## 1 Introduction  
Directional sensor networks (DSNs) consist of low-energy, low-cost, small-size and multi-function directional sensor nodes which cooperate to collect video monitoring data of target area and communicate with a remote base station (BS) [1]. DSNs have wide application prospects in industry control, environment monitoring and city management etc. [2]. Coverage is an important factor which directly reflects the service quality of DSN. In actual application scenarios, random deployment of sensor nodes will lead to many coverage overlapping areas and coverage blind areas. Besides, the directional sensor nodes are generally powered by battery with limited energy, the premature death of nodes will also affect the service quality of network. Therefore, in a random deployed DSN with limited energy, we should not only consider how to reduce coverage overlapping areas and coverage blind areas but also consider how to prolong network lifetime by balancing energy consumption of each node. In a randomly deployed DSN, the directional sensor nodes are rotatable and with limited energy. Considering that the position, sensing radius and sensing angle of sensor nodes are invariable, it is necessary to improve coverage ratio and reduce redundancy ratio by rotating sensing direction. Traditional coverage optimization methods generally consider that coverage ratio and redundancy ratio have a negative correlation. The redundancy ratio will certainly drop with the improvement of coverage ratio. However, the correlation between coverage ratio and redundancy ratio is not obvious in the application scenario where the target area is multiply covered. If the coverage ratio is improved, the redundancy ratio may also improve if the new covered area has redundancy. On the contrary, with decrement of coverage ratio, the redundancy ratio may also get reduced if reduced coverage area has redundancy. To address the problems of multiple area coverage, this paper proposes a sensing direction rotation approach to achieve high coverage ratio and low redundancy ratio by searching for optimal sensing direction group. This problem is essentially a combinatorial optimization problem which is NP-hard [3]. For a directional sensor node, it is generally believed that the energy consumption consists of two main components: the sensing energy consumption for data collection and the communication energy consumption for data transmission. Considering that all the sensor nodes in a monitoring area are opened during the service period, it is not necessary to consider how to control the sensing energy consumption of the nodes by turning the nodes on or off. In order to achieve energy consumption balance of the network, this paper only considers how to control the communication energy consumption. Communication energy consumption mainly comes from data communication between nodes, and it is important to reduce redundant data communication by optimizing the topology of the network and designing a reasonable data communication protocol. Cluster is one of the most effective network layering optimization technologies which is employed to conserve energy of sensor nodes [4]. The structure of a clustered DSN is shown in Figure 1.  
The network is divided into many clusters, and each cluster consists of a cluster head and multiple member nodes. The member node is only responsible for collecting and transmitting data. The cluster head receives monitoring data from member nodes within cluster and send them to a remote BS after fusion in addition to having the same function as the member node. In the process of clustering, cluster head selection plays a vital role in distributing communication energy consumption on each node as it has a direct effect on the energy conservation of member nodes. This paper mainly considers the total residual energy ratio and energy consumption balance degree of the cluster head candidate nodes.