Data Aggregation In Wireless Sensor Networks Research Articles

A survey on network lifetime maximization using data aggregation trees

SummaryThe sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges.

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

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

A Statistical Analysis of Wireless Body Area Network using Clustering Based Routing Protocols

Recently, research on wireless body-area sensor networks (WBASN) or wireless body area networks (WBANs) has gained great importance in medical applications, and now plays an important role in patient monitoring. Clustering is the most important task in wireless sensor networks (WSNs) by data aggregation through each cluster head (CH). The development of technology has seen comfort in the domestic and professional life of an individual. However, such survival was not able to meet medical emergencies during the pandemic COVID-19 and other health surveillance scenarios. As a result, it is important to design an energy-efficient routing system for WBAN. Existing routing algorithms focus more on energy efficiency than security. A Secure Optimal Path-Routing (SOPR) protocol is proposed to identify and discover routes in WBAN that are secure against black-hole attacks. In this paper the sensor's wireless capability has been tested with a simulator to detect and transmit signals across a wide range of frequencies and to ensure that data is transferred successfully. CARF reduces the maximum work done by relay nodes in case of overloaded nodes and thereby increases the energy distribution in the network. The proposed CARF design reduces packet loss and time delay, thereby increasing network lifetime compared to other fuzzy and heuristic methods used for routing in WSNs. The proposed algorithm maintains more than 75% of the residual energy in the network at a minimum initial energy level of 1 J to enable data syncing. A SAC-TA approach for data aggregation in WSNs is presented. False injection attacks are identified based on traffic analysis at the time of the route discovery process. One-time key generation algorithm is introduced to eliminate malicious nodes from the network. The presented paper proposes a congestion-free routing framework for data transmission from source to sink nodes in WSNs. Congestion-aware routing mechanisms modeled using fuzzy sets (CARF) include operations such as establishing non-localized nodes, by which more paths can be created for traffic distribution, and predicting an optimal congestion.

A secure and effective data aggregation in WSN for improved security and data privacy

The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.

CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization

SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

Wireless sensor network (WSN) combines and transmit the data in internet of things (IoT) applications. The nodes in pervasive Wireless sensor network are battery-operated and it needed to develop an energy-efficient approach, which lessens the energy consumption and increases the network lifetime. The existing methods not provide effective cluster head (CH) selection and node computation.In this manuscript, Multi-Objective Cluster Head Based Energy Aware Routing using Self-attention based progressive generative adversarial network optimized with African vulture optimization is proposed for Secured Data aggregation in WSN (MOCH-SAPGAN-AVO). Initially, the proposed model performs the routing process through Cluster heads (CH). So, the data aggregation using Self-attention based progressive generative adversarial network selects CH based on the multi-objective fitness function. These multi-objective fitness functions consider factors, like energy, delay, throughput, distance between the nodes and cluster density. After the cluster head selection, the optimal path is used to transfer the data to the base station. The optimal path is based on three parameters, such as degree of satisfication, connectivity and Rate of service (RoS). Hence, these three parameters are optimized with the help of African vulture optimization approach for optimal path selection. Finally, the optimal path is used for transferring the aggregated data to the base station (BS) and vice versa. The proposed method is implemented in network simulator (NS2) tool and the performance of proposed method is estimated. The proposed method attains 30.39%, 34.64%, 45.36%, and 33.32% lower delay, 39.056%, 44.12% and 28.97% improved packet delivery ratio and 45.96%, 48.06%, and 38.16% lower packet drop compared with existing methods, like hybrid and energy-efficient secure data aggregation algorithm and fuzzy logic based secure data aggregation in wireless sensor network (FL-CHESDA-WSN), multi objective fostered male lion optimization algorithm (DA-WSN-MLOA) and an Adaptive Source Location Privacy Preservation Technique and Principle Component Analysis (IDAF-ASLPP-RR).

Hierarchical autoregressive bidirectional least-mean-square algorithm for data aggregation in WSN based IoT network

The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147.

