Nature-Inspired Techniques for Avoiding Congestion in Wireless - PowerPoint PPT Presentation

Need for orientation University of Cyprus • Repulsion/attraction forces allow packets form flocks but move in any direction without orientation to a global attractor  Rooting loops in the network • Orientation and attractiveness to a global attractor can be extracted from the orientational movement of migratory birds towards the poles • Make sink artificial magnetic pole in a WSN »Goal: guide packets move sinkwards under the influence of the artificial magnetic field 21/5/2012 Pavlos Antoniou - Ph.D. Defence 19

Magnetic field and Field of View (FoV) University of Cyprus • Artificial magnetic field should point to the sink • Hop distance h n (k) – # of hops between node n and the sink at the kth sampling period – shows proximity to sink: nodes closer to sink have smaller hop distances and hence stronger ‘magnetic field’ – sink extends forward in the direction of decreasing hop distance • In order for birds to move sinkwards – turn their head toward the sink • Mimic visual system of birds: FoV – FoV includes packets on nodes at 20 equal or smaller hop distance to sink

Redefinition of Zones University of Cyprus • Zone of Repulsion and Zone of Attraction refined within the FoV REPULSION FORCES ATTRACTION FORCES 21/5/2012 Pavlos Antoniou - Ph.D. Defence 21

Recap: Bird Flocking Elements University of Cyprus • Rules followed by each packet: – repel from neighboring packets on nodes at close distance – attract to neighboring packets on nodes at medium distance – orient toward global attractor Number of hops (sink) to the sink – experience perturbation (exploration) + – Mimic visual system of birds ( Limited visual perception: Field of View (FoV) ) 22 22 Transmission range

Flock-CC Protocol (1/4) University of Cyprus • At each node, each packet chooses its new hosting node (from M nodes one hop away in FoV) • Packet chooses its new hosting node on the basis of a desirability function for each node attraction repulsion – synthesizes the attraction and repulsion forces – measures tendency of a packet on node n to move towards each neighboring node m • evaluated once per time period k (every T sec.) – T: sampling period 21/5/2012 Pavlos Antoniou - Ph.D. Defence 23

Flock-CC Protocol (2/4) University of Cyprus • Attraction to packets moving to nodes 2 hops away – s nm norm (k) : measure of wireless channel loading around node m •  1 channel not congested,  0 channel congested – s nm (k) : number of packets successfully transmitted from node m to nodes two hops away from node n (# of packets in ZoA) within period k – s’ nm (k) : number of total transmission attempts at node m within period k – ξ : spreading variable [0,1] – allows attraction to idle nodes (at the borders of the flock) • low ξ values weak attraction to idle nodes; coherent flock motion (low 24 spreading)

Flock-CC Protocol (3/4) University of Cyprus • Repulsion from packets on nodes 1 hop away – q nm norm (k) : queue occupancy at node m •  1: high queue occupancy,  0 queue nearly empty – q m (k) : number of packets in the queue of node m (# of packets in ZoR) within period k – Q m : queue capacity of node m ξ -spreading variable and T-sampling period are only two tuneable parameters – their behaviour is well understood 21/5/2012 Pavlos Antoniou - Ph.D. Defence 25

Flock-CC Protocol (4/4) University of Cyprus • Orientation : choosing the next hop hosting node: – choose set of nodes with shorter hop distance than the current hosting node having available buffer space • if this set is empty, choose set of nodes with equal hop distance having available buffer space – if this set is empty, choose set of nodes with longer hop distance • Involve perturbation when selecting new hosting node from the chosen set (introduce exploration) – rank-based selection: rank nodes from chosen set by increasing desirability ( J = no. of nodes in chosen set) • weakest node has fitness f i ’=1 • fittest node has fitness f i ’=J – probability to choose a node  21/5/2012 Pavlos Antoniou - Ph.D. Defence 26

Flock-CC implementation University of Cyprus • Every time a packet is about to be sent, the decision making process is invoked by the current hosting node to determine the new hosting node. • The decision process employs three stages: a) selection of direction (forward, sideways, backwards) using the notion of the FoV and the magnetic fields, b) sorting of all nodes in the selected direction in descending order by their desirability function (calculated once per T), and c) probabilistic, biased (proportional to desirabilities) selection of the new hosting node. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 27

Performance evaluations University of Cyprus • Performance evaluations focus on four directions: – Parameter selection ( ξ , T) – Demonstration of: • emerging behavior • self-adaptation to changing network and traffic conditions • robustness against failing nodes • scalability as network size changes – Comparative evaluations • between previous and current Flock-CC models • against related (nature-inspired and conventional) congestion control approaches 21/5/2012 Pavlos Antoniou - Ph.D. Defence 28

