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A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks
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A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks

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Hady Abdel-Salem's PhD Defense Slides …

Hady Abdel-Salem's PhD Defense Slides
Department of Computer Science
Old Dominion University
November 1, 2010

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  • 1. Agenda  Introduction. [5 min]  Motivation & Related work. [5 min]  Backbone Construction Protocol. [10 min]  Mitigating Network Challenges:  Clustering & Sensor Localization. [5 min]  Energy-Aware Task Management.  Workforce Selection [10 min]  Centralized Approach.  Distributed Approach.  Sensor Sleep Scheduling [10 min]  Data Aggregation & Routing. [5 min]  Energy Hole Problem. [5 min]  Conclusions & Future Work [5 min]
  • 2. • Collected data are locally aggregated then forwarded for further processing at sink nodes which in terms of energy, computation, and communication have more powerful capabilities compared to sensors. Introduction • Sensor Networks are special type of Ad-Hoc networks which include two types of nodes, “Sensor nodes’’& “ Sink nodes’’. • Sensor nodes are tiny electronic devices with limited sensing, computational, and communicational capabilities. • In a typical scenario, sensors are massively deployed in an area of interest to collect data that serve the mission of the network.
  • 3. • Sensor networks have many applications … some of them are military related:  battlefield surveillance.  borders monitoring. • Others are civilian:  fire and habitat monitoring  home automation.  traffic control.  target tracking.  body sensor networks Sensor Network Applications
  • 4. Design Limitations • Limited and non-renewable energy budget. • Short duty cycle. • Limited computing power. • Small transmission range. • High failure rate (unreliable). • Location unawareness (no GPS). • Random nature of deployment. • Working unattended (autonomously). • Dynamic topology. Sensor: Network:
  • 5. Motivation & Related Work • Several techniques have been proposed to address different problems in WSNs.  For Localization, we have APIT, DV-Hop, Centroid, Weighted Centroid, …  For Routing, we have GPSR, GFS, MFR, …  For clustering, we have Hierarchical clustering, LEACH, … • A common drawback when using any of these techniques, they attempt to solve one problem in isolation from the others, hence protocol designers have to face the same common challenges again and again. • This, in turn, has a direct impact on the complexity of the proposed protocols and on energy consumption.
  • 6. Motivation • In this thesis, we present a different approach to address the problems mentioned earlier. • Specifically,  In a one-time investment, we construct a lightweight network backbone which provides some kind of an infrastructure that makes the network easier to manage.  On top of the constructed backbone, we provide solutions for the following problems:  Sensor Localization  Clustering  Energy-Aware Task Management  Workforce Selection  Sleep Scheduling.  Data aggregation & routing.  Energy hole problem.
  • 7. Backbone Construction Protocol
  • 8. Construction Protocol • Initially, the deployment area is hypothetically tiled with identical hexagons starting from the sink outward. • The first hexagon is placed so its center coincides with the center of the sink node. • Hexagons are placed next to each other in the following directions: • The geometry of the gaps between the hexagons in any two consecutive directions allow perfect tiling. • The size of the hexagon is determined by the distance between the centers of any two neighbored hexagons which is chosen to be ≈ tx Sensor Sink Tx
  • 9. • Hence, the deployment area is divided into six sectors.• We use a ternary coordinate system <sector, row, column> to uniquely identify hexagons.• The closest sensor to the center of each hexagon is determined (backbone sensor). These sensors collectively form the network backbone. Construction Protocol (cont.)
  • 10. Order of Backbone Sensors Selection • Selection process goes recursively, backbone sensors in any row are responsible for selecting backbone sensors in the next row. • Each backbone sensor with odd column coordinate selects 2 sensors in the next row. i.e Sensor <s, r, 2c-1> selects sensors <s, r+1, 2c-1> and <s, r+1, 2c > • Backbone sensors in the first column in even rows select 3 sensors i.e Sensor <s,2r,1> selects sensors <s,2r+1,1> , <s,2r+1,2> and <s-1,r+1,r+1 > • Backbone sensors can be selected in many ways that may result in different order. To reduce collisions, we propose the following rules. • Selection of backbone sensors with the same row and column coordinates in different odd sectors (i.e. s=1,3,5) occurs in the same time. A similar rule applies for even sectors (i.e. s=2,4,6).
