Tuning the parameters that control the operation of a wireless sensor network, such as sampling rate, is not a simple task. This is partly due to the distributed nature of the problem, but is also a result of the time-varying dynamics that a network experiences. Inspired by the way in which cells alter their behaviour in response to diffused protein concentrations, an abstract representation, termed a discrete gene regulatory network (dGRN), is introduced.

Tuning the parameters that control the operation of a wireless sensor network, such as sampling rate, is not a simple task. This is partly due to the distributed nature of the problem, but is also a result of the time-varying dynamics that a network experiences

In the following paper, we study the tradeoff between network utility and network lifetime for energy-constrained wireless sensor networks (WSNs).

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding and routing actions of each node in order to maximize the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each node’s energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding and routing actions of each node in order to maximize the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each node’s energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data.