Perform Data Aggregation Research Articles

[formula omitted] : An adaptive data aggregation algorithm for IoT infrastructure

In IoT infrastructure, high-frequency sensing and subsequent transmission of sensed data to computational facilities can lead to redundant data storage and processing, consuming significant storage and processing capacity. As a result, the IoT infrastructure needs more data transmission cycles, leading to data redundancy and low network up-time due to the drainage of limited battery capacity. Conversely, if the data is communicated at a lower rate, it may cause absolute data delivery to the processing unit, which is useless. As a result, a well-designed data aggregation algorithm is required. This paper proposes the ADA2−IoT, an Adaptive Data Aggregation Algorithm for IoT Infrastructure tailored to optimize parameters such as low data redundancy, limited data communication cycles, and high IoT infrastructure up times. The proposed algorithm consists of two key components: the Route Data Aggregator (RDA) performs aggregation during data transit towards the Edge node or gateway, and the Node Data Aggregator (NDA) performs data aggregation during capturing or sensing data. The algorithm employs metrics like Age of Information (AoI) and data freshness factor during the node and route data aggregation phase to capture and timely deliver data to the Edge node, where this data is processed for informed decision-making. The proposed algorithm was efficiently tested on a simulation and IoT hardware deployment environment. Both simulation and hardware results demonstrate a substantial improvement in QoS parameters, such as a decrease in data redundancy and packet exchanges, leading to considerable energy savings and prolonging the lifespan of IoT infrastructure.

Secondary cluster head based SEP in heterogeneous WSNs for IoT applications

AbstractA wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP.

Federated-SRUs: A Federated-Simple-Recurrent-Units-Based IDS for Accurate Detection of Cyber Attacks Against IoT-Augmented Industrial Control Systems

The security of Industrial Control Systems (ICS) against cyber-attacks is essential in modern era since ICSs are vital constituent of modern societies and smart cities. However, the augmentation of legacy ICS networks with smart computing and networking technologies (such as, IoT) have intensely enlarged the surface of attacks against these critical infrastructures. This augmentation makes these networks more vulnerable to cyber-attacks and despite the current security solutions, attackers still find ways to proliferate these networks. Intrusion Detection System (IDS) is one of the key security aspect to prevent these networks from contemporary cyber-attacks. Therefore, this paper proposes a new IDS model named Federated-SRUs (simple recurrent units) for the security of IoT-based ICSs. Specifically, the Federated-SRUs IDS model uses an improved simple recurrent units architecture to reduce computational cost and alleviate the gradient vanishing issue in recurrent networks. Then, it performs data aggregation through several communication rounds in federated architecture which allows multiple ICS networks and stakeholders to build a comprehensive IDS model in a privacy-preserving manner. The performance of Federated-SRUs IDS model is validated through experiments using real-world gas pipeline-based ICS network data, which indicates that it is able to accurately detect intrusions in real-time without compromising privacy and security. Experiments also verify that the Federated-SRUs model outperforms existing state-of-the-art approaches and thus can serve as a viable IDS method in IoT-based ICS networks.

Improved buffalo optimized deep feed forward neural learning based multipath routing for energy efficient data aggregation in WSN

Wireless sensor networks comprised inadequate computational power and memory for application developers.Data aggregation is the method of gathering the useful information. Data aggregation is the new process for reducing the energy consumption. In WSN, data aggregation is an efficient method with limited resources. Many classification methods were introduced for performing data aggregation in WSN. But, the energy consumption and delay was not reduced by existing techniques. In order to address these problems, an Improved Buffalo Optimized Route Selective Deep Feed Forward Neural Learning (IBORSDFFNL) Model is introduced. The main objective of the IBORSDFFNL Model is to construct multiple paths for efficient data aggregation in WSN. IBORSDFFNL Model carried out route path discovery process and optimal route path selection process. The multiple route paths are constructed from distributed sensor nodes. Among the constructed route paths, an optimal route path is chosen by using improved buffalo optimization for performing efficient data aggregation in WSN. After selecting efficient optimal route path, data aggregation process is carried out with minimal delay and higher packet delivery ratio. Experiments are carried out to determine the performance in terms of end-to-end delay, energy consumption and packet delivery ratio.Simulation results show that the proposed model attained lesser energy consumption and end-to-end delay for data aggregation and increases the packet delivery ratio in WSNs.