  • 11. Order of Backbone Sensors Selection
  • 12. How backbone sensors are selected ? • Backbone sensors are selected to be the closest sensors to the centers of the hexagons they represent. • To determine these sensors, our approach requires sensors to be able to measure their angle to the sink node. • Hence, we find it more appropriate to start by showing how a sensor can estimate it angle to the sink node.
  • 13. Directional Transmission Pattern The transmission pattern of directional antennas consists of a major lobe oriented towards the transmission direction and several minor and side lobes oriented in other directions.
  • 14. Measuring Angles Received Power Initially, the sink uses it omnidirectional transmitter to send a sequence of wakeup messages to make sure sensors in its neighborhood are awake. While rotating its unidirectional antenna, the sink sends a sequence of beaconing messages attaching current transmission angle to each message. From received messages, a sensor can estimate its angle to the sink as the average of the received angles weighted by the received power pr. θ Sensor Sink
  • 15. Measuring Angles Received Power The process can be repeated for better accuracy. θ
  • 16. • Sensors that receive the message, verify that the absolute difference between their measured angle and ϕ is less than a predetermined threshold, otherwise, they ignore the message. • Sensor S1 estimates the angle ϕ between the sink node and the target hexagon and starts the search process by broadcasting a message that includes ϕ to its neighbors. ϕ S1 3 7 5 6 14 9 2 6 4 5 13 8 1 5 3 4 12 7 New Backbone Sensor ∞ ∞ ∞ Selection of backbone sensors • Sensors within range, estimate the square of their distance to the center of the target hexagon, and initialize a countdown timer to this value. • The sensor that has the first timer to expire broadcast a message to its neighbors to announce itself as the newly selected backbone sensor. Sink
  • 17. Estimation of Distance e2 Case 1 Case 2
  • 18. Common intermediate terms can be reused to evaluate the Sine and the cosine functions using 10 multiplication operations only, with an accuracy up to 4 decimal digits.
  • 19. Problem of Voids
  • 20. Problem of Voids (Cont.) Void
  • 21. Even Neighbor Replacement • Recall that in our basic selection rules, only sensors with odd column coordinates are allowed to select. • Note that, every even neighbor can receive selection messages transmitted from its two immediate odd neighbors.
  • 22. Even Neighbor Replacement • If an odd sensor is missing due to voids, then all its selection tree will be pruned. • Idea: allow immediate even neighbor sensors to replace missing sensors in order to continue the selection chain.
  • 23. Recovering From Voids (Even Neighbor Replacement)
  • 24. Recovering From Voids Backward Selection • If selection of a backbone sensor was initiated by a sensor other than the one determined by the initial rules. (e.g. Even neighbor replacement or backward selection), Then the newly selected sensor should try to select the odd sensor that was supposed to select it.
  • 25. Recovering From Voids (Backward Selection)
  • 26. Backbone Switching θ Rotation Angle θ • To balance the load on backbone sensors, we use alternative backbones.• Idea: rotate sector orientation angles by θ1, θ2…
  • 27. Actual Hexagons obtained through Simulation
  • 28. Actual vs. theoretical positions of backbone sensors
  • 29. Sensor Localization & Clustering  During the selection of backbone sensors, sensors can use their knowledge about their angle to the sink along with the computed distance to the sink to fully localize themselves.  Moreover, the hexagonal structure of the backbone provides an implicit clustering mechanism in which each hexagon represents a cluster and the backbone sensor around its center represents its cluster head.  Hence, sensor localization & clustering are direct by-products of the backbone construction protocol.
  • 30. Average Localization Error for Different Localization Protocols
  • 31. Energy-Aware Task Management
  • 32. Workforce Selection • Sensing tasks are issued to sensors through sink nodes. • A task T identified by the tuple (x, y, w), where  (x,y): is the position where data need to be collected.  w : QoS requirements expressed in terms of number of sensors participating in the task (we refer to them as task workforce). • Obviously, only sensors whose sensing range cover the point (x,y) can join the workforce for the task T.
