# Social Network of Sensors

Model was written in NetLogo 5.3.1
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extensions [profiler] breed [ sensors sensor ] breed [ events event ] globals [ current-showing ; current sensor layer showed nmessages ; number of exchanged messages dist ; The distance of spreading from the center of the environment excor ; Save x cordinate of the sensors that get the event eycor ; Save y cordinate of the sensors that get the event n-degree total ; for test purposes CDT ;; Cumulative Dynamic Threshold strong-estimate ; The estimated number of strong ties weak-estimate ; The estimated number of weak ties reported ; flag to stop the simulation if the event has been reported to the sink training ; flag to let the simulation run if we are under the training time ] patches-own [ ;; keep track of how many times the patch has been covered ;; that is, how many times fell in sensor radius (but might not be visited!) overlapping ] ;; these variables are available for sensors and events turtles-own [ ;; virtual coordinates home-x home-y current-x current-y ] ;; sensor specific variables sensors-own [ jump-size ;; length of the jump waiting-time ;; time to wait before the next jump mobile ;; True if the sensor is mobile sink ;; True if the sensor is the sink found ;; True if has knowledge of the event sensor-radius ;; radius of the sensor displacement ;; sum of displacements from the home position Pnew ;; probability of a new jump. We need this for plotting visited-locations ;; list of visited locations with the frequency S ;; keep track of the number of disctinct visited locations sum-of-frequencies ;; keep track of the sum of the visited location (number of jumps) allmeet ;; Time of Last Encounter list ;; in Gradient (FRESH) routing is the time of last encounter of the sink ;; in DoNothing is the time of last encounter of other sensors TLE ;; message copies to spray in [Binary] Spray & Wait routing nMC ;; This list should be used to store probabilities or related quantities ;; Delivery Predictability metric in PROPHET routing ;; Encounter frequency in YAP and DoNothing P ;; Cumulative Score of Device d; ;; Barry Lavelle, Daragh Byrne, Cathal Gurrin, Alan F. Smeaton, Gareth J.F. Jones ;; "Bluetooth Familiarity: Methods of Calculation, Applications and Limitations." CS_d ;; total probability delivery of a sensor in YAP Pdelivery ;; OPTIMIZATION: keep track of the sum of encounter frequencies (for YAP) F ;; Total intervals where device d is present. I_d ;; Dunbar's Number D Ti-list; The history of encounter of sensor i CSTi ; The list of Strong Ties CWTi ; The list of Weak Ties strong-ties ;Strong Ties Estimate weak-ties ;Weak Ties Estimate ] to setup clear-all set-default-shape events "x" set-default-shape sensors "triangle" set current-showing -1 ;; show-next display sensor 0 set reported false ;; event has not been reported yet ;============================= Deploy Sensors ============================= ;; deploy sensor first so their agent number start from 0 to avoid problems with sensor-index deploy-sensors mobile-sensor-distribution n-of-mobile-sensors ask sensors [ set mobile true set S 1 ;; the first location is the sensor's "home" ] deploy-sensors static-sensor-distribution n-of-static-sensors ask sensors with [ mobile = 0 ] [ set mobile false ] ;; we need to do this since NetLogo defaults variables to 0 ;; WATCH-OUT!!! If you deploy sensors in a lattice then the actual number of them might have been changed by deploy-sensors procedure ;; so we set n-of-mobile-sensors and n-of-static-sensors to the actual value set n-of-mobile-sensors count sensors with [mobile] set n-of-static-sensors count sensors with [not mobile] ;; WATCH-OUT!!! Sinks MUST be deployed before events since the who variable is used as index in P deploy sink-location "sink" 1 deploy event-location "event" 1 ;============================= Intialize Sensors' State ============================= ask sensors [ set size 2 ;; size 2 is cached by netlogo, so it will run faster! set found false set home-x current-x set home-y current-y setxy current-x current-y ;; send sensors to "home" ifelse (mobile = true) [ set shape "person" set color green set sensor-radius mobile-radius set sum-of-frequencies 0 set displacement 0 ;; [initial-patch-index frequence