## 1 Introduction  
Recent advances in microelectro-mechanical systems are driving the developments of low-cost and and low-power wireless sensors, with diverse applications in the physical world in areas such as environmental monitoring, disaster recovery, industrial process control, and smart environments. With sensors placed close to an event, wireless sensor networks can observe the phenomenon and receive data. However, having too few active sensors or excessive ones may result in reduced sensing coverage or severe interference, which will have a great influence on network performance features such as energy and bandwidth efficiency, and sensing quality. Therefore, sensing scheduling schemes may be implemented to tackle basic problems of sensor networks (e.g. energy constraints and communication interference) in order to reduce energy consumption and prolong network lifetime.  
Sensor scheduling aims to maintain a balance of network resources. Recent research has found that significant energy savings can be achieved by dynamic power management in sensor networks [1-7]. To achieve this sensing process, sensors are scheduled to execute the sensing task. Hence, reducing the sensing redundancy and maintaining sufficient sensing coverage and network connectivity are critical requirements in sensor networks. In addition, the two issues of energy constraint and communication interference have to be considered together with both the network connectivity and data gathering strategy. In this work, two sensor scheduling protocols, _Centralized Adaptive Scheduling Algorithm_ (CASA) and _Distributed Adaptive Scheduling Algorithm_ (DASA), are proposed to address the application scenario of typical surveillance systems in a cluster-based network topology, where both connectivity and coverage constraint are taken into consideration to achieve performance balance.  
For the CASA scheme, given the local information such as neighboring connectivity, the round determination problem may be solved centrally by the clusterheads. For the DASA scheme, as the clusterhead broadcasts a message to start the scheduling assignment, each sensor initializes a random waiting timer with a value which is related to the cluster topology and the neighbor information. If the random waiting timer expires, then the sensor broadcasts a message proclaiming that it is a good candidate to be a group member, which also serves to notify its neighbors that it has a higher priority for the sensing task. Based on the received messages from its neighboring cluster members, each cluster member may use the data gathering strategy (detailed in Section 3.3) to schedule itself to a specific round.  
In order to facilitate performance evaluation of a sensor scheduling protocol design, two analytical models, a neural network model and a probabilistic model, are proposed. For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry is applied to abstract the properties of the algorithm. Moreover, based on the analysis, the sensor lifetime and cluster lifetime is further explored to show how the operations of the proposed schemes may prolong the network lifetime.  
The organization of this paper is as follows: Section 2 reviews the current literature on the sensor scheduling management. Section 3 describes the system model and algorithm for sensor scheduling in a cluster-based network topology. In Section 4, a neural network model and a probabilistic model are built up to approximate the desire performance and estimate the sensing rounds of the proposed schemes. Section 5 summarizes the performance of the proposed scheduling methodology. Finally, Section 6 draws conclusions and shows future research directions.