  • 33. • Assume that each of the tasks T1 and T2 requires 3 sensors. • Case 2: assign < a, b, c> to T1 We can run T2 using < e, g, i >. • Case 1: assign < b, e, f > to T1 No way to run T2. • Considering sensor remaining energy during workforce selection can enhance network reliability and durability. • Assigning tasks to sensors improperly can consume sensor energy unevenly which in turn may result in many problems e.g. reducing network density, creating energy holes, … Example
  • 34. Workforce Selection Centralized Approach
  • 35. • A task starts when the sink sends a sequence of CTW to get the attention of enough number of sensors (including backbone sensors across routing path) Workforce Selection • CTW message should contain: • W: required workforce w, • (x,y): task position, • Emax: sink estimate of maximum energy in target hexagon. • S: number of bidding slots in first bidding round.
  • 36. 10 14 14 12 12 13 14 13 • Candidate sensors whose sensing range cover the point (x, y) participate in the task with probability • Obviously, the formula shown above gives sensors with higher energy higher chance to participate in the task than other sensors. • Immediately, after the last CTW message, time line is divided into s time epochs (slots). Sensors willing to participate should choose a random slot and transmit a short message that contains its current energy level. ( For the example assume S = 8) Last CTW 13, 14 14 10,12 13 14 12 Workforce Selection
  • 37. Last CTW 13, 14 14 10,12 13 14 12 • Immediately, after the last bidding slot, the task coordinator (the backbone sensor within the hexagon) transmits a message that contains the results of the last bidding round. Last Bidding Slot 0 0 1 0 2 0 3 4 Info . • While evaluating the bidding process, only single-bidder slots are considered, other slots are ignored. A temporary id is assigned to sensors that bid in any of these slots, this id determines the order by which sensors send their sensory data to the task coordinator during data aggregation. • The info field in the bidding result message should include information needed for next bidding round (if any): • S: number of slots , • W: number or remaining sensors needed, • Emax: maximum energy among sensors.
  • 38. • The bidding process continues till the whole workforce is recruited or a maximum number of bidding rounds is reached. • Task execution starts immediately after collecting required workforce. When task execution is completed, sensors start to send their sensory data to the backbone sensor in the order assigned during workforce selection. • Finally, the backbone sensor within the hexagon sends the locally aggregated sensory data to the sink node along with Emax, the maximum energy among sensors within this hexagon, hence the sink can attach this value with CTW messages transmitted for any upcoming tasks within this hexagon.
  • 39. • We provided mathematical derivation of all parameters needed for our workforce selection protocol, including Number of slots in any bidding round Expected number of candidate sensors collected by CTW messages Awake probability of sensors. Number of CTW messages needed
  • 40. Workforce Selection Distributed Approach • Estimation of Maximum Energy • Workforce Selection
  • 41. • Assume that sensor energy can be quantized into 2n levels, (i.e it can be encoded in a string of n bits). Estimation of Maximum Energy For illustration, we assume, n = 4 E1 Last CTW E2 E3 E4 clock drift margin Time line is divided into 4 time epochs • Immediately after the last CTW message, time line is divided into n time epochs.
  • 42. • Sensors in the same sensing area transmit short messages that represent their energy levels bit by bit starting from the most significant bit. • A value of 0 is not transmitted, while a value of 1 is transmitted. • Sensors stop transmitting if they have a 0 and received a packet or detected a collision in the corresponding epoch. Estimation of Maximum Energy (cont.) For illustration, assume we have 3 sensors with the following energy levels, S1 [1110], S2 [1100], S3 [1011] clock drift margin E1 Last CTW S2 S1 S1 S2 S1 S3 E2 E3 E4
  • 43. • Sensors that pick up the values transmitted should use the following disambiguation scheme:  No packets are received: 0 is recorded.  A packet is received or collision is detected: 1 is recorded. Estimation of Maximum Energy (cont.) S1 [1110], S2 [1100], S3 [1011] clock drift margin E1 Last CTW S2 S1 S1 S2 S1 S3 E2 E3 E4 Maximum: 1 1 1 0 Using appropriately long epochs, synchronization should not be a problem.
