Situation Awareness Mechanisms for Cognitive Networks

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Abstract—The paper provides a study of situation awareness mechanisms in modern telecommunication networks on the essential basis of the actual Self-NET ...
Situation Awareness Mechanisms for Cognitive Networks Andrej Mihailovic Centre for Telecommunications Research, King’s College London London, United Kingdom [email protected]

Ioannis P. Chochliouros, Evangelia Georgiadou, Anastasia S. Spiliopoulou, Evangelos Sfakianakis and Maria Belesioti Research Programs Section, Labs & New Technologies Division Hellenic Telecommunications Organization S.A. (OTE) Athens, Greece [email protected]

Gerard Nguengang and Julien Borgel

Nancy Alonistioti

Thales Communications S.A. Colombes, France gerard.nguengang;julien.borgel@fr. thalesgroup.com

Dept. of Informatics and Telecommunications, National Kapodistrian University of Athens Athens, Greece [email protected]

Abstract—The paper provides a study of situation awareness mechanisms in modern telecommunication networks on the essential basis of the actual Self-NET research Project effort. Since cognition has today become one of the key features of most systems, novel mechanisms for identification, comprehension and corresponding reaction to altering environmental conditions are continuously being developed. The necessity of situation awareness techniques as well as potential ways for their realization is pointed out, in the broader context of the Future Internet. Keywords-Autonomic networking; cognitive systems; Future Internet (FI); self-management; situation awareness (SA)

I.

INTRODUCTION

When the Internet first began its operation, its structure was based on a decentralized distributed design, substantially uniting individual networks together. But through the years, as the number of users grew and the provided services were multiplied both in scale and in heterogeneity, the increased network complexity called for crucial changes of the underlying architecture. Expansion of the Internet was not only performed geographically but also in terms of everyday usage. Thus, new intriguing challenges regarding the viability, management and operation control of the system came to the foreground and led to the vision of the Future Internet (FI). The latter is envisaged as a huge cognitive autonomic network, capable of organizing itself according to uprising demands and circumstances. The first step for such a self-management approach is the network ability to achieve situation awareness. More specifically, all involved elements are expected to monitor their status, observe changes in their vicinity, take appropriate decisions and execute corrective actions in order to overcome 9781-4244-3941-6/09/$25.00 ©2009 IEEE

possible anomalies and to maintain their performance above a minimum threshold. The derivation of conscience is therefore a matter of primary importance and an issue that needs to be further analyzed. In Section 2, a detailed definition of situation awareness is given, starting from the collection, observation and understanding of raw data, to the projection of the current status in the near future. Section 3 explains why situation awareness is crucial for decision-making mechanisms of cognitive networks and presents a relevant fundamental scope, on the basis of the actual Self-NET research Project effort. Section 4 discusses the building/structuring of specific situation awareness concepts and/or mechanisms models in the context of the original Self-NET effort. II.

SITUATION AWARENESS – THEMATIC CONTEXT

The term “Situation Awareness” (SA) mainly refers to the collection of sufficient information about the environment and operational state of a system, which is a “vital” prerequisite for the further conducting of appropriate decision-making and related executing procedures. In the literature, numerous definitions can be found for situation awareness, not only related to telecommunication networks, but broadly referring to various dynamic systems. One of the most common and quite efficient theoretical frameworks was early developed by Endsley [1], while another very concise interpretation was given by Smirnov et al. [2]. Other definitions [3] follow Endsley’s model and affirm its key elements, while simpler perspectives translate situation awareness as the process of “knowing what is going on so you can figure out what to do” [4], or just “keeping track of what is going on around you in a complex, dynamic environment” [5].

SA is quite often met in autonomic networking related research. A. Essentials of Endsley’s Model Endsley [1] states that situation awareness is “the perception of elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future”. In other words, the relevant model introduces time factors, “space” (i.e. environment) assessments, and interpretation & prediction aspects, in the near future. The model was originally related to dynamic systems and development of operator interfaces, automation concepts and training programs in fields such as aircraft, air traffic control, power plants and advanced manufacturing systems. SA formation can be analyzed into three distinct levels (or “stages” or “steps”) [6], as illustrated in Fig.1.

