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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 [1] ).
The first definition of the cognitive network was provided by Theo Kanter 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 Kanter focused on cognition in the network. Mitola's Licentiate thesis, published in August, 1999 includes the following quote "Over time, the [Radio Knowledge Representation Language] 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 Kanter published a bit later.
IBM's autonomic networks challenge of 2001 instigated the introduction of a cognition cycle into networks. Cognitive radio, Kanter'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. [2] and is based on an older idea of the Knowledge Plane described by Clark et al. in 2003 . [3] B.S. Manoj et al. proposed a Cognitive Complete Knowledge Network System in 2008. [4] Since then, several research activities in the area have emerged. A survey [5] and an edited book [6] 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" . [3]
The concept of large scale cognitive network was further made in 2008 by Song, [7] 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. [2] 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, [5] [6] [7] [8] and. [9]
Balamuralidhar and Prasad [8] 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, [5] 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.).
Furthermore, in [7] and, [9] a detailed cross-layer network architecture was proposed for CNs, where CN is interpreted as a network that can utilize both radio spectrum and wireless station resources opportunistically, based upon the knowledge of such resource availability. Since CR has been developed as a radio transceiver that can utilize spectrum channels opportunistically (dynamic spectrum access), the CN is therefore a network that can opportunistically organize CRs.
The cross-layer network architecture of CN in [9] is also named as Embedded Wireless Interconnection (EWI) as opposed to Open System Interconnection (OSI) protocol stack. The CN architecture is based on a new definition of wireless linkage. The new abstract wireless links are redefined as arbitrary mutual co-operations among a set of neighboring (proximity) wireless nodes. In comparison, traditional wireless networking relies on point-to-point "virtual wired-links" with a predetermined pair of wireless nodes and allotted spectrum.
This network architecture also has the following three primary principles:
Wireless link modules provide system designers with reusable open network abstractions, where the modules can be individually updated, or new modules may be added into the wireless link layer. High modularity and flexibility could be essential for middleware or application developments.
EWI is also an organizing-style architecture, where the system layer organizes the wireless link modules (at the wireless link layer); and peer wireless link modules can exchange module management information by padding packet headers to the system-layer information units.
Five types of wireless link modules were proposed, including broadcast, peer-to-peer unicast, multicast, to-sink unicast, and data aggregation, respectively. Other arbitrary types of modules may be added, establishing other types of abstract wireless links without limitation. For example, the broadcast module simply disseminates data packets to surrounding nodes. The peer-to-peer unicast module can deliver data packets from source to destination over multiple wireless hops. The multicast module sends data packets to multiple destinations, as compared to peer-to-peer unicast. The to-sink unicast module can be especially useful in wireless sensor networks, which utilizes higher capabilities of data collectors (or sinks), so as to achieve better data delivery. The data-aggregation module opportunistically collects and aggregates the context related data from a set of proximity wireless nodes.
Two service access points (SAP) are defined on the interface between the system layer and the wireless link layer, which are WL_SAP (Wireless Link SAP) and WLME_SAP (Wireless Link Management Entity SAP), respectively. WL_SAP is used for the data plane, whereas WLME_SAP is used for the management plane. The SAPs are utilized by the system layer in controlling the QoS of wireless link modules.
In computer networking, multicast is a type of group communication where data transmission is addressed to a group of destination computers simultaneously. Multicast can be one-to-many or many-to-many distribution. Multicast differs from physical layer point-to-multipoint communication.
The Open Systems Interconnection (OSI) model is a reference model from the International Organization for Standardization (ISO) that "provides a common basis for the coordination of standards development for the purpose of systems interconnection." In the OSI reference model, the communications between systems are split into seven different abstraction layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application.
A wireless network is a computer network that uses wireless data connections between network nodes. Wireless networking allows homes, telecommunications networks and business installations to avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. Admin telecommunications networks are generally implemented and administered using radio communication. This implementation takes place at the physical level (layer) of the OSI model network structure.
Software-defined radio (SDR) is a radio communication system where components that conventionally have been implemented in analog hardware are instead implemented by means of software on a computer or embedded system. While the concept of SDR is not new, the rapidly evolving capabilities of digital electronics render practical many processes which were once only theoretically possible.
A communication channel refers either to a physical transmission medium such as a wire, or to a logical connection over a multiplexed medium such as a radio channel in telecommunications and computer networking. A channel is used for information transfer of, for example, a digital bit stream, from one or several senders to one or several receivers. A channel has a certain capacity for transmitting information, often measured by its bandwidth in Hz or its data rate in bits per second.
