Energy-efficient Capture of Stochastic Events by Global- and Local-periodic Network Coverage David K. Y. Yau

SensorNet: Plume Detection by In-situ Sensor Network

Motivations  Unattended operation of many low-cost and small form factor sensors  Dense static network  Uncontrolled (random) placement  Significant overlap in sensing regions  Possibility to duty-cycle sensors to save energy  Eliminate redundant coverage  Load balance between sensors for maximum network lifetime  Unattended operation of many low-cost and small form factor sensors  Dense static network  Uncontrolled (random) placement  Significant overlap in sensing regions  Possibility to duty-cycle sensors to save energy  Eliminate redundant coverage  Load balance between sensors for maximum network lifetime

Motivations (cont’d)  Exploitation of real-world event properties and dynamics  Events may stay and hence can be captured with delay by (q,p)-periodic sensor  When events stay, “quality of monitoring” may be much higher than q/p  high energy saving potential  If p small enough, arbitrarily high fraction of events can be captured no matter how small q/p  (q,p) schedule can be optimized for maximum event/information capture given event dynamics  Exploitation of real-world event properties and dynamics  Events may stay and hence can be captured with delay by (q,p)-periodic sensor  When events stay, “quality of monitoring” may be much higher than q/p  high energy saving potential  If p small enough, arbitrarily high fraction of events can be captured no matter how small q/p  (q,p) schedule can be optimized for maximum event/information capture given event dynamics

Basics  Stochastic event arrivals/departures at each PoI  Distribution of event staying times X  Distribution of event absent times Y  X and Y for same event may be dependent; but different events are i.i.d.  Capture of events by sensor of range r (binary perfect-disk model)  Anisotropic sensing is possible without affecting main conclusions  Many sensors placed according to Poisson Point Process of intensity  ; may communicate within given wireless range  Stochastic event arrivals/departures at each PoI  Distribution of event staying times X  Distribution of event absent times Y  X and Y for same event may be dependent; but different events are i.i.d.  Capture of events by sensor of range r (binary perfect-disk model)  Anisotropic sensing is possible without affecting main conclusions  Many sensors placed according to Poisson Point Process of intensity  ; may communicate within given wireless range

Basics (cont’d)  Sensors can be turned on/off (as a whole)  Energy model: energy rate of k 1 when on, k 2 when off; constant energy c to switch between on/off  In principle, sensing/communication/computation can be independently controlled  Performance metrics (Step utility function)  Probability of instantaneous event capture P in (over all events that could be captured only)  Probability of event capture (with or without delay) P c  Other types of events (utility functions) can be analyzed  Sensors can be turned on/off (as a whole)  Energy model: energy rate of k 1 when on, k 2 when off; constant energy c to switch between on/off  In principle, sensing/communication/computation can be independently controlled  Performance metrics (Step utility function)  Probability of instantaneous event capture P in (over all events that could be captured only)  Probability of event capture (with or without delay) P c  Other types of events (utility functions) can be analyzed

Energy Efficiency Techniques  (q,p)-periodic sensor schedule to exploit event dynamics (mainly, staying times)  Coordinated sleep between sensors to eliminate redundant coverage  Sensor x is redundant if its sensing region is completely covered by those of its active neighbors (conservative condition)  Sensors exchange their location, active schedule, remaining energy, etc (Hello protocol)  Safe to turn off x without affecting performance  Different neighbors can go to sleep at different times; permission to sleep renegotiated for energy balance  (q,p)-periodic sensor schedule to exploit event dynamics (mainly, staying times)  Coordinated sleep between sensors to eliminate redundant coverage  Sensor x is redundant if its sensing region is completely covered by those of its active neighbors (conservative condition)  Sensors exchange their location, active schedule, remaining energy, etc (Hello protocol)  Safe to turn off x without affecting performance  Different neighbors can go to sleep at different times; permission to sleep renegotiated for energy balance

Coordinated Sleep Protocol  Sensor roles  Regular, supporting, redundant  Regular sensors identify their support sets regularly; a sensor ranks support set by  Minimum residual energy (energy balance)  Overlap of active time between itself and support members (maximally productive sleep; in particular, a > 2 c / (k 1 - k 2 ))  Sensor roles  Regular, supporting, redundant  Regular sensors identify their support sets regularly; a sensor ranks support set by  Minimum residual energy (energy balance)  Overlap of active time between itself and support members (maximally productive sleep; in particular, a > 2 c / (k 1 - k 2 ))