Level 3 (Projection) involves the ability to “project” the future actions of the elements in the surrounding environment of interest. It is realized through knowledge of the status and dynamics of the elements and comprehension of the situation (i.e. the previous Levels 1 and 2 SA), and then extrapolating this information “forward in time” to conclude the manner it will have effect(s) on future states of the operational environment. The model also incorporates several variables that can influence the development and maintenance of SA, including individual, task, and environmental factors and “provides the primary basis for subsequent decision making and performance in the operation of complex, dynamic systems”. However, it appears extensive and complex for considerations in modern FI-related approaches. B. Essentials of Smirnov’s Model The Smirnov SA model [2] is “more oriented” in the telecommunications world and precisely in autonomic networking; it provides the most precise definition of situation awareness with specific angle and relevance to autonomic networking but reusing the main concepts of Endsley’s definition. According to Smirnov’s contextual approach, SA is a prerequisite for making good decisions in networks, since it facilitates selective perceptions and targeted analysis of network information. In particular, it represents processes that describe knowledge about current situations and build the constantly evolving picture of the state of the environment. The three distinct levels of awareness realization are as follows: Level 1 (Perception): The first level deals with perceiving critical factors in the “environment” of concern. Level 2 (Inference): The second level aims in the appropriate understanding of “what those factors mean” for the specific decision maker’s goals. Level 3 (Prediction): The third level aims to “predict” what will happen in the near future. The above can be considered as a “start” for interpretation of SA in autonomic networking, clearly depicting a “three stage” process.

Figure 1. SA Model in dynamic decision-making [1]

Level 1 (Perception) deals with perception of the status, attributes/characteristics, and dynamics of all related elements in the surrounding environment. It is the most essential level of SA, since it includes the processes of monitoring, cue detection, and simple recognition, directing to an awareness of several situational elements (such as objects, events, people, systems, environmental factors) and their current states (i.e. locations, conditions, modes, actions). Level 2 (Comprehension) involves a kind of “synthesis” of disjointed Level 1 SA elements, through the processes of pattern recognition, interpretation, and evaluation. It entails integrating such information in order to recognize how it will “impact” upon the individual’s goals and objectives. This also comprises the development of a comprehensive picture of the corresponding environment (or of that specific portion of the environment) of concern.

III.

SA AND DECISION-MAKING MECHANISMS FOR COGNITIVE NETWORKS

The operation of complex dynamic cognitive networks comprises decision-making mechanisms and corrective procedures, based on the results of SA. The consideration of several variables influencing the development of a system, including spatial and temporal components, reflects the dynamic and location-based nature of SA. So, even though alone it cannot guarantee successful subsequent decisionmaking, SA provides a primary base and supports all the necessary input processes, like cue recognition, situation assessment and situation prediction, upon which “good” and efficient decisions are based [7]. Understanding of what is happening in the system prior to invoking decision-making steps is crucial for any kind of autonomic networking activity. Thorough observation and analysis of a network’s node (or of any appropriate network’s element) behaviour and information flows in the direct

neighbourhood of a “network entity”, as well as efficient cooperation in order to supply information on remote events, provides the basis to decide and act, according to the current state of the related network [8]. Moreover, capturing of situational data and effectively exploiting them, promotes the capability of services to autonomously adapt to the context of alternating demands related to the environment in consideration, independently of its full complexity [9]. Fig.2 illustrates the basic instances and processes of monitoring, decision-making and execution stages which correspond to situation awareness acquisition. Through appropriate sensing and filtering of triggering events, a knowledge base is shaped, leading in turn to decision enforcement. It is therefore evident, that SA constitutes the step that precedes the foundation for decision-making 1 . It can be thought of as “feed-forward”, since anticipating the outcome of an action helps the network to achieve its (expected) specific goals. Conversely, by comparing the results of the network’s reactions with set goals, the system can modify or “adjust” its behaviour, this time using situation awareness as valuable feedback. The significance of situation deduction lies in the fact that the latter is not related to processing of a single information or state, but to the comprehensive assessment of various aspects and conditions of the overall situation that is being analyzed. It resembles a conscious process of determining what has happened and what needs to be done in order to assess system operations and potentially invoke further executions, just like the process a virtual network administrator would follow.

