Optimal data collection in wireless sensor networks with correlated energy harvesting

Abstract

We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy-chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate.

Value of Information in Wireless Sensor Network Applications and the IoT: A Review

“…Apart from filtering or prioritization, VoI is used in WSNs as a metric used in tradeoffs to plan the allocation of system resources. Examples of such tradeoffs are cost versus timeliness [81], processing speed versus accuracy [37], cost versus amount of information [9], or QoS requirements versus energy consumption [34], all of which are effectively tradeoffs between system utility and internal or external system costs.…”

“…Age of information (AoI) refers to a category of techniques that emphasize time as a factor for the value of an information item [16], [19], [27], [29], [33], [79]- [81], implying that the value of an information item decreases with time. Most approaches in this category combine a base value V max with a decay function f (t, t 0 ), where an information object generated at time t 0 has value V max which then decays over time.…”