An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> The growing interests in underwater wireless sensor networks(UWSNs) for a wide variety of purposes from Tsunami monitoring to commercial oilfield exploration projects have been encouraging. This paper deals with the problem of accurately tracking a single target moving through UWSNs employing acoustic sensors. This paper addresses the issues of estimating the target position, improving energy efficiency by applying a Kalman filter in a distributed architecture. Each underwater wireless sensor nodes composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides an algorithm which increases energy efficiency of each sensor through a wake-up/sleep(WuS) and a valid measurement selecting(VMS) scheme. Simulation results illustrate the performance of the tracking filter according to the sensor node displacement and sensor detecting area. <s> BIB001 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> Target tracking is one of the major applications of Wireless Sensor Networks (WSN). In traditional target tracking with directional sensors, the target can be detected only when it is in the field of view (FoV) of a sensor. The target's track is obtained with the help of filtering algorithms/techniques at the base station (BS). These techniques sometimes lead to incorrect location estimation of the target due to measurement errors and noise. It is ideally required that the main sensing lobe/FoV of the sensor follow the target, whenever the target is in the FoV of the sensor. Based on the above considerations, this paper proposes an efficient solution to target tracking with the help of beam steering enabled directional sensors mounted on the nodes for tracking the targets. At every step of beam steering, the sensor node updates the location of the target to the nearest cluster head or base station. Each sensor will continuously monitor the target till it crosses the FoV or sensing range of the sensor. The proposed method has been implemented in OMNeT++ and the performance of the algorithm has been evaluated with directional active and passive sensors. Different target mobilities and different sensor FoV angles have been considered. The results show that the proposed method successfully tracked the target and located the target at every instant of time. Directional active sensors yield more accurate results when compared to passive sensors. It was also seen that the location accuracy of the target was mainly dependent on the FoV angle and FoV/sensing radius of the sensor. <s> BIB002 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> In this paper, underwater sensor network with static sensor nodes is applied in environmental monitoring. The scope of underwater sensor applications can be enhanced by combining underwater sensor network and cloud computing. The underwater sensor cloud management is done by proposing a middleware based on metamodeling. The underwater sensor cloud architecture provides a way for underwater sensor motes to collect, store and retrieve environmental data. The proposed Hadoop framework serves as a middleware for underwater sensor cloud. The middleware that is been considered to use in our research is an intelligent context-aware middleware software that will glue together the network hardware, operating systems, network stacks and applications. It contain a runtime environment that supports and coordinates multiple applications based on multi-agents, and standardized system services such as data aggregation, control and management policies. Underwater Sensor cloud framework enables to ; Extract knowledge from the data it collects and use the information to intelligently react and adapt to its surroundings. It links a remote end user's cognizance with the observed environment. ; Sensor Web is an amorphous network, creating an embedded, distributed monitoring presence that provides a dynamic infrastructure for sensors. ; Reduce the Labor effort and have continuous monitor of the environment ; Provide more intensive and accurate data on the current status of the environment and reduces the reaction time towards the environmental changes ; Since there is no specific routing of information, all pods share everything with one another. This global data sharing allows each pod to be aware of situations beyond its specific location ; Usage of historical data to predict future actions and foresee the disaster to be caused quickly and more precisely ; Framework can support extensibility, easy maintainability and control over the sensors deployed. <s> BIB003 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> Recent research in communications and computer science has been considered to advance the performances of monitoring water environments. However, constrains produced by the water environments, caused by the specific channel propagation and harsh operating conditions must be taken into account. The purpose of this paper is to define and describe a monitoring system for the water environments, based on a previous study regarding both the underwater, but also technologies that are appropriate for such surroundings. The system is based on an underwater sensors network which is connected to a cloud platform by means of a reconfigurable wireless transceiver. The sensor network integrates several low cost sensors that can measure different parameters such as water level, the water flow, temperature, pressure etc. The measured parameters will be transmitted through an operational communication node, which should be able to ensure a reliable communication with timing and variation delay constraints. Finally, the paper describes the platform interface available to end users, providing real time visualization of the water environment events. <s> BIB004 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> Softwarization approaches