# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 2 Related Work  
Pervasive applications that monitor loosely-coupled mobile work should consider the dynamism of the interaction context, in which smartphone users can be while performing a collaborative activity. Therefore, pervasive data sensing solutions should provide services for implicit and explicit data sharing, and these services should work in both infrastructure-based and ad hoc networks. Participatory sensing [2; 11] usually involves the voluntary cooperation between smartphone users in order to collect, analyse and share information about their local context. These processes usually require explicit participation of the user, who is actively involved in the data collection process. An example of this voluntary information sharing is when an individual uses his smartphone to take a picture, provide descriptions of his particular context (e.g., in a meeting, cycling, etc.) or tags his current location (e.g., my favourite cafeteria, my brother's place, etc.). Similarly, mobile crowdsensing [10; 13; 14] also requires user intervention to provide sensor information. However, it reuses user-entered data from Internet services and social networking sites. These capabilities have allowed people to become active participants of these processes and get a benefit for that. Some services based on these sensing paradigms allow people, for example to identify opportunities for hitchhiking [15] or to evaluate their personal security [16]. Typically, these sensing approaches use infrastructure-based communication that allows mobile sensors to access centralized data repositories, which are in charge of supporting the data sharing process. These approaches are not particularly designed to perform opportunistic sensing, where heterogeneous mobile devices (i.e., devices with distinct sensing capabilities) collaborate to provide each other with contextual data that each device alone could not otherwise sense. For this reason, a different sensing approach is also necessary. Opportunistic sensing [2; 6], provides a method to capture contextual information automatically from sensors available in the smartphone. In this case, the user is not directly involved in the data collection process and the information is sensed unobtrusively. Although there are various applications that provide specific solutions for addressing particular problems, most of them are not easy to use in other contexts, and also require specialized development expertise. For this reason, the development of these types of applications requires to address various challenges related to the limitations of the communication networks and types of interactions in certain contexts, such as unstable communication links, devices heterogeneity, energy constraints, user mobility patterns, etc. There are also platforms that consider unstable communication scenarios and provide interaction autonomy to the devices participating in a sensing activity, for instance, by using mobile ad hoc networks (MANETs). Nevertheless, they do not take advantage of long-range communication infrastructures when they are available (e.g., Internet connections, cellular networks, etc.) [8; 17], which limits the system capability to interact with remote components when they are available. Given the communication in MANETs is usually unstable, there are some proposals (e.g., to support mobile sensing task scheduling [9; 18]) that are focused on reducing the data missing rate of mobile nodes, and thereby increasing the gathering rate of the required sensing data. Some other platforms, like Remora [19] and C-SPINE [20] are designed to exploit device proximity through body sensor networks (BSN). Neighbouring BSNs can opportunistically perform freeriding by using the overheard data from all in-range sensors. The contrary approach is adopted by middleware, like MobIoT [21], which support mobile collaborative sensing at a large scale. However, most these middleware have scalability limitations that are addressed by controlling the participation of redundant sensing devices.