visit-time] set frequence = 0 because it is updated to 1 at first move call. set visited-locations n-values 1 [(list (p-index pxcor pycor) 0 0)] ] [ set color violet set sensor-radius static-radius ] ] ask sensors with [sink = true] [ set shape "flag" set color yellow set size 2 set mobile false ] ask events [ set color red set size 2 ] ;============================= Initialize Protocol Variables ============================= ;; some protocols need to consider the sink as a special node, others do not consider sinks ask sensors [ ;; Time of Last Encounter is used by Gradient (FRESH), PRoPHET, and DoNothing protocols set TLE n-values (n-of-mobile-sensors + n-of-static-sensors + 1) [0] ;; [Binary] Spray & Wait OR 2000000000000 & Wait set nMC 0 ;; PRoPHET protocol if Routing = "PRoPHET" [ set P n-values (n-of-mobile-sensors + n-of-static-sensors + 1) [0] ;; P(A,x) set P replace-item who P 1 ;; P(A,A) = 1 ] if Routing = "ExtractingFriendshipRelations" [ set P n-values (n-of-mobile-sensors) [0] ;; [F(A,x)] set CS_d n-values (n-of-mobile-sensors) [0] ;; [CS_d(A,d)] set I_d n-values (n-of-mobile-sensors) [0] ;; [I_d(A,d)] ] if Routing = "SpreadingToStrongOrWeakTies" [ set Ti-list [] ;; The List of Encounter Frequencies set CSTi [] ;; The List of Strong Ties set CWTi [] ;; The List of Weak Ties set strong-estimate []; The list of strong ties estimate set weak-estimate [] ;The list of weak ties estimate set strong-ties 0 ;The estimated number of strong ties set weak-ties 0 ;The estimated number of weak ties ] if Routing = "DoNothing" [ set P n-values (n-of-mobile-sensors) [0] ;; [F(A,x)] set CS_d n-values (n-of-mobile-sensors) [0] ;; [CS_d(A,d)] set I_d n-values (n-of-mobile-sensors) [0] ;; [I_d(A,d)] ] ] ;; these kind of walks are incompatible with Song's model, so disable it!!! if (type-of-walk = "Brownian motion (Wiener)") or (type-of-walk = "correlated directions") [ set preferential-return false ] ;; If training time is set, let's the model warm up before starting the simulation reset-ticks ; initialize tick counter set training false if Training-Time > 0 [ set training true repeat Training-Time [go] set training false ;; reset the overlapping so that % of area covered start from 0% ask patches [ set overlapping 0 ] ;; reset sensor state ask sensors with [ mobile ] [ ; send sensors back to home so that they reflect the initial spatial distribution set current-x home-x set current-y home-y setxy home-x home-y set displacement 0 set jump-size 0 set waiting-time 0 ; set time of last visit to 0 because ticks starts back from 0 set visited-locations map [(list (item 0 ?) (item 1 ?) 0)] visited-locations ] ] clear-all-plots ; so the initial state is the state of the model after warm up reset-ticks end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;; Deployment Functions ;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; to deploy [place-in which number] if (place-in = "none") [ set number 0 ] ;; do not deploy sinks or events if (which = "event" and number > 1 and event-location != "random") [ error "Only random deployment is supported in multi event environment" ] if (which = "sink" and number > 1 and sink-location != "random") [ error "Only random deployment is supported in multi sink environment" ] if (event-location = "diagonal") xor (sink-location = "diagonal") [ error "Both event and sink must be on diagonal" ] repeat number [ let x 0 let y 0 if place-in = "random"[ set x random-xcor set y random-ycor ] if place-in = "center"[ if (any? sensors with [sink = true and xcor = 0 and ycor = 0]) or (any? events with [xcor = 0 and ycor = 0]) [ error "You cannot have both event and sink in the center" ] set x 0 set y 0 ] if place-in = "corner"[ let corners (list max-pxcor max-pycor min-pxcor min-pycor) ;; since there are only 4 corners it's highly probable an overlap of target and sink, so we check to avoid it! set x one-of corners set y one-of corners while [(any? sensors with [sink = true and xcor = x and ycor = y]) or (any? events with [xcor = x and ycor = y])] [ set x one-of corners set y one-of corners ] ] if place-in = "diagonal" [ let diag 0 ifelse diag-dist > 1 [ set diag (sqrt ((world-width - 1) ^ 2 + (world-height - 1) ^ 2)) ][ error "Please specify a greater distance" ] ifelse (diag - diag-dist) > 0 [ ;; we put sink and target equally distant from