found themselves in a worst position. Facebook, through its inappropriate data aggregation gained a competitive advantage. The Bundeskartellamt’s decision is, therefore, reflecting the anticompetitive dangers that data aggregation might pose. Nevertheless, it is debated whether the Bundeskartellamt, as a competition authority, is competent to determine the compliance or lack of compliance of business terms with the provisions of the GDPR. This paper analyzes the Bundeskartellamt’s decision as to where an anticompetitive nature of data processing has been identified, and tries to answer the question why it is problematic that it was the Bundeskartellamt and not a data protection supervisory authority that has issued such a decision.

With the continuous development of multimedia technology, a growing number of multimedia applications have emerged. The demand for multimedia services continues to grow. But the development of multimedia services is still hampered by inherent security. Privacy and integrity of multimedia data are two key issues for implementing mobile multimedia security. At present, most data privacy-preserving solutions have a trusted third party, which may become the bottleneck of the system. And, the computational efficiency of the client is inefficient. In this paper, a lightweight and privacy-preserving data aggregation for mobile multimedia is proposed. In the proposed scheme, the terminal calculation is lightweight and there is no trusted third party in our scheme. Besides, multimedia big data and personal multimedia data are balanced by creating virtual aggregation areas and the system performance is improved by adopting batch verification in our scheme. Security analysis shows that the presented scheme can guarantee the privacy, confidentiality and integrity of the personal multimedia data. The performance analysis indicates the proposed scheme is lightweight.

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA) , which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA , as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA , which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.

Network lifetime maximization is the primary challenge in the wireless sensor network (WSN) due to its resource limitations. E2R2 routing algorithm uses a hierarchical WSN model to minimize energy consumption in the WSN. However, it increases complexity, time consumption, and energy consumption due to the hierarchical model. To resolve these problems, this paper proposes a novel ring partitioned based MAC (RP-MAC) protocol for the energy-efficient WSN with a mobile sink node. Energy efficiency is achieved by the following phases: clustering phase, MAC scheduling phase, data aggregation phase, and routing phase. Clustering phase is initiated by a weighted Voronoi diagram (WVD) algorithm by assigning a weight value for each node. Energy consumption due to idle listening is minimized by enabling novel RP-MAC scheduling in each cluster. Involvement of RP-MAC protocol also achieves collision-free data transmission in the network. A two-fold data aggregation (TFDA) scheme is proposed for the data aggregation phase to minimize energy consumption by reducing the number of transmissions. Routing phase supports both intra-cluster routing and inter-cluster routing. For intra-cluster routing, a hybrid chicken swarm optimization algorithm is proposed. For inter-cluster routing, position-based routing tree is constructed based on the sink node’s position. Our proposed RP-MAC protocol minimizes energy consumption in all possible ways for improving the network lifetime. Extensive simulation in ns-3 shows that the RP-MAC protocol achieves promising results in the following performance metrics: the number of dead nodes, average energy consumption, network lifetime, and throughput.

Releasing state samples generated by a dynamical system model, for data aggregation purposes, can allow an adversary to perform reverse engineering and estimate sensitive model parameters. Upon identification of the system model, the adversary may even use it for predicting sensitive data in the future. Hence, preserving a confidential dynamical process model is crucial for the survival of many industries. Motivated by the need to protect the system model as a trade secret, we propose a mechanism based on differential privacy to render such model identification techniques ineffective while preserving the utility of the state samples for data aggregation purposes. We deploy differential privacy by generating noise according to the sensitivity of the query and adding it to the state vectors at each time instant. We derive analytical expressions to quantify the bound on the sensitivity function and estimate the minimum noise level required to guarantee differential privacy. Furthermore, we present numerical analysis and characterize the privacy-utility trade-off that arises when deploying differential privacy. Simulation results demonstrate that through differential privacy, we achieve acceptable privacy level sufficient to mislead the adversary while still managing to retain high utility level of the state samples for data aggregation.

