management of ubiquitous sensor network -...

Jong T. ParkKyungpook National University

([email protected])

Management of Management of

Ubiquitous Sensor NetworkUbiquitous Sensor Network

2APNOMS 2005 Tutorial, Okinawa, Japan

Outline

Brief Overview on Ubiquitous Sensor Network TechnologyUbiquitous Sensor Network and its ApplicationsUSN Characteristics and Research Projects Network Protocol Architecture of USN Current Standardization ActivitiesLocation and Synchronization Technology in Sensor Network

USN Management RequirementsUSN Management Architecture

USN Management ArchitectureUSN Management Functional AreasRelated Research on USN Management Architecture

Specific USN Management FunctionsPower ManagementTopology ManagementSecurity ManagementContext Management

Conclusion


What is Ubiquitous Sensor Network?

Infrastructure network for realizing ubiquitous computing environment using may sensor nodes with sensing,processing & wireless communication capabilitiesCheap and smart sensor node deployed, and monitoringand controlling target environment

Internet Sink

Sensor ADCProcessor

StorageTransceiver

Sensing unit Processing unit Transmission unit

Target

Sensor node

User

Power UnitPower

Generator

Position finding system Mobilizer


Sensor Networks

Sensor network is composed of a large number of sensor nodesSensor nodes are small, low cost, low-power devices that have following functionality:

Communication on short distances due to power limitationSense environment dataPerform limited data processing

Network usually also contains “sink” node which connects it to the outside world


Sensor Node Hardware

19.1 Kbps 20m Range Light Sensing Temperature Sensing 4 MHz – 3.0 V 8 Kbytes –Program Memory 512 Bytes – Data Memory Available from: CrossBow Inc. $900 for a complete kit

UC Berkeley Motes

MIT µAMPS-I node


Sensor Network Applications

Military ApplicationsEnvironment and Habitat MonitoringManufacturingTransportation Seismic StudyHealth Care Home Network


Sensor Network Applications (Cont.)

Military: battlefield surveillance, enemy/friendly forces monitoring & tracking, biological attack detection, battle damage assessmentEnvironmental and Habitat : file detection, flood detection, agricultural usesHabitat monitoringManufacturing: inventory controlTransportation: traffic control, shipping and commodity managementHealth: human physiological data monitoringMiscellaneous: car theft detection


Characteristics of USNHardware Constraints

Low cost (wasteful): temperature, lightning condition, object presence and movement, pressure etc.Low power, and limited processing/memory capacities: sensing node, sink node, and manager node

High DensityLarge number of sensor nodes (maybe 10 to 100,000 nodes): Scalability problemNode position may not be predetermined

Low Energy ConsumptionLife time of sensor network depends on battery life timeTo relocate & recharge large number of sensing nodes is impossibleNeed adaptable power management strategy: multi-hop communication mode, energy-conservative applications

Network Self-configurationLarge number of nodes in hostile locations-> manual configuration unfeasibleNodes may fail & new nodes join the network: node failures should be regarded as normal conditionAd-hoc sensor network protocols may be applied

Task-specific Application and Data-centric routingNetwork application is defined and developed specifically for each sensing taskData-centric routing is usually employed so that attribute (or location)-based naming are used.

Data fusion and query for collaborative/distributed processingLocally carry out simple computation -> forwards and aggregate dataQuery for single node or group of nodes based on attribute and/or locationBase nodes collect data from given area & create summary messages


USN and Ad-Hoc Network Comparison

Global ID(IP Address)No Global IDID

Node IDAttribute/Location-BasedAddressing/Naming

Relatively highLimitedResource (Processing)

Almost steadyVery frequentTopology Change

Not Prone to FailureProne to failureFailure

RechargeableVery Limited Power Consumption

Point-to-pointMulti-hop, Power-conservative Broadcast/multicast,

Communication mode

Relatively sparselyDensely (about 20/meter cube)Deployment

10 ~ 100100 ~ 1000Number of Nodes

Ad-hoc NetworkSensor NetworkItems for Comparison

Needs new paradigm on communication protocols and its MANAGEMENT !


USN Research Projects

SMART DUST – UC Berkeley•Autonomous Node 1mm3, MEMS Tech.•Optical Comm. Module, CCR•Sensing, Processing, Communication•Small Size, Low Power, Low Cost

SMART DUSTSMART DUST – UC Berkeley•Autonomous Node 1mm3, MEMS Tech.•Optical Comm. Module, CCR•Sensing, Processing, Communication•Small Size, Low Power, Low Cost

Things The Think (TTT) - MIT Media Lab.•Embedding computation into everyday things. (such as clothing, jewelry, and tables.)•Large amount of Project.(Wearable Health, Smart City, UbER-Badge etc.)