corners let cosine (world-width - 1) * (diag - diag-dist) / (2 * diag) let sine (world-height - 1) * (diag - diag-dist) / (2 * diag) if which = "event" [ set x min-pxcor + cosine set y min-pycor + sine ] if which = "sink" [ set x max-pxcor - cosine set y max-pycor - sine ] ][ error "Distance between sink and event is higher than length of diagonal" ] ] if which = "event" [create-events 1 [setxy x y]] if which = "sink" [ create-sensors 1 [ set current-x x set current-y y set sink true ] ] ];;END of repeat number end to deploy-sensors [ sensor-distribution n-of-sensors ] if n-of-sensors = 0 [stop] if sensor-distribution = "lattice" [ if (n-of-sensors < 4) [ error "You need at least 4 sensors!" ] let x-side floor sqrt(n-of-sensors) let x-increment (max-pxcor - min-pxcor) / (x-side - 1) let y-side ceiling (n-of-sensors / x-side) let y-increment (max-pycor - min-pycor) / (y-side - 1) let x min-pxcor let y min-pycor repeat y-side [ repeat x-side [ create-sensors 1 [ set current-x x set current-y y ] set x x + x-increment ] set y y + y-increment set x min-pxcor ] ] ;; END "lattice" if sensor-distribution = "uniform" [ create-sensors n-of-sensors [ set current-x random-xcor set current-y random-ycor ] ] if sensor-distribution = "exponential" or sensor-distribution = "normal" or sensor-distribution = "power-law" [ let next-one FALSE create-sensors n-of-sensors [ let hypotenuse 0 while [ not next-one ] [ set heading random-float 360 if sensor-distribution = "exponential" [ ;; we want 1 - e^(-lambda * world-width / 2) = 0.95; we pass the mean mu = 1 / lambda set hypotenuse exponential (- 0.5 * world-width / ln (1 - 0.95)) ] if sensor-distribution = "normal" [ ;; We want 2*sigma = world-width / 2 => P(X <= world-width / 2) = 0.954499736104 ;; 3*sigma => 0.997300203937 set hypotenuse Rayleigh (0.5 * world-width / 2) ] if sensor-distribution = "power-law" [ ;; P (X > x) = x^(1-alpha) = 0.05 => x = (1/0.05)^(1/(alpha-1)) = world-width / 2 ;; => alpha = 1 + 1 / (log (world-width / 2) (1 / 0.05)) set hypotenuse Levy ( 1 + 1 / (log (world-width / 2) (1 / 0.05)) ) ] if not (patch-at-heading-and-distance heading hypotenuse = nobody) [ set next-one true ] ] set current-x hypotenuse * dx set current-y hypotenuse * dy set next-one FALSE ] ] end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## to go ;; if simulation is running ripristinate overlapping layer if (current-showing >= 0) [ display-overlapping-layer ] if (Routing = "Gradient") and (sink-location != "none") [ ;; TLE of sinks is always the most recent (that is, the time elapsed from the last encounter is 0) ask sensors with [sink = true] [set TLE replace-item who TLE ticks] ] ;profiler:start ask sensors with [ mobile ] [ move ] ;profiler:stop ;; WATCH OUT!!! Do NOT use WITH-hack here because we need sensors to check in a random (not synchronized) order!!! ;profiler:start ask sensors [ if not training [ if (event-location != "none") and (not found) and (any? events in-radius sensor-radius) [ set nMC L ;; if sensor found an event then spray L copies set found true set color red ] if (sink-location != "none") and (found) and (any? sensors with [sink = true] in-radius sensor-radius) [ set nMC 0 set reported true ;; if we reach the sink simulation should stop, so exit from ask sensors stop ] ] ;; Forward events towards the sink accordingly to the chosen algorithm ; let route-date out of if not training to let sensors update their delivery probabilities ;profiler:start route-data ;profiler:stop ] ;;END ask sensors if Routing = "ExtractingFriendshipRelations" [ set CDT (ticks ^ (1 / 3)) ;update Cumulatyve Dynamic Threshold ] ;; if the event has been reported to a sink then stop the simulation if reported [stop] tick end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;; Move Functions ;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; to move if (jump-size <= 0) ;and (waiting-time <= 0) ;a new flight must be chosen (note that waiting-time will be always = 0 if wait-time is false) [ let sensor-index who ;; we MUST do all this here because only the destination of the travel count for displacement, ;; location frequencies and preferential return!!! set displacement ( displacement + sqrt ((home-x - current-x) ^ 2 + (home-y - current-y) ^ 2) ) set sum-of-frequencies sum-of-frequencies + 1 ;; the following code