Outline Network structure and objectives Routing Routing protocol protocol System analysis Results Conclusion Slide 2

Similar documents
Performance Evaluation of Queuing Systems

Solutions to COMP9334 Week 8 Sample Problems

Queuing Networks: Burke s Theorem, Kleinrock s Approximation, and Jackson s Theorem. Wade Trappe

Energy Optimal Control for Time Varying Wireless Networks. Michael J. Neely University of Southern California

WiFi MAC Models David Malone

Mathematical Analysis of IEEE Energy Efficiency

Call Completion Probability in Heterogeneous Networks with Energy Harvesting Base Stations

Quiz 1 EE 549 Wednesday, Feb. 27, 2008

Delay QoS Provisioning and Optimal Resource Allocation for Wireless Networks

NICTA Short Course. Network Analysis. Vijay Sivaraman. Day 1 Queueing Systems and Markov Chains. Network Analysis, 2008s2 1-1

Stochastic Optimization for Undergraduate Computer Science Students

Queueing Theory I Summary! Little s Law! Queueing System Notation! Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K "

Channel Selection in Cognitive Radio Networks with Opportunistic RF Energy Harvesting

Introduction to queuing theory

Fraud within Asymmetric Multi-Hop Cellular Networks

Power Allocation and Coverage for a Relay-Assisted Downlink with Voice Users

Stability Analysis in a Cognitive Radio System with Cooperative Beamforming

Networking = Plumbing. Queueing Analysis: I. Last Lecture. Lecture Outline. Jeremiah Deng. 29 July 2013

A Simple Energy Model for the Harvesting and Leakage in a Supercapacitor

Energy Harvesting Multiple Access Channel with Peak Temperature Constraints

Open Loop Optimal Control of Base Station Activation for Green Networks

SAMPLING AND INVERSION

Time Reversibility and Burke s Theorem

Computer Networks More general queuing systems

Optimal Utility-Lifetime Trade-off in Self-regulating Wireless Sensor Networks: A Distributed Approach

Microprocessor Power Analysis by Labeled Simulation

Decodability Analysis of Finite Memory Random Linear Coding in Line Networks

Location Determination Technologies for Sensor Networks

Queueing Theory and Simulation. Introduction

Homework 1 - SOLUTION

Optimal Sleep Scheduling for a Wireless Sensor Network Node

Course Outline Introduction to Transportation Highway Users and their Performance Geometric Design Pavement Design

EP2200 Course Project 2017 Project II - Mobile Computation Offloading

Intro to Queueing Theory

Broadcasting with a Battery Limited Energy Harvesting Rechargeable Transmitter

Approximate Queueing Model for Multi-rate Multi-user MIMO systems.

Chapter 2 Queueing Theory and Simulation

requests/sec. The total channel load is requests/sec. Using slot as the time unit, the total channel load is 50 ( ) = 1

Chapter 5. Elementary Performance Analysis

ABSTRACT WIRELESS COMMUNICATIONS. criterion. Therefore, it is imperative to design advanced transmission schemes to

Detecting Wormhole Attacks in Wireless Networks Using Local Neighborhood Information

Markov decision processes with threshold-based piecewise-linear optimal policies

AN ENHANCED ENERGY SAVING STRATEGY FOR AN ACTIVE DRX IN LTE WIRELESS NETWORKS. Received December 2012; revised April 2013

NODE ACTIVATION POLICIES FOR ENERGY-EFFICIENT COVERAGE IN RECHARGEABLE SENSOR SYSTEMS

On the Performance of Two-Hop Relay Mobile Ad Hoc Networks under Buffer Constraint

COMP9334: Capacity Planning of Computer Systems and Networks

Analysis and Performance Evaluation of Dynamic Frame Slotted-ALOHA in Wireless Machine-to-Machine Networks with Energy Harvesting

Link Models for Packet Switching

ABSTRACT ENERGY HARVESTING COMMUNICATION NETWORKS WITH SYSTEM COSTS. Title of dissertation: Ahmed Arafa, Doctor of Philosophy, 2017

Fairness and Optimal Stochastic Control for Heterogeneous Networks

Adaptive Contact Probing Mechanisms for Delay Tolerant Applications. Wang Wei, Vikram Srinivasan, Mehul Motani

Dynamic Power Allocation and Routing for Time Varying Wireless Networks

MAT SYS 5120 (Winter 2012) Assignment 5 (not to be submitted) There are 4 questions.

