Data Transfer In Wireless Sensor Network Research Articles

An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92j), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.

An Elliptic Curve based Key Management Scheme for Wireless Sensor Networks

Wireless sensor network (WSN) is a growing expertise which has been used in many areas such as airports, smart watches, smart cities, smart health organizations, and smart homes. It has provided mankind with a lot of benefits in different areas and domains. They are also used in disaster management, habitat monitoring in forests, and many applications in hostile areas such as military; therefore, there is a need to enforce security policy. Also, in many instances of data transfer in WSNs, there is a need of stricter authentication process. Presently, authentication in WSNs is commonly achieved through the use of usernames and passwords. A wicked attacker can impersonate to the network as a genuine user by using stolen password(s). This is chiefly due to the lack of proper key management scheme(s) currently available for WSNs. Moreover, because of limited memory and computing power, it is problematic to decide the proper key management system for WSNs. We propose a key management scheme (KMS) for WSNs using an elliptic curve in this paper. The proposed KMS is implemented in Java SE 6(Mustang) and evaluated for critical performance by using an elliptic curve in a WSN of varied cluster head (CH) sizes.

Quadratic Programming for TDMA Scheduling in Wireless Sensor Networks

This paper presents a novel Multihop Aperiodic Scheduling (MAS) algorithm which guarantees energy-efficient data collection by Wireless Sensor Networks (WSNs) under delay constraints. Present Medium Access Control (MAC) protocols in WSNs typically sacrifice packet latency and/or the reliability of packet transfer to achieve energy-efficiency. Thus, the paper is concerned with developing a novel protocol to achieve energy efficient and reliable multihop data transfer in WSNs satisfying given latency requirements. Energy efficiency is achieved by optimizing the scheduling of the underlying Time Division Multiple Access (TDMA) system by minimizing the wake-up number of the nodes. Schedule optimization is transformed into a quadratic programming (QP) task, which is then solved by the Hopfield net in polynomial time. In this way, an energy efficient scheduling can be obtained which meets a given delay requirement in TDMA systems. The performance of the new algorithm has been evaluated by simulations and compared to the performance of well-known scheduling methods, such as SMAC, UxDMA (a slot assignment algorithm for WSN), and traditional tree-based protocols. The simulations have demonstrated that our method reduces global power consumption for time-driven monitoring.