An energy-efficient prediction model for data aggregation in sensor network

Singh

2021

Preprint

Self Cite

Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.

Section: Literature Surveymentioning

confidence: 99%

A Two Vector Data-Prediction Model for Energy-Efficient Data Aggregation in Wireless Sensor Network

Singh

2021

Preprint

Self Cite

“…A prediction model based on extended cosine regression (ECR) for data aggregation is proposed to manage the energy depletion in Reference 29 efficiently. In intracluster transmissions, ECR is a double‐queue‐based prediction technique to synchronize the standard measurements and minimize cumulative errors from continuous predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Another technique to address these challenges differently in reducing radio transmission as it ingests higher energy after sensing data. Dual data prediction 21,24‐32 emerges to be an influential approach in this direction. An identical data prediction model is used in dual data prediction between the SNs or CHs and the BS.…”

Section: Introductionmentioning

confidence: 99%

A lightweight data transmission reduction method based on a dual prediction technique for sensor networks

Trans Emerging Tel Tech

Kumar

2021

Self Cite

An essential design concern in a resource‐constraint sensor network is optimizing data transmission for each sensor node (SN) to prolong the network lifetime. Many research works cited that the dual prediction technique remains the most efficient technique for data reduction. A large amount of redundant data is usually transmitted across the network, leading to collisions, loss of data, and energy dissipation. This article proposes a data transmission reduction method (DTRM) to solve these problems, implemented on the cluster heads and operates in rounds. DTRM is lightweight in processing, has low complexity costs, and needs a limited memory footprint, but it is robust and effective. It can be combined with any form of cluster‐based data aggregation. We have incorporated the proposed DTRM with the data aggregation‐adaptive frame method (DA‐AFM), implemented on the SNs within the clusters. DA‐AFM can eliminate temporal redundancies and correlations in the sensor's time‐series readings. This helps the SN take fewer readings, which improves the efficiency of reducing data transmission and decreases the amount of energy spent during sensing. The proposed DTRM approach decreases the average transmission rates of data while maintaining data quality. This study is evaluated on real data obtained from the Intel Berkeley Lab and compared with three recent data reduction techniques focused on prediction. The results show that DTRM consumes up to 70% less energy while preserving the expected quality of data and reducing transmission.

“…Data Aggregation will be done at the CHs to reduce the number of data packets. Clustering-base schemes are promising techniques [22] as they offer good scalability and support for data aggregation in WSNs. The communication within a cluster is managed by the CH, which is on the second layer of the architecture.…”

Section: Network Architecture and Set-upmentioning

confidence: 99%

An efficient secure key establishment method in cluster-based sensor network

Singh¹,

2021

Telecommun Syst

Self Cite

The main issue for the security of wireless sensor networks (WSNs) is how to allow sensor nodes (SNs) to establish and share cryptographic keys in an energy-efficient, storage-efficient, and authentic manner for their secure data transmission. Furthermost recent studies carried out in this direction is concerned with homogeneous networks in which all sensor has identical characteristics and key administration mechanisms. Although Cluster-based sensor networks have demonstrated better achievements and performance as compared to homogeneous networks because of the several benefits of clustering. This inspired us to propose a secure key-establishment method for cluster-based sensor networks based on symmetric-key cryptography. Since symmetric key cryptography has small energy consumption, they are a great choice to prefer for securing the networks. Even though symmetric key cryptography has high storage needs, this deficiency can be reduced by using suitable methods-the evaluation of the proposed work that the storage needs are reduced along with reduced energy consumption. The work offers a favorable level of security against various intruders and possible security threats and is additionally scalable than the state-of-the-art techniques.