DAMFO-Based Optimal Path Selection and Data Aggregation in WSN

Wireless Sensor Network (WSN) encompasses several tiny devices termed as Sensor Nodes (SN) that have restriction in resources with lower energy, memory, together with computation. Data Aggregation (DA) is required to optimize WSN for secured data transmission at Cluster Head (CH) together with Base Station (BS). With regard to the Energy Efficiency (EE) along with the privacy conservation requirements of WSN in big-data processing and aggregation, this paper proposed Diversity centered Adaptive Moth-Flame Optimization (DAMFO) for Optimal Path Selection (OPS) and DA in WSN. In the proposed work, initially, the Trust Evaluation (TE) process is performed. The Pompeiu Distance-centered Fuzzy C-Means (PDFCM) is employed for Cluster Formation (CF) in addition to Cluster Head Selection (CHS) and then DAMFO algorithm chooses the optimal path to gather the data together with cluster centroids. The DHECC algorithm then generates keys and encrypts the aggregated data. The encrypted data is finally passed on to the BS. The experimentation outcomes exhibited that the proposed algorithm outweighs the traditional methods with respect to Energy Consumption (EC) 6.35 J, Packet Delivery Ratio (PDR) of 93%, Throughput of 0.956 bps, end-to-end delay 6.547 s, together with a lifetime of networks. Additionally, the proposed system exhibits the best Security Level (SL) of 94.2% amid the transmission.

Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation

In modern days, Wireless Sensor Network (WSN) is an emerging research area in which various resources constrained sensor nodes are associated by wireless radio with minimum bandwidth. Generally, WSN includes more devices, which are microsensors, wireless radios, and microprocessors. The sensor node in WSN has capable of sensing, self-controlling, offering wireless communication and estimation processing. The information attained by sensors is accumulated resourceful devices, named actuator nodes or central unit, termed as Sink node or base station. The sink node assists in transferring data gathered from a network and vice versa. Therefore, several WSN applications need data collection from sensor nodes based on sink nodes. A productive approach is needed for obtaining data efficiency through decreasing nodal energy consumption. In this research, a Hierarchical Fractional quantized kernel least mean square (HFQKLMS) filter was devised for data aggregation in WSN. Moreover, the HFQKLMS technique was devised by combining Kernel Least Mean Square and Hierarchical Fractional Bidirectional Least-Mean-Square (HFBLMS) approach. Besides, data redundancy is attained by broadcasting the required data using data predicted at the sink node. Besides, the performance of the developed HFQKLMS technique for data aggregation obtained less energy consumption of 0.021 J, and a prediction error of 7.45 based on 100 nodes in the localization database.

A secure and efficient privacy-preserving data aggregation algorithm

As a significant part of the Internet of things, wireless sensor networks (WSNs) is frequently implemented in our daily life. Data aggregation in WSNs can realize limited transmission and save energy. In the process of data aggregation, node data information is vulnerable to be eavesdropped and attacked. Therefore, it is of great significance to the research of data aggregation privacy protection in WSNs. We propose a secure and efficient privacy-preserving data aggregation algorithm (SECPDA) based on the original clustering privacy data aggregation algorithm. In this algorithm, we utilize SEP protocol to dynamically select cluster head nodes, introduce slicing idea for the private data slicing, and generate false information for interference. A comprehensive experimental evaluation is conducted to assess the data traffic and privacy protection performance. The results demonstrate that the proposed SECPDA algorithm can effectively reduce data traffic and further improve data privacy of nodes.