is doing the same operation as ;; ask patch-here [ set frequencies replace-item sensor-index frequencies (item sensor-index frequencies + 1) ] ;; but more efficiently let patch-index (p-index pxcor pycor) ;let v-loc array:from-list visited-locations let location-index position patch-index (n-values (length visited-locations) [ item 0 (item ? visited-locations)] ) ;let location-index position patch-index (n-values (array:length v-loc) [ item 0 (array:item v-loc ?)] ) ifelse location-index != false [ set visited-locations replace-item location-index visited-locations (list patch-index (item 1 (item location-index visited-locations) + 1) ticks) ;array:set v-loc location-index (list patch-index (item 1 (array:item v-loc location-index) + 1)) ;set visited-locations array:to-list v-loc ] [ set visited-locations lput (list patch-index 1 ticks) visited-locations ] ifelse preferential-return [ ;; avoid use of (count patches with [ item sensor-index frequencies > 0 ] ) ^ (- gamma) for better performance ;set Pnew ro * (S ^ (- gamma)) ifelse ( random-float 1 < ro * (S ^ (- gamma)) ) [ ;; if true Explore let newLocation patch-here while [ (newLocation = nobody) or ;( member? (patch-at-heading-and-distance heading jump-size) ([self] of patches with [ item sensor-index frequencies > 0 ]) ) member? ( p-index ([pxcor] of newLocation) ([pycor] of newLocation) ) (n-values (length visited-locations) [ item 0 (item ? visited-locations)] ) ][ ;; we want a new location!!! That is, a patch not visited before set heading random-float 360 set jump-size FlightLength set newLocation patch-at-heading-and-distance heading jump-size ] set S S + 1 ] [ ;; else do a Return jump ifelse (random-float 1 < lambda) [ do-frequency-return ][ do-recency-return ] ] ][ while [ ((patch-at-heading-and-distance heading jump-size) = nobody) or (jump-size = 0)] [ ifelse type-of-walk != "correlated directions" [ set heading random-float 360 ] [ rt random-normal 0 stdev-angle ] set jump-size FlightLength ] ] if wait-time [ set waiting-time round Levy-cutoff beta (1 / cutoff-time) ] ] ;; END if jump-size <= 0 ;; sensors go back "home" at regular time steps (e.g., 24h-48h-72h) if back-to-home [ if (ticks mod back-time = 0) [ let x (home-x - current-x) let y (home-y - current-y) if (x != 0 and y != 0) [ set heading atan x y ;; set sensor heading towards home set jump-size sqrt(x ^ 2 + y ^ 2) ] ] ] ifelse (waiting-time > 0) [ set waiting-time waiting-time - 1 ] [ ;; sensors moves at a fixed speed V <= 1 step/tick ifelse(jump-size < 1) [ set current-x current-x + jump-size * dx ; dx and dy are like cos and sin set current-y current-y + jump-size * dy set jump-size 0 ][ set current-x current-x + dx set current-y current-y + dy set jump-size jump-size - 1 ] ;; update sensor position setxy current-x current-y foreach [self] of patches in-radius sensor-radius [ ask ? [ set overlapping overlapping + 1 ;; we visited the patch so color it! set pcolor scale-color blue overlapping 1 150 ] ] ];;END of if waiting-time > 0 end ;; END of movend to do-frequency-return let throw random sum-of-frequencies let flag false let partial-sum 0 let x 0 let y 0 foreach visited-locations [ if not flag [ set partial-sum partial-sum + item 1 ? if (partial-sum > throw) [ set flag true set x (px-index (item 0 ?)) set y (py-index (item 0 ?)) ] ] ] facexy x y ;; set heading towards the new location set jump-size sqrt((current-x - x) ^ 2 + (current-y - y) ^ 2) end to do-recency-return ; obtain the value of the quantile function from a zipfian distribution and round it to closest integer. This is the recency rank selcted. let k (round (Levy nu) - 1) ; because xmin = 1 while [ k >= length visited-locations] ; TODO: fix the way to select recent location because we loop many times when length of visited-locations is small [ set k (round (Levy nu) - 1) ] ; order locations according to the visiting time let location (item k (sort-by [item 2 ?1 > item 2 ?2] visited-locations)) let x (px-index (item 0 location)) let y (py-index (item 0 location)) facexy x y ;; set heading towards the new location set jump-size sqrt((current-x - x) ^ 2 + (current-y - y) ^ 2) end to-report FlightLength if (type-of-walk = "Brownian motion (Wiener)") or (type-of-walk = "correlated directions") [ report 1 ] if type-of-walk = "exponential" [ report exponential (1 / lambda) ] if type-of-walk = "Rayleigh flight" [ report Rayleigh stdev ] if type-of-walk = "Cauchy flight" [ report Cauchy ] if type-of-walk = "Levy flight" [ report Levy alpha ] if type-of-walk = "Levy with Exp cutoff" [ report Levy-cutoff alpha (1 / cutoff-length) ] end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;; ;;; Routing Functions ;;; ;;;;;;;;;;;;;;;;;;;;;;;;; to route-data if Routing = "Epidemic" and found [ ;; if event not found we don't need to update data ask other sensors in-radius sensor-radius with [not found] [updateData] stop ] if Routing = "Probabilistic Flooding" and found [ ask other sensors in-radius sensor-radius with [not found] [ if ((random-float 1) < delta) [updateData] ] stop ] if Routing = "Gradient" and found [ if any? sensors with [sink = True] in-radius sensor-radius [ set TLE replace-item who TLE ticks ;; update Time of Last [sink] Encounter ] let myTLE item who TLE ;; we look for sensors which encountered sink more recently ask other sensors in-radius sensor-radius with [ (item who TLE) > myTLE ] [updateData] stop ] if Routing = "PRoPHET" [ ;; we must update deliver predictability even if we didn't find the event let sensors-in-radius [who] of other sensors in-radius sensor-radius ;; remove myself because P(A,A) == 1 ;; 1 - When two nodes A and B meet, the first thing they do is to update the delivery predictability for each other if (empty? sensors-in-radius) [stop] foreach sensors-in-radius [ let Pold (item ? P) ifelse ( Pold < 0.1 ) ;; if delivery predictability is less than 1% than it's like if it never encountered that node ;; If node B has not met node A for a long time or has never met node B, such that P_(A,B) < P_first_threshold, then P_(A,B) should be set to P_encounter_first. ;; P_encounter_first SHOULD be set to 0.5 unless the node has extra information obtained other than through PRoPHET about the likelihood of future encounters. [ set P replace-item ? P 0.5 set TLE replace-item ? TLE ticks ] ;; P_(A,B) = P_(A,B)_old + ( 1 - delta - P_(A,B)_old ) * P_encounter [ set P replace-item ? P (Pold + (1 - 0.01 - Pold) * Pinit) set TLE replace-item ? TLE ticks ] ] let other-sensors [who] of other sensors ;; 2 - The predictabilities for all other destinations must be 'aged'. ;if (not empty? sensors-in-radius) [ ;; age probabilities of other sensors only if a sensor is met, because update of delivery predictability id done only in that case foreach other-sensors [ let Pold (item ? P) let tle-x (item ? TLE) ;; If a pair of nodes do not encounter each other during an interval, they are less likely to be good forwarders of bundles to each other, ;; thus the delivery predictability values must age. ;; The delivery predictabilities are aged before being passed to an encountered node so that they reflect the time ;; that has passed since the node had its last encounter with any other node. ;; P_(A,B) = P_(A,B)_old * gamma^K ;; where 0 <= gamma <= 1 is the aging constant, and K is the number of time units that have elapsed since the last time the metric was aged. if not ((member? ? sensors-in-radius) or (Pold = 0)) [ set P replace-item ? P ( Pold * aging ^ (ticks - tle-x) ) set TLE replace-item ? TLE ticks ] ] ;; 3 - Predictabilities are exchanged between A and B and the 'transitive' property of predictability is used to update the predictability of destinations C ;; for which B has a P(B,C) value on the assumption that A is likely to meet B again: foreach sensors-in-radius [ set nmessages nmessages + 1 ;; we consider 1 message each time we exchange predictabilities ;; P_(A,C) = MAX( P_(A,C)_old, P_(A,B) * P_(B,C)_recv * beta ) ;; where 0 <= beta <= 1 is a scaling constant that controls how large an impact the transitivity should have on the delivery predictability. let Pab (item ? P) let tle-x (item ? TLE) let current-sensor ? foreach other-sensors [ ;; skip P(A,A) let Pac_old (item ? P) let Pbc (item ? [P] of turtle current-sensor) let Pac_new (Pab * Pbc * trans) if not ((Pac_new < Pac_old) or (member? ? sensors-in-radius) or (Pbc = 0)) [ ;; skip P(B,B) == 1 set P replace-item ? P Pac_new set TLE replace-item ? TLE tle-x ] ] ] if found [ let PA_sink item 0 (item (n-of-mobile-sensors + n-of-static-sensors ) P) ;; implements GRTR forwarding strategy described here: http://tools.ietf.org/html/draft-irtf-dtnrg-prophet-10#section-3.6 