Evaluation setup University of Cyprus – Traffic load: light (25 pkts/s), • Evaluation topologies medium (35 pkts/s), heavy (45 – Lattice (300 homog. nodes) pkts/s) – Random (300 homog. nodes) • Evaluation measures: • Evaluation parameters – Packet Delivery Ratio (PDR) – Sampling period T: 0.5, 1, – End-to-End Delay (EED) 1.5, 2 sec. – Energy tax – Node queue size: 50 packets – Throughput – IEEE 802.11: 2Mbps, 250Kbps Sink activated at t=10 s node reactivated at t=70 s deactivated at t=70 s 15 nodes activated failed at t=40 s at t=50 s 20 nodes 20 nodes 29 (a) Active nodes, Scenario 1 (b) Active nodes, Scenario 3 Active nodes, Scenario 2 Dead nodes,

Results – Scenario 1 (35pkts/sec) University of Cyprus • Low ξ {0, 0.25}  low spreading  available paths left unexploited  high overload in popular paths  high number of collisions and buffer overflows  low PDR & high EED • High ξ {1}  high spreading  high number of collisions • Good compromise values: ξ = {0.5, 0.75} • T=1 sec. : compromise between keep network updated without high control packet overhead 21/5/2012 Pavlos Antoniou - Ph.D. Defence 30

Results – Scenario 1 (35pkts/sec) University of Cyprus • High number of retransmissions for: 35pkts/sec – ξ =0 and ξ =0.25 • buffer overflows & collisions – ξ =1 • collisions Good compromise values: ξ = {0.5, 0.75} 21/5/2012 Pavlos Antoniou - Ph.D. Defence 31

Results – Scenario 3 (failing nodes,35 pkts/s) University of Cyprus • Buffer overflows followed same behavior with increase of T as in scenario 1 • Unlike scen. 1, collisions increased with increase of T – High T: Infrequent control packet exchange and desirability evaluation  packet flock incapable of adapting to rapidly changing network conditions – Low T: fast adaptation of flock movement to network conditions – Good compromise value in failing node conditions: T = 0.5 sec. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 32

Energy tax University of Cyprus • Lowest tax paid in scen 2 – packets traveled shorter paths to the sinks • Highest tax paid in scen 3 – failing nodes => packets traveled longer paths to the sink whilst maneuvering around the “dead” zone • Frequent updates (T=0.5s) – Highest tax for scens 1 & 2 • Higher number of control pkts sent – Lowest tax for scenario 3 • entities need to be updated about network state otherwise pkt drops, retransmissions • Changes in energy tax fairly insensitive to ξ 21/5/2012 Pavlos Antoniou - Ph.D. Defence 33

Throughput 10 active nodes University of Cyprus 35 pkts/sec • Flock-CC achieves ξ =0.75, T=0.5s fairness between active nodes – active nodes achieve similar throughput • Fluctuations in throughput as new active nodes added & network capacity reached • Steep decline in throughput during extreme failing node phenomena Fast adaptation to network conditions (10 sec after failures) 34

Results (Low data rates & random topologies) University of Cyprus • Low data rate WSNs, 250Kbps – Parameter setting similar to high data rate WSNs – Majority of packet loss attributed to collisions • Low rates  buffers rarely fill up • Random topologies – Sparse and dense topologies of 300 nodes – High density topos  increased collisions  lower PDR • highest PDR + lowest EED  ξ = 0.5 and 0.75, T = 2 sec – Low density topos: limited network resources  limited paths to sink  increased buffer overflows (x10 more than dense topos) • up to 20% lower PDR compared to dense • Overall recommended values: 35 ξ =0.75, T = 0.5s or 1s

Need for adapting T value University of Cyprus • Value of T can be adapted to dynamically changing network conditions (e.g. failures) • Initial (simple approach) – Initially set T=1s to avoid high control packet overhead – Change to T=0.5s after failures only nodes 1 hop away from failures for a small amount of time • If change to 0.5 for 2secs, results close to scen. having 0.5s • Design choices need further study – Which nodes (number of hops away from failure point) will participate? – For how long? – Other rule-based/equation-based approach for optimally tuning T 21/5/2012 Pavlos Antoniou - Ph.D. Defence 36

Emergent behavior: visualizations University of Cyprus Packets form flocks Packets generated at After deactivation and ‘fly’ over the the bottom create two of front nodes, network subflocks that bypass subflocks re-join the congested area A number of paths to the sink are exploited Nodes Nodes Nodes sending sending idle Nodes Nodes Nodes idle sending sending Scenario 2 37

