A Brief Survey on Data Aggregation and Data Compression Models using Blockchain Model in Wireless Sensor Network

Most of the time, nodes in Wireless Sensor Networks (WSNs) are set up in an unmonitored, potentially dangerous setting. Battery life, storage space, processing power, and network connectivity are all severely lacking in these nodes. Therefore, it is difficult to protect a WSN without negatively impacting the efficiency of the network. The issues listed above can be effectively addressed with a well-designed plan that makes use of Data Aggregation (DA) in conjunction with a robust security system In this work, we provide a thorough analysis of Secure Data Aggregation (SDA) in WSNs, highlighting both the security benefits sought and the challenges currently faced by this approach. Both established and up-and-coming network topologies are covered, with comparisons drawn between them to highlight key differences in application landscapes and security provisions afforded by each. Compressive data gathering (CDG), which employs Compressive Sensing (CS), can significantly enhance energy effieiency with the advantages gained from data compression. The data collection performance and longevity of Internet of Things (IoT) based WSNs are found to be significantly improved by CS based data collecting techniques. However, in hostile and sophisticated network deployments, they encounter significant difficulties with key distribution and adversary attacks. This research explores how wireless sensor networks' security measures affect their ability to collect and analyze data. This research provides a brief survey on data aggregation and data compression using blockchain in WSN for secure data transmission.

1. Introduction

Most WSNs are made up of a large number of individual sensornodes, each of which performs its function across a certain geographical area. With its own sensor, CPU, and wireless transceiver, each sensor node may collect data and relay it to other nodes in the network [1]. It keeps monitoring the surrounding environment [2], gathers sensing data, and sends it on to a selected sink node for storage and analysis [3]. These WSNs have seen widespread deployment in real-world settings to facilitate data collection for the benefit of users seeking a deeper comprehension of their surrounding environments [4]. Many uses for WSNs require the deployment of numerous sensor nodes, each with a small battery and often located in inaccessible areas. For this reason, it is important that sensor nodes be as energy-efficient as possible, as their batteries can only last for so long. Maximum efficiency in the face of energy constraints has been the subject of a great deal of research [5].