  • 44. 1 10 0 1 01 0 1 110 0 11 1 # Channel Status Bit 4 <c> 1 3 Ambient 0 2 Collision <b,c> 1 1 Collision <a,b,c> 1 Maximum: 1101Estimation of Maximum Energy
  • 45. Workforce Selection • The sensing area is divided into k regions with equal size • Workforce selection goes in decision rounds, each round is associated with an energy level (Emax, Emax-1, Emax-2,…etc). • Each decision round has k slots corresponding to the regions determined above. k R2 . Max Estimation Decision Round Emax Decision Round Emax-2 Decision Round Emax-1 …… clock drift margin S1 S2 Sk Round E+1 Round E-1
  • 46. • Based on task and sensor position, each sensor estimates its region and transmits a short message in the decision round/slot that matches its energy and region. • Protocol terminates when required workforce is recruited. • Sensors keep track of number of recruited sensors as follows:  Collision is detected: increment by 2.  No packets are received: 0 is recorded.  One packet is received: increment by one. S1 Round E+1 S3 S2 S1 S2 S3 S4 Round E-1 +2 +0 +1 S6 S5S4 +2 Workforce Selection
  • 47. # Sensors Wnew Wtotal 4 Ambient 0 3 3 c<S4 , S5> 2 3 2 Ambient 0 1 1 S2 1 1 Round-1 (E = 10) Assume required workforce is 4
  • 48. # Sensors wnew Wtotal 1 S1 1 4 Round-2 (E = 9) Workforce collected, Protocol terminates
  • 49. Sensor Sleep Schedule  Due to their limited energy budget, sensors spend most of their lifetime in a sleep mode.  Various deterministic and probabilistic schemes can be used to determine the schedule based on which sensors sleep and wake up.  Each scheduling scheme has its impact on :  The effective sensor density (ESD), defined as the density of awake sensors.  The coverage capability of the network.
  • 50. Scheduling Schemes  Static Schemes  Dynamic Schemes  Energy-Aware Schemes Sensor sleep for a random amount of time uniformly selected in the interval [Ts, TS], and stay awake for another random amount of time uniformly selected in the interval [Ta, TA]. Dynamic scheduling schemes which adaptively change the upper bounds TA and TS based on sensor remaining energy. In general, we can classify scheduling schemes into, Sensors sleep for Ts time units and stay awake for Ta time units.
  • 51. PASTA & Sleep Scheduling in WSN  The PASTA property stands for Poisson Arrivals See Time Average.  We show that the PASTA property can be applied to effective sensor density in WSNs.  Specifically, we proved that fraction of events that occur in a certain area while k sensors are awake equals the fraction of time this area is under the surveillance of k sensors.  This result allowed us to use the time-invariant probability distribution of k-coverage to analyze different scheduling schemes.  PASTA has been used for too long in queuing systems and in general it implies the equivalence between the time average view seen by an internal observer who has been watching the system for long time and the view seen by external observers that arrive at the system according to a Poisson process.
  • 52. Energy-Aware Scheduling  We proposed a backbone-guided energy-aware scheduling scheme designed to extend network reliable lifetime by balancing energy consumption among sensor nodes.  The main idea of our scheme is to continuously and probabilistically adjust sleep and awake times of sensors based on the differences in their remaining energy.  In other words, the proposed scheme prolongs the sleeping periods of sensors with relatively low energy and compensate for their absence by shortening sleeping periods of sensors with relatively high energy.  This goal has to be done without probabilistically affecting the effective sensor density.
