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마스터제목스타일편집 14. 6. 24 최증원 (jwchoi@add.re.kr) 국방과학연구소 Public Relations Team

발표순서 1. 미래전양상과전술통신구조 2. 생체모방네트워킹기술 3. 집단생태체계기반자율망기술 4. 결론

마스터제목스타일편집 1. 미래전양상과전술통신구조 Public Relations Team

미래전성격 필요무기체계 1. 동시 통합 다차원전쟁 지휘통제체계및통신체계 (C4I) 2. 속도전 신속 / 기동전 3. 정밀타격 / 무인전쟁 4. 전자전 / 사이버전 정보 감시 정찰체계 (ISR) 정밀유도무기 / 무인무기체계 (PGM) 미래전은 C4ISR+PGMs 개념의 NCW 기반복합무기체계운용이보편화 4

NCW(Network Centric Warfare) 전장의여러전투요소를결합하여전장상황을공유하고통합적, 효율적전투력을만들어내는개념 (K. Cebroski, 미해군제독 ) [ 미래전 : NCW&NCOE] [OODA LOOP] 5

NCW : Network Centric Warfare 6

통신망역할 - 미래전수행개념 (NCOE) 진지전 기동전 ( 진격전 ) 기동전 ( 공지전투 ) 정보전 효과중심전 결심주기단축 : 80분 ( 걸프전 ) 20분 ( 이라크전 ) 감시정찰체계 ( 눈, 귀 ) 지휘통제체계 ( 뇌 ) NCOE (Network Centric Operational Environment) : 네트워크중심작전환경. 1) 전장의제전투요소를네트워킹화하여 2) 전장상황을공유함으로써 3) 효과중심의동시. 통합작전을보장할수있는개념으로전투력승수효과를창출할수있는작전환경을조성. 정밀타격체계 ( 손, 발 ) 통신체계 ( 신경망 ) Networking 이모든것을가능케한다. 7

상용 vs. 전술통신발전추세 ( 전송속도측면 ) 전술통신은상용통신에비해전송속도는낮지만, 군고유요구사항인네트워크의 분산성, 강인성, 항재밍, 보안등에대한기술적해결이우선됨. 9

전술통신체계요구사항발전 10

SDR : Software Defined Radio CR : Cognitive Radio 11

다계층광역통합네트워크개념도 Resource layer 서비스 / 플랫폼 융합된전장 자원관리 Integrated Network layer Cloud network 수직 (layer 간 ) 통합 Ubiquitous 수평 (layer 내 ) 통합 User layer 수집체계 지휘통제체계 정밀타격체계 13

다계층광역통합네트워크구조 14

다계층광역통합네트워크구조 15

마스터제목스타일편집 2. 생체모방네트워킹기술 Public Relations Team

생물모방 (Biomimetics) / 생물영감 (Bioinspiration) 생물의구조와기능을모방또는영감을얻어아이디어를얻는분야 재료공학 : 연잎효과 (lotus effect) 로봇공학 : 끈적이로봇, 파리지옥로봇 건축공학 : 흰개미둔덕모방 ( 이스트게이트센터 ) [ 자연은위대한스승이다 ], 2012/05 < 현대경제연구원 > 17

생체모방네트워킹 (Bio-Inspired Networking) 기술 자연계에존재하는생체간커뮤니케이션의특성을통해얻은영감 (inspiration) 을기반으로, 이를통해현재의통신시스템이직면하고있는기술적난제를해결하기위한네트워킹기술 [ 생체모방네트워킹기술의예 ] 18

Swarm Intelligence collective foraging by ants (a) Starting from the nest, a random search for the food is performed by foraging ants (b) Pheromone trails are used to identify the path for returning to the nest (c) The significant pheromone concentration produced by returning ants marks the shorted path Nest Food Nest Food (a) (b) Nest Food (c) 19

Natural behavior of ant * Ant Algorithms (P. Koumoutsakos based on notes L. Gamberdella (www.idsia.ch) 20

Working on a connected graph G = (V,E), the ACO algorithm is able to find a shortest path between any two nodes Capabilities A colony of ants is employed to build a solution in the graph A probabilistic transition rule is used for determining the next edge of the graph on which an ant will move; this moving probability is further influenced by a heuristic desirability The routing table is represented by a pheromone level of each edge indicating the quality of the path * AntNet : G. Di Caro, et al., M. Dorigo, et al. 21

The most important aspect in this algorithm is the transition probability p ij for an ant k to move from i to j p k ij l 0 t) J k i ij ( ij k if j J i il il ( t) otherwise J i k : the tabu list of not yet visited nodes, i.e. by exploiting J ik, an ant k can avoid visiting a node i more than once η ij : the visibility of j when standing at i, i.e. the inverse of the distance τ ij : the pheromone level of edge (i, j), i.e. the learned desirability of choosing node j and currently at node i α & β : adjustable parameters that control the relative weight of the trail intensity τ ij and the visibility η ij, respectively The pheromone decay is implemented as a coefficient ρ with 0 ρ < 1 τ ij (t) (1 ρ) τ ij (t) + Δτ ij (t) 22

AntHocNet (ACO 알고리즘을적용한 Ad-hoc 라우팅알고리즘 ) (1) When a data session is started at node s with destination d, s checks whether it has up-to-date routing information for d. (2) If not, it reactively sends out ant-like agents, called reactive forward ants, to look for paths to d. (3) These ants gather information about the quality of the path they followed, and at their arrival in d they become backward ants which trace back the path and update routing tables. (4) Once paths are set up and the data session is running, s starts to send proactive forward ants to d: * AntHocNet : G. Di Caro, et al 23

