1. Using Pattern of Social Dynamics in the Design of Social Networks of Sensors - Marello Tomasini, Franco Zambonelli, Ronaldo Menezes 한국기술교육대학교 전기전자통신 공학부 CIS LAB 지선호

2. Outline Ⅰ Ⅰ II III Ⅳ Ⅳ Ⅴ Ⅴ

3. What is the issue? Issue Model features - Mobile sensors are carried by people - The human mobility patterns have been modeled based on the most common social mobility pattern for urban. - Static sensors are assumed to be deploy uniformly around the environment. Elucidated the relationship between detection and report time in SNoS. the existence of a lower and a higher threshold in the percentage of mobile sensors Evaluate the performance of urban SNoS Social Network of Sensors(SNoS) de facto ; sensor networks Social Dynamics - carried by people. - SNoS can be mobile without requiring any energy to achieve mobility. Mobile node in SNoS - patrolling and monitoring environment - wireless sensor node is not be possible or economically feasible - improve network efficiency - eliminate “blind spot of coverage”

4. The Model Benchmarking of two issues in sensor networkMain GoalRepresentation of environment Static Sensors Mobile Sensors Sensor Distributed along the regular lattice Distributed based on a negative exponential probability from the center of the city

5. The Model Representation of environment Static Sensors Mobile Sensors Distributed along the regular lattice sink event the sink is the place to report the event (e.g., a police station) The event is the item we want to detect (e.g., a fire, an explosion) Distributed based on a negative exponential probability from the center of the city

6. The Model Representation of environment Mobile Sensor s placed the sink and the event in the environment at a distance D = 60 from each other approximately 80% of mobile sensors are included within this circle Fig. 1. Setup of a simulation with 441 static nodes (violet triangles) and 220 mobile nodes (green people). The event is marked with a red cross and the sink with a blue flag. Symbols are just for display convenience and they do not reflect the real size of a sensor.

7. The Simulation NetLogo fully parameterized and supports many spatial distributions of sensors and different kinds of walks A simulation starts in a given configuration and while time is passing, mobile sensors move accordingly to the specified model, exploring the environment. A sensor will detect the event. From that point the information spread to other sensors when they are within communication range. The simulation stops when one of the sensors with the information about the event finds the sink node. Start and Stop Simul

8. The Simulation Assume a boolean sensor network with fixed sensor radius r both for mobile and static sensors (Routing Condition) It is hard to synchronize a tick to real - world units (e.g. seconds), so the wait-time cutoff has been arbitrarily set to k2 = 5 ticks Value setting

9. The Expreimental Result Assume A network made of only static sensors Value setting -the simulator reaches tick number 5 -Cost problem : decrease the radius of the sensors so that r = 2.5 then tsim 10 ticks which is 2 times longer than the reference setup : the number of sensors would have to quadruple to a minimum of ns = 1681 Result

10. The Expreimental Result Assume Add an increasing percentage of mobile sensors - 10% (44), 20% (88), 30%(132), 40% (176), 50% (220), 60% (265) Value setting -the detection time of static sensors is always 0 if none of them can see the event or 1 if at least one can see the event. Result

11. The Expreimental Result Assume A hybrid sensor network is using normalized values - values according to the worst-case with (tD10% and tR10%) representing the scenario where only 10% of mobile sensors are present. -The normalized values clearly show that detection time drops very quickly to a little fraction of tD10%, while report time improves but more slowly. -This leads to another observation that detection time becomes less relevant with the increase in the number of mobile sensors and the bottleneck is the time to report the event to the sink -Preferential return pattern : asymmetry between detection time and report time Result

12. The Expreimental Result Assume -SPEEDUP RELATIVE THE SLOWEST CONFIGURATION WITH 10% OF MOBILE SENSORS -a scenario where all nodes are mobile. -we can set a lower bound as tmin = tDmin +tRmin that must be respected. -The weights ωD = 1− ωR and ωR = tR/(tD + tR) of tD and tR to calculate the general speedup as Sp = ωDSD + ωRSR. -a limited amount of mobile sensors the mixed configuration is faster. -Lower detection time is a consequence of the better coverage of the periphery and the high sensor density allows a quick spreading of the event till the sink. Result

13. The Expreimental Result -they move, they are able to “see” areas of the environment that would not otherwise be seen. -With this in mind the total time of the simulation, tsim = tD + tR, decreases rapidly, as shown in Figs. 3 and 4, which tells us that adding more mobile sensors may not help after some saturation point -it should be noted that the performances both of a fully mobile or a mixed network cannot be compared to an ideal fully connected static network because a fully connected network is fast (although expensive). Result

14. Conclusion Since most of mobile sensors are near the center of the city, it is worthless to have static sensors in that place because it is already well covered. Due to the urban population density distribution and the greater relevance of report time over detection time, to maximize performances the best sink deployment is in the center of the city. There are two thresholds. A lower-bound threshold that ensures that mobile sensors achieve a high enough density and a higher-bound threshold over which it is not worth the addition of more mobile sensors because they do not provide a significant improvement in the performance. The human mobility model may not be the best to patrol an area; this is because the preferential return should lead to a strong overlapping of areas, which in a real environment is only partially mitigated by the different locations different users visit.