## 2 Related Work  
### Sensing Scheduling Strategies  
The research on energy-saving sensing scheduling strategies involves two levels. The first level is adaptive duty cycle scheduling, concerning active time, nap time and the idle listening period. The goal is to best model the event arrival patterns. The second level, collaborative sensing, considers the sensing coverage: how numerous sensor nodes (not just a single node) with different spatiotemporal coordinates in a public area can cooperate to achieve adequate coverage, which is conducive to global optimization of energy consumption. We know that sensors consume the most energy when they are on duty and the least when they are sleeping. As a result, almost all scheduling strategies, such as ELECTON [6] and additive increase/multiplicative decrease (AIDM) [7], make full use of the energy-saving characteristic of the sleep mode. DANCE [8] improves AIDM [7] by incorporating the behavior of neighboring nodes into consideration scope. In DANCE, the sensor abandons its task if its neighbor has already done it, thus down-scaling the energy inefficiency. The authors argue that the data sampling rate affects the computing and communication load on the central server. Controlling the sampling rate within a specific range (if it exceeds, then the central server and nodes renegotiate a range) through Kalman filtering is believed to be capable of tackling this problem [9]. [PERSON] et al. [10] recommend fine-tuning the sensing frequency of each sensor dynamically. As the sensory readings increase, the detection accuracy improves. The challenge, however, lies in modeling the actual world. Maintaining the greatest possible coverage is a primary goal when designing a sensor network. [PERSON] et al. [11] defined a sensing coverage metric that measures a wireless sensor network's QoS (quality of service). Furthermore, a polynomial-time complexity optimization algorithm using graphing theory and computational geometry is proposed to achieve optimum coverage [12] broadens the work in [11] by enhancing the algorithm. The first work that considers both energy consumption and sensing coverage was completed by [13], which introduces an energy-efficient surveillance system (ESS). This was followed by lightweight deployment-aware scheduling (LDAS) [14; 15], probing environment and adaptive sensing (PEAS) [16], probing environment and collaborating adaptive sleeping (PECAS) [17], and randomized independent scheduling (RIS) [18], and so forth. These studies make the following two common assumptions: (1) each sensor is power-constrained, and (2) the network is expected to run for a long time. Other assumptions include:  
* Network Structure. The network structure can be flat or hierarchical. * Sensor Placement. The sensing coverage is usually affected by how sensors are initially placed. In most cases, the sensors follow a random distribution or two-dimensional (2-D) Poisson distribution [12; 19]. * Sensing Area: The sensing area can be 2-D circular or 3-D spherical. * Time Synchronization. The sensors are synchronized in time, and can be woken up simultaneously for the next scheduling round. * Failure Model. Almost all the studies assume that sensors fail when energy is exhausted. * Sensor Mobility. Most studies have assumed that the sensor is immobile. * Location Information. These studies typically associate location information with whether, or how much, a sensor's sensing area overlaps with its neighbor's. * Distance Information. The distance information can be inferred from the location information. Randomized independent scheduling (RIS) [18] can extend the sensor lifetime and obtain an asymptotic \(k\)-coverage, and, it is simple. RIS does not need to provide location information or distance information, nor does it need adjustable transmission range and mobility. However, it is based on strict distribution assumptions--for example, the sensor is Poisson/uniform/grid distributed, the sensing range follows a uniform distribution and the network is flat and two-dimensional.