## 2 Literature Review  
A large number of sensor scheduling and coverage maintenance protocols have been proposed [8-35]. However, due to the sensing objectives, these management protocols can be different. [PERSON] _et al_. [1] presented an energy-efficient sensing protocol to achieve the desired sensing coverage. Nodes decide their active periods by exchanging reference points among neighbors. In [2], the authors investigated coverage intensity of the proposed sleep scheduling protocols. [PERSON] _et al_. [3] provided a generic analytical framework that can be widely used for sensing scheduling protocol design with detection quality requirements. [PERSON] _et al_. [4] tried to route packet with the minimum time and energy and aimed to distribute the transmission time slots dynamically among sensor nodes such that the network congestion can be avoided.  
[PERSON] _et al_. [5] proposed a scheduling scheme for considering energy savings in a data collection process. [PERSON] _et al_. [6] applied an ant colony optimization method for scheduling the visiting order of targeted areas in the sensing field such that their energy consumptions are minimized. [PERSON] _et al_. [7] developed a scheduler to manage the competition for resources among different sensing tasks at a single sensor node. [PERSON] _et al_. [8] investigated the relationship between sleeping duration, detection delay and energy consumption in a stationary sensing field. References [9; 10; 11] are clustering-based protocols that attempt to minimize the energy dissipation in sensor networks.  
[PERSON] _et al_. [12] proposed a bio-inspired scheduling scheme which is a kind of adaptive scheduling scheme which uses only local information for making scheduling decisions. [PERSON] _et al_. [13] considered the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. In [14] and [15], randomized scheduling algorithms are proposed for monitoring a field to detect intrusion objects. The authors study the performance of the randomized scheduling algorithm and explore the impact of the size of intrusion object on the sensor network's configuration.  
Since energy efficiency and reasonable sensing coverage can be achieved by exploiting the sensing spatial redundancy, redundant sensors may be turned off to save energy [16; 17; 18; 19]. However, the network connectivity is not considered in those schemes. In order to further reduce energy and computational overhead, some scheduling schemes [2; 16; 19; 20] operate without the location information or time synchronization. Although the joint problem of coverage and connectivity is considered in [21; 22; 23; 24; 25], the optimization of the sensing spatial redundancy is not taken into account. A survey of energy-efficient scheduling mechanisms in sensor networks is detailed in [26].  
In contrast, the approaches of this paper consider coverage, connectivity, and sensing spatial redundancy simultaneously in order to improve energy efficiency in a hierarchical network structure. For the CASA approach, the clusterhead collects local topology information to manage the sensing schedule centrally. By approximating the network behavior throughout the neural network learning process, the clusterhead may be able to roughly predict the performance of the scheduling management. For the DASA approach, the setting of the random waiting timer allows each sensor to exploit the information about coverage, connectivity, and sensing spatial redundancy such that a balance of network resources can be maintained. Due to the randomized property of the waiting timer, the probabilistic model is proposed to abstract global network behavior. The comparison of the proposed approaches and the other cluster-based schemes [10][11] is further discussed in Section 5.