Cooperative Energy Harvesting Communications with Relaying and Energy Sharing

6 Solving Queueing Models

Stochastic Models in Computer Science A Tutorial

Modelling, Simulation & Computing Laboratory (msclab) Faculty of Engineering, Universiti Malaysia Sabah, Malaysia

Energy Efficient Transmission Strategies for Body Sensor Networks with Energy Harvesting

Discrete-event simulations

Relaying Information Streams

Exercises Solutions. Automation IEA, LTH. Chapter 2 Manufacturing and process systems. Chapter 5 Discrete manufacturing problems

Answers to the problems from problem solving classes

HYPOTHESIS TESTING OVER A RANDOM ACCESS CHANNEL IN WIRELESS SENSOR NETWORKS

Introduction to Markov Chains, Queuing Theory, and Network Performance

A Quantitative View: Delay, Throughput, Loss

Delay, feedback, and the price of ignorance

Cooperative HARQ with Poisson Interference and Opportunistic Routing

Quality of Real-Time Streaming in Wireless Cellular Networks : Stochastic Modeling and Analysis

Modeling the Residual Energy and Lifetime of Energy Harvesting Sensor Nodes

Performance Evaluation of Large Scale Communication Networks

How do Wireless Chains Behave? The Impact of MAC Interactions

Modeling and Simulation NETW 707

Power Controlled FCFS Splitting Algorithm for Wireless Networks

Data Gathering and Personalized Broadcasting in Radio Grids with Interferences

Markov Chain Model for ALOHA protocol

An Algebraic Approach to Network Coding

Generating Random Variates II and Examples

STABILITY OF FINITE-USER SLOTTED ALOHA UNDER PARTIAL INTERFERENCE IN WIRELESS MESH NETWORKS

CE351 Transportation Systems: Planning and Design

Wireless Internet Exercises

Design of discrete-event simulations

ENERGY harvesting (EH), a technology to collect energy. Optimal Relaying in Energy Harvesting Wireless Networks with Wireless-Powered Relays

An engineering approximation for the mean waiting time in the M/H 2 b /s queue

Temporal Reachability Graphs

Extracting mobility behavior from cell phone data DATA SIM Summer School 2013

EE 550: Notes on Markov chains, Travel Times, and Opportunistic Routing

Multi-Dimensional Online Tracking

Queueing Theory II. Summary. ! M/M/1 Output process. ! Networks of Queue! Method of Stages. ! General Distributions

Reducing The Data Transmission in Wireless Sensor Networks Using The Principal Component Analysis

Chapter 3 Balance equations, birth-death processes, continuous Markov Chains

Energy Balanced Data Propagation in Wireless Sensor Networks

ATM VP-Based Ring Network Exclusive Video or Data Traffics

Signalling Analysis for Adaptive TCD Routing in ISL Networks *

CS 798: Homework Assignment 3 (Queueing Theory)

Data analysis and stochastic modeling

Energy Cooperation and Traffic Management in Cellular Networks with Renewable Energy

DATA ROUTING WITH WIRELESS SENSOR NETWORKS

Dynamic Power Management under Uncertain Information. University of Southern California Los Angeles CA

LECTURE 3. Last time:

The Factor: Inferring Protocol Performance Using Inter-link Reception Correlation

Transcription:

2007 Radio and Wireless Symposium 9 11 January 2007, Long Beach, CA. Lifetime-Aware Hierarchical Wireless Sensor Network Architecture with Mobile Overlays Maryam Soltan, Morteza Maleki, and Massoud Pedram Department of Electrical Engineering University of Southern California Slide 1

Outline Network structure and objectives Routing protocol System analysis Results Conclusion Slide 2

Hierarchical Network Structure Sensor nodes Event Aggregation Relay ( (EAR EAR) )nodes Mobile Aerial Infrastructure overlay (MAIL)) nodes Base Station Flight Trajectory MAIL Node EAR Node Sensor Node M# of fmail nodes Base N # of EAR nodes Station Base Station Recurrent cycle Network Monitoring Lifetime (MoL) Flat architecture Slide 3

Objectives Analysis of concurrent controllable mobility and multi-hop routing in a multi-tier tier network Base Station Design and Analysis of Mobility-aware routing protocol Slide 4

Motivation Lower energy dissipation Longer lifetime. Energy consumption for wireless transmission: ε = ed β t d : Distance et : Energy dissipation for transmitting unit of data over unit of distance β : Path loss exponent Hierarchical network structure and multi-hop routing lowers energy dissipation. Mobility brings symmetry in battery depletion Slide 5

Bounded Hop Count Routing (BHR) Multi-hop routing between EAR nodes Less network delay and smaller storage size Shorter distance Less transmission power Dynamic Hop Count (DHC) vs. Initial Hop Count (IHC) mobility Routing delays transmission, propagation and queueing Bounded number of hops route if hop count <= H Storage, delay, and energy trade-off Slide 6

