## 2 Related Works  
The coverage problem is regarded as a key task in both WSNs or DSNs that are intended to collect information from an area of interests or to monitor targets in a predetermined region. Numerous studies have presented solutions for the coverage problem in omnidirectional WSNs [24; 25; 26; 27; 28]. However, the algorithms proposed in these WSNs cannot be directly applied to DSNs because of the limited angle of view of directional sensors. Therefore, new solutions specific to DSNs are necessary. The remainder of this section reviews studies conducted on the target coverage problem in DSNs. [PERSON] and [PERSON] [29] presented a model of DSNs in which the orientations of the sensor nodes are static. They analyzed the probability of full coverage (i.e., each target is covered by at least one sensor). [PERSON] and [PERSON] [30] presented a model of a sensor network in which the orientations of the sensors' nodes are adjustable. They defined the maximum-coverage-with-minimum-sensor (MCMS) problem, in which the coverage rate is maximized while minimizing the number of active sensors. They also demonstrated that the MCMS problem is NP-complete and presented two greedy algorithms, namely, the centralized greedy algorithm (CGA) and distributed greedy algorithm (DGA) for MCMS problems. [PERSON] et al. [31] developed a weighted centralized greedy algorithm by modifying the CGA with adjustable weight functions to obtain a higher coverage rate. [PERSON] et al. [4] addressed the multiple directional cover set (MDCS) problem of organizing sensor directions into a group of nondisjoint sets to prolong the network lifetime. Only one cover set is activated at a time, in which all targets are covered. They also demonstrated that the MDCS problem is NP-complete and proposed several algorithms for obtaining solutions to this problem. [PERSON] et al. [32] addressed the maximum set cover for DSN (MSCD) problems, which are NP-complete, and proposed an algorithm to provide energy-efficient cover sets that could cover all targets. The network lifetime was maximized by activating each cover set for various durations on a schedule. [PERSON] and [PERSON] [33] presented a greedy target coverage scheduling algorithm for the MSCD problem and a GA that could identify optimal cover sets that could maximize the network lifetime while monitoring all targets by using the evolutionary global search technique. [PERSON] et al. [34] used a bounded service delay constraint to extend the network lifetimes on the target Q-coverage problem in DSNs. They proposed an algorithm to identify a collection of coverage sets that the bounded service delay constraint and the coverage quality requirement are satisfied; the target in each coverage set was not required to be served continuously but could be served with a tolerable service delay. [PERSON] et al. [35] reduced sensor energy consumption by partitioning the DSN into several cover sets, each of which could cover all targets, and activating these covers successively. They presented an irregular cellular learning automata-based distributed algorithm to identify a near-optimal solution for selecting an appropriate working direction for each sensor. [PERSON] et al. [36] addressed the target coverage problem in a visual sensor network (VSN) that comprised a number of self-configurable visual sensors with adjustable spherical sectors with limited angles. [PERSON] et al. [37] investigated priority-based target coverage with adjustable sensing ranges. They proposed two scheduling algorithms--greedy-based and learning-automata-based algorithms--to organize the sensors into a few cover sets that were successively activated to maximize network lifetime. The simulation revealed that the learning automata-based scheduling algorithm was superior to the greedy-based algorithm in terms of extending network lifetime. [PERSON] et al. [38] addressed heterogeneous coverage in VSNs, in which the coverage requirements of targets vary.