Presented by Anwar Saipulla Umass Lowell Slides are adopted from original authors’ presentation 1

Papers An Optimization Framework for Joint Sensor Deployment, Link Scheduling and Routing in Underwater Sensor Networks Leonardo Badia, Michele Mastrogiovanni, Chiara Petrioli, Stamatis Stefanakos, Michele Zorzi, WUWNet’06 Surface-Level Gateway Deployment For Underwater Sensor Networks Saleh Ibrahim, Jun-Hong Cui, Reda Ammar, Milcom07 Deployment Analysis in Underwater Acoustic Wireless Sensor Networks D. Pompili, T. Melodia, I.F. Akyildiz, WUWNet’06 2

Leonardo Badia*, Michele Mastrogiovanni +, Chiara Petrioli +, Stamatis Stefanakos +, Michele Zorzi § *IMT Lucca, Italy § University of Padova, Italy + University of Rome “La Sapienza”, Italy 3

Outline Underwater sensor networks Joint Optimization of Link Scheduling, Routing and Sensor Placement Underwater challenges (propagation, delay, interference) Proposed solution Conclusions and future works 4

Underwater Sensor Networks Sensor networks are generally used for surveillance, monitoring and low-cost communication. In underwater scenarios, they can be employed to detect earthquakes, incoming tsunamis, water pollution, global warming, and other important phenomena. 5

Underwater Sensor Networks The network can comprise columns of submarine acoustic sensors, e.g. linked to moored buoys and hence to the land. 6

Underwater Sensor Networks For the modeling purpose, the network can be often represented as a directed graph, where the sensor nodes need to deliver their measurements to a sink. If nodes’ position are assumed to be stationary and link gains are sufficiently stable, it can be thought of optimizing this delivery. 7

Underwater Sensor Networks This optimization involves an underlying TDMA access, where transmission slots of a periodic frame are assigned in a centralized and coordinated manner. The optimization must account for: Power expenditure of the nodes (the traditional energy saving needs assumed for RF sensors is even more important for underwater networks) Transmission and interference constraints. 8

Joint Optimization of… Instead of optimizing each network layer separately, we go for a cross-layer solution. We seek to optimize jointly: Link scheduling (including Power Control) Routing Node placement This is obtained through a proper ILP framework. 9

Joint Optimization of… This can be done by representing with binary variables: The positioning of a node in a candidate position i, represented by y i The activation of a link (i,j) at time t, represented by X ij (t) The allocation of these variables must respect several constraints. 10

Underwater challenges Such optimization procedures have been studied for traditional wireless networks. However, the underwater medium has special characteristics that need to be addressed. In particular, the most important differences are in the propagation and delay aspects. 11

Underwater challenges Usually, in wireless radio networks the signal received power is a strongly decreasing function of the distance d (e.g., ~ d -4 ) For underwater scenarios: where in particular:

Underwater challenges For RF networks, physical propagation delay is negligible (when it is not zero, it is mainly because of the processing delay). Underwater, it is not. It can be comparable to or even larger than the packet transmission time. bb time Node a cc time Node b aa cc cc

ILP Formulation main constraints: For any case where t e and t f overlap 14

Proposed solution For our preliminary investigations: Power Control is dealt with by considering a single link for any available power level. Only links with a gain high enough are considered. Interference constraints are modeled by means of a simple two-step approach: the ILP considers the protocol interference model and check the SIR a posteriori, if it is violated adds a constraint and re-runs. 15

Proposed solution Network scenario sink 2 layers of vertically aligned sensors (candidate) 16

Proposed solution We used GLPK version 4.8, an exact solver of ILP problems. The numerical data reproduce UWM1000 LinkQuest underwater acoustic modem. The grid size is 600  600  200 meters. Two power levels are available (2W and 8W). 17

Proposed solution The higher the parallelism of the solution, the better. Hence, the optimal solution has a high degree of parallelism. Shown links are not activated at the same time, but this does not guarantee they do not interfere!! Links 2  18 and 4  18 are activated at the same time but do not interfere, thanks to the different delays! 18

Conclusions and Future Work Optimal link scheduling and routing can obtain the highest possible performance in severely constrained scenarios such as underwater acoustic networks. However, their evaluation is subject to many design constraints. Optimized solution are not easy to find. 19

Conclusions and Future Work Capturing the interference phenomena into a viable mathematical model can be an interesting topic for future research. Possible ways to further improve the optimization framework are under study. 20

