Cognitive network

In communication networks, cognitive network (CN) is a new type of data network that makes use of cutting edge technology from several research areas (i.e. machine learning, knowledge representation, computer network, network management) to solve some problems current networks are faced with. Cognitive network is different from cognitive radio (CR) as it covers all the layers of the OSI model (not only layers 1 and 2 as with CR ). In communication networks, cognitive network (CN) is a new type of data network that makes use of cutting edge technology from several research areas (i.e. machine learning, knowledge representation, computer network, network management) to solve some problems current networks are faced with. Cognitive network is different from cognitive radio (CR) as it covers all the layers of the OSI model (not only layers 1 and 2 as with CR ). The first definition of the cognitive network was provided by Theo Kantor in his doctoral research at KTH, The Royal Institute of Technology, Stockholm, including a presentation in June 1998 of the cognitive network as the network with memory. Theo was a student of Chip Maguire who also was advising Joe Mitola, the originator of cognitive radio. Mitola focused on cognition in the nodes, while Kantor focused on cognition in the network. Mitola's Licentiate thesis, published in August, 1999 includes the following quote 'Over time, the RKRL-empowered network can learn to distinguish a feature of the natural environment that does not match the models. It could declare the errors to a cognitive network.' This is the earliest publication of the concept cognitive network, since Kantor published a bit later. IBM's autonomic networks challenge of 2001 instigated the introduction of a cognition cycle into networks. Cognitive radio, Kantor's cognitive networks, and IBM's autonomic networks provided the foundation for the parallel evolution of cognitive wireless networks and other cognitive networks. In 2004, Petri Mahonen, currently at RWTH, Aachen, and a member of Mitola's doctoral committee organized the first international workshop on cognitive wireless networks at Dagstuhl, Germany. In addition, the EU's E2R and E3 programs developed cognitive network theory under the rubric of self* - self organizing networks, self-aware networks, and so forth. One of the attempts to define the concept of cognitive network was made in 2005 by Thomas et al. and is based on an older idea of the Knowledge Plane described by Clark et al. in 2003 . B.S. Manoj et al. proposed a Cognitive Complete Knowledge Network System in 2008. Since then, several research activities in the area have emerged. A survey and an edited book reveal some of these efforts. The Knowledge Plane is 'a pervasive system within the network that builds and maintains high level models of what the network is supposed to do, in order to provide services and advice to other elements of the network' . The concept of large scale cognitive network was further made in 2008 by Song, where such Knowledge Plan is clearly defined for large scale wireless networks as the knowledge about the availability of radio spectrum and wireless stations. Thomas et al. define the CN as a network with a cognitive process that can perceive current network conditions, plan, decide, act on those conditions, learn from the consequences of its actions, all while following end-to-end goals. This loop, the cognition loop, senses the environment, plans actions according to input from sensors and network policies, decides which scenario fits best its end-to-end purpose using a reasoning engine, and finally acts on the chosen scenario as discussed in the previous section. The system learns from the past (situations, plans, decisions, actions) and uses this knowledge to improve the decisions in the future. This definition of CN does not explicitly mention the knowledge of the network; it only describes the cognitive loop and adds end-to-end goals that would distinguish it from CR or so called cognitive layers. This definition of CN seems to be incomplete since it lacks knowledge which is an important component of a cognitive system as discussed in, and. Balamuralidhar and Prasad gives an interesting view of the role of ontological knowledge representation: “The persistent nature of this ontology enables proactiveness and robustness to ‘ignorable events’ while the unitary nature enables end-to-end adaptations.” In, CN is seen as a communication network augmented by a knowledge plane that can span vertically over layers (making use of cross-layer design) and/or horizontally across technologies and nodes (covering a heterogeneous environment). The knowledge plane needs at least two elements: (1) a representation of relevant knowledge about the scope (device, homogeneous network, heterogeneous network, etc.); (2) a cognition loop which uses artificial intelligence techniques inside its states (learning techniques, decision making techniques, etc.).