in networks, storages, M2M, services, and smart things aim to optimize costs and processes and bring new infrastructure definitions and functional values. A recent integration of wireless and mobile cyber physical systems with the dramatically growing smart sensors enables a new type of pervasive smart and mobile urban surveillance infrastructures, which opens up new opportunities for boosting the accuracy, efficiency, and productivity of uninterrupted target tracking and situation awareness. In this paper, we present a design and prototype of a mobility-inspired efficient and effective fog system using software-defined control over a mobile and wireless environment (MIST). Fog Computing, a recently proposed extension and complement for cloud computing, enables computing at the network edge in a smart device without outsourcing jobs to a remote cloud. We investigated an effective softwarization approach in the Fog environment for dynamic big data driven, real-time urban surveillance tasks of uninterrupted target tracking. We address key technical challenges of node mobility to improve the system awareness. We have built a preliminary proof-of-concept MIST architecture on both Android and Linux based smart devices and tested various collaboration scenarios among the mobile sensors. <s> BIB005 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> E. CLOUD AND FOG COMPUTING <s> we propose a method of Cloud-Ocean Computing to push the intelligent processing of the data to the front end. Ocean Computing and Cloud Computing work together, Ocean Computing is an important support for Cloud Computing. For Ocean Computing, we put forward two ways. The first way is to take a compressed sensing method which extracts valuable information from the original data intelligently. The second way is to fuse multi-sensor data and make intelligent identification and prediction. We did the experiment on the first way, and the experiment is shown to exhibit significantly enhanced performance with respect to reducing energy consumption and shortening transmission time. <s> BIB006
Cloud computing for underwater networks seems to be the best candidate to distribute and visualize the complex data collected from the oceans through the gateways (surface buoys or AUVs). An intelligent context-aware middleware BIB003 is proposed to combine underwater sensor network and cloud computing that supports different underwater applications through multi-agents. Another method BIB004 connects underwater sensor network to the cloud by means of a wireless transceiver to elaborate and analyze the data. An integration of ocean and cloud computing is proposed in BIB006 to sense, identify, and predict the multisensor data. All these research works transfer data to the cloud by means of gateways. Likewise, terrestrial communications, plethora of underwater devices increase the amount of data to be transferred to the cloud, which might cause congestion and bottleneck at the gateways. Therefore, fog computing and edge computing notions can be applied in underwater networks to overcome the issues of cloud computing. We will discuss it further in our proposed scheme below. Taking advantages form all the novel notions such as SDN, NFV, fog, and cloud computing, we propose novel target detection and tracking scheme. The scheme considers underwater sensor nodes as the fundamental units that sense the presence of the target and report the sensing results to any of the AUV moving around the cluster of the sensor nodes. The AUV collects the sensing results, apply any information gathering algorithm such as decision-based methods or neural networks, and forward the collaborative output to the nextlevel AUV as shown in Fig. 1 to reach the local controller (any surface buoy). Different surface buoys communicate with the SDN controller on land that is inside the fog cloud. The fog cloud serves as the local cloud to provide flexibility among different services and to preserve network latency. Each fog cloud forwards the collected data to the server cloud. This is nextgeneration hybrid underwater communications where both acoustic and radio waves serve as a medium of communications. The NFV hypervisor inside each fog cloud is responsible for launching different underwater application services such as surveillance, monitoring, safety, oceanographic, and oil inspection. SDN controller is responsible for monitoring these application services. The integration of these different notions in nextgeneration underwater target detection and tracking systems allow any node of interest for any other service to collect information from any fog cloud and take full advantage of the whole network. This scheme allows detecting either a single or multi, mobile or fixed target, by collecting the sensing information at nearby AUVs which forwards the collected data to the local/server cloud for stronger processing. The cloud then estimates the position of the target and predicts its trajectory more accurately. The scheme does not involve a single entity; it is a hybrid communication of several underwater devices such as sensors, AUVs, surface buoys, and base stations on land, each performing the tasks in a collaborative manner. Therefore, we can say that, this is an alternative solution to detect and track the mobile target more accurately and precisely. The problem of energy consumption for sensor nodes can be overwhelmed by utilizing the waking-up sleep mechanism BIB001 , BIB002 where only those sensor nodes near the area where the target is moving, or static keep themselves activated. The other energy-efficient target detection and tracking algorithms described in Section II can be used to solve this issue. Also, SDN controller along with virtualization hypervisor, i.e. fog cloud is responsible for adjusting the energy level of each underwater sensor node BIB005 .