Evidence of emergent behavior University of Cyprus • Full Flock-CC, highest PDR • Exclusion of randomization  reduced path exploration  deterioration of PDR (scen1: 9-17% , scen3: 5-11% ) • Exclusion of local interactions  lack of social activity  lack of knowledge on neighboring buffer & channel conditions  high number of overflows & collisions  deterioration of PDR (all scenarios) • Exclusion of both features  further PDR deterioration 38

Robustness against failures University of Cyprus • Flock-CC approach achieves robustness against failures • Packet flock exhibits the obstacle avoidance behavior of the bird flocks VIDEO Packets maneuver When hole in the around the zone of middle, packets re- dead nodes align to include middle path to sink Node activation 21/5/2012 Pavlos Antoniou - Ph.D. Defence 39

Scalability University of Cyprus • Lattice topologies of 200, 300 and 400 nodes in same area • Higher PDR in large scale nets – number of nodes scales up  available resources increase  flock spreads in network  packet losses reduced – small scale nets  packets “forced” to move in coherent formations • Lower EED in large scale nets – large scale nets  multiple paths to sink  lower buffer occupancy  lower time to reach sink • Graceful degradation 40

Comparative evaluations University of Cyprus • Flock-CC outperformed No Congestion Control (NCC) and Congestion-aware Routing (CAwR) protocols in all scens • NCC sends over shortest paths • CAwR allows multipath routing over shortest paths choosing node with lowest queue • Scen1: 15%, 23%, 19% higher PDR than NCC for 25, 35, 45 pkts/sec • Scen1: 2-8% higher PDR than CAwR • Smaller differences in scens 2, 3 • Flock-CC allows for controlled packet spreading, exploits available resources through multiple paths to sink • NCC, CAwR significantly higher 41 number of overflows 41

Comparative evaluations University of Cyprus • Qualitatively compared against AntHocNet and AntSensNet – quite complicated protocols involving large number of parameters and equations (2x & 4x more respectively) – parameters have to be tuned for variety of network and traffic conditions; sensitive to environment – control packets much larger and a lot more (forward+backward ants) + need lots of memory space – AntSensNet requires modifications in the queueing policies of the underlying MAC protocol • Flock-CC approach – quite simple involving only 2 parameters and 1 equation (desirability function), – much smaller and lot less control packets. No modification of the underlying protocols needed Comparison table 42

Conclusions University of Cyprus • Control motion of packets through WSNs by mimicking synchronized group behavior of bird flocks and their ability to avoid obstacles (congested & “dead” nodes) • Design embodying simple behavioural rules • Results showed that congestion is alleviated by balancing the offered load through alternative (unexploited) paths to the sink • Robust against failures, self-adaptable to variable network conditions • Flock-CC outperformed related approaches in all traffic loads 21/5/2012 Pavlos Antoniou - Ph.D. Defence 43

Future Work University of Cyprus • Investigate alternative methods of evaluating attraction/repulsion forces and desirability functions – e.g. take direct account of energy in desirability • Make design parameters adaptive to network changes – study when (immediate/delayed actions?) and how (rule-based, equation-based) to tune parameter values • Investigate Flock-CC applicability in the presence of multiple sinks and/or mobile sinks – what happens if multiple magnetic poles? • devise criteria for differentiating the influence of each pole – devise moving strategy for mobile sinks 21/5/2012 Pavlos Antoniou - Ph.D. Defence 44

Lotka-Voltera Congestion Control (LVCC) University of Cyprus Department of Computer Science 45

Lotka-Volterra competition model University of Cyprus • Lotka (1925) and Volterra (1926) independently developed a general model of competition between species Alfred James Lotka (1880 - 1949) • Lotka-Volterra competition model – simple deterministic model of mathematical biology – describes how species population change over Vito Volterra time as a result of species competition for (1860-1940) some limiting resource (e.g. food, space) – detailed description 21/5/2012 Pavlos Antoniou - Ph.D. Defence 46

Ecosystems vs. WSNs University of Cyprus • Ecosystem • Sensor Network – species live in nature – nodes initiate traffic flows – species interact with each – flows interact each other other & non-living parts of their – flows compete for available surroundings resources located at each – compete for resources (e.g., node (e.g., buffer, bandwidth) food, water) – Goal: co-existence of flows – Result: co-existence of species species traffic flows resources buffer capacity 47