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Wireless sensor network (WSN) comprises the distributed sensors for aggregating and organizing the data. Data aggregation is the major concern in WSN since it relies on several factors, namely energy constraints of sensors, network topology, links conditions and so on. The conventional approach does not perform efficient data aggregation due to their battery power of nodes and degrade the network lifetime. To improve data aggregation and network lifetime, An Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering based Data Aggregation (EEEEFCC-DA) method is designed. Initially, residual energy of each sensor node (SN) is calculated. To determine the number of clusters, the elbow method is used in fuzzy c-means clustering algorithm. Then, Centroids value is calculated for every cluster to group SNs. Bray-Curtis Similarity Index is used to compute the similarity between the SN and Centroids value of cluster. SNs are grouped depends on the similarity value. The process gets iterated until every SNs gets clustered to the suitable clusters. After that, the SN with higher residual energy is selected as cluster head (CH). CH gathers data from each SNs and send to sink node. This, assist to enhance the data gathering accuracy and lessen the energy consumption. Simulation of EEEEFCC-DA method is carried out with various metrics namely energy consumption, network lifetime, data aggregation accuracy (DAA) and data aggregation time with number of SNs and number of data packets (DP). Results show that EEEEFCC-DA method provides better performance in term of DAA , network lifetime , energy consumption and data aggregation time than the conventional methods.

Data Aggregation in Wireless Sensor Networks: Emerging Research Areas

Wireless Sensor Networks (WSNs) generate an immense measure of explicit data usage. Such data, which is an exorbitant issue, should be prepared and then transmitted to the base station. Efficient data handling and energy monitoring are primary challenges since WSN hubs are asset-compelled. The point of wireless sensor networks is not confined to data collection in the present climate. Yet the retrieval of beneficial data still depends on it. The term used for the retrieval of beneficial data is data aggregation. In social affairs, data aggregation helps to collect data in energy-efficient ways to elongate the network's duration. The majority of the detected data by the sensors were seen to be excessive. On the off chance that data repetition can be reduced, it will prompt the organization's extended lifetime and decreased inertness at that point. This paper explains a wide methodological analysis of literature on data aggregation in WSNs. Review of various strategies to reduce data repetition, and specifically through aggregation, as well as scientific categorization of data aggregation, challenges, and broken-down aggregation methods proposed over the last ten (10) years, are discussed.

IoT Based Energy-Efficient Data Aggregation Wireless Sensor Network in Agriculture: A Review

Sensor nodes generate Wireless Sensor Networks (WSNs), these networks have considerable application in the areas of habitat safety, disaster management, surveillance in defense, security & many more areas. WSNs are compact in size, with short battery power & additionally their processing capabilities are low. This restriction of battery power makes them vulnerably faulty. In order to save this limited power, redundant data must be stored inside the sensor node during aggregation which will result in a reduction power dissipation associated with the sending of unnecessary data. By aggregating data, we can control energy consumption by reducing redundancy. Data aggregation is a really effective technique for WSN. In this paper we discuss the aggregation of data and their complex energy-efficient approach used for data aggregation in WSN. This paper highlights the latest innovations in WSNs vital for the research in agricultural domain, further we present their classification & did a comparative analysis of the discussed protocols, the nomenclature of energy saving & harvesting strategies used in agricultural monitoring. Further it discuss the difficulties and drawbacks of WSNs in context of agriculture, The presented comparative study will helpful in increasing number of data processing opportunities available through the Internet of Things (IoT).

Security‐aware authorization and verification based data aggregation model for wireless sensor networks

AbstractIn Wireless Sensor Network (WSN), the sensor nodes aggregate the environment and transmit the aggregated data to the Cluster Head (CH) or aggregator. Even though various data aggregation model is introduced, the resource‐constrained issues in WSN are complex to solve in the research community. Therefore, the authorization and verification‐based data aggregation model is proposed in this research to perform the secure data transmission. The proposed network model contains three functioning blocks, such as sensor nodes, aggregators, and data centers, respectively. The proposed data aggregation model involves five different phases, setup and key generation phase, signing, verification, authorization, and data aggregation phase. The sensor node creates the ID and private key to perform the data communication between the Cluster Head and data center. The data center generates the control message and transfers the message to CH and the sensor node. Finally, the data aggregation is carried out using the trimodel Least Mean Square (LMS) filter to achieve the data aggregation in WSN. Moreover, the proposed authorization and verification‐based data aggregation model obtained a lower prediction error of 0.0526 and consumed higher energy of 0.5567 J with the computational and the communication cost as 0.008 second, and 0.0139 second, respectively.