Things The Think (TTT)Things The Think (TTT) - MIT Media Lab.•Embedding computation into everyday things. (such as clothing, jewelry, and tables.)•Large amount of Project.(Wearable Health, Smart City, UbER-Badge etc.)

Smart Kindergarten – NESL-UCLA•iBadge, Childhood education environment •Monitoring & AnalysisEvaluations of students progress“How well is student A reading the story book B?”

Smart Kindergarten Smart Kindergarten – NESL-UCLA•iBadge, Childhood education environment •Monitoring & AnalysisEvaluations of students progress“How well is student A reading the story book B?”

TRON (The Real-time OS Nucleus) - Japan•ITRON(Industry TRON), BTRON(Business TRON)•T-Engine (2002. 6)

- Tron based Development platform- 400 corporations (2004) : MS, Samsung

TRON (The RealTRON (The Real--time OS Nucleus) time OS Nucleus) -- JapanJapan•ITRON(Industry TRON), BTRON(Business TRON)•T-Engine (2002. 6)

- Tron based Development platform- 400 corporations (2004) : MS, Samsung


USN Research Projects (cont’)

Oxygen – MIT Computer Science Lab• Computing service available like Oxygen• Computing access using human centered interface such as

natural language and vision

OxygenOxygen – MIT Computer Science Lab• Computing service available like Oxygen• Computing access using human centered interface such as

natural language and vision

CoolTown – HP• Real World Wide Web• Person, place or thing existing on the Web space,

communicating each other• Education, Medicare, ITS, Fire and Safety Service

CoolTownCoolTown – HP• Real World Wide Web• Person, place or thing existing on the Web space,

communicating each other• Education, Medicare, ITS, Fire and Safety Service

SMART TAG – MIT Auto-ID Center•RFID, Supply Chain Management•Collaboration with EPC Global•EPC Code, ONS, PML•Low Cost TAG (Cost < 5 Cent)

SMART TAGSMART TAG – MIT Auto-ID Center•RFID, Supply Chain Management•Collaboration with EPC Global•EPC Code, ONS, PML•Low Cost TAG (Cost < 5 Cent)

Global Supply Chain

Easy Living Project – Microsoft•Intelligent Environment•Info. Gathering Tradition I/O Device – Mouse, Keyboard, MICSensing Device – Cameras, Active Badge

Easy Living ProjectEasy Living Project – Microsoft•Intelligent Environment•Info. Gathering Tradition I/O Device – Mouse, Keyboard, MICSensing Device – Cameras, Active Badge


USN Research Projects (cont’)

u-Korea – Ministry of Information and Communication• Development of Key Technologies in Wireless Personal Area

Network, UWB (~hundred Mbps), Electronic Tag (RFID), Intelligent Wireless Sensor Network

• Ubiquitous Home Network and Commodity Circulation Network

uu--KoreaKorea – Ministry of Information and Communication• Development of Key Technologies in Wireless Personal Area

Network, UWB (~hundred Mbps), Electronic Tag (RFID), Intelligent Wireless Sensor Network

• Ubiquitous Home Network and Commodity Circulation Network

Smart-Its Project – ETH, TecO, VTT•Disappearing Computer Initiative (16Projects)•Development Device (Disappearing Computer Initiative)

•Smart Cup (Temp. & User Detecting)

SmartSmart--Its ProjectIts Project – ETH, TecO, VTT•Disappearing Computer Initiative (16Projects)•Development Device (Disappearing Computer Initiative)

•Smart Cup (Temp. & User Detecting)

U-Network – Ministry of Internal Affairs & Communications•Net. Roaming Based On Small Chip & Terminal•Anywhere Connection•Hotspot Net. Service Mobile Network Service•Project: TRON (Tokyo UNIV.) eHII (Matsushita)

UU--NetworkNetwork – Ministry of Internal Affairs & Communications•Net. Roaming Based On Small Chip & Terminal•Anywhere Connection•Hotspot Net. Service Mobile Network Service•Project: TRON (Tokyo UNIV.) eHII (Matsushita)

Network Protocol Architecture of USN


Sensor Network Protocol Architecture(Akyildiz, etc, GIT)

Pow

er man

agemen

t plane

Mobility m

anagemen

t plane

Task man

agemen

t plane

Application layer

Transport layer

Network layer

Data link layer

Physical layer

Application specific protocols

Frequency selection, carrier frequency generation, signal detection, modulation, data encryption

Schedule the sensing tasks given to a specific region and/or power level

Movement detection, neighbor detection and registration

Manage how a sensor node uses its power

Needed when access through the Internet or other external networks

Multihop wireless routing protocols between the sensor nodes and the sink nodes

Multiplexing of data streams, data frame detection, medium access and error control