LVCC: The concept University of Cyprus Source Nodes Relay (SNs) Node (RN) • Source nodes (SNs) compete for available buffer space at the parent (relay) node • SNs self-regulate and adapt the rate of their traffic flows so as to co-exist • SNs send packets to their parent node only when it has the available buffer space to hold the packets 48

LVCC: Cong. detection & avoidance University of Cyprus • Congestion detection – Parent (relay) node measures its queue length – Broadcasts to all potential children (source nodes) • Congestion avoidance – Rate adaptation – Every source node regulates and adapts its traffic flow rate on the basis of the LV competition model • queue length of parent node is taken into account – Goal: Avoid buffer overflow at parent (relay) node 21/5/2012 Pavlos Antoniou - Ph.D. Defence 49

Lotka-Volterra competition model University of Cyprus • Generalized Lotka-Volterra model for n species Species have same characteristics       r r 0 , i 1 , n i       K K 0 , i 1 , n i        a a 0 , i , j 1 , n , i j ij         0 , i 1 , n i x i (t) : biomass (population size) of species i at time t  number of bytes sent by each children node i r i : growth rate of species i β i : intra-specific competition coefficient (competitive effects among individuals of species i ) α ij : is the inter-specific competition coefficient (competitive effects of species j on growth of species i ) K i : is the carrying capacity of species (maximum number of individuals that can be sustained by the biotope in the absence of all other species competing for the same resource)  resource capacity 50

Equilibria and stability analysis University of Cyprus • Equilibria of the generalised Lotka-Volterra model can be evaluated by:      n dx x a          i i rx 1 x 0 , i [ 1 , n ] i j dt K K    j 1    j i • Coexistence non-negative equilibrium solution x i * = x * K   * x i , i 1 ,..., n     ( n 1 ) • Stability analysis (using eigenvalues) of coexistence solution – all flows (species) co-exist (survive) when β > α , α >1 • inter-specific competition is weaker than intra-specific competition 21/5/2012 Pavlos Antoniou - Ph.D. Defence 51

LVCC: Conditions to be satisfied University of Cyprus • Equilibrium stability conditions: , • Buffer overflow avoidance: – when system of n active nodes converges to the K coexistence solution, *   x i , i 1 ,..., n     ( n 1 ) – each node i should send less than or equal to K / n bytes at equilibrium * – denominator of x i > n • x • To ensure both conditions: 21/5/2012 Pavlos Antoniou - Ph.D. Defence 52

LVCC: Rate evaluation (1/2) University of Cyprus • Each node i evaluates its flow rate using the solution of the LV differential equation – rate evaluation every period T • Solution of LV differential equation by node i requires:      n dx x a      i  i  rx 1 x 0 i j dt  K K  – knowledge of variables r, K, α , β  j 1    j i – number of bytes sent by node i within previous period T, x i n  – number of bytes sent by all other competing nodes j, , x j  within previous period T : j 1  j i • difficult to be obtained in a distributed decentralized network n   • set  parent node’s queue length – x i C x i j  j 1  j i         n dx x a dx x a            i i i i rx 1 x 1 rx C i j i i   dt  K K  dt K K  j 1    21/5/2012 j i Pavlos Antoniou - Ph.D. Defence 53

LVCC: Rate evaluation (2/2) University of Cyprus • Solution of LV differential equation: w ( 0 ) x ( 0 )     i x ( t ) , w ( 0 ) K C ( 0 ) i i w ( 0 ) r    t     x ( 0 ) w ( 0 ) x ( 0 ) e K i i – x i (t) : number of bytes send by node i at time t • Discrete-time equation of x i at the k+1 th period: w ( kT ) x ( kT )      w ( kT ) K C ( kT ) i x (( k 1 ) T ) , i i w ( kT ) r    T     K x ( kT ) w ( kT ) x ( kT ) e i i – used by source nodes (SNs) – slightly modified equation used for relay nodes (RNs) 54

Performance evaluations University of Cyprus • Performance evaluations focus on three directions: – Parameter selection ( α , β , r) – Demonstration of: • self-adaptation to changing network and traffic conditions • scalability as the network size changes • fairness among active nodes – Comparative evaluations • against related congestion control approaches 21/5/2012 Pavlos Antoniou - Ph.D. Defence 55

Evaluation setup University of Cyprus • Cluster-based evaluation topology (all links are wireless) Grey-shaded area: collision domain • Evaluation parameters – Buffer capacity (K): 35KB – Time period between successive sending rate evaluations: T = 1sec – α , β , r > 0, β > α • Evaluation measures – Bandwidth (number of pkts sent) – Packet delivery ratio – End-to-end delay Pavlos Antoniou - Ph.D. Defence 56