Combination of Fitness-Mated Lion Algorithm with Neural Network for Optimal Query Ordering Data Aggregation Model in WSN

Query processing can be briefly defined as a database that comprises of an organized collection of data for one or more users either in digital form or in analog form such that it can portray exactly. A Wireless Sensor Network (WSN) is a specialized network of minimum cost, and power sensor nodes that can be described as the ability of performing some processing, gathering sensory information and communicating with each other. Query ordering with data aggregation is the process of scheduling of the nodes to receive the useful data from sensors. Data aggregation is considered as one of the fundamental processing procedures for saving the energy. In WSN data aggregation is an effective way to save the limited resources. This paper proposes a novel query-based data aggregation model with the aid of intelligent techniques. The framing of the query order takes place and the frames are ranked on the basis of a multi-objective function. The newly developed multi-objective function includes Latency, Throughput, and Data freshness. Initially, the solution corresponding to query order is trained in NN using the proposed Fitness-Mated Lion Algorithm (FM-LA). The optimally generated query order from NN is further given for second-level solution generation, which is again applied to FM-LA for subsequent query order optimization. Hence the two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, throughput, and data freshness. Hence, substantiated performance and comparative analysis validate the improved performance of the proposed model.

A HEURISTIC DATA AGGREGATION IN HETEROGENEOUS LOW POWER DEVICES FOR INTERNET OF THINGS

Internet of Things (IoT) has been envisioned as smart objects’ (things) communication and integration. The key challenge with regards to IoT is privacy. Within the IoT context, vertically-partitioned data learning is quite an applicable and well-known strategy. The issue happens whenever an event or state has to be predicted or identified, depending on the values assessed features at diverse nodes. An identity may be uncovered due to Quasi-Identifiers (QI), which are an attribute set of the information of a user. Wireless Sensor Network (WSN) is largely used as data collection for IoTs. Energy utilization can be decreased with data aggregation in WSN. In WSNs, an Energy-Efficient Hierarchical Clustering Algorithm (EEHCA) can attain good performance with regards to network lifespan through adjusting the nodal energy load and reducing the usage of energy for communication. Energy usage, load balance, and so on are sensor network performance factors which can be greatly boosted with the Particle Swarm Optimization (PSO) algorithm. In comparison to other mathematical and heuristic methods, this algorithm has better throughput and more efficient. Vertically-partitioned data aggregation that makes use of Quasi-Identifiers (QI) in the Internet of Things (IoT) has been proposed in this work and optimize the WSN using PSO to improve the performance of the entire system.

Energy aware decision stump linear programming boosting node classification based data aggregation in WSN

Data collection is a considerable operation in Wireless sensor network (WSN) to minimize the energy dissipation of sensor nodes and thereby increasing the network lifetime. A lot of research works have been designed for data gathering in WSN. A multi-mobile agent itinerary planning based energy and fault aware data aggregation and zone-based energy-aware data collection routing protocol was introduced to obtain better energy usage and data delivery in WSN. However, data aggregation accuracy was not increased and energy consumption during data aggregation was not reduced. In order to overcome such limitations, an Energy Aware Decision Stump Linear Programming Boosting Node Classification based Data Aggregation (EADSLPBNC-DA) Model is proposed. Initially, residual energy for every sensor node is calculated for performing the node classification. After that, linear programming boosting classification (LPBC) model is applied in EADSLPBNC-DA Model which increased the margin between the training samples (i.e., sensor nodes) of different classes. The LPBC model constructs a strong classifier by combining number of weak decision stump result. Subsequently, strong classifier in EADSLPBNC-DA Model accurately classifies each input sensor node as higher energy or lower energy node which reduced the misclassification error. Then, the lesser energy sensor nodes in WSN transmit the data packets to the neighboring higher energy sensor nodes through calculating the distance by Manhattan distance formula. Finally, the sink node gathers the data packets from the higher energy sensor nodes. As a result, EADSLPBNC-DA Model attains energy efficient data collection in WSN. Experimental evaluation of EADSLPBNC-DA Model is carried out on factors such as energy consumption, delay, data aggregation accuracy, network lifetime and data aggregation time with respect to number of sensor nodes and number of data packets. From the simulation results, proposed EADSLPBNC-DA Model significantly reduces the energy consumption by 22%, delay by 35% and data aggregation time by 28% when compared to the existing techniques. In addition, the data aggregation accuracy and network lifetime gets increased by 13% and 10% respectively compared to state-of-the-art works.