Zigbee is an IEEE 802.15.4-based specification for a suite of high-level communication protocols used to create personal area networks with small, low-power digital radios, such as for home automation, medical device data collection, and other low-power low-bandwidth needs, designed for small scale projects which need wireless connection. Hence, Zigbee is a low-power, low-data-rate, and close proximity wireless ad hoc network.
In telecommunications, a point-to-point connection refers to a communications connection between two communication endpoints or nodes. An example is a telephone call, in which one telephone is connected with one other, and what is said by one caller can only be heard by the other. This is contrasted with a point-to-multipoint or broadcast connection, in which many nodes can receive information transmitted by one node. Other examples of point-to-point communications links are leased lines and microwave radio relay.
A wireless mesh network (WMN) is a communications network made up of radio nodes organized in a mesh topology. It can also be a form of wireless ad hoc network.
Contiki is an operating system for networked, memory-constrained systems with a focus on low-power wireless Internet of Things (IoT) devices. Contiki is used for systems for street lighting, sound monitoring for smart cities, radiation monitoring and alarms. It is open-source software released under the BSD-3-Clause license.
In telecommunications networks, a node is either a redistribution point or a communication endpoint.
A cognitive radio (CR) is a radio that can be programmed and configured dynamically to use the best channels in its vicinity to avoid user interference and congestion. Such a radio automatically detects available channels, then accordingly changes its transmission or reception parameters to allow more concurrent wireless communications in a given band at one location. This process is a form of dynamic spectrum management.
An overlay network is a computer network that is layered on top of another network. The concept of overlay networking is distinct from the traditional model of OSI layered networks, and almost always assumes that the underlay network is an IP network of some kind.
IEEE 802.22, is a standard for wireless regional area network (WRAN) using white spaces in the television (TV) frequency spectrum. The development of the IEEE 802.22 WRAN standard is aimed at using cognitive radio (CR) techniques to allow sharing of geographically unused spectrum allocated to the television broadcast service, on a non-interfering basis, to bring broadband access to hard-to-reach, low population density areas, typical of rural environments, and is therefore timely and has the potential for a wide applicability worldwide. It is the first worldwide effort to define a standardized air interface based on CR techniques for the opportunistic use of TV bands on a non-interfering basis.
IEEE 802.11w-2009 is an approved amendment to the IEEE 802.11 standard to increase the security of its management frames.
E-UTRA is the air interface of 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) upgrade path for mobile networks. It is an acronym for Evolved UMTS Terrestrial Radio Access, also known as the Evolved Universal Terrestrial Radio Access in early drafts of the 3GPP LTE specification. E-UTRAN is the combination of E-UTRA, user equipment (UE), and a Node B.
A wireless ad hoc network (WANET) or mobile ad hoc network (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such as routers or wireless access points. Instead, each node participates in routing by forwarding data for other nodes. The determination of which nodes forward data is made dynamically on the basis of network connectivity and the routing algorithm in use.
In computing, Microsoft's Windows Vista and Windows Server 2008 introduced in 2007/2008 a new networking stack named Next Generation TCP/IP stack, to improve on the previous stack in several ways. The stack includes native implementation of IPv6, as well as a complete overhaul of IPv4. The new TCP/IP stack uses a new method to store configuration settings that enables more dynamic control and does not require a computer restart after a change in settings. The new stack, implemented as a dual-stack model, depends on a strong host-model and features an infrastructure to enable more modular components that one can dynamically insert and remove.
Connectionist Learning with Adaptive Rule Induction On-line (CLARION) is a computational cognitive architecture that has been used to simulate many domains and tasks in cognitive psychology and social psychology, as well as implementing intelligent systems in artificial intelligence applications. An important feature of CLARION is the distinction between implicit and explicit processes and focusing on capturing the interaction between these two types of processes. The system was created by the research group led by Ron Sun.
Opportunistic mesh (OPM) is a wireless networking technology that aims to provide reliable and cost-effective wireless bandwidth when used to build the networking infrastructure of large-scale wireless systems.
Deterministic Networking (DetNet) is an effort by the IETF DetNet Working Group to study implementation of deterministic data paths for real-time applications with extremely low data loss rates, packet delay variation (jitter), and bounded latency, such as audio and video streaming, industrial automation, and vehicle control.