  • 53. Energy-Aware Scheduling  We proved that if sensors independently updated their upper bounds for TA & TS after the execution of each task according to the following equations, their energy consumption will almost be even.  The figure below show the Energy-Aware scheme in action S9 S8 S7 S6 S5 S4 S3 S2 S1 Sensors Sleep / Awake Cycle
  • 54. Sink Local Aggregation Backbone Sensor Regular Sensor Inter-backbone sensor aggregation through reversed selection rules Selection rules Data Aggregation & Routing
  • 55. Energy Hole Problem Energy Hole
  • 56. Rendezvous-based Routing
  • 57. Source 2 Δ c Source 1 Destination 2 Δ r Δ c Δ r Destination 1 Δ𝑐 = 𝑐 𝑑𝑠𝑡 − 𝑐 𝑠𝑟𝑐 + Δc anti-clockwise - Δc clockwise 𝑐 𝑠𝑟𝑐 = 𝑠𝑠𝑟𝑐 − 1 𝑟 𝑚𝑖𝑛 + 𝑐 𝑠𝑟𝑐 𝑐 𝑑𝑠𝑡 = 𝑠 𝑑𝑠𝑡 − 1 𝑟 𝑚𝑖𝑛 + 𝑐 𝑑𝑠𝑡 𝑟 𝑚𝑖𝑛 = Min(𝑟𝑑𝑠𝑡, 𝑟𝑠𝑟𝑐) If (Δ𝑐 > 3𝑟 𝑚𝑖𝑛) Δ𝑐 = 6𝑟 𝑚𝑖𝑛 − Δ𝑐else If (Δ𝑐 < −3𝑟 𝑚𝑖𝑛) Δ𝑐 = Δ𝑐 − 6𝑟 𝑚𝑖𝑛 Hexagon to Hexagon Routing Δ𝑟 = 𝑟𝑑𝑠𝑡 − 𝑟𝑠𝑟𝑐 + Δr outward direction - Δr inward direction (𝒔 𝒔𝒓𝒄, 𝒓 𝒔𝒓𝒄, 𝒄 𝒔𝒓𝒄) >> (𝒔 𝒅𝒔𝒕, 𝒓 𝒅𝒔𝒕, 𝒄 𝒅𝒔𝒕)
  • 58. Source 2 Δ c Source 1 Destination 2 Δ r Δ c Δ r Destination 1• Once Δr & Δc are known, each sensor can adaptively select its next hop as follows:  If (|Δr| > 0) & (|Δc| > 0) , whenever possible, select the hop which decrements both |Δc| and |Δr|. Otherwise,  If (routing outward i.e Δr > 0) select the next hop which decrements |Δc|  If (routing inward i.e Δr < 0) select the next hop which decrements |Δr| Hexagon to Hexagon Routing (𝒔 𝒔𝒓𝒄, 𝒓 𝒔𝒓𝒄, 𝒄 𝒔𝒓𝒄) >> (𝒔 𝒅𝒔𝒕, 𝒓 𝒅𝒔𝒕, 𝒄 𝒅𝒔𝒕)
  • 59. Conclusions  In this work, we explored the construction and the advantages of having an infrastructure for WSNs.  The proposed backbone proved to be useful in mitigating many of the typical challenges inherent to WSN including:  Sensor Localization.  Data Aggregation & Routing  Energy-Aware Workforce Selection  The construction protocol initially tiles the deployment area around sink nodes using identical hexagons.  After that backbone sensors are selected to be the closest sensors to the centers of the hexagons they represent.  Clustering  Energy Hole Problem  Energy Aware Sleep Schedule
  • 60. Future Research Directions Despite the encouraging results, many important challenges and research questions remained unanswered.  In the construction protocol, it would be useful to reduce the amount of inaccuracy due to RSS. One way around this difficulty would be to:  Estimate the initial distance through several readings.  Continuously update distance estimates form different messages received throughout the network lifetime.  Given the limited on-board energy budget available to sensors, it would be of interest to see how far can one streamline the computational requirements of the construction protocol.  If backbone sensors in different columns can be selected in parallel, can we also select backbone sensors in different rows in parallel ?
  • 61.  Let the sink node estimate the positions of all the hexagon centers and broadcast them to sensors which can work in parallel to estimate the closest sensor to the center of each hexagon. Future Research Directions (cont.) Any Problems?  Sensor Synchronization is one of the most challenging problems in sensor networks. It would be of an interest to show, if possible, how our proposed backbone can be useful in simplifying the synchronization problem.  Network Security is something that we completely overlooked in our presented work. Revisiting the proposed protocols from a security point of view would definitely open new dimension of research challenges.  Finding more applications of the PASTA property in in sensor networks analysis.
  • 62. Publications Journals: Conferences/Workshops:
  • 63. Conferences/Workshops (cont.)
  • 64. Conferences/Workshops (cont.)
  • 65. Questions ? Thank You !

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