T 6 s 7 T 5 EAR Node Cluster and State Transitions in Each Cycle... Wait (S1) state Forward Chain γ S2 state μ 1... MAIL Coverage Area (Single Hop Routing) v... Backward Chain i j j+1 j+2 μ 2 k k+nc-1 k+nc k+nc+1 l... k + 1 k+nc-2 S3 state S4 state S5 S6 state S7 state (Nc -2) nodes T 7 s 1 T 1 Active States s7 s 6 s 2 s 5 s 3 T 2 s 5 3 T 4 s 4 T 3 Network of queues with vacation and variable service rate over time Slide 7

Queuing Analysis of Each EAR Node Wait (S1) state S2 state Forward Chain (Single Hop Routing) i j j+1 j+2 μ k 2... γ... v... Backward Chain k+nc-1 k+nc k+nc+1... μ 1 k + 1 k+nc-2 S3 state S4 state S6 state S7 state (a) Temporal variation of arrival rate for node i (b) Temporal variations of departure rate μ or μ 1 2 if Qi ( t ) > ( ) { t> T 0 1 Ui () t = For, Ai() t if Qi() t = 0 otherwise U ( t) = 0 i (c) () Temporal Variations of queue size l A i () t Ut i() Q i (t) + H ( H 1) (a) H 2 + μ 1 H.<μ 2 <μ 1 Δt B ( ) T1 T2 T3 T4 T5 T6 T7 μ H 1 2 μ ( H 1) 2 ( H 2 ) Δt B T1 T2 T3 T4 T 5 T6 7 L 1 μ 1 L 2 + μ μ L 3 1 2 L 4 μ2 s 1 s 2 s 3 s 4 s 5 s 6 s 7 T 1 T2 T3 T 4 5 T T T 6 7 2 T T Slide 8 (b) (c) M

Waiting Time in Queues EAR-to to-ear link delay: t link1 State durations: T i Avg. waiting in each EAR node: W t = W + ( 1/ μ ) link 1 1 t : link 1 Set as a variable T i Queueing analysis as a function of t link1 W as a function of t link1 System of two equations W Slide 9

Average Number of Hops for a Packet Transmission Hop count for packet delivery from an EAR node hi () t to a MAIL node Between 1 to H DHC seen by an EAR node during one cycle H 1 H Temporal average of DHC for a packet delivery 1 DHC t1 t2 t3 1 H 1 + h = H ( T1 + T2) + ( T4 + T5) + ( ) ( T3 + T6 ) T 2 M Δt s (H-1) Δt s (c+n ) Δ t tc c s (n ) Δt c s (H-1) Δts tn= Tcyc Slide 10

Network Delay Average delay for a packet to reach a MAIL node, D net D net ( h 1) 1 = h W + + μ μ 1 2 Waiting time in queues EAR-to-MAIL communication EAR-to-EAR communications Slide 11

Network Lifetime Average energy consumption for a packet delivery: E = ( h 1) ee + em e E : Avg. energy consumption for EAR-to-EAR communication e E M : Avg. energy consumption for EAR-to-MAIL communication Average # of packets generated during lifetime of the network: N T net 0 ( ) E N T N E T. 0 net E net = = E Slide 12

Max T vh,, Nc sys T = Min( T, c T ) sys net e Optimization Problem s.. t D D, B B, c C C net max p max M max Convex epigraph form: f 1 T f, f 1/ ct 1/ T sys e net Min f vhr,, c T e : MAIL endurance time T net : Network lifetime B p : Peak queue size C M M: : Recharge Cost c : # of charge occasions st.. ke f E 0, ρ v f c α E M 0 and D D, B B, c C C net max p max M max 3 ( T 3 1 ) e v Slide 13

Sample scenario: Simulation Results 1000 EAR nodes Q i (t) 1 Random distribution ib i Two MAIL nodes μ 2 e t for EAR-to-EAR: 0.0013 e t for EAR-to-MAIL: 10 (pjoul/bit/m 2 ) 1 T2 T3 : 0.3 4 μ + μ1 μ2 s 1 s 2 s 3 s 4 s s 5 s 6 7 T T T T T 5 6 7 Slide 14

Simulation Results Cont. Delay: Dnet 1 vm H 2 Lifetime: T net 1 H 2 Slide 15

Concluding Remarks Lifetime and delay aware deployment strategy A mobility-aware multi-hop routing protocol (Bounded hop count routing (BHR)) To control the trade off between delay, buffer size and lifetime Analysis of lifetime, delay, and buffer size Optimization problem formulation Packet level simulator Slide 16