Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses detecting in-band wormholes in wireless ad hoc networks. The detection scheme requires collecting the end-to-end delay of packets at the receiver and then applying a sequential change point detection algorithm to detect abrupt changes in the delay time series. A new change point detection algorithm, named SW-CLT, is proposed. The algorithm is based on the Central Limit Theorem (CLT) and does not involve using a preset detecting threshold. The algorithm is compared with the non-parametric cumulative sum (NP-CUSUM) because the non-parametric version is believed to be more robust to highly dynamic data than the parametric version. SW-CLT has the ability to adjust its detection threshold with the variance of the data, and therefore is more robust than NP-CUSUM, which uses a preset threshold. Simulation results from ns3 verified the advantage of SW-CLT over NP-CUSUM in all simulated scenarios.
['Maggie Xiaoyan Cheng', 'Yi Ling', 'Wei Biao Wu']
I am writing a research paper on a idea In-band wormhole detection in wireless ad hoc networks using change point detection method
[{"from": "human", "value": "I am writing a research paper on a idea In-band wormhole detection in wireless ad hoc networks using change point detection method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses detecting in-band wormholes in wireless ad hoc networks. The detection scheme requires collecting the end-to-end delay of packets at the receiver and then applying a sequential change point detection algorithm to detect abrupt changes in the delay time series. A new change point detection algorithm, named SW-CLT, is proposed. The algorithm is based on the Central Limit Theorem (CLT) and does not involve using a preset detecting threshold. The algorithm is compared with the non-parametric cumulative sum (NP-CUSUM) because the non-parametric version is believed to be more robust to highly dynamic data than the parametric version. SW-CLT has the ability to adjust its detection threshold with the variance of the data, and therefore is more robust than NP-CUSUM, which uses a preset threshold. Simulation results from ns3 verified the advantage of SW-CLT over NP-CUSUM in all simulated scenarios."}]