ask other sensors in-radius sensor-radius with [ (item (n-of-mobile-sensors + n-of-static-sensors) P) > PA_sink and not found] [updateData] ] stop ] ;; END "PRoPHET" if Routing = "Spray & Wait" [ if (nMC <= 1) [stop] ;; wait phase if found [ ;; not needed actually, I keep it just for safety reasons if I mess with go procedure foreach ( [self] of other sensors in-radius sensor-radius with [not found] ) [ if (nMC > 1) [ ask ? [updateData] ] ;; spray phase set nMC nMC - 1 ] ] stop ] ;; END Spray & Wait if Routing = "Binary Spray & Wait" [ if (nMC <= 1) [stop] ;; wait phase if found [ foreach ( [self] of other sensors in-radius sensor-radius with [not found] ) [ if (nMC > 1) [ ;; spray phase ask ? [ updateData set nMC int ([nMC] of myself / 2) ] ] set nMC nMC - int (nMC / 2) ] ] stop ] ;; END "Binary Spray & Wait" ;; ############ Peer-to-Peer Communication Protocols ########## if Routing = "ExtractingFriendshipRelations" [ let sensors-in-radius [who] of other sensors in-radius sensor-radius ;; 1 - When two nodes A and B meet, the first thing they do is to update encounter frequency foreach sensors-in-radius [ ifelse (? < n-of-mobile-sensors) [ let fk (item ? P) ;; encounter frequency at time t-1 set P replace-item ? P (fk + 1) ;; F(A,B) = F(A,B)_old + 1 ] [ error "Out of P range!" ] ] if (ticks mod aging) = 0 [ ;; 2 - Update the Cumulative Score of Device d ;; we recompute Dunbar's number every so often, so reset it set D 0 ;; compute AVG(F) Average of all encounter frequencies within given interval let avgF (mean P) if avgF = 0 [ set avgF 1 ] let pos 0 foreach P [ if (? > 0) [ let CS_d_old (item pos CS_d) let I_d_old (item pos I_d) set I_d replace-item pos I_d (I_d_old + 1) ;; T_i is the length in second (in our case ticks, but it doesn't really matter!) of the interval ;; let T_i aging ;; let F_d ? ;(item pos P) let CS_d_new ( CS_d_old + (? / avgF) * aging / T_d ) set CS_d replace-item pos CS_d CS_d_new ] ;; 3 - compute Dunbar's number estimate ;; Pinit is the Static Baseline threshold, in the paper is named alpha ;; CDT is the Cumulative Dynamic Threshold, in the paper is named beta ;; I_d is the total intervals the devic d were present ;; if true then it is a "Familiar" device if item pos CS_d > (Pinit + CDT * (item pos I_d)) [ set D (D + 1) ] set pos (pos + 1) ] ;; reset frequencies set P n-values (n-of-mobile-sensors) [0] ] stop ] ;; ExtractingFreiendshipRelations if Routing = "SpreadingToStrongOrWeakTies" [ let sensors-in-radius [who] of other sensors in-radius sensor-radius ;show sensors-in-radius ; when a sensor meet another this encounter is reported in Ti-list foreach sensors-in-radius [ ;Multiply a sensor id by 0.0003 in order to use the form (f.id) ;the integer part (f) represents the frequancy while the float represents sensor id ;for example, 2.0001 means sensor1's frequency is 2. let temp1 ? let temp2 (? * 0.0001) ; If the history of encounters is empty THEN ; we put the currrent encounter into Ti-list and make its frequency = 1 ifelse(empty? Ti-list) [set Ti-list fput (temp2 + 1) Ti-list] [ foreach Ti-list [ ; we check whether the current encounter is in the history of encounters ; if YES we just update the encounter frequency by 1 ; if NO this means a new item will be inserted into the history of encounters and make its frequency = 1 ifelse(member? temp1 map[round((? - int ?) * 10000)] Ti-list) [ set Ti-list replace-item position ? Ti-list Ti-list (? + 1) ] [ set Ti-list fput (temp2 + 1) Ti-list] ] ; foreach Ti-list ] ;ifelse 1 ] ; foreach sensor-in-radius ; sort Ti-list according to the frequencies set Ti-list sort Ti-list ;Extracting the number of strong ties set strong-ties 0 ;reset the strong-ties buffer set strong-ties 0 ;reset the weak-ties buffer ; Get the length of 20% (strong ties) in Ti list set strong-ties length (sublist Ti-list round(length Ti-list * 0.8) round( length Ti-list)) ; Get the length of 80% (weak ties) in Ti list set weak-ties length (sublist Ti-list 0 round(length Ti-list * 0.8)) ; NOW this part invovlves CSTi and CWTi ; putting the strong and weak ties in their lists when they are empty ;Extracting the strong and weak ties according to 80/20 rule ; 1- Strong Ties foreach map [round((? - int ?) * 10000)] sublist Ti-list (length Ti-list * 0.8) ( length Ti-list) [ ; if this strong tie sensor is