Simulations University of Cyprus • Control system type simulations (Matlab) for theoretical model analysis – Evaluate validity of analytical results • Realistic network simulations (NS2) – Two-ray ground radio propagation model – CSMA-based IEEE 802.11 MAC, 1 Mbps 21/5/2012 Pavlos Antoniou - Ph.D. Defence 57

Matlab results University of Cyprus α =1, r=1, β =2 • Buffer overflows never occur – sending rates < buffer capacity • Scalability – as # of active nodes scales up, their sending rates decrease – graceful performance degradation • Adaptation – each active node self-adapts its sending rate – responsiveness to changes in the number of active nodes • Fairness Clusterheads Clusternodes – Clusterheads’ buffer capacity is fairly shared among active K cluster nodes  * x i     ( n 1 ) Pavlos Antoniou - Ph.D. Defence 58

Matlab results (cnt’d) University of Cyprus • no analytical upper bound for β α =3, r=1, β =4 • β cannot grow unboundedly – Increase of β decreases coexistence solution => decrease of transmission rate • α < β for system stability • Increase of α : – decreases coexistence solution K  – smooth traffic sending rates are * x i     ( n 1 ) not preserved – close to stability limits • Results showed that r can not grow unboundedly • Smooth traffic sending rates are not preserved with the increase of r • r ≤ 2 for system stability 21/5/2012 Pavlos Antoniou - Ph.D. Defence 59

NS2: Parameters setting, 3 nodes University of Cyprus • Decrease in PDR perceived for low values of α and β • Mainly attributed to the increase in transmission rates at equilibrium: – increased traffic load provoked channel contention, packet loss. • Sharp decrease in PDR was observed when the stability condition was threatened, e.g., 3.5< α <4 and β =4 21/5/2012 Pavlos Antoniou - Ph.D. Defence 60

Validation of stability & buffer overflow avoidance conditions University of Cyprus 61

NS2: Parameters setting, 5 nodes University of Cyprus • In Matlab, stability was achieved for r<2 • Realistic experiments showed that for r<1 calculated transmission rate does not converge • Extensive simulations showed that system stability is achieved for 1<r<2 21/5/2012 Pavlos Antoniou - Ph.D. Defence 62

NS2: Parameters setting, 10 nodes University of Cyprus • Highest PDR ( 0.9 ) achieved for 6< β <7 and 1.8< α <2.1 • Lowest EED ( 10 μ s ) achieved for 6< β <7 and 1.8< α <2.1 21/5/2012 Pavlos Antoniou - Ph.D. Defence 63

Parameter Setting University of Cyprus • Values of parameters α , β and r should be chosen to ensure convergence, stability and buffer overflow avoidance • r=1 : preserves convergence to equilibria and smooth flow rate regulation • α and β values depend on number of active nodes: 21/5/2012 Pavlos Antoniou - Ph.D. Defence 64

Comparative Evaluations University of Cyprus • 3 and 5 active nodes • LVCC vs. AIMD rate adaptation • AIMD is involved in many recent CC protocols for WSNs • LVCC achieved: – controlled behavior in wireless environments – smooth throughput – friendliness among competing flows • AIMD caused saw-tooth behavior of traffic flow rates, proved ineffective for wireless streaming environments 21/5/2012 Pavlos Antoniou - Ph.D. Defence 65

Conclusions University of Cyprus • Lotka-Volterra competition model is employed in order to avoid congestive phenomena: – control of traffic flows originating from source nodes – avoid overwhelming parent node’s buffer – allow co-existence of multiple flows • Self-adaptation of traffic flow rate at each source node is achieved • Responsiveness to changes is maintained • Available buffer capacity at parent node is fairly shared among active children • For small configurations (<20 nodes), system scales up with number of flows, offering graceful performance degradation 21/5/2012 Pavlos Antoniou - Ph.D. Defence 66

Future work University of Cyprus • Adaptation of parameter values – analytically optimized using conventional techniques – Or adopt nature-inspired optimization techniques • Modify LVCC approach to cope with different priority classes – Different kind of traffic flows – different species in nature • Evaluation of LVCC approach on a real testbed – collaboration with Prof. Ahmet Sekercioglu, Monash University, Australia – Initial very-small scale experiments are encouraging, involve higher number of active nodes 21/5/2012 Pavlos Antoniou - Ph.D. Defence 67

