The United Arab Emirates (UAE) is the eighth largest oil producing country in the world. It has about 800 km of coastline. Beyond the coastline, its territorial water and exclusive economic zone have very rich and extensive marine life and natural resources. Most of the oil and natural gas in the UAE is produced from the offshore oil fields. Maritime oil exploration and transportation has increased more steeply due to the expansion of the world crude oil and natural gas production, and the trend of using larger-shaped and higher-speed container vessels. The probability of oil rigs pollution, burning and explosion continues to rise. All these factors stimulate a greater danger for vessels, oil operation safety and maritime environment. Therefore, maritime security and environmental protection are of great interest, both from the academia and petroleum industry point of view. The continuous surveillance of the offshore oil fields is essential to secure the production flow, avoid trespassing and prevent vandalism from intruders and pirates. With the emergence of the new technologies such as maritime wireless mesh network (MWMN) and camera sensor network (CSN), maritime surveillance systems have been gradually improving due to the accuracy, reliability and efficiency of the maritime data acquisition systems. However, in order to realize the oil operation security, it is necessary to control and implement a dynamic system to monitor the maritime environment. The monitoring objects include vessels, fishery boats, pollution, navigational and sailing conditions. By the same token, the legacy monitoring systems such as very-high frequency (VHF) communication, marine navigational radar, vessel traffic service (VTS) and automatic identification system (AIS) are still insufficient to satisfy the increasing demand of the maritime surveillance. The objective of the paper is to provide full-view coverage CSN for a triangular grid based deployment and to reduce the total transmission power of the image to cope with the limited available power in the CSN. The rough and random movements of the sea surface can lead to a time-varying uncovered area by displacing the CSN from its initial location. Thus, it is important to investigate and analyze the effects of the sea waves on the CSN to provide full-view coverage in such complex environments. The main challenges in the deployment of the CSN are the dynamic maritime environment and the time-varying full-view coverage provided by the sea waves. Therefore, quasi-mobile platforms such as buoys are envisaged to hold the camera sensor nodes. The buoys will be nailed at the sea floor to limit the movement of the buoys due to the sea waves. In addition, cooperative transmission method has been proposed to reduce the total transmission power of the image in the CSN. A CSN is formed by autonomous, self-organized ad-hoc camera sensor nodes that are equipped with wireless communication devices, processing unit and power supply. The design, implementation and deployment of a CSN for maritime surveillance stimulate new challenges different to that which exist on the land, as the maritime environment hinders the development of such a network.

The main differences are summarized as follows:

• Dynamic aspect of the maritime environment which requires rigorous levels of device protection.

• Deployment characteristic of a CSN in maritime environment which is highly affected by wind direction and speed, sea wave, and tide.

• Requirement of flotation and anchoring platforms for a CSN and the possible vandalism from intruders and pirates.

• Coverage problem of a CSN due to the random and rough sea movement.

• High energy consumption if battery-power based cameras are used continuously.

• Communication signals are highly attenuated by the constant sea movement.