not in CSTi if(not member? ? CSTi) [ if(member? ? CWTi) [ ; remove it from CWTi set CWTi remove-item position ? CWTi CWTi ] ;add it to CSTi set CSTi fput ? CSTi ] ] ; Foreach - For Adding Strong Ties ; ; ; 2- Weak Ties foreach map [round((? - int ?) * 10000)] sublist Ti-list 0 (length Ti-list * 0.8) [ if(not member? ? CWTi) [ set CWTi fput ? CWTi ] ] ; Foreach - For Adding Weak Ties ;; Spreading Phase foreach sensors-in-radius [ if(DataSpreadingTo = "Strong Ties") [ if(not found and member? ? CSTi) [ updatedata ; Transfer the event from sensor ? to those which are in CWTi ] ] if(DataSpreadingTo = "Weak Ties") [ if(not found and member? ? CWTi) [ updatedata ; Transfer the event from sensor ? to those which are in CWTi ] ] ] stop ] ;"SpreadingToStrongOrWeakTies" end ;; END Route-Data to updateData ;; increase the number of messages exchanged set nmessages nmessages + 1 ;print (word "from " [who] of myself " to " who " at time " ticks) set color red set found true end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;;; ;;; Quantile Functions ;;; ;;;;;;;;;;;;;;;;;;;;;;;;;; to-report exponential [ rlambda ] ;; step length is chosen from an exponential distribution, with mean (1/lambda) = rlambda report random-exponential rlambda end to-report Rayleigh [ sigma ] ;; uses a normal distribution with standard deviation (sigma) = sigma and mean (mu) = 0 report abs random-normal 0 sigma end to-report Cauchy ;; quantile function (inverse cdf) of the Cauchy distribution is: ;; x0 + gamma * tan[pi* (p - 1/2)] ;; x0 is the location parameter, specifyng the location of the peak of the distribution ;; gamma is the scale parameter and is sometimes called the probable error ;; when x0 = 0 and gamma = 1 is called standard Cauchy distribution report abs tan((random-float 1 - 0.5) * 180 ) end to-report Levy [ scaling ] ;; length of flight is given by: ;; x = xmin * (1 - r)^(-1/(alpha-1)) ;; r is a random uniformly distributed real number ;; xmin is the lower bound to the power-law behaviour. We assume xmin = 1 ifelse(scaling > 1) [ report (1 - random-float 1) ^ (1 / (1 - scaling)) ] [ error "power law distribution must have exponent greater than 1" ] end to-report Levy-cutoff [ scaling lambd ] if(scaling < 1) [ error "power law distribution must have exponent greater than 1" ] ;; length of fly is choosed according to power law with exponential cutoff ;; x^(-alpha) * e^(-x*lambda) ;; where alpha = scaling and lambda = cutoff (in `Understanding individual human mobility patterns - Nature-2008`, lambda = 1/k) ;; For the case of the power law with cutoff there is no closed-form expression for quantile, ;; but one can generate an exponentially distributed random number using the formula ;; x = xmin − 1/lambda ln(1 − r) ;; where r is uniformly distributed and then accept or reject it with probability p or 1 − p respectively, ;; where ;; p = (x/xmin)^(-alpha). ;; Repeating the process until a number is accepted then gives an x with the appropriate distribution. ;; ;; This algorithm is a port of randht.py (Python, by Joel Ornstein) showed here: http://tuvalu.santafe.edu/~aaronc/powerlaws/ let x (list) let y (list) let xmin 1. let n 1 ;; number of samples to return let mu (1. / lambd) ; try to avoid recomputing it when q < 0 ;repeat (10 * n) [ set y lput (xmin - mu * ln(1 - random-float 1)) y ] let samples n-values (10 * n) [?] ;; this is a list [ 0 1 2 3 ... 10*n-1 ] loop [ set y (list) ;repeat (10 * n) [ set y lput (xmin + random-exponential mu) y] repeat (10 * n) [ set y lput (xmin - mu * ln(1 - random-float 1)) y ] let ytemp (list) foreach samples [ ;if ( random-float 1 < ((item ? y) / xmin ) ^ (- scaling) ) [ set ytemp lput (item ? y) ytemp ] if ( random-float 1 < (item ? y) ^ (- scaling) ) [ set ytemp lput (item ? y) ytemp ] ; do not divide by xmin because xmin = 1 ] ;;set y ytemp ;;set x sentence x y ;; concatenates lists set x sentence x ytemp ;; no point of setting y when it is not used later because we either return or overwrite it let q (length x - n) if (q = 0) [ report item 0 x ] if (q > 0) [ let r n-values (length x) [?] ;; this is a list [ 0 1 2 3 ... length(x)-1 ] let perm shuffle r let xtemp (list) foreach r [ if (not member? ? (sublist perm 0 q)) [ set xtemp lput (item ? x) xtemp ] ] ;set x xtemp ;report item 0 x report item 0 xtemp ; no need to reallocate