Questions? 21

Papers An Optimization Framework for Joint Sensor Deployment, Link Scheduling and Routing in Underwater Sensor Networks Leonardo Badia, Michele Mastrogiovanni, Chiara Petrioli, Stamatis Stefanakos, Michele Zorzi, WUWNet’06 Surface-Level Gateway Deployment For Underwater Sensor Networks Saleh Ibrahim, Jun-Hong Cui, Reda Ammar, Milcom07 Deployment Analysis in Underwater Acoustic Wireless Sensor Networks D. Pompili, T. Melodia, I.F. Akyildiz, WUWNet’06 22

Saleh Ibrahim, Jun-Hong Cui, Reda Ammar Computer Science & Engineering Dept. University of Connecticut 23

Underwater Sensor Networks Radio Does Not Work Well in Water Acoustic Communication More Practical High Propagation Delay Propagation speed 1500 m/s for sound vs. 3x10 8 m/s for EM waves Low Available Bandwidth Heavily depending on transmission range & frequency Most acoustic systems operate below 30kHz Range x Rate product less than 40 km x kbps 24

Motivation Use Surface Gateways to Improve Performance of UWSN: Delay, Energy Consumption, etc. Multiple Surface Gateway Nodes Relay Traffic between Underwater Nodes and the Control Center

Surface Gateways Deployment Given : UWSN, Node Locations Data Gen. Rates Candidate Locations for Surface Gateways Find : Optimal Deployment Locations Minimizing : Expected End-to-end Delay Expected Per Packet Energy Consumption Required Number of Surface Gateways 26

Plan Integer Linear Programming Problem Solve Sample Problems to Investigate Effect of: Number of Surface Gateways Network Load Underwater Deployment Pattern 27

ILP Constraints (1) For each candidate location t i Limit number of surface gateways 28

ILP Constraints (2) No flow f in edge e i going into a candidate location t i, unless a node is deployed there 29

ILP Constraints (3) Flow conservation at each node End-to-End Flow conservation

ILP Constraints (4) Channel Capacity Underwater Nodes Surface Nodes 31

Minimizing Expected Delay Delay t of Edge e L message length, B bit-rate, l(e) distance, v p propagation velocity. Minimize Expected End-to-End Delay Minimize 32

Minimizing Expected Energy Energy  Consumption at Edge e  s (e) is the transmission power at e Minimize Expected End-to-End Per-Packet Energy Consumption Minimize 33

Sample Uniform UW Problem Underwater 7x7 2D-Mesh of sensors at 100m deep. 600x600m horizontal area Comm. range R=150m Data generation g=1pkt/sec Candidate locations 5x5 mesh Fixed packet length, propagation velocity, transmission power

Results and Observations (1) End to End Delay vs. # of Gateways with Varying Bit-rate Due to Transmission time Minimum # Gateways that Makes the Problem Feasible.

Results and Observations (2) Energy Per Packet vs. # of Gateways with Varying Bit-rate Due to Transmission time

Sample Random UW Problem Underwater 49 Randomly-located UW nodes Same horizontal area Multiple problem instances Candidate Locations Same 5x5 mesh Same communication range, data generation rate, packet length, propagation velocity, transmission power.

Results and Observations (3) Random U.W. deployment results very similar to Uniform case.

Results and Observations (4) 39

Conclusion and Future Work Conclusion Benefits of Multiple Surface Gateways Effect of Network Load Effect of Underwater Deployment Pattern Future Work Developing Heuristic Solutions Candidate Deployment Location Schemes Joint Optimization of Surface and UW 40

Questions? Discussions [1] Key Assumption: known Candidate Deployment Locations [2] Other dangerous assumption: symmetric communication links; A virtue sink: ignoring communication issues between sink nodes.. My solution: grid point approximation 41

Papers An Optimization Framework for Joint Sensor Deployment, Link Scheduling and Routing in Underwater Sensor Networks Leonardo Badia, Michele Mastrogiovanni, Chiara Petrioli, Stamatis Stefanakos, Michele Zorzi, WUWNet’06 Surface-Level Gateway Deployment For Underwater Sensor Networks Saleh Ibrahim, Jun-Hong Cui, Reda Ammar, Milcom07 Deployment Analysis in Underwater Acoustic Wireless Sensor Networks D. Pompili, T. Melodia, I.F. Akyildiz, WUWNet’06 42

Deployment Analysis in Underwater Acoustic Wireless Sensor Networks D. Pompili, T. Melodia, I.F. Akyildiz Broadband and Wireless Networking Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology 43/29

Outline Propose 2D and 3D architectures for UW-ASNs State objectives of the paper Study graph properties of 2D ocean-bottom UW-ASNs Propose and evaluate 3D deployment strategies Conclusions and future work 44/29