Online battery health diagnosis for electric vehicles based on DTW-XGBoost

With the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The feature extraction method of multi-source data fusion based on clustering was adopted. DTW clustering was used to perform data aggregation and feature extraction for real-time battery data during charging process, and XGBoost algorithm was used to establish SOH prediction model. Build an online battery health diagnosis platform including acquisition and control module, modeling and analysis module and application service module by using cloud platform to improve charging operation and maintenance management level.

PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT.

Optimized Clustering Protocol for Balancing Energy in Wireless Sensor Networks

While wireless sensor networks (WSNs) are increasingly equipped to handle more complex functions and in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network lifetime. Cluster-based Hierarchical Routing Protocol using compressive sensing (CS) theory (CBHRP-CS) divides the network into several clusters, each managed by a set of CHs called a header. Each member of the header compresses the collected data using CS. This paper proposes an optimized clustering protocol using CS (OCP-CS) to improve the performance of WSNs by exploiting compressibility. In OCP-CS, each cluster is managed by a cluster head (CH). CHs are selected based on node concentration and sensor residual energy, and performs data aggregation using CS to reduce the energy consumed in the process of data sampling and transmission. Simulations show that our proposed protocol is effective in prolonging the network lifetime and supporting scalable data aggregation than existing protocols.

WSN and Data Aggregation Techniques in Wireless Sensor Network

Wireless sensor network are the collection of hundreds and thousands of tiny, battery powered sensor nodes. Sensor’s nodes co-operatively send the sense data to base station. Wireless sensor network has several applications like habitat monitoring, building monitoring, health monitoring, military surveillance and target tracking. Sensor nodes have limited energy and bandwidth. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use the networks for long duration. Hence, network lifetime is most challenging task and it became necessary to reduce data traffic inside sensor networks, reduce amount of data that need to send to base station. This reduction of communication data extends the network lifetime and it is possible only if sensor nodes perform data aggregation instead of directly sending the sensed data to sink (Base station). This paper presents various data aggregation techniques in wireless sensor network and a novel Data Routing for In-network aggregation called DRINA. The main goal of data aggregation is to gather and aggregate data in energy efficient manner in order to enhance the network life time.

A multi-Mobile Agent and optimal itinerary planning-based data aggregation in Wireless Sensor Networks

Due to the energy constraints in Sensor Nodes (SN) and to increase energy efficiency in Data Aggregation (DA) approaches, Wireless Sensor nodes (WSN) requires a suitable network’s life cycle. In order to address the energy efficiency, a new system model is proposed for competent Data Aggregation (DA), and Data processing (DP) mechanism with the amalgamation of Mobile Agent (MA) in WSN intended towards collaborative signal systematized with information processing. Furthermore, with the expansion of Mobile Agent (MA) centred WSN, unique resource-efficient potentials for Data Aggregation (DA) and Data processing (DP) applications are identified. In the proposed system model, a single Mobile Agent (MA) is employed for data processing. Hence the MA-centred paradigm’s fundamental difficulty is determining the best route for agent traversal. Henceforth, using the Brownian Motion-Based Flower Pollination Algorithm (BMFPA), a new multi-MA-centred optimal itinerary planning strategy for performing data aggregation in WSN is proposed. Further, the proposed approach uses Fitness based Fuzzy C-Means (FFCM) for cluster creation and Cluster Head (CH) selection. Crossover Mutation based Firefly Algorithm (CM-FFA) is used. Moreover, the proposed model uses optimal itinerary planning-based Brownian Motion-Based Flower Pollination Algorithm (BMFPA) for MA migration and Data Gathering (DG) in WSN. Finally, after data gathering, the proposed model, the mobile Agent collects the information and communicates it to the base station or sink based on various applications scenarios. The system model’s investigation outcomes evidently exhibit that the proposed work competently performs well than prevailing algorithms for Data Gathering (DG) in WSN. Furthermore, the proposed model method can be fully utilized in a virtual wireless network’s scenario.

Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the [Formula: see text] encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

A Survey on Various Clustering Algorithms in WSN for Optimal Energy Utilisation

Wireless sensor networks comprise of an expansive number of distributed sensor gadgets, which are associated and composed through multi-hop steering. Because of the presence of related data and excess in measuring data, data messages can be joined and converged by performing data aggregation work in the steering procedure. To diminish energy utilization is a noteworthy enhancement target of data aggregation approaches, which can be accomplished by diminishing the mandatory correspondence load of steering. To improvise the network lifetime as much as possible in Wireless Sensor Networks (WSNs) the ways for data move are picked in a way that the aggregate energy used along the way is limited. To help high adaptability and better data aggregation, sensor nodes are routinely collected into disjoint, non-covering subsets called clusters. Clusters make various leveled WSNs which consolidate proficient use of constrained assets of sensor nodes and in this manner broadens network lifetime. The objective of this paper is to demonstrate a forefront survey on clustering calculations announced in the writing of WSNs. This paper presents different energy effective clustering calculations in WSNs. From the hypothetical level, an energy show is proposed to approve the advantages of data aggregation on energy utilization. The key parameters which may affect the aggregation execution are additionally examined.

Lightweight, Divide-and-Conquer privacy-preserving data aggregation in fog computing

With the increasing popularity of the Internet of Things’ (IoT) and fog computing paradigm, aggregating IoT data considering privacy concerns over fog networks can be seen as one of the biggest security challenges. Numerous schemes address this problem. However, most of the existing schemes and their associated methods are heavyweight, facing issues related to performance overhead. Furthermore, performing data aggregation at a single aggregator fog node causes an overly computational burden on the node, which results in high latency, degraded reliability and scalability leading to a single point of failure risks. To fill these gaps, this paper presents a lightweight, Divide-and-Conquer privacy-preserving data aggregation scheme in fog computing to improve data privacy, data processing, and storage capabilities. Particularly, we design a data division strategy based on the Level of Privacy (LoP) defined by data owners. The data division strategy not only effectively divides data according to LoP and distributes it among participating fog nodes for aggregation and storage processing, but also reduces computational and memory overhead in the processing simultaneously. Moreover, we perform a privacy analysis of our scheme and perform comprehensive experiments to compare it with other traditional schemes to evaluate performance efficiency. The results demonstrate that our scheme can efficiently achieve data privacy in fog computing and outperforms the other schemes in computational and memory costs.

Scalable and efficient routing protocol for internet of things by clustering cache and diverse paths

This paper proposes SENR, a scalable and efficient named-based routing protocol for the internet of things (IoT). SENR explores semantic notification and opportunistic caching strategy to enhance the data retrieval efficiency for smart systems. First, SENR performs data aggregation according to the hierarchy of namespace, and stores data over in-network storage node. It also marks each route notification at the edge nodes, and allows the interest with the same name prefix to choose the same edge routing node towards to the storage node. Second, SENR adopts link-state routing protocols to collect network topology and compute the shortest path to the content source nodes, moreover, it uses distance-vector routing protocols to select multiple alternative paths to cope with node failures. Third, SENR employs partial routing update to explicit support of dynamic paths and caching capabilities. Simulation results demonstrate that SENR can significantly reduce the content retrieval delay and routing overheads.

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning. Wearable activity recognition experiments and real Parkinson's disease auxiliary diagnosis application have evaluated that FedHealth is able to achieve accurate and personalized healthcare without compromising privacy and security. FedHealth is general and extensible in many healthcare applications.

E-MHMS: enhanced MAC-based secure delay-aware healthcare monitoring system in WBAN

Chronic patients are adapting to an emerging healthcare system assisted by the wireless body area network (WBAN). Medical data are not normal at all times; hence, the preference for critical data is expected to be high, and the requirement of security has become essential. This paper addresses a novel healthcare monitoring system (HMS) that adopts the IEEE 802.15.6 standard for WBAN. A modified IEEE 802.15.6 MAC is designed for predicting data types and residual energy on body sensors. An enhanced MAC-based HMS (E-MHMS) is developed for delay aware data transmission to perform data aggregation, key distribution, channel selection and data classification. A smartphone acts as a coordinator that aggregates data from the WBAN; upon receiving the data, it determines the best channel from the available multiple inputs for assured data transmission. E-MHMS uses the novel time-based elliptic curve algorithm and the ASCII RSA algorithm for key distribution and encryption. Finally, the data reaches the monitoring servers that classify the data using hybrid naive Bayesian neural network. The proposed E-MHMS setup in an OMNeT++ simulation environment and the improvements are demonstrated in terms of important network parameters such as delay, throughput, packet drop, security and accuracy.

