Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , the coverage optimization problem for mobile sensing networks is examined from a team-based perspective. The objective is to arrange robots in a given environment to minimize the serving cost based on a given density function, which defines the probability of events in the environment. A team concept is introduced here to allow adjusting to the changes in the environment or the assigned task(s) in a collaborative way as a team. Firstly, an optimization problem is solved in the team level in order to assign a sub-region to each team. The region assigned to each team is then divided among its members by minimizing a locational cost. A distributed communication algorithm is also given, in which each team member, e.g., robot can access the information of its neighbors in order to compute the associated region. Finally, numerical simulation results are given to demonstrate the effectiveness of the presented approach. The results indicate that agents in both teams- and agents-level reach the optimal configuration considering the given density function.
['Farshid Abbasiy', 'Afshin Mesbahiy', 'Javad Mohammadpoury']
I am writing a research paper on a idea Team-based coverage control of moving sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Team-based coverage control of moving 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: , the coverage optimization problem for mobile sensing networks is examined from a team-based perspective. The objective is to arrange robots in a given environment to minimize the serving cost based on a given density function, which defines the probability of events in the environment. A team concept is introduced here to allow adjusting to the changes in the environment or the assigned task(s) in a collaborative way as a team. Firstly, an optimization problem is solved in the team level in order to assign a sub-region to each team. The region assigned to each team is then divided among its members by minimizing a locational cost. A distributed communication algorithm is also given, in which each team member, e.g., robot can access the information of its neighbors in order to compute the associated region. Finally, numerical simulation results are given to demonstrate the effectiveness of the presented approach. The results indicate that agents in both teams- and agents-level reach the optimal configuration considering the given density function."}]