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Data collected and transmitted by Internet of things (IoT) devices are typically used for control and monitoring purposes; and hence, their timely delivery is of utmost importance for the underlying applications. However, IoT devices operate with very limited energy sources, severely reducing their ability for timely collection and processing of status updates. IoT systems make up for these limitations by employing multiple low-power low-complexity devices that can monitor the same signal, possibly with different quality observations and different energy costs, to create diversity against the limitations of individual nodes. We investigate policies to minimize the average age of information (AoI) in a monitoring system that collects data from two sources of information denoted as primary and backup sources, respectively. We assume that each source offers a different trade-off between the AoI and the energy cost. The monitoring node is equipped with a finite size battery and harvests ambient energy. For this setup, we formulate the scheduling of status updates from the two sources as a Markov decision process (MDP), and obtain a policy that decides on the optimal action to take (i.e., which source to query or remain idle) depending on the current energy level and AoI. The performance of the obtained policy is compared with an aggressive policy for different system parameters. We identify few types of optimal solution structures and discuss the benefits of having a backup source of information in the system.

The definition of “energy topologies” based on energetic cooperation (exploitation and exchange) between interconnected objects is an important feature that can be implemented in Smart Cities. Based on the presence of energy harvesting devices, it is aimed at providing system-wide sustainability by allowing exchange of stored and supplied energy in a similar fashion to communication of data. In this paper, we investigate the possibility of integrating energy cooperation within the design of the energy topology, or, in other words, by establishing energy links between objects, in particular wireless smart nodes powered by harvesting renewable energy sources. To do so, we construct an optimization model, where it is guaranteed that wireless nodes during operation will not be depleted and the optimal energy transfer does not exceed the energy demands of other communication nodes. We analyze how the system conditions can affect the energy topology, in particular, energy harvesting capabilities, energy levels, and energy thresholds. We also identify some theoretical limits for the system to guarantee complete sustainability, that is, nodes do not go out of charge. Also we demonstrated the effectiveness of the model comparing it with the system operation without applied optimization.

Energy harvesting is an important feature that can be implemented in mobile devices to provide them with extended autonomy, yet it poses several challenges in terms of optimal battery usage. In this paper, we highlight that some non-ideal effects, especially the so-called "charge recovery," can have a dramatic impact on the operation policy of autonomous devices. To do so, we construct a Markov model, where we introduce a bidimensional battery value, including the apparent energy level, which is what available at the electrodes to power the device, and the actual energy level stored in the battery. When power is not drained by the device, these levels tend to equalize due to charge recovery, but, in the case of intense battery usage, they can be significantly different. We show that this non- ideality leads to considerably different estimates of undesirable events such as battery outages, and may cause a general underutilization of the devices if not properly accounted for.