**6. Conclusion**

The increasing potential of Autonomous Underwater Vehicle (AUV) swarm operations and the opportunity to use multi-hop networking underwater has led to a growing need to work with a short-range acoustic communication channel. Understanding the channel characteristics for data transmission is essential for the development and evaluation of new MAC and Routing Level protocols that can better utilise the limited resources within this harsh and unpredictable channel.

The constraints imposed on the performance of a communication system when using an acoustic channel are the high latency due to the slow speed of the acoustic signal (compared with RF), and the signal fading properties due to absorption and multipath signals, particularly due to reflections off the surface, sea floor and objects in the signal path. The shorter range acoustic channel has been shown here to be able to take advantage of comparatively lower latency and transmitter power as well as higher received SNR and signal frequencies and bandwidths (albeit still only in kHz range). Each of these factors influence the approach needed for developing appropriate protocol designs and error control techniques while maintaining the required network throughput and autonomous operation of each of the nodes in the swarm.

Significant benefits will be seen when AUVs can operate as an intelligent swarm of collaborating nodes and this will only occur when they are able to communicate quickly and clearly between each other in a underwater short range ad-hoc mobile sensor network.

#### **7. References**

24 Will-be-set-by-IN-TECH

in a different manner. In this case all the link state information is periodically transmitted to all nodes in the network. In case of any change of state of a link, all nodes get notification and modify their routing table. In a swarm network link qualities will be variable which will require regular reconfiguration of routing tables. The performance of routing algorithms is generally determined by a number of factors including the convergence delay. In the case of a swarm network the convergence delay will be a critical factor because of high link delays. For underwater swarm applications, each update within a network will take considerably longer time than a RF network, causing additional packet transmission delays. Hence, it is necessary to develop the network structure in different ways than a conventional sensor network. For example, it may be necessary to develop smaller size clustered networks where cluster heads form a second tier network. Within this topology, local information will flow within the cluster and inter-cluster information will flow through the cluster head network. Cluster based communication architectures are also being used in Zigbee based and wireless personal communication networks (Karl & Willig, 2006). Further research is necessary to develop appropriate routing algorithms to minimise packet transmission delay in swarm networks. Readers can consult the following references to follow some of the recent progress in the area (Aldawibio, 2008; Guangzhong & Zhibin, 2010; LeonGarcia & Widjaja, 2004; Zorzi

Discussion in this section clearly shows that the MAC and routing protocol designs require transmission channel state information in order to optimise their performance. Due to the high propagation delay of an underwater channel, any change of link quality such as SNR will significantly affect the performance of the network. Hence, it is necessary to develop a new class of protocols which can adapt themselves with the varying channel conditions and

The increasing potential of Autonomous Underwater Vehicle (AUV) swarm operations and the opportunity to use multi-hop networking underwater has led to a growing need to work with a short-range acoustic communication channel. Understanding the channel characteristics for data transmission is essential for the development and evaluation of new MAC and Routing Level protocols that can better utilise the limited resources within this

The constraints imposed on the performance of a communication system when using an acoustic channel are the high latency due to the slow speed of the acoustic signal (compared with RF), and the signal fading properties due to absorption and multipath signals, particularly due to reflections off the surface, sea floor and objects in the signal path. The shorter range acoustic channel has been shown here to be able to take advantage of comparatively lower latency and transmitter power as well as higher received SNR and signal frequencies and bandwidths (albeit still only in kHz range). Each of these factors influence the approach needed for developing appropriate protocol designs and error control techniques while maintaining the required network throughput and autonomous operation of each of

Significant benefits will be seen when AUVs can operate as an intelligent swarm of collaborating nodes and this will only occur when they are able to communicate quickly and clearly between each other in a underwater short range ad-hoc mobile sensor network.

offer reasonable high throughput in swarm networks.

et al., 2008).

**6. Conclusion**

harsh and unpredictable channel.

the nodes in the swarm.


Pedro Patrón, Emilio Miguelañez and Yvan R. Petillot

**Presence of Underwater Networks** 

**Embedded Knowledge and Autonomous** 

**Planning: The Path Towards Permanent** 

Oceanographic observatories, year-round energy industry subsea field inspections and continuous homeland security coast patrolling now all require the routine and permanent

These applications require underwater networks of fixed sensors that collaborate with fleets of unmanned underwater vehicles (UUVs). Technological challenges related to the underwater domain, such as power source limitations, communication and perception noise, navigation uncertainties and lack of user delegation, are limiting their current development and establishment. In order to overcome these problems, more evolved embedded tools are needed that can raise the platform's autonomy levels while maintaining the trust of the

Embedded decision making agents that contain reasoning and planning algorithms can optimize the long term management of heterogeneous assets and provide fast dynamic response to events by autonomously coupling global mission requirements and resource capabilities in real time. The problem, however, is that, at present, applications are mono-domain: Mission targets are simply mono-platform, and missions are generally static procedural list of commands described *a-priori* by the operator. All this, leaves the platforms in isolation and limits the potential of multiple coordinated actions between adaptive

In a standard mission flow, operators describe the mission to each specific platform, data is collected during mission and then post-processed off-line. Consequently, the main use for underwater platforms is to gather information from sensor data on missions that are static and incapable to cope with the long term environmental challenges or resource changes. In order for embedded service agents to make decisions and interoperate, it is necessary that they have the capability of dealing with and understanding the highly dynamic and complex environments where these networks are going to operate. These decision making tools are

Shared knowledge representation between embedded service-oriented agents is therefore necessary to provide them with the required common situation awareness. Two sources can provide this type of information: the domain knowledge extracted from the expert (orientation) and the inferred knowledge from the processed sensor data (observation). In both cases, it will be necessary for the information to be stored, accessed and shared efficiently

constrained to the quality and scope of the available information.

**1. Introduction**

operator.

collaborative agents.

presence of underwater sensing tools.

*Ocean Systems Laboratory, Heriot-Watt University*

*United Kingdom*

**9**

