A communications system comprising an access network node and a plurality of resource limited nodes, the access network node comprising: a control unit being operable to estimate and dynamically update a belief on a network condition for a resource limited node of the plurality of resource limited nodes based on network observations and a history profile of the network observations by calculating a probability according to a two-player dynamic Bayesian game in which a first player, i, represents an overall sensing phenomenon and a second player, j, represents the plurality of resource limited nodes in which the posterior belief of player j's belief on player i's type being critical is: P P T i = critical | O A i x, H i x = μ x P T i = critical | H i x × P O A i x | P T i, H i x ∑ P T i ̅ μ x P T i ̅ | H i x × P O A i x | P T i ̅, H i x where H i ( t) is the history profile of player i up to the game stage t, OA i ( t) is the observed action of player i at the game stage t, and PT i is the private type of player i (critical or normal), PT i is the set of all possible private types of player i; determine whether the severity of the updated belief in the network condition is indicative that a change in sleep duty cycle of the resource limited node is required; determine whether a correlation between the resource limited nodes within the plurality of resource limited nodes is indicative that a change in sleep duty cycle of the resource limited node is required; and update the sleep duty cycle of the resource limited node to optimize the overall energy consumed in the resource limited nodes when the severity of the updated belief in the network condition as well as the correlations between resource limited nodes indicates that a change in sleep duty cycle of the resource limited node is required. a control unit being operable to estimate and dynamically update a belief on a network condition for a resource limited node of the plurality of resource limited nodes based on network observations and a history profile of the network observations by calculating a probability according to a two-player dynamic Bayesian game in which a first player, i, represents an overall sensing phenomenon and a second player, j, represents the plurality of resource limited nodes in which the posterior belief of player j's belief on player i's type being critical is: P P T i = critical | O A i x, H i x = μ x P T i = critical | H i x × P O A i x | P T i, H i x ∑ P T i ̅ μ x P T i ̅ | H i x × P O A i x | P T i ̅, H i x where H i ( t) is the history profile of player i up to the game stage t, OA i ( t) is the observed action of player i at the game stage t, and PT i is the private type of player i (critical or normal), PT i is the set of all possible private types of player i; determine whether the severity of the updated belief in the network condition is indicative that a change in sleep duty cycle of the resource limited node is required; determine whether a correlation between the resource limited nodes within the plurality of resource limited nodes is indicative that a change in sleep duty cycle of the resource limited node is required; and update the sleep duty cycle of the resource limited node to optimize the overall energy consumed in the resource limited nodes when the severity of the updated belief in the network condition as well as the correlations between resource limited nodes indicates that a change in sleep duty cycle of the resource limited node is required.