The approach depicted in Fig.2, relates to the Self-NET research Project [10]. Its main objective is to design and validate new paradigms for the management of complex and heterogeneous network infrastructures and systems, taking in to consideration the next generation Internet environment and the convergence of Internet and mobile/wireless networks, around a novel feedback-control cycle, i.e. the Monitoring, DecisionMaking and Execution (M-D-E) cognitive cycle. Self-NET defines requirements for characterized and frequently complex structures of information on various pre-defined and dynamic aspects of a system’s operations. In several corresponding systems (as described in [11]), a network element’s (NE) or network domain’s (ND) M-D-E cycle would be potentially exposed to a large input of various forms, coming from: (a) monitoring processes, that include collecting data and triggering events; (b) being assesses and interpreted in the M part of the Generic Cognitive Cycle Model (GCCM); (c) being derived from the continuous processes of collecting and interpreting system’s operational states, as defined by selfawareness, and; (d) interacting and exchanging information between the levels of the Distributed Cognitive cycle for System and Network Management (DC-SNM). The need for these structures of information is firstly formulated in the objectives and the scope of the Project targeting development of knowledge-based systems (and situation awareness) and in the analysis of the state-of-the-art in related areas of research. In basic terms, SA in the Self-NET context can be interpreted as “the ability of the cognitive system to known and deduce what is happening in the network, involving the comprehensive set of data inputs and related to the environment in consideration”. Thus, for understanding the processes, Fig.2 can be used to demonstrate the fundamental examples and procedures of the M-D-E cycles indicating the stages, which correspond to SA. Hence, a simple conclusion is that the M-D-E is required to conduct the Decision-Making after collecting sufficient information about the environments and/or operational states. As noted in the general objectives of the Project and as derived by the above described issues, Self-NET systems are continuously involved in processes where some of them can be generalised into SA processes constituting an important aspect of the M-D-E cycle. IV.

BUILDING SA IN THE SELF-NET SCOPE

In order to be “self-manageable”, a “system” must have: (i) an internal representation of “what it feels and experiences” as it perceives entities, events and situations in the world; (ii) an internal model that captures the richness of what it knows and discovers, and; (iii) a proper mechanism for computing values and priorities that enables it “to decide what it wishes to do/perform”. This required internal knowledge on the environment must express the human expert know-how and be machine readable. Figure 2. Monitoring, decision-making and execution cycle processes in the scope of a Generic Cognitive Cycle Model (GCCM) 1 At this particular point it is logical to assume three Levels/Steps of the situation awareness that can lead to decision-making, as discussed in the previous sub-sections 1.1 and 1.2.

Definition of SA in the Self-NET can be framed 2 by initially assessing outside definitions related to the Project’s 2 A broad collection of possible inputs including monitoring data and triggering events, interpretations of data, self-awareness processes and interactions and deductions between levels of DC-SNM for 13 distinct use-

objectives, scope and results [12]. From the analysis of the corresponding use-cases, it appears that a network element should be able to:

Many definitions can be found about data3, information4 and knowledge 5 from the existing literature, explaining the relationships and limits between them [13], [14].



Understand its internal state, the high level operator’s goals, the network services it delivers and their expected behaviors.

Figures 3 and 4 both describe some fundamental concepts and the essential representation of Self-NET approach to SArelated issues, as these will be further developed.



Analyze network events and handle any abnormal behavior.



Infer efficient corrective actions for the detected anomalies.

In particular, the distinct separate “modules” (or constructional elements) depicted in Fig.3, are briefly identified as follows:



Cooperate with other NEs, at different levels, in order: (i) to understand more precisely the network status when local information is not sufficient; (ii) to elaborate efficiently the appropriate solution to network faults and; (iii) to apply corrective actions where relevant, and to avoid side-effects of a unilateral local reconfiguration.





Monitoring: This includes separate activities like sensing and correlating/filtering; it refers to data corresponding to the information process of interpretation.



Level 1 of SA: This leads to a “proper” characterisation of the operational states (i.e. to an elementary knowledge interpretation), where information is primarily interpreted, i.e. “suitably characterised”, giving the meaning and relevance to overall system status. (For instance, information like “85% of a link is utilised” can lead to the concluding characterisation that “the link is congested above the tolerable threshold” where any such characterisation is predetermined by system designers).



Situation Trigger: This is an abstraction to indicate triggers that leads to Level 2 of SA.