45/29 Two-dimensional Architecture

Three-dimensional Architecture 46

Objectives 2D Architecture: Determine the minimum number of sensors and uw- gateways to achieve communication and sensing coverage Provide guidelines on how to choose the optimal deployment surface area, given a target region 3D Architecture: Evaluate different deployment strategies Determine the minimum number of sensors needed to achieve the target sensing coverage 47

Graph Properties of Bottom UW-ASNs We analyze the graph properties of devices (sensors and uw-gateways) when they are deployed on the ocean surface, sink, and reach the bottom We study the trajectory of sinking devices deployed on the ocean surface when: Sensors are randomly deployed on the ocean surface (e.g., scattered from an airplane), or Sensors are accurately positioned on the surface (e.g., released from a vessel) 48/29

Triangular-grid Surface Deployment Sensors with same sensing range r Optimal deployment to cover a 2D area with minimum number of sensors: Center sensors at the vertex of a grid of equilateral triangles 49/29 d A FE D d d r r r ½*d = √(3)/2*r d/r = 1.732*r

Triangular-grid Coverage 50/29 Coverage=0.95 Ratio of sensor distance and sensing range=d/r=1.95

Minimum No. of Sensors in Tri-grid A 1 =100x100m 2 A 2 =300x200m 2 r in[10,35]m d*/r=1.95 N = ½{100*100/[(√3/4)*(1.95*15)^2]} = 26.99N = ½*{300*200/[(√3/4)*(1.95*15)^2] }=

Assumptions on Ocean Currents Assumptions in this paper: No significant vertical movement of ocean water, i.e., the considered area is neither an upwelling nor a downwelling The velocity of the ocean current depends on depth H different ocean current layers with different width Current in each layer has a fixed module and angular deviation (with known statistics) This allows modeling the thermohaline circulation (the ocean’s conveyor belt), i.e., deep ocean currents that flow with constant velocity and direction within certain depths 52/29

Trajectory of a Sinking Device 53/29

Dynamic System of a Sinking Object F W is the weight force F B is the buoyant force (Archimede’s principle) F R is the fluid resistance force F C is the force of the current Projecting onto x, y, z

Average Horizontal Displacements 55/29 Sensor displacement depths= 50,100,500m Uw-gateway displacements depths= 50,100,500m

Sensor-gateway Dist. vs. No. gateways 56/29 V 1 =100x100x50m 3 V 2 =300x200x100m 3 V 3 =1000x1000x500m 3 Maximum sensor- gateway distance Average sensor- gateway distance

3D Deployment Strategies We propose three deployment strategies for three- dimensional UW-ASNs to obtain a target coverage of the 3D region 3D-random strategy Bottom-random strategy Bottom-grid strategy Winch-based sensor devices are anchored to the bottom of the ocean in such a way that they cannot drift with currents Sensors are assumed to know their final positions by exploiting localization techniques 57/29

3D-random Deployment Strategy 3D-random Does not require any form of coordination from the surface station Sensors are randomly deployed on the bottom, where they are anchored Sensors randomly choose a depth and float to the selected depth 58/29

Bottom-random Deployment Strategy Bottom-random Sensors are randomly deployed on the bottom, where they are anchored Surface station is informed about their position on the bottom Surface station calculates the depth for each sensor to achieve the target coverage ratio Sensors are assigned a target depth and float to the desired position 59/29 Where does this position come from? Remember, NO GPS on the ocean bottom! Any question to this slide?

Bottom-grid Deployment Strategy Bottom-grid Needs to be assisted by one or multiple AUVs, which deploy the sensors Grid deployment on the bottom of the ocean Each sensor is also assigned a desired depth by the AUV and accordingly floats to achieve the target coverage ratio 60/29

3D-random Coverage 61/

Bottom-random Coverage 62/

Bottom-grid Coverage 63/

Sensing Range Bound Minimum normalized sensing range that guarantees coverage ratios of 1 and 0.9 with the bottom-random strategy and the theoretical bound on the minimum normalized sensing range derived in [2] 64/29

Conclusions Deployment strategies for 2D and 3D architectures for UW-ASNs Deployment analysis in order to: Determine the minimum number of sensors to achieve the application-dependent target sensing and communication coverage Provide guidelines on how to choose the deployment surface area, given a target region Determine the minimum number of uw-gateways, given some desired communication properties of clusters 65/29

Future Work Two-dimensional UW-ASNs: We will extend the current worst-case study to set surface margins in order to take into account statistical information about currents Three-dimensional UW-ASNs: We will develop a mathematical framework to study the 3D sensing coverage We will devise a distributed algorithm to set and adjust the depth of sensors 66/29

Questions? 67 Thank you.