if we are going to return ] ;if (q < 0) [ we do not need to check, if we didn't get out of the loop this condition is always true, so I moved it at the beginning of loop ; set y (list) ;repeat (10 * n) [ set y lput (xmin + random-exponential mu) y] ; repeat (10 * n) [ set y lput (xmin - mu * ln(random-float 1)) y ] ; 1 - random-float 1 = random-float 1 ;] ] ;; END loop end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;; Monitor & Reporters ;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;; to-report p-index [px py] ;; this is a linear transform from (x,y) matrix style coordinates to [0,(max-pxcor-min-pxcor)] ==> y * (world-width) + x ;; since min-pxcor <= 0 and min-pycor <= 0 we can easily traslate pxcor and pycor to be >= 0 report (py - min-pycor) * (world-width) + px - min-pxcor end to-report px-index [pindex] report ifelse-value (pindex > 0) [ pindex mod world-width + min-pxcor ] [min-pxcor] end to-report py-index [pindex] report ifelse-value (pindex > 0) [ int ( pindex / world-width ) + min-pxcor] [min-pycor] end to-report fraction-of-covered-area report (count patches with [ overlapping > 0 ] ) / (count patches) end to-report fraction-of-acknowledged-nodes report (count sensors with [found]) / (count sensors) end to-report sensor-density ;; unit square here is 10x10 patches report (n-of-mobile-sensors + n-of-static-sensors) / (world-width * world-height) * 100 end to-report mean-S ;;n-values count sensors [ count patches with [item ? frequencies > 0] ] report mean ([S] of sensors with [mobile]) end to-report mean-D let Ds [D] of sensors with [mobile and D > 0] report ifelse-value (not empty? Ds) [mean Ds][0] end to-report MSD report ifelse-value (ticks > 0) [mean [(displacement / sum-of-frequencies) ^ 2] of sensors with [mobile]][0] end to export-patches-own-variables if file-exists? (word "frequencies-run-" behaviorspace-run-number ".csv") [ stop ] file-open (word "frequencies-run-" behaviorspace-run-number ".csv") let nsensors count sensors with [ mobile = true ] ;; write the header file-type "\"pxcor\",\"pycor\",\"overlapping\"" let sensor-index 0 repeat nsensors [ file-type (word ",\"f" sensor-index "\"") set sensor-index sensor-index + 1 ] file-type "\n" file-flush ;; write values ask patches [ file-type (word pxcor "," pycor "," overlapping) set sensor-index 0 let patch-index (p-index pxcor pycor) let freq 0 repeat nsensors [ ask sensor sensor-index [ let visited-patches (n-values (length visited-locations) [ item 0 (item ? visited-locations)] ) let location-index position patch-index visited-patches ifelse (location-index != false) [set freq (item 1 (item location-index visited-locations))][set freq 0] ] file-type (word "," freq) set sensor-index sensor-index + 1 ] file-type"\n" ] file-close end ;######################################################################################################################################################## ;######################################################################################################################################################## ;######################################################################################################################################################## ;;;;;;;;;;;;;;;;;;;;;;;;; ;;; Display Functions ;;; ;;;;;;;;;;;;;;;;;;;;;;;;; to display-overlapping-layer ask patches [ set pcolor scale-color blue overlapping 1 1000 ] set current-showing -1 end to display-sensor-layer [ sensor-index ] if (sensor-index >= 0) and (sensor-index < n-of-mobile-sensors) [ ;; update current index set current-showing sensor-index ;; change color with sensor frequency ask patches [ set pcolor 0 ] ask sensor sensor-index [ foreach visited-locations [ ask patch (px-index (item 0 ?)) (py-index (item 0 ?)) [ set pcolor scale-color lime (item 1 ?) 1 20 ] ] ] ] end

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## Attached files

File | Type | Description | Last updated | |
---|---|---|---|---|

parsing-utils.zip | data | Utils to parse the data generated by BehaviorSpace experiments and import it to R [UPDATED] | over 8 years ago, by Marcello Tomasini | Download |

Social Network of Sensors.png | preview | Preview for 'Social Network of Sensors' | over 9 years ago, by Marcello Tomasini | Download |

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