Machine type communication (MTC) is a fundamental technology to realize the concept of fully connected world in fifth generation (5G) Internet of Things (IoT). The massive roll out of the MTC devices is a serious challenge for cellular networks from operational and management perspective, including massive access and network congestion. Many proposals have been put forward by the research community to cater to the nuisance of massive MTC (mMTC) access in cellular networks. Recently, data aggregation has attracted a lot of research attention owing to its robust ability to resolve the above-mentioned challenges. In this paper, we review the recent development in data aggregation techniques, including their application scenarios, design, and limitations. Commencing with the application scenarios and current challenges in the MTC network, the classification of various proposed solutions for massive access along with the family of data aggregation techniques is discussed in detail. By doing so, it provides an insight about the future design trends that can propel the current research efforts to curb the mMTC access in a cellular network.

This paper proposes an efficient data aggregation and dynamic billing system that it uses anonymous communication for exchanging information between smart meters and CC. Moreover, the given scheme consists of a permissioned blockchain. This blockchain contains the ledger that keeps users’ anonymized identities and electricity consumption for predefined time ranges. Using consumption data of users, a billing mechanism can bill the users accordingly. In the construction, since all the parties in the system have the ledger, every party has the aggregated usage of the electricity without using very heavy cryptographic operations such as homomorphic encryption, bilinear pairing, etc. Using the ledger in our model, the aggregation of the users’ electricity consumption can be computed by anyone in the system. Moreover, users can verify their bills and check any data using a signature scheme. This results in that the integrity of all data is going to be preserved. The proposed approach mainly uses hash functions to provide the same functionality (aggregation of the users’ data consumption, data integrity check, and dynamic pricing and billing) with preserving data privacy of the users.

Wireless Sensor Networks (WSNs) is a fast-emerging technology which has become an integral part of the research. WSNs have various applications covering military, environment monitoring, health care, surveillance, national security, etc. Due to the inherent nature of wireless communication, such types of networks are more vulnerable to security attacks, and the authentication and confidentiality of wireless networks are much more critical. WSNs needs include efficient clustering methods, data aggregation methods, data compression methods, data encryption and authentication methods, and data gathering methods. WSNs are more vulnerable to attacks due to their ad hoc nature, so the design of a good key management scheme to provide security is necessary. In this paper, different methods of clustering, data aggregation, data compression, data encryption and authentication, and data gathering are analyzed. A survey is conducted on the key management schemes of WSNs.

In dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource-constrained devices, these communication overheads should be controlled in order to conserve battery. In this study, the authors focused mainly on routing protocol for low power and lossy network (RPL), IPv6 RPL networks, which is fully compatible with limited resources and Internet of things expectations. They proposed a dynamic method in order to extract the most suitable value for the frequency of executing the objective function which are rules governing how to construct a network graph. In this method, first nodal data is collected and aggregated along the path to the sink, and then a novel metric is used to determine the degree of the environmental changes and control the signalling overhead in RPL routing protocol. They evaluated their scheme with the Contiki operating system and Cooja simulator. They aimed to decrease the packet loss ratio, routing overhead and energy consumption. The simulation results showed the effectiveness of the proposed method.

Wireless Sensor Networks (WSNs) are used widely in many mission critical applications like battlefield surveillance, environmental monitoring, forest fire monitoring etc. A lot of research is being done to reduce the energy consumption, enhance the network lifetime and fault tolerance capability of WSNs. This paper proposes an ANFIS estimator based data aggregation scheme called Neuro-Fuzzy Optimization Model (NFOM) for the design of fault-tolerant WSNs. The proposed scheme employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) estimator for intra-cluster and inter-cluster fault detection in WSNs. The Cluster Head (CH) acts as the intra-cluster fault detection and data aggregation manager. It identifies the faulty Non-Cluster Head (NCH) nodes in a cluster by the application of the proposed ANFIS estimator. The CH then aggregates data from only the normal NCHs in that cluster and forwards it to the high-energy gateway nodes. The gateway nodes act as the inter-cluster fault detection and data aggregation manager. They pro-actively identify the faulty CHs by the application of the proposed ANFIS estimator and perform inter-cluster fault tolerant data aggregation. The simulation results confirm that the proposed NFOM data aggregation scheme can significantly improve the network performance as compared to other existing schemes with respect to different performance metrics. Keywords: Wireless Sensor Networks, Fault tolerance, Data aggregation, ANFIS, Estimator

The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data. Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger. Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes. The synergy between Blockchain and HE is rapidly increasing in the computing environment. This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies. Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract. On the top, HE will be used for data encryption allowing private aggregation operations. The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution. This work has been partially supported by the Basque Country Government under the ELKARTEK program, project TRUSTIND (KK- 2020/00054). It has also been partially supported by the H2020 TERMINET project (GA 957406).