Level 2 of SA: This implicates an assessment of the surrounding environment, and it is somehow the “analogy” of a network administrator training. When there is a situation trigger, the environment needs to be assessed, in order to prepare for decision-making (e.g. to identify any alternative links available, to relieve congestion).



Knowledge Base - Interpretation Library of operational states: This refers to the database(s) where characterizations are stored (for instance, thresholds, hysteresis, etc.).



Knowledge Base - Procedure for Assessment of the Environment: This refers to the database(s) of the procedures for assessment of the environment, i.e. analogous to operator training.

Adapt the decision-making process, thanks to machine learning.

The key-reference concept and definition are captured in Endsley’s generic definition and the definition for autonomic networking, as previously discussed. This approach suggests that foundations for definitions are similar -if not identical- and that interpreting SA is related to specific technical processes, where details and specifics can become more evident and would be particularly identified and/or confirmed in the context of the work to be performed. Thus, SA can be “interpreted” as “the ability of the cognitive system to known and deduce what is happening in the network, involving the comprehensive set of data inputs and related to the environment in consideration”. In the Self-NET systems, “situation” is considered as a point at which an instance of SA is completed, hence progressing the overall M-D-E cycle to the decision-making part. Under this scope, triggering event, information or characterized operational state are not “situations”. Additionally, a “validated situation” is related to the completed assessment of the environment invoking event(trigger)/ information/data collected and analyzed from the current operational aspects in the system, analogous to a view of a network administrator. The intention is to successfully replace (or decrease) the role of network administrator in some aspects and, consequently, to introduce a kind of a “virtual” administrator (or intermediate/auxiliary administrator), the latter being the Self-NET intelligence. For this reason, situation awareness and situation deduction look like a conscious process of determining what has happened and what needs to be done in order to analyze and assess system operations, and potentially invoke further actions or executions. This assessment is not related to processing of single information and state, but to comprehensive assessment relevant to many aspects and conditions of the overall situation that is analyzed.

cases (classified as categories of congestion, performance, failure, intrusion detection, fault prediction and deployment) has been considered in the context of the Self-NET, as also described in [12].

3

Data is “raw”. It can exist in any form, usable or not. In Self-NET systems for example, it can be associated to basic numbers, interrupts/triggers collected by sensing tools. 4 Information is data that has been given meaning by way of relational connection. In Self-NET systems, information relates to the assembly of information that gives operational state of an element of the environment, e.g. data for link utilization is just a number, interpretation that a link is being utilized with specific percentage/ratio or just a throughput figure assembled for the link in consideration is an synthesized information. 5 Knowledge is the appropriate collection of information, such that its intent is to be useful. It is a deterministic process. It refers to and is applicable to different structures of information and various stages of cognitive processes. It also has the property of being predetermined and used for interpretation purposes but also dynamic and used for generating the updated statues of the system (i.e. self-awareness).



Knowledge Base - Deduction Completion: It is an abstraction to indicate finalisation of all “checks” for the environment’s assessment. These are conditions for moving to the decisionmaking stage. In case of predictions (i.e. needed Level 3 SA) this additionally refers to what needs to further happen, to deduce a situation and proceed to the decision-making stage.



Level 3 of SA: This includes “projections”, i.e. prediction on what would happen in the future and/or what conditions need to be met, to satisfactorily proceed to the decision-making.

Figure 4. Depiction of generalized situation deuction steps

V.

CONCLUSION

In the context of the present work we have discussed the unanimous importance of situation awareness mechanisms for modern cognitive systems, the latter forming an indispensable part of Future Internet. SA is a critical issue for the proper functioning of complex dynamic cognitive networks, where decision-making mechanisms and corrective procedures need to be deployed and become effective.

