## 2 Related Work
[1] proposed the monitoring query management method (MQM) and the QR-tree method (QRT), respectively, which aim to reduce the communication cost and the server workload by leveraging heterogeneous computational capabilities of moving objects through the concept of resident domain. Recently, the safe region techniques for moving range queries over stationary objects have also been proposed in [3, 32]. Similarly to the safe region assigned to a moving object, the safe region assigned to a query \(q\) is the region that (i) contains \(q\)'s location (_i.e._, the center point of \(q\)'s spatial query range \(q.R\)) and (ii) guarantees that while \(q\)'s location remains inside it, the result of \(q\) remain unchanged. Focusing on the evaluation of continuous moving queries over moving objects, [PERSON] _et al_. [15] proposed the _Scalable INcremental hash based Algorithm_ (_SINA_) to achieve the system scalability based on the notions of _shared execution_ and _incremental evaluation_. [PERSON] _et al_. [5] presented the _MobiEyes_, where moving objects play an active role in the query evaluation task similar to those in MQM and QRT. In SINA, moving objects report their location-updates periodically, while in Mobieyes, moving objects rely on _location estimation_ to reduce the number of sending location-updates as well as moving query issuers (_i.e._, moving clients). [PERSON] _et al_. [14] employed two kinds of communication methods for moving query evaluation: _on-demand access_ and _periodic broadcasting_ to reduce communication costs and energy wastes of handheld devices carried by moving objects and query issuers. Recently, assuming moving objects periodically send their location-updates, [PERSON] _et al_. [16] have introduced the _broadcast grid index_ (_BGI_), which employs the periodic broadcasting for communications between the server and query issuers to evaluate moving queries. All the methods reviewed above cannot adequately deal with the CM range monitoring queries. Although some existing researches have addressed the spatial queries that involve non-spatial specifications, their methods are restricted to snapshot queries over stationary objects [10, 11, 20]. We note that the problem that is slightly related to the evaluation of CM range monitoring queries is the evaluation of _spatial keyword queries_[33, 34, 35, 36, 37]. [PERSON] _et al_. [33] studied the problem of evaluating _boolean range queries_ over stationary geo-textual objects, where keywords are used as boolean predicates to filter out the objects, which do not contain the query keywords, among all the objects that are inside the given spatial query ranges. On the other hand, [PERSON] _et al_. [34] studied the problem of evaluating _top-k queries_ over stationary geo-textual objects, where spatial proximity and textual relevance (_i.e._, textual similarity between the textual descriptions of the objects and query keywords) are combined by a linear function to rank the objects. Several variants of spatial keyword queries have also been studied such as _m-closest keywords queries_[35, 36] and _region based spatio-textual queries_[37]. However, the methods for spatial keyword query evaluation are also restricted to snapshot queries over stationary objects.