Generalization of approaches (1/2) University of Cyprus • Generalization of both approaches to other man made systems • Flock-CC – Road transportation • Capture interactions in an urban road transportation system • Flock-CC for navigating vehicles through congested road networks • Example: Google driverless car: like any car, but – Uses a series of cameras and laser radar to ”see” its environment, react to other vehicals, stop signs, stop lights and other traffic signs – It can steer itself while looking out for obstacles, accelerate to the correct speed limit, stop and go based on any traffic condition » Nevada, US, 1 st state to allow driverless vehicle to be legally operated on public roads, 1 st license May 2012 – Co-operation of a swarm of robots or Unmanned Aerial Vehicles (UAVs) moving towards a given target 68

Generalization of approaches (2/2) University of Cyprus • LVCC – Transportation engineering • Control of traffic flow injection into freeways/highways • Manage traffic flows on access ramps to freeways in order to avoid congestion phenomena, and thus delay for motorists • Autonomous Real-time Traffic Injection Control system – minimize the overall delay for motorists according to the traffic input load and freeway congestion situation 21/5/2012 Pavlos Antoniou - Ph.D. Defence 69

Thank you ! University of Cyprus Department of Computer Science Supported by: Are we there yet?

Publications University of Cyprus Book Chapters [1] Pavlos Antoniou , and Andreas Pitsillides “Congestion Control in Wireless Sensor Networks based on the Lotka Volterra Competition Model”, Biologically Inspired Networking and Sensing: Algorithms and Architectures, edited by Dinesh C. Verma and Pietro Lio, IGI Book, August 2010, pp. 158-181. Journal Papers [2] Pavlos Antoniou and Andreas Pitsillides, “A Bio-Inspired Approach for Streaming Applications in Wireless Sensor Networks based on the Lotka- Volterra Competition Model”, Elsevier Computer Communications, Special Issue on Applied Sciences in Communication Technologies, Vol. 33, No. 17, November 15, 2010, pp. 2039-2047. [3] Charalambos Sergiou, Pavlos Antoniou and Vasos Vassiliou, “Congestion Control Protocols in Wireless Sensor Networks: A Survey”, submitted to the IEEE Surveys and Tutorial Journal (accepted, subject to minor revision). Submitted Journal Papers [4] Pavlos Antoniou , Andreas Pitsillides, Tim Blackwell, Andries Engelbrecht and Loizos Michael, “Congestion Control in Wireless Sensor Networks based on Bird Flocking Behavior”, submitted to the Elsevier Computer Networks Journal.

Publications (cnt’d) University of Cyprus Conference/Workshop Papers [5] Pavlos Antoniou , Andreas Pitsillides, Andries Engelbrecht and Tim Blackwell, “Applying Swarm Intelligence to a Novel Congestion Control Approach for Wireless Sensor Networks”, 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2011), Invited Paper, Barcelona, Spain, October 26-29, 2011. [6] Pavlos Antoniou , Andreas Pitsillides, Andries Engelbrecht and Tim Blackwell, “Mimicking the Bird Flocking Behavior for Controlling Congestion in Sensor Networks”, 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), Invited Paper, Rome, Italy, November 7-10, 2010. [7] Pavlos Antoniou , Andreas Pitsillides, Tim Blackwell, Andries Engelbrecht and Loizos Michael, “Congestion Control in Wireless Sensor Networks based on the Bird Flocking Behavior”, IFIP 4th International Workshop on Self-Organizing Systems (IWSOS 2009), Zyrich, Switzerland, December 9-11, 2009, pp. 200-205. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 72

Publications (cnt’d) University of Cyprus [8] Pavlos Antoniou , and Andreas Pitsillides, “Congestion Control in Autonomous Decentralized Networks based on the Lotka-Volterra Competition Model”, 19th International Conference on Artificial Neural Networks (ICANN 2009), Limassol, Cyprus, September 14-17, 2009, pp. 986-996. [9] Pavlos Antoniou , Andreas Pitsillides, Tim Blackwell and Andries Engelbrecht, “Employing the Flocking Behavior of Birds for Controlling Congestion in Autonomous Decentralized Networks”, 2009 IEEE Congress on Evolutionary Computation (IEEE CEC 2009), May 18-21, Trondheim, Norway. [10] Pavlos Antoniou and Andreas Pitsillides, “Towards a Scalable and Self-adaptable Congestion Control Approach for Autonomous Decentralized Networks”, 3rd European Symposium on Nature- inspired Smart Information Systems (NiSIS2007), St. Julians, Malta, November 2007. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 73