Data Aggregation Privacy in WSN

When WSN is applied to monitoring, the privacy of monitoring data from monitoring objects becomes an important issue for the successful application of WSN, which requires effective privacy protection for data aggregation. CPDA (Cluster-based Private Data Aggregation) has a problem that energy costs become higher with increase of nodes within a cluster. In this paper, UCPDA (Upgrade Cluster-based Private Data Aggregation) is established to perform data aggregation while preserving data privacy. According to required privacy, the sensors in a cluster are partitioned into some groups. Data preprocessing is performed only in the same group. Compared to CPDA, UCPDA has lower energy consumption for required privacy.

Energy-Efficient and Secure Data Forwarding Mechanism for Balancing Cluster Lifetime for Huge Size Wireless Sensor Networks

In designing Wireless Sensor Networks, energy efficiency and security should be considered very critically. Energy efficiency is achieved through data aggregation which eliminates the transmission of redundant data while security is achieved by preserving confidentiality among sensor node and the base station. In this paper, an energy efficient and secure cluster based aggregation mechanism is presented. In this model, for energy efficiency the network is divided into tracks and sectors so the cluster head’s are uniformly selected from the whole network. To achieve security the cluster head’s perform data aggregation with the help of some pattern codes and only distinctive data is transmitted from sensor nodes in encrypted form. To perform aggregation, the sensor nodes do not need to know about the actual sensor data therefore there is no need to use any encryption or decryption schemes between nodes and cluster head. Performance evaluation shows proposed model works better to enhance the network lifetime, security, average residual energy, and average packet transmission ratio than conventional data aggregation models.

DABPR: a large-scale internet of things-based data aggregation back pressure routing for disaster management

In this paper, we propose a data aggregation back pressure routing (DABPR) scheme, which aims to simultaneously aggregate overlapping routes for efficient data transmission and prolong the lifetime of the network. The DABPR routing algorithm is structured into five phases in which event data is sent from the event areas to the sink nodes. These include cluster-head selection, maximization of event detection reliability, data aggregation, scheduling, and route selection with multi attributes decision making metrics phases. The scheme performs data aggregation on redundant data at relay nodes in order to decrease both the size and rate of message exchanges to minimize communication overhead and energy consumption. The proposed scheme is assessed in terms of packet delivery, network lifetime, ratio, energy consumption, and throughput, and compared with two other well-known protocols, namely “information-fusion-based role assignment (InFRA)” and “data routing for in-network aggregation (DRINA)”, which intrinsically are cluster and tree-based routing schemes designed to improve data aggregation efficiency by maximizing the overlapping routes. Meticulous analysis of the simulated data showed that DABPR achieved overall superior proficiency and more reliable performance in all the evaluated performance metrics, above the others. The proposed DABPR routing scheme outperformed its counterparts in the average energy consumption metric by 64.78% and 51.41%, packet delivery ratio by 28.76% and 16.89% and network lifetime by 42.72% and 20.76% compared with InFRA and DRINA, respectively.

Network lifetime maximization in wireless mesh networks for machine-to-machine communication

In this paper we present new optimization formulations for maximizing the network lifetime in wireless mesh networks performing data aggregation and dissemination for machine-to-machine communication in the Internet of Things. We focus on heterogeneous networks in which multiple applications co-exist and nodes may take on different roles for different applications. Moreover, we address network reconfiguration as a means to increase the network lifetime, in keeping with the current trend towards software defined networks and network function virtualization. To test our optimization formulations, we conducted a numerical study using randomly-generated mesh networks from 10 to 30 nodes, and showed that the network lifetime can be increased using network reconfiguration by up to 75% over a single, minimal-energy configuration. Further, our solutions are feasible to implement in practical scenarios: only few configurations are needed, thus requiring little storage for a standalone network, and the synchronization and signalling needed to switch configurations is low relative to each configuration’s operating time.

An Enhanced Distributed Data Aggregation Method in the Internet of Things.

“Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g., human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.