© 2018 IEEE. Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users' data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.

The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.

With the widespread adaptation of the Internet of things (IoT), we are already witnessing a deluge of IoT data analytics applications. IoT data analytics can be defined as a process to control and optimize decision making in real time, by analyzing huge chunks of IoT sensor data. Moreover, data analytics stands on the shoulders of sensor data aggregation that includes data pre-processing and routing. This paper envisions two open problems of data aggregation, first, raw IoT sensor data is highly uncertain, second, the traditional algorithms are not fit for processing highly uncertain sensor data. This is formally known as data veracity problem. This paper proposes a data aggregation scheme for highly uncertain raw IoT sensor data collected using the device to device communication. The approach initially reconstructs the subspace using sample data and then it iteratively tracks down the low-rank approximation of the dominant subspace in the presence of high uncertainties at the fog server. Later, the robust dominant subspace is used to estimate a more reliable true sensor data matrix from the highly uncertain raw IoT sensor data traffic matrix. Moreover, the proposed scheme achieves the aforementioned tasks while processing the raw IoT sensor data without any prior information, i.e., in a fully unsupervised fashion. The existing literature based on sampling, approximation, and data reduction either causes random data reduction or destruction of global characteristics of the raw data. However, unlike the existing solutions, the proposed method removes the uncertainties while preserving the global characteristics of the raw data. Performance evaluations conducted using both the real world sensor data and synthetic data injected with noise, outliers, and missing values. Experimental results show that the proposed approach can estimate a reliable true sensor data matrix in the presence of high uncertainties and the energy efficient device to device communication-based data delivery mechanism can accommodate a large number of IoT devices.

Vast volumes of time series are becoming more common in part to recent research advancements in big data analytics and sensor/monitoring networks. However, tranmission of time series for visualization services can cause serious bandwidth wastage and extensive network delays if there is no efficient data management. Existing work such as M4 aim to solve this problem by providing high data reduction rates through data aggregation and guaranteeing the reliability of visualization results at the same time. However, current work on M4 does not consider verification in a more practical environment; for example experimentation on web-based servers that are openly accessible by users. In this paper, we propose inter-pixel gradient-based M4 (IGM4) for enhancing the existing M4 scheme, and conduct a study to reduce the amount of data and delay without distorting the results of visualized graph. We build user-friendly web-based system to dealing with the data processing technique, and perform test on the empirical environment. Finally, we present the results of performance evaluation through comparison among the original data, M4, and IGM4 reflecting the various kinds of time series and resolutions.

Bringing together the information latent in distributed medical databases promises to personalize medical care by enabling reliable, stable modeling of outcomes with rich feature sets (including patient characteristics and treatments received). However, there are barriers to aggregation of medical data, due to lack of standardization of ontologies, privacy concerns, proprietary attitudes toward data, and a reluctance to give up control over end use. Aggregation of data is not always necessary for model fitting. In models based on maximizing a likelihood, the computations can be distributed, with aggregation limited to the intermediate results of calculations on local data, rather than raw data. Distributed fitting is also possible for singular value decomposition. There has been work on the technical aspects of shared computation for particular applications, but little has been published on the software needed to support the "social networking" aspect of shared computing, to reduce the barriers to collaboration. We describe a set of software tools that allow the rapid assembly of a collaborative computational project, based on the flexible and extensible R statistical software and other open source packages, that can work across a heterogeneous collection of database environments, with full transparency to allow local officials concerned with privacy protections to validate the safety of the method. We describe the principles, architecture, and successful test results for the site-stratified Cox model and rank-k Singular Value Decomposition (SVD).

Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.