Publications (cnt’d) University of Cyprus Poster [11] Pavlos Antoniou and Andreas Pitsillides, “Wireless Sensor Network Control: Drawing Inspiration from Complex Systems”, Poster Proceedings of the 6th IFIP Annual Mediterranean Ad Hoc Networking Workshop (MedHocNet2007), Corfu, Greece, June 2007. Technical Reports [12] Pavlos Antoniou , Andreas Pitsillides, Tim Blackwell, Andries Engelbrecht and Loizos Michael “From Bird Flocks to Wireless Sensor Networks: A Congestion Control Approach”, Technical Report TR-05- 11, Department of Computer Science, University of Cyprus, September 2011. [13] Pavlos Antoniou and Andreas Pitsillides “Understanding Complex Systems: A Communication Networks Perspective”, Technical Report TR-07-01, Department of Computer Science, University of Cyprus, February 2007. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 74

Repulsion forces University of Cyprus • Packet i repelled from packets Representation of a sensor network on grey-shaded nodes • Repulsion force proportional to the number of these packets – obtained through control packets* broadcasted periodically – control packets can be seen as means of transferring knowledge (propagate information) within packet i on node n the environment (sensor network) that is observable by birds' eyes (*) Control packets are broadcasted periodically (every T seconds, 75 sampling period)

Repulsion forces University of Cyprus Representation of a sensor network 7 6 8 5 n 1 i 4 2 3 packet i on node n 21/5/2012 Pavlos Antoniou - Ph.D. Defence 76

Attraction forces University of Cyprus • Packet i attracted to packets Representation of a sensor network on black-shaded nodes • Attraction force proportional to the number of these packets – cannot be obtained timely through control packets – black-shaded nodes outside of transmission range – use only locally available information packet i on node n – packet i can perceive packets ‘flying’ from nodes one hop away to nodes two hops away 21/5/2012 Pavlos Antoniou - Ph.D. Defence 77

Attraction forces University of Cyprus Representation of a sensor network 7 6 8 5 n 1 i 4 2 3 packet i on node n 21/5/2012 Pavlos Antoniou - Ph.D. Defence 78

Results – Scenario 1 University of Cyprus • Low T: keeps network updated, frequent evaluation of desirabilities  desirable nodes change at a fast pace – Low number of buffer overflows/high number of collisions – Effective when packet spreading is enabled ( ξ =0.5, 0.75, 1) and a high number of paths to the sinks are available • individuals in the flock are allowed to exploit the whole space and move on a balanced way over multiple paths to the sink – Ineffective at low ξ : coherent flock formation • next hop nodes belong to a very small number of closely located paths to the sink • proximity of these paths led to very high number of collisions • High T: infrequent control packet exchanges and desirability evaluations – High number of buffer overflows/low number of collisions 21/5/2012 Pavlos Antoniou - Ph.D. Defence 79

Flock-CC vs AntHocNet vs AntSensNet University of Cyprus 21/5/2012 Pavlos Antoniou - Ph.D. Defence 80

Flock-CC vs AntHocNet vs AntSensNet University of Cyprus 21/5/2012 Pavlos Antoniou - Ph.D. Defence 81

2 species Lotka-Volterra model University of Cyprus • Start with logistic growth model for each of the two species. • Population growth of species 1 depends on population size of species 1 (intra-specific comp.). • Population growth of species 2 depends on population size of species 2 (intra-specific comp.). • Now expand models so that growth depends on number of members of the same species and number of individuals of other competing species. (inter-specific) • α and β are termed the competition coefficients 82

2 species Lotka-Volterra model University of Cyprus • α is a measure of the effect of species 2 on growth of species 1. • β is a measure of the effect of species 1 on growth of species 2. • Competition coefficients measure strength of inter-specific competition effects relative to intra-specific competition. • If α > 1, then competitive effect of species 2 on population growth of species 1 is greater than that of an individual of species 1. • If α <1, then competitive effect of species 2 on population growth of species 1 is less than that of an individual of species 1. 21/5/2012 Pavlos Antoniou - Ph.D. Defence 83

Equilibria and Linearization University of Cyprus dx dy • System of non-linear differential equations:   F ( x , y ), G ( x , y ) dt dt • Study continuous models for two (or more) interacting populations: linearization at equilibria   * * * * F ( x , y ) 0 , G ( x , y ) 0 – behaviour of solutions near an equilibrium – periodic orbits cannot be revealed • Classification of equilibria : * , y * x – Stable ( node ): if every solution (with sufficiently x ( t ), y ( t ) x ( 0 ), y ( 0 )  close to equilibrium) remains close to equilibrium for all t 0   • Asymptotically stable: solutions tend to equilibrium as t – Saddle point: there is a curve through the equilibrium, orbits starting on this curve tend to the equilibrium, orbits starting off this curve cannot stay near the equilibrium – Spiral point or focus: every orbit wings around the equilibrium – Center: every orbit is periodic – Unstable 21/5/2012 Pavlos Antoniou - Ph.D. Defence 84