Knowledge Base

Intelligence for Decision-making Procedures for Assessment of Environment

Deduction Completion Situation triggers

Interpretation library of operational states

Level 3 Projections

Assessment of Environment Level 2 Characterisation of operational states Level 1

On the findings of the original Self-NET Project effort, we have presented the essential framework for the structuring of appropriate SA mechanisms, to achieve an active network management system based on network elements capable of autonomous action, by using local knowledge and operator defined policies [15], [16]. ACKNOWLEDGMENT The work has been performed in the context of the SelfNET (“Self-Management of Cognitive Future Internet Elements”) Project and has been supported by the Commission of the European Communities, Information Society and Media Directorate General, in the scope of the 7th Framework Program ICT-2008, Grant Agreement No.224344. REFERENCES

Monitoring

Sensing

Filtering/ correlating

Situation Awareness

Self-Awareness

Situation Deduced

Decision making / Reasoning

Update of operational states

Action

For indication and completeness, Fig.3 additionally contains decision-making steps and knowledge base applied with updates of self-awareness plane and its interaction with the SA steps (also learning is not included in this stage of explanation).

Figure 3. Framework for SA and decision enforcement models, in the SelfNET Project

Fig.4 shows another perspective of SA, indicating the SA Levels leading to the deduction of a situation.

[1] M.R. Endsley, “Toward a theory of situation awareness in dynamic systems”, Human Factors 37(1), 1995, pp.32-64. [2] M. Smirnov, T. Zseby, and R. Roth, “Situation aware composition of lowlevel traffic handling functions”, Proceedings of the 1st IEEE International Workshop on Modelling Autonomic Communications Environments (MACE2006), Dublin, Ireland, October 25-26, 2006. [3] P.M.D. Gray, A. Preece, N.J. Fiddian, W.A. Gray, T.J.M. Bench-Capon, M.J.R. Shave et al., “KRAFT: knowledge fusion from distributed databases and knowledge bases”, Proceedings of the 8th International Workshop on Database and Expert Systems Applications (DEXA’97), pp. 682-691, Toulouse, France, September 1-2, 1997. [4] E.V. Adam, “Fighter cockpits of the future”, Proceedings of the 12th IEEE/AIAA Digital Avionics Systems Conference (DASC), pp. 318-323, New York, NY, October 25-28, 1993.

[5] N. Moray, “Ou sont les neiges d’antan? (Where are the snows of yesteryear?)”, Human performance, situation awareness and automation: Current research and trends, pp. 1-31, Lawrence Erlbaum Associates, Mahwah, 2004.

[12] A. Mihailovic, I.P. Chochliouros, A. Kousaridas, G. Nguengang, C. Polychronopoulos, J. Borgel et al., “Architectural principles for synergy of self-management and Future Internet evolution”, Proceedings of the ICT Mobile Summit 2009, Santander, Spain, June 10-12, 2009.

[6] http://en.wikipedia.org/wiki/Situational_awareness [7] H. Artman, “Team Situation assessment and information distribution”, Ergonomics 43(8), 2000, pp.1111-1128. [8]http://www.emobility-ca.eu/deliverables/ D3.2%20White_ Paper_on_Future_Internet_in_a_Post_IP_era_FINAL.pdf [9] CASCADAS (Component-ware for Autonomic Situation-aware Communications, and Dynamically Adaptable Services) Project, [http://www.cascadas-project.org/]. [10] Self-NET (Self-Management of Cognitive Future Internet Elements) Project, FP7-ICT-2007-2, Grant Agreement No.224344 [http://www.ictselfnet.eu/]. [11] Self-NET Project, “Deliverable 1.1: System Deployments Scenarios and Use Cases for Cognitive Management of Future Internet Elements”, October 2008 [http://www.ict-selfnet.eu/].

[13] R. L. Ackoff, “From Data to Wisdom”, Journal of Applies Systems Analysis, 16, 1989 (pp.3-9). [14] C. Fortuna, M. Mohorcic, “Trends in the development of communication networks: Cognitive networks”, Computer Networks 53(9), 2009, pp.13541376. [15] A. Mihailovic, G. Nguengang, J. Borgel and N. Alonistioti, “Building Knowledge Lifecycle and Situation Awareness in Self-Managed Cognitive Future Internet Networks”, Proceedings of The First International Conference on Emerging Network Intelligence - EMERGING 2009, (part of NexTech 2009 conferences), Sliema, Malta, October 11-16, 2009, unpublished. [16] Self-NET EU Project, Deliverable D2.1 “First Report on Mechanisms for Situation Awareness of Cognitive Network Elements and Decision Making Mechanisms for Goal-oriented Task Planning”, INFSO-ICT-224344, [https://www.ict-selfnet.eu], April 2009.