Example Support Set J B C A I H G D E F A’s support sets: C, D, H, I D, F, H, I Hello message

Negotiation of Permission to Sleep J B C A I H G D E F Request to sleep (RTS) Clear to sleep (CTS) Confirm (CNF) Supporting Redundant If two neighbors both want to go to sleep, they defer sending CTS by random delay (probabilistically longer if less remaining energy)

Syncrhonous and Asynchronous Periodic Network  Synchronous periodic network  All sensors start their (q,p) schedule at the same time (global-periodic)  Network of sensors behave as one big periodic sensor  Maximum coordinated sleep opportunities  Leverage lightweight time synchronization protocols  Asynchronous periodic network  Each sensor starts (q,p) schedule at an independent random point in time (local-periodic)  Spread-out on periods for better event capture  Reduced coordinated sleep opportunities (less temporal redundancy)  Zero coordination for periodic operation  Synchronous periodic network  All sensors start their (q,p) schedule at the same time (global-periodic)  Network of sensors behave as one big periodic sensor  Maximum coordinated sleep opportunities  Leverage lightweight time synchronization protocols  Asynchronous periodic network  Each sensor starts (q,p) schedule at an independent random point in time (local-periodic)  Spread-out on periods for better event capture  Reduced coordinated sleep opportunities (less temporal redundancy)  Zero coordination for periodic operation

Coordinated Sleep Opportunities  Spatial redundancy  Deployment density  Temporal redundancy  Overlapping q active time for synchronous periodic scheduling  Opportunistic overlapping time for asynchronous periodic scheduling  Higher q  higher overlap probability  Spatial redundancy  Deployment density  Temporal redundancy  Overlapping q active time for synchronous periodic scheduling  Opportunistic overlapping time for asynchronous periodic scheduling  Higher q  higher overlap probability

Design Points  Periodic scheduling can be used together and orthogonally with coordinated sleep  Four design points  Synchronous network with/without coordinated sleep  Asynchronous network with/without coordinated sleep  Periodic scheduling can be used together and orthogonally with coordinated sleep  Four design points  Synchronous network with/without coordinated sleep  Asynchronous network with/without coordinated sleep

Energy-aware Optimization of Synchronous Network  Required P in specified by user  P c of single sensor given by [CoNext 2008]  Required P in specified by user  P c of single sensor given by [CoNext 2008]

P c as function of p For Step utility, P c monotonically decreasing in p (full information captured instantaneously  no need to remain on)

Information Capture under Limited Energy When energy also considered, extremely fine q/p wastes energy to turn on/off the sensor frequently  optimal event capture per unit of energy occurs at intermediate p Energy model: k 1 q + k 2 (p - q) + 2c Standard techniques apply for single dimension optimization of continuous function

Analysis of Capture Delay

Capture Delay for Exponential Staying times

Illustration of Capture Delay

Event Capture of Asynchronous Network  Events not captured by one sensor may be captured by another sensor  All sensors within distance r of event are “within range”  Consideration for all in-range sensors needed  By Poisson Point Process, probability of k such sensors given by  Events not captured by one sensor may be captured by another sensor  All sensors within distance r of event are “within range”  Consideration for all in-range sensors needed  By Poisson Point Process, probability of k such sensors given by

Non-capture Probability by One Sensor  For P in, simply 1 - q/p  For P c, given by  Hence, we have …  For P in, simply 1 - q/p  For P c, given by  Hence, we have …

Probability of Instantaneous Capture (Asynchronous Network)

Probability of Capture (Asynchronous Network)

Optimization of Asynchronous Network  User-specified P in  q/p (Theorem 3)  P in increases linearly with q/p for synchronous network, but  Increase is exponential for asynchronous network  Optimization of p given q/p (Theorem 4)  User-specified P in  q/p (Theorem 3)  P in increases linearly with q/p for synchronous network, but  Increase is exponential for asynchronous network  Optimization of p given q/p (Theorem 4)

P in as Function of q/p (Asynchronous Network)

