Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. ,  propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path.  demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks.  evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works ll in a wireless sensor network environment. Through simulation,  demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation.
['Qingquan Zhang', 'Gerald E. Sobelman', 'Tian He']
I am writing a research paper on a idea Gradient-driven target acquisition in mobile wireless sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Gradient-driven target acquisition in mobile wireless sensor networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. ,  propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path.  demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks.  evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works ll in a wireless sensor network environment. Through simulation,  demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation."}]