Comparison of Proposed Data Dissemination Protocols for Sensor Networks Using J-Sim

TL;DR

This paper compares various data dissemination protocols for wireless sensor networks through simulations, providing insights into their scalability, energy efficiency, and data delivery performance to aid network design.

Contribution

It offers a comparative analysis of four data dissemination protocols using J-Sim, highlighting their strengths, weaknesses, and potential improvements for sensor network applications.

Findings

CBDDP has significantly lower routing overhead than DDDP and FDDDP.

EAGDDP improves data delivery by 80% and balances energy consumption.

Static configuration of protocol parameters impacts performance and overhead.

Abstract

A distinguishing characteristic of wireless sensor networks is the opportunity to exploit characteristics of the application at lower layers. This paper reports on the results of a simulation comparison of proposed data dissemination protocols using the J-Sim simulator for the WSN protocols: Forwarding Diffusion Data Dissemination(FDDDP), Decentralized Data Dissemination(DDDP), Credit Broadcast Data Dissemination (CBDDP), Energy Aware & Geographical Data Dissemination (EAGDDP) .Our performance provides useful insights for the network designer such as which protocols (and design choices) scale control traffic well, improve data delivery or reduce overall energy consumption,improves routing overhead and maximizes the bandwidth utilization. The static pre configuration of the cell size in DDDP, is one of the reasons why DDDP exhibits larger routing overhead than FDDDP by 74.2% on average. Although CBDDP produces approximately 94.6% smaller overhead than DDDP and 90.7% smaller than FDDDP, because of statically configured amount credit CBDDP delivers on average 7.5 times more of the redundant data packets than DDDP and FDDDP.EAGDDP improves the delivery by 80% on average and makes a balance of energy consumption .We suggest that making these protocols truly self-learning can significantly improve their performance.