## 1 Introduction  
Wireless communication and mobile computing technologies are changing the way in which we perform our everyday activities, and they are already part of our lives. The 'always connected' paradigm is more a reality than an expectation. More and more mobile collaborative applications involving opportunistic or participatory sensing are used by people to determine, e.g., the status of the vehicular traffic, the security level of a certain area, and the location of interesting places while performing tourist activities [1; 2; 3; 4]. In order to do that, mobile users can utilize their smartphones as a sensor able to detect other computing devices or antennas nearby. Thus, a passer-by could determine the presence and location of friends in the area (e.g., detecting their smartphones) or an interesting museum (e.g., detecting the signal spread by an antenna of the museum). These applications are usually network intensive, therefore their energy consumption becomes a major concern for both users and software designers. There are two basic approaches used by these applications to perform the sensing activities: opportunistic and participatory. In opportunistic sensing the user is not particularly aware of the activities being performed by his mobile device (e.g., a smartphone) [5], however in a participatory approach the user is an active participant in the sensing activities [1]. In this paper we consider the use of a Human-centric Wireless Sensor Network (HWSN) [6] as the support of the sensing activities, because these networks support both approaches, and also their combinations. Reducing the energy consumption of the nodes participating in these networks allows the collaborative sensing solutions to increase their availability. This also contributes to improve the trustworthiness of the information provided by them, due a larger number of nodes participate in the sensing process. In a previous work, the authors proposed to use a predictor of the HWSN topology to help reduce the energy consumption of the nodes participating in these networks [7]. Such a proposal was based on Optimized Link State Routing with prediction (OLSRp) [8] and it included adaptations to be used on a HWSN. The evaluation of such a topology prediction strategy showed promising results in terms of energy savings; however it considered only a particular work scenario. Therefore it is not clear which are the limits of that proposal. This paper extends that previous work trying to determine how the node mobility, the network degree and the shape of the application area, affect the amount of energy that can be saved when using this prediction strategy. Several simulations were done to determine the impact of these variables. The obtained results help identify favorable scenarios where the topology control (TC) strategy can be used to support these mobile collaborative applications. These results also allow quantifying the energy savings that each participating device can expect, considering the features of the working area and the node behavior. Designers of mobile collaborative sensing applications can use these results to determine the expected autonomy of the mobile devices, the lifetime of a HWSN, and the trustworthiness of the information provided by these applications. The autonomy and availability of these solutions can be improved using the proposed energy saving strategy. As shown by [PERSON] _et al_. addressing these non-functional requirements is critical in this type of systems [9, 10]. The use of the proposed strategy is not exclusive, therefore it could be used together with other similar proposals to increase even more the energy savings. The following section briefly describes the main components of a human-centric wireless sensor network and its communication dynamics. Section 3 presents the related work. Section 4 describes the message prediction process that allows HWSN to reduce the energy consumption.