Movement-Assisted Sensor Deployment in WSN Heon-jong Lee iwantwine@kut.ac.kr Korea University of Technology and Education Laboratory of Intelligent Networks http://link.kut.ac.kr

Today’s Topic : Sensor Deployment Deploying more static sensors cannot solve the problem due to wind or obstacles Not Covered!! Coverage under random deployment Mobile Sensor Robots

Today’s Topic : Sensor Deployment Solution : Utilizing Mobile Sensors! Direct the movement of sensors to increase coverage General idea: detecting coverage hole  move to heal the hole Utilizing Voronoi Diagram Utilizing Virtual Grid Mobile Sensor Robots

Mobility-Assisted Sensor Deployment Guiling Wang, Guohong Cao, and Tom La Porta IEEE INFOCOM 2004 4/30 Mobile Sensor Robots

Problem statement Problem statement is: Scenario given the target area, how to maximize the sensor coverage with less time, movement distance and message complexity Scenario discover the existence of coverage holes (the area not covered by any sensor) in the target area the proposed protocols calculate the target positions of these sensors, where they should move VEC(VECtor-based), VOR (VORonoi-based), and Minimax based algorithm Mobile Sensor Robots

Voronoi Polygon Voronoi polygon O is the set of Voronoi vertices of O is the set of Voronoi edges of O is the set of Voronoi neighbors of O Mobile Sensor Robots

VEC(VECtor-based Algorithm) Motivated by the attribute of electro-magnetic particles : expelling forces Virtual Forces pushes sensors away from dense area B C A B C A Mobile Sensor Robots

VEC(VECtor-based Algorithm) Notation : distance between si, sj : average distance between two sensors(calculated beforehand) Three types of Virtual Force( ) A B B A B A Voronoi Polygons are not completely covered a Voronoi Polygon is not completely covered Voronoi Polygons are completely covered Mobile Sensor Robots

VEC(VECtor-based Algorithm) Field boundary also exert force( ) : will push sensor to move Algorithm distance of si to the boundary if the local coverage is not increased, the sensor should not move the target location Mobile Sensor Robots

VEC(VECtor-based Algorithm) Execution of VEC 35 sensors in a 50m by 50m flat space Sensing range is 6m Round 0 (75.7%)  Round 1 (92.2%)  Round 2 (94.7%) Mobile Sensor Robots

VOR(VORonoi-based Algorithm) Pull-based algorithm : pulls sensors to their maximum coverage hole move towards its farthest Voronoi vertex( ) d(Si, A) > Sensing range moves along line SiA to Point B, where d(A, B) is equal to the Sensing range B M B M Mobile Sensor Robots

VOR(VORonoi-based Algorithm) limit the maximum moving distance to be at most half of the communication range Si can’t see me(Sj) Mobile Sensor Robots

VOR(VORonoi-based Algorithm) check whether its moving direction is opposite to that in the previous round Mobile Sensor Robots

VEC(VECtor-based Algorithm) Execution of VOR 35 sensors in a 50m by 50m flat space Sensing range is 6m Round 0 (75.7%)  Round 1 (89.2%)  Round 2 (95.6%) Mobile Sensor Robots

Minimax Concept Notation chooses the target location inside the Voronoi polygon whose distance to the farthest Voronoi vertex (Vfar) is minimized a more regular shaped Voronoi polygon Notation Om : Minimax point C(O, r) : A circle centered at point O with radius r C(Vu, Vv, Vw) : The circumcircle of three points Vu, Vv, Vw B M N B M N Mobile Sensor Robots

Minimax Mobile Sensor Robots

Minimax To find the Minimax point, we only need to find all the circumcircles of any two and any three Voronoi vertices. Among those circles, the one with the minimum radius covering all the vertices is the Minimax circle. The center of this circle is the Minimax point. Mobile Sensor Robots

Minimax Algorithm (O(n3)) Mobile Sensor Robots

Minimax Execution of Minimax 35 sensors in a 50m by 50m flat space Sensing range is 6m Round 0 (75.7%)  Round 1 (92.7%)  Round 1 (96.5%) Mobile Sensor Robots

Performance Evaluation Measure the performance from two aspects: deployment quality : measured by the sensor coverage and the time(the number of round) to reach this coverage cost : sensor cost, energy consumption(moving distance) Environment 30, 35, 40,and 45 sensors 50m ∗ 50m or 150m * 150m initial deployment : random or normal communication range : 10m to 28m (normal: 20m) sensing range : 6m using NS2, 802.11, DSDV Mobile Sensor Robots