Wireless sensor networks (WSNs) have become one of the most vigorous techniques in the network domain. However, the sensor nodes of WSNs tend to become the target of attackers due to the broadcast communication mode and the unattended deployment nature. Although it can prevent the sensitive data from being compromised, Slice-Mix-AggRegaTe (SMART) needs to exchange messages frequently in a network, which put tremendous overhead on the sensor nodes with limited resources. Faced with these issues, this paper proposes an energy-efficient privacy-preserving data aggregation protocol based on slicing (EPPA) where a novel slicing mode is adopted to reduce the numbers of slices, which can significantly prevent the data from being compromised and decrease the communication overhead. Meanwhile, an enhanced scheme based on EPPA, called multi-function privacy-preserving data aggregation protocol (MPPA), is presented and it supports multiple functions in the process of data aggregation, such as max/min, count, and mean. The theoretical analysis and the simulation evaluation show that the proposed aggregation protocols demonstrate a better performance in the privacy preserving and the communication efficiency.

International audience; Data aggregation is an effective traffic-saving solution in Wireless Sensor Networks. A group of aggregation functions are proposed to save traffic and network capacity, thereby extending network lifetime. While when given an application and a target accuracy, how to select an appropriate function is not well being discussed. Considering the forecasting aggregation functions, we propose classification of the functions to guild how to select the appropriate one. Using target accuracy (the accuracy requirement predefined by the application), the distribution of raw data and performance of the functions (modeled by a Markov Decision Process), we classify the functions into a map, that shows which function performs better depending on target accuracy and the characterized data distribution.

Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

In recent days, IoT has been widely accepted and WSN (Wireless Sensor network) is being used for variety of the applications such as transportation, medical, environmental, military, it moreover the main aim to deploy the WSN is to collect the data about the given set of phenomena. The common task of WSN is to sense the data and send over the network. Moreover, due to the various purpose such as statistical analysis, the data aggregation is required. However, the when the dynamic network topology is considered, it is considered to be the very difficult task to provide the secure and efficient data aggregation. The main issue here is to ensure the security and accuracy of the data aggregation. Hence, in this research we have proposed an algorithm named as E-SDA (Efficient Secure Data Aggregation) in order to provide the secure data. In this, the algorithm provides the flexibility to detect the dishonest honest through neighbor monitoring. Later, extensive simulation has been done in order to prove the convergence of our algorithm.

Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature , these issues are addressed individually and most of the proposed solutions are either application-specific or too complex that make their implementation unrealis-tic, specifically, in a resource-constrained environment. In this paper, we propose a novel node level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, i.e., the residual energy of neighboring nodes and their importance from a network's con-nectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard, and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in term of various performance metrics such as throughput, lifetime, data accuracy, computational time and delay.

Published in Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, Proceedings of SOHOMA 2019, Studies in Computational Intelligence book series, Theodor Borangiu, Damien Trentesaux, Paulo Leitão, Adriana Giret Boggino, Vicente Botti editors, volume 853, p. 414-425, Springer, Cham 2019; International audience; The rapid development of Internet of Things has enabled intelligent products that can be applied to the Industrial 4.0, Smart City, Smart Supply Chain and Smart Buildings. Its autonomy and flexibility make possible to construct intelligent manufacturing systems. In this paper, we present the McBIM project that is based on the concept of communicating materials. A holonic manufacturing approach is proposed to handle these problems. It aims to include a WSN to the product to make it more sensitive and aware of its own internal conditions. In our approach, the "sensing device" is an embedded Wireless Sensor Network that can collect "internal" data of the intelligent material. In this context, physical and digital parts are defined and related to four main challenges. Some existing solutions of data collection architectures are discussed for the physical part. Furthermore, these algorithms are compared with the required performance metrics of our application. Finally we conclude with some perspectives for future work.

International audience; Wireless sensor networks consist of different sensor nodes, able to monitor inaccessible environments. Our objective is to improve the lifetime of specific WSNs in the context of communicating material. And we apply it to smart concrete design. The McBIM project, its objectives and constraints are introduced with some existing solutions for data collection. Protocols including aggregation features have been preferred, in order to reduce the number of messages and optimize network lifetime. Three main methods in data aggregation are presented with their main advantages and drawbacks: chain-based, cluster-based and tree-based methods. Furthermore, a comparison of the different associated algorithms is proposed based on required performance metrics, related to our context of application. Finally, some perspectives for the future work are presented.