Equilibria and Linearization (cnt’d) University of Cyprus • Stability/Instability of an equilibrium for the linearization implies stability/instability of the equilibrium of the non- linear system • Asymptotic stability/instability for a linear system is determined using the community matrix of the system at the equilibrium • Describes the effect of the size of each species on the growth rate of itself and the other species at equilibrium   * * * * F ( x , y ) F ( x , y )    x y A   * * * * G ( x , y ) G ( x , y )   x y   det( rI A ) 0 85

Classification of equilibria University of Cyprus Stable point proper node (sink) improper node Saddle point (unstable) Unstable point proper node Center (periodic orbit) Stable Stable spiral Unstable spiral 21/5/2012 Pavlos Antoniou - Ph.D. Defence 86

Lotka-Volterra Equilibria University of Cyprus • In general, model predicts coexistence of two species when inter-specific competition is weaker than intra- specific competition for both species. • Otherwise, one species is predicted to exclude the other eventually. • Equilibrium (steady state) population densities at which population growth for the two species stops:   K K     * 1 2 N * * * 0 N 0 N N K   1 1 1 1 1 1    * * *   N K N 0 N 0 K K  2 2 2 2 * 2 1 N   2 1   r 0    1 A     r r      * *   1 1 0 r N N 1 1 2   K K  Unstable A 1 1   r r    * * 2 2  N N  node 2 2   K K 2 2 21/5/2012 Pavlos Antoniou - Ph.D. Defence 87

Lotka-Volterra Isoclines University of Cyprus • Isoclines of zero population growth are straight lines, where everywhere along the line population growth is stopped. (dN1/dt = 0 and dN2/dt = 0)       * * N K N N K N 2 2 1 1 1 2 Isocline for species 2 Isocline for species 1 21/5/2012 Pavlos Antoniou - Ph.D. Defence 88

Outcomes of Lotka-Volterra model University of Cyprus Case 1 Case 2 Stable steady state, N2 wins Stable steady state, N1 wins • Isoclines do not cross and isocline • Isoclines do not cross and isocline for species 2 lies above that of for species 1 lies above that of species 1. species 2. • Species 2 wins (species 1 excluded) • Species 1 wins (species 2 excluded) with equilibrium for species 2 at its with equilibrium for species 1 at its carrying capacity. carrying capacity. Competitive exclusion principle: species less suited 89 to compete for resources should either adapt or die out

Outcomes of Lotka-Volterra model University of Cyprus Case 3 Case 4 Unstable Stable equilibrium Steady equilibrium (node) states, either N1 or Saddle or N2 wins point • Isoclines cross • Isoclines cross • Intra-specific competition is stronger • Inter-specific competition is stronger than inter-specific competition. than intra-specific competition. • Stable coexistence at equilibrium. • Unstable equilibrium with eventual exclusion of one of the two species. Coexistence Competitive exclusion principle 90

Stability analysis University of Cyprus K • Linearization (Taylor) at equilibrium point  * x     ( n 1 ) • Stability is achieved if all eigenvalues of the community matrix (A) are negative       r r                      2 2 2 0   2 r r – n =2:              2 det( I A ) det 0                 r r  2   0                   r           0    1 , 2      – n =3: using Routh theorem  iff 0 1 , 2 , 3 K  • Stability of is achieved when *    x     ( n 1 ) • Model predicts coexistence of two (or more) species when inter-specific competition is weaker than intra-specific competition for all species 91

Extreme scenario (1/8) University of Cyprus Set of active nodes, 35 pkts/sec 21/5/2012 Pavlos Antoniou - Ph.D. Defence 92

Extreme scenario (2/8) University of Cyprus Nodes failed at t=40s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 93

Extreme scenario (3/8) University of Cyprus Nodes failed at t=45s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 94

Extreme scenario (4/8) University of Cyprus Nodes failed at t=50s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 95

Extreme scenario (5/8) University of Cyprus Nodes failed at t=55s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 96

Extreme scenario (6/8) University of Cyprus Nodes failed at t=60s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 97

Extreme scenario (7/8) University of Cyprus Nodes failed at t=65s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 98

Extreme scenario (8/8) University of Cyprus Nodes failed at t=70s 21/5/2012 Pavlos Antoniou - Ph.D. Defence 99

Extreme scenario 2 (1/11) University of Cyprus Set of active nodes, 35 pkts/sec 21/5/2012 Pavlos Antoniou - Ph.D. Defence 100