Optimization of Q E as Function of p (Asynchronous)

Network Simulations  Synchronous network  With coordinated sleep (S-CSP)  Without coordinated sleep (S-nc)  Asynchronous network  With coordinated sleep (A-CSP)  Without coordinated sleep (A-nc)  Role-alternating, Coverage-preserving protocol (RACP) [Hsin & Liu, IPSN 2004]  Synchronous network  With coordinated sleep (S-CSP)  Without coordinated sleep (S-nc)  Asynchronous network  With coordinated sleep (A-CSP)  Without coordinated sleep (A-nc)  Role-alternating, Coverage-preserving protocol (RACP) [Hsin & Liu, IPSN 2004]

A-nc vs. RACP  =4, required P in =  q/p=0.4 A-nc has 75% longer network lifetime, w/ little loss in P in A-nc requires no zero synchronization between sensors A-nc achieves perfect load balancing (trivially)

A-CSP vs. RACP A-CSP starts to die at about same time as A- nc, but … Death is much more gradual A-CSP has less good load balancing as RACP, because shifted on periods reduce chance for coordinated sleep

S-CSP vs. RACP (Probability of Instantaneous Capture) P in = 0.4  q/p = 0.4 S-CSP achieves required P in S-CSP lasts twice as long as RACP  tradeoff between performance and energy efficiency

S-CSP vs. RACP (Probability of Capture) S-CSP closes perfomance gap significantly in terms of event capture (0.8 vs. 0.4), because … S-CSP is designed to take advantage of event staying time to work less hard and capture events at a delay

S-CSP vs. A-CSP (Probability of Capture) S-CSP starts to die later because aligned on periods provide maximum sleep opportunities, but … A-CSP achieves better event capture almost all the time, in spite of its degraded performance earlier

S-CSP vs. S-nc Coordinated sleep prolongs network lifetime by about 1/3 Coordinated sleep achieves pretty good load balancing (complete network death happens rather quickly, cf. asynchronous network)

Summary of Results  Synchronous network provides performance/energy tradeoff by exploiting event staying time to capture events at a delay  If user is willing to relax requirement on P in (so we can use smaller q/p)  Performance gap closes significantly in terms of P c  At low/moderate density, asynchronous network provides similar tradeoff, but tradeoff becomes more attractive  P in increases exponentially w/ q/p (cf. linear increase for synchronous network)  Synchronous network provides performance/energy tradeoff by exploiting event staying time to capture events at a delay  If user is willing to relax requirement on P in (so we can use smaller q/p)  Performance gap closes significantly in terms of P c  At low/moderate density, asynchronous network provides similar tradeoff, but tradeoff becomes more attractive  P in increases exponentially w/ q/p (cf. linear increase for synchronous network)

Summary of Results (cont’d)  For high-density asynchronous network, tradeoff becomes mostly not necessary  Loss of P in is very small  Gain in network lifetime is quite large  Asynchronous network provides better performance than synchronous network, but …  Asynchronous network provides less chance for coordinated sleep (load balancing also less effective)  For high-density asynchronous network, tradeoff becomes mostly not necessary  Loss of P in is very small  Gain in network lifetime is quite large  Asynchronous network provides better performance than synchronous network, but …  Asynchronous network provides less chance for coordinated sleep (load balancing also less effective)

Related Work  Offline computation of subsets of k-cover sensors [Slijepcevic & Potkonjak 01]  Not adaptive to dynamic networks  Online coordinated sleep protocols [Hsin & Liu 0; Yan, He & Stankovic 03; Tan & Georganas 02]  Don’t consider event dynamics and optimization of periodic networks  Network optimization for dynamic events [Bisnik, Abouzeid & Isler 06; Yau et al. 08]  Sparse mobile sensor networks; no on-line sensor coordination  Offline computation of subsets of k-cover sensors [Slijepcevic & Potkonjak 01]  Not adaptive to dynamic networks  Online coordinated sleep protocols [Hsin & Liu 0; Yan, He & Stankovic 03; Tan & Georganas 02]  Don’t consider event dynamics and optimization of periodic networks  Network optimization for dynamic events [Bisnik, Abouzeid & Isler 06; Yau et al. 08]  Sparse mobile sensor networks; no on-line sensor coordination