Key Contributions

We propose to tackle the above issues by :-

a) Model Predictive Control - We propose the use of MPC as a feedback mechanism to allow sensors to react not only based on past observations but also on possible future events. To correlate MPC to a real-life example of chess; we play our next move not only based on past opponent moves but also on his/her anticipated future moves.

b) Coopetitive Interaction approach - We propose the use of Coopetitive Interaction approach between sensors such that they ‘compete’ as well as ‘cooperate’ with each other to perform the designated task in the best possible manner. The key is to obtain optimization at a global level rather than at each individual instance. To correlate to a real-life example, partners in a card game of Bridge often outbid each other. Hence though they are competing in local instances they are still cooperating at a global level to obtain best results for their team.