In this context, CSNs with ubiquitous and substantive camera sensor nodes can be utilized to monitor offshore oil fields to secure the production flow, avoid trespassing and vandalism from intruders and pirates. However, camera sensor nodes can generate various views of the same target if they are captured at different viewpoints if the image is taken near or at a frontal viewpoint, then, the target will be more likely to be recognized by the recognition system. It is fundamental to understand how the coverage of a given camera depends on different network parameters to better design numerous application scenarios. The coverage of a particular CSN represents the quality of surveillance provided by the CSN. As the angle between the target's facing direction and the camera's viewing direction increases, the detection rate drops severely. Consequently, the camera's viewing direction has a considerable effect on the quality of surveillance in a CSN. Recently, a novel concept, which is called full-view coverage, has been introduced to characterize the intrinsic property of camera sensor nodes and assess the coverage in CSNs. A target is full-view covered if its facing direction is always within the scope of a camera, regardless of the target's actual facing direction. Simply, the underlying contribution of full-view coverage tackles the pledge of capturing the target's face image. Consequently, designing a CSN with full-view coverage is of major importance, as the network does not only provide the detection of a target, but also the recognition of it. In many network configurations, camera sensor nodes are not mobile and they remain stationary after the initial deployment. In a stationary CSN, when the deployment characteristic and sensing model for the CSN are defined, the coverage can be deduced and remain unchanged over time. In order to address the hostile maritime environment, there has been a strong desire to deploy sensors mounted on quasi-mobile platforms such as buoys. Such quasi-mobile CSNs are extremely beneficial for offshore oil field surveillance where buoys move with the sea waves. Hence, the coverage of a quasi-mobile CSN depends not only on the initial network deployment, but also on the mobility pattern of the CSN. Nevertheless, the full-view coverage under a quasi-mobile CSN in maritime network has not been investigated. This problem is pivotal for the network design parameters and application scenarios of CSNs where conventional deployment characteristics such as air-drop fail or is not appropriate in a maritime environment. Since a priori knowledge of the terrain is available, a grid-based deployment can be utilized for the given terrain. The endeavor to design a practical mobility pattern for CSNs gives rise to model the cable attached to the buoy as a spring. In this practical mobility pattern, buoys start from an initial coordinate assignment, then oscillate based on spring force, sea wave, wind direction and speed and ultimately converge to a consistent solution. Specifically, this mobility pattern is based on two stages. The first stage is the effects of sea wave, wind direction and speed that move a buoy. The second stage is the spring reaction, based on the previous effects. Then, the design concept is followed and extended to develop a mobility pattern for CSNs in maritime environment. A CSN is considered that constitutes of a small number of buoys whose locations are initially known and consecutively their locations will be derived based on spring relaxation technique. With this technique, coverage issues arisen in a CSN and design a cooperative transmission method to reduce the total transmission power in the CSN are studied. One primary problem is how to design a realistic sea wave model for a given deployed CSN to achieve full-view coverage. Compared with the traditional sea wave model which assumes sine wave model for simplicity analysis, there are two elements that increase the complexity of the problem in a realistic sea wave model. First, the force that acts on the surface of the sea, which is supposed to be the main driving force for the creation of waves in deep water. Second, the force from the sea surface and ground interaction, which is the main contributing force near shoreline, however, this type of force becomes dominant in deep water when seaquakes occur. The realistic sea wave model should encounter the extruding of a two dimensional sine wave model into a three dimensional sea wave model. However, there should be some order of variation along the wave propagation direction for the finite wide waves. In conventional wireless sensor networks (WSNs), scalar phenomena can be traced using thermal or acoustic sensor nodes. In camera sensor networks (CSNs), images and videos can significantly enrich the retrieved information from the monitored environment, and hence provide more practicality and efficiency to WSNs. Recently; there has been enormous development of applications in surveillance, environment monitoring and biomedicine for CSNs that has brought a new spectrum to the coverage problem. It is indispensable to understand how the coverage of a camera depends on various network parameters to better design numerous application scenarios. In many network configurations, cameras are not mobile and they remain stationary after the initial deployment. However, different from a stationary CSN, maritime environment poses challenges on the deployment characteristic and mobility pattern for CSNs. In stationary CSNs, when the deployment characteristic and sensing model are defined, the coverage can be deduced and remain unchanged over time. In the maritime environment, camera sensors are mounted on quasi-mobile platforms such as buoys. This paper aims to provide full-view coverage CSN for maritime surveillance using cameras mounted on buoys. It is important to provide full-view coverage because in full-view coverage, targets facing direction is taken into account to judge whether a target is guaranteed to be captured. Image shot at the frontal viewpoint of a given target considerably increases the possibility to detect and recognize the target. The full-view coverage has been achieved using equilateral triangle grid-based deployment for the CSN. To accurately emulate the maritime environment, a mobility pattern has been developed for the buoy which is attached with a cable that is nailed at the sea floor. The buoy movement follows the sea wave that is created by the wind and it is limited by the cable. The average percentage of full-view coverage has been evaluated based on different parameters such as equilateral triangle grid length, sensing radius of camera, wind speed and wave height. Furthermore, a method to improve the target detection and recognition has been proposed in the presence of poor link quality using cooperative transmission with low power consumption. In some parameter scenario, the cooperative transmission method has achieved around 70% improvement in the average percentage of full-view coverage of a given target and total reduction of around 13% for the total transmission power PTotal(Q).


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