AntHocNet ACO 알고리즘을적용한 Adhoc 라우팅알고리즘 24

In South-East Asia huge swarms of fireflies emit light flashes in perfect synchronicity. The firefly synchronization scheme is reviewed and challenges related to the implementation in ad hoc networks are addressed. 25

Almost traditional TDMA in ad hoc networks implementations still encounter the following difficulties Message Overhead : Negotiation of the slot schedule Wasted Slots : Unused slot management 26

Fireflies can simply be abstracted as oscillators that emit a pulse of light periodically. This type of oscillators is referred to as pulse coupled oscillators. As a simply mathematical representation, a pulse-coupled oscillator is completely described by its phase function 27

DESYNC Algorithm: (Inverse of Firefly Sync.) 1. Record firing times of phase neighbors 2. Compute the average 3. When back neighbor fires, jump towards the average i t 1 i t mid t Ankit Patel, Julius Degesys, Radhika Nagpal Desynchronization: Self-Organizing Algorithms for Periodic Resource Scheduling. (to appear) In. Proc. SASO 2007. 28

DESYNC can be easily used for TDMA Time Division Multiple Access (TDMA) 1 2 3 Protocol for broadcast channel sharing, where nodes divide time into equal slots, and each node owns a slot 4 5 Nice Properties Collision-free message transmission Fair allocation of bandwidth High bandwidth coverage in high load 1 2 3 4 5 1 2 3 4 5 29

DESYNC-TDMA Algorithm: Define slots at midpoints of firings from the round before Slot Properties Non-overlapping Full bandwidth coverage Well-defined (regardless of state) Can always send Fairness over time 30

마스터제목스타일편집 3. 집단생태체계기반자율망기술 Public Relations Team

Mobile Ad-hoc Network (MANET) A collection of mobile platforms nodes where each node is free to move about arbitrarily Distributed, mobile, wireless, multi-hop networks Without the benefit of any exiting infrastructure Each node logically consists of a router DARPA (Defense Advanced Research Project Agency) 1970 년대부터연구진행 MANET 의특성 Dynamic topology Possibly uni-directional links Constrained resources Battery power Wireless transmitter range 33

기존 Ad-hoc 네트워크기술의당면과제 기존 Ad-hoc 네트워크기술에비해진보된형태의차세대자율망구성기술필요 34

미래전장운용환경특성 대규모네트워크로인한제어복잡성 동적으로변화하는주변환경 자원제한성 ( 예. 주파수 ) 해상 / 지상 / 공중통합전력간이기종환경 중앙통제적용이불가한환경 단위전력요소의예측불허장애 / 손실수시발생 전통적인기존네트워킹기술로지원제한 Complexity Dynamic Nature Resource Constraints Heterogeneity No Control Frequent/Potential Failures 자연계특성 미래전장환경에서의정보통신체계한계를 집단생태특성을접목한새로운전술네트워크기술을통해해결 집단생태체계기반 (Bio-Inspired) 차세대자율망기술 35

개요 집단자연생태계의적응능력, 자가구성력, 상호반응등과같은동작특성을모방하여, 미래통신체계가요구하는자율성, 분산성, 강인성, 확장성등을위한차세대자율기동네트워크기초 / 기반기술개발 개념도 36

연구범위 자율기동망구조및공학적통신모델연구 집단생태특성기반자율기동망네트워크구조설계 통신모델정립및세부통신계층별기능설계 전장환경의임무및지휘통제를위해요구되는운용시나리오및유스케이스연구 BXT-01 집단생태특성기반차세대자율기동망기술개발 BXT-02 BXT-03 BXT-04 자율기동망 M&S 자율기동망 M&S 기반기술및시나리오연구 자율기동망성능평가구조및방법론연구 NLS 기반 M&S 성능분석도구개발 성능검증및분석 자율기동망무선자원제어기술연구 자율기동망자가구성라우팅기술연구 집단생태특성기반무선자원제어기반기술및운용시나리오연구 전장환경의무선자원제어구조및프로토콜연구 집단생태특성기반자율기동망동기제어알고리즘연구 집단생태특성기반자가구성라우팅기반기술및운용시나리오연구 전장환경의자가구성라우팅알고리즘및프로토콜연구 집단생태특성기반자율기동망라우팅경로인지및전파알고리즘연구 37

접근방안 OSI 7-layer model Application Presentation Session Transport Network (Routing) Media Access (MAC) Physical (PHY) ACO, BA 등의생태특성알고리즘을적용한 Tactical Ad-Hoc 라우팅기술개발군네트워크의생존성및로드밸런싱을고려한라우팅알고리즘연구라우팅프로토콜오버헤드및탐색지연시간경감화방안연구대규모네트워크환경에서라우팅성능저하감소방안연구라우팅알고리즘의자율판단을위한 Routing Policy Rule Set 정의및적용방안연구 PCO, FS 등의생태특성알고리즘을적용한 Dynamic-TDMA Ad-Hoc MAC 기술개발군네트워크환경에적합한주파수자원공유및동기화알고리즘연구 군 IER에적합한 Fairness, QoS 제공구조고려멀티홉다중중계환경에서 MAC 오버헤드및지연시간경감화방안연구대규모네트워크환경에서 MAC 성능저하감소방안연구 38