A Novel Energy-Efficient and Privacy-Preserving Data Aggregation for WSNs

Data aggregation is a fundamental and efficient algorithm to reduce the communication overhead and energy consumption in wireless sensor networks (WSNs). However, WSNs need both energy-efficient and privacy-preserving when being deployed in an unprotected area. In this paper, we propose an energy-efficient and privacy-preserving data aggregation algorithm CBDA (the chain-based data aggregation). In the CBDA, sensor nodes will be organized as a tree topology. The leaf nodes of the tree sequentially reconnect with each other to form many chain topologies. For assuring data privacy, after data gathering, (1) the tail nodes (the nodes which on the tail of chain) need to slice their sensing data into J fragments. One fragment is kept by themselves, and they distribute the J -1 data fragments to their neighbor nodes. (2) Each tail node will inject some fake fragments into its J -1 fragments to interfere with adversaries. The CBDA can achieve less energy consumption and higher aggregation accuracy during data aggregation. We perform a comprehensive simulation to make a comparison among the CBDA with existing algorithms. The experimental results demonstrate that the CBDA outperforms the existing algorithms.

MCEEP-BDA: Multilevel Clustering Based -Energy Efficient Privacy-Preserving Big Data Aggregation in Large-Scale Wsn

In current scenario, the Big Data processing that includes data storage, aggregation, transmission and evaluation has attained more attraction from researchers, since there is an enormous data produced by the sensing nodes of large-scale Wireless Sensor Networks (WSNs). Concerning the energy efficiency and the privacy conservation needs of WSNs in big data aggregation and processing, this paper develops a novel model called Multilevel Clustering based- Energy Efficient Privacy-preserving Big Data Aggregation (MCEEP-BDA). Initially, based on the pre-defined structure of gradient topology, the sensor nodes are framed into clusters. Further, the sensed information collected from each sensor node is altered with respect to the privacy preserving model obtained from their corresponding sinks. The Energy model has been defined for determining the efficient energy consumption in the overall process of big data aggregation in WSN. Moreover, Cluster_ head Rotation process has been incorporated for effectively reducing the communication overhead and computational cost. Additionally, algorithm has been framed for Least BDA Tree for aggregating the big sensor data through the selected cluster heads effectively. The simulation results show that the developed MCEEP-BDA model is more scalable and energy efficient. And, it shows that the Big Data Aggregation (BDA) has been performed here with reduced resource utilization and secure manner by the privacy preserving model, further satisfying the security concerns of the developing application-oriented needs.

N-Tier Modelling of Robust Key management for Secure Data Aggregation in Wireless Sensor Network

Security problems in Wireless Sensor Network (WSN) have been researched from more than a decade. There are various security approaches being evolving towards resisting various forms of attack using different methodologies. After reviewing the existing security approaches, it can be concluded that such security approaches are highly attack-specific and doesnt address various associated issues in WSN. It is essential for security approach to be computationally lightweight. Therefore, this paper presents a novel analytical modelling that is based on n-tier approach with a target to generate an optimized secret key that could ensure higher degree of security during the process of data aggregation in WSN. The study outcome shows that proposed system is computationally lightweight with good performance on reduced delay and reduced energy consumption. It also exhibits enhanced response time and good data delivery performance to balance the need of security and data forwarding performance in WSN.