Performance Evaluation Coverage Moving distance Mobile Sensor Robots

Performance Evaluation Communication range Mobile Sensor Robots

Mobility-Assisted Sensor Networking for Field Coverage Dan Wang, Jiangchuan Liu, Qian Zhang IEEE GLOBECOM 2007 23/30 Mobile Sensor Robots

Motivation and Previous Work Concept : A mix of mobile and static sensors Motivation Mobile sensors are expensive When all sensors are in random motion, packet routing and information dissemination will be much more complicated [13] suggested a one-time reposition of the mobile sensors after the initial deployment, but the coverage is still unbalanced black : static sensors white : mobile sensors Mobile Sensor Robots

The goal The mobile sensors are always in motion to assist the static sensors Main challenges (1) to clarify the necessary coverage contributions from the static and mobile sensors (2) to find a mobility model for the mobile sensors to achieve their desired coverage contribution Mobile Sensor Robots

Mobility-Assisted Architecture n2 virtual grid (0, 1, …, n2-1) The sensors are aware of their location information (using GPS) The size of each grid is  any active sensor in a grid can cover the whole grid The sensing range of a mobile sensor can be smaller. e.g., The static sensors in one grid are equivalent, they do not have to be active The mobile sensors are always active, and can stay in a grid or move to other grids  covering the holes Mobile Sensor Robots

Mobility-Assisted Architecture Static sensors: a random activation scheduling Mobile sensors: a random walk model Two stages Parameter Initialization (1~4) Field Monitoring (each time slot) (5~6) initial deployment one or more MS travel (collect information of SS) Determine the activation probability of SS and notify them SS activates itself with p and then monitors its grid MS decides to move into one neighboring grid or to stay in the current grid, and then monitors its grid p p p p p p p p p p Mobile Sensor Robots

Mobility-Assisted Architecture The advantages of using a probabilistic operation easier to implement involves simple optimization in the initial stage substitute mobile sensor can easily follow the mobility model the behavior of each type of the sensors are statistically identical this is useful especially for recharging or replacement of mobile sensors more resistant to intruders that try to learn the sensor behavior Mobile Sensor Robots

Objective and Optimization Define a measure of how well a location is covered (δ is minimum coverage ratio required by the user) Our objective is to ensure this quality(δ-covered), while maximizing the lifetime of the network. lifetime : the death of the first sensors serves as a good signal to the end of the steady-state operation(because of domino effect) Mobile Sensor Robots

Objective and Optimization Notation assign static sensors with an identical activation probability p The probability that mobile sensor travels to the grid i, πi π = The density of grid i, d(i). i.e., the number of static sensors in i The number of mobile sensors in the network, M Formulation maximizes the network lifetime contribution constraint of each mobile sensor ensure the coverage ratio of the grids Mobile Sensor Robots

Objective and Optimization Heuristic Algorithm (O(N2)) The grid to have a low activation probability p Let li is the index of the grid in terms of density, i.e., Iteration (until (fully exploited) or K == n2) find key parameter K (start from 0), the index after which the grids are dense enough to be covered by the static sensors only evaluate p by with p, find a valid increase index K We have an upper bound on p corresponding to K-1 and a lower bound on p corresponding K Mobile Sensor Robots

A Random Walk Mobility Model A mobile sensor will either stay in a grid, or move into an adjacent grid along four direction in a boundary grid, it has 3 or 2 direction Consider decisions depending only on the current grid (Markov chain : given the present state, future states are independent of the past states(wikipedia)) Let Pij is the transition probability from grid i to grid j Mobile Sensor Robots

A Random Walk Mobility Model Given the long-run distribution π Markov chain obeys the following balance equations standard steady-state constraints for Markov chain no transition is possible for two non-adjacent grids Mobile Sensor Robots

A Random Walk Mobility Model The mobile sensors cannot be trapped in a grid Add another conditions Summary the probability that it will stay in this grid should be less than 1 mobile sensor always has chance to move into a neighboring grid. Mobile Sensor Robots

Performance Evaluation Focus on coverage quality and network lifetime Environment 1000 static sensors in a field 140m x 140m 100 virtual grids 10000mAh battery power for each sensor (it can last for one day) coverage quality δ = 0.85 time slot is 1 minute the average of 100 independent experiments The cumulative distribution curve of the residual energy after the death of the first sensor Mobile Sensor Robots

Performance Evaluation 50 Mobile Sensor Robots

Performance Evaluation A small set of MS assists load balancing while the cheaper SS carries the main duty for field coverage randomly and uniformly generated from time to time Mobile Sensor Robots