Logical Functions of USN Protocol Layers

Sensor Query, data dissemination/aggregation,

Task assignment, Management

Maintain connections to external Internet: TCP splitting

Data-centric routing, Self-configuration, Geo-Routing

MAC, Time and Location, Adaptive

Communication, Sensing, Actuation

Physical

Data Link

Network

Transport

ApplicationCoordinated to minimize power consumption

Adaptive topologyEnergy-aware RoutingAdaptive MAC

In-network processingData-centric routingMulti-hop communicationNo direct communication toservers


Physical Layer

NeedsSimple, but robust modulation, transmission, and receiving technique

Transmission mediaRadio

ISM (Industrial, Scientific, Medical) 915MHz band widely suggested

InfraredOptical media

Open research issueModulation scheme

Needed simple and low-power modulation scheme

Hardware designTiny, low-power, low-costPower efficient hardware management strategy


Data Link Layer

Medium Access Control (MAC)Creation of the network infrastructureFair and efficient sharing of communication resources between sensor nodes

Reasons: Existing MAC Protocols Cannot be UsedCellular system

Mobile node is only a single hop away from nearest base stationGoal : high QoS, bandwidth efficiencyImpractical for sensor network due to no central controlling agentPower efficiency is of prime importance in sensor network

Bluetooth and MANETClosest peers to sensor networksSensor network may have a much larger number of nodes

Transmission power Transmission range


Data Link Layer (cont.)

MAC for Sensor NetworkSMACS (Self-Organizing Medium Access Control for Sensor Networks) and EAR (Eaves-drop-And-Register) AlgorithmCSMA-Based MAC schemeHybrid TDMA/FDMA-Based MAC scheme

Power Saving Modes of OperationError Control

FEC (Forward Error Correction)ARQ (Automatic Repeat Request)

Open Research IssueMAC for mobile sensor networksPower-saving modes of operationError control coding schemes


Data Link Layer (cont.)

Contention-based random access

Centralized freq. and time division

Fixed allocation of duplex time slots at fixed freq.

Method

Uses constant “listening time”to min energy

Min energy use in Hardware. Picks the mix of TDMA/FDMA to min energy use

Random wake up during setup and turning radio off while idle. Exploits large bandwidth available compared to sensor data rate

Power Conservation

Should use more computation over handshaking. Use more Power-saving modes (sleeping). Need to derive bounds on energy needed by sensorsCSMA-based

Hybrid TDMA/FDMA

Not great for very mobile networks (good for mostly stationary nodes)

SMACS and EAR

What we still need to improveMAC Protocol


Network Layer

NeedsData Routing

RequirementPower efficiency Data-centric : What is the temperature on Region A ?Data aggregation

Region A

SinkSink

Query is sent to no t node but region

< Data Centric >


Network Layer (Cont.)

Energy-efficient route

B (PA=2)

F (PA=4)

Sink

A (PA=2)

C (PA=2)

D (PA=3)

E (PA=1)

α1 = 1

α2 = 1

α3 = 2

α4 = 2

α5 = 2

α6 = 2

α7 = 1α8 = 2

α9 = 2α10 = 2

T

Route 1 : Sink-A-B-T, total PA = 4, total α = 3Route 2 : Sink-A-B-C-T, total PA = 6, total α = 6Route 3 : Sink-D-T, total PA = 3, total α = 4Route 4 : Sink-E-F-T, total PA = 5, total α = 6

Maximum PA route : Route 4Minimum energy(ME) : Route 1Minimum hop(MH) : Route 3


Sensor Network Layer Routing Scheme

Sets up gradients for data to flow from source to sink during interest dissemination

Directed diffusion

Forms clusters to minimize energy dissipationLEACH

Creates multiple trees where the root of each tree is one hop neighbor from the sink

SAR

Sends data to sensor nodes only if they interested;

- has three types of messages (i.e., ADV, REQ, and DATA)SPIN

Sends data to one randomly selected neighborGossiping

Broadcasts data to all neighbor nodes regardless if they receive it before or not

Flooding

DescriptionNetwork layer scheme


SPIN

Step1

ADV

Step3

DATA

Step2

REQ

Step4

ADV

Step5

REQ

Step6

DATA

SPIN (Sensor Protocols for Information via Negotiation)


Directed Diffusion

SinkSource

Step 1 : propagate interest

SinkSource

Step 2 : set up gradient

SinkSource

Step 3 : send data


Transport Layer

NeedsMaintain the flow of data, and end-to-end connection if the sensor networks applications requires it

ResearchTCP splitting needed

TCP connections are ended at sink nodes, and special transport layer protocol can handle the communications between the sink node and sensor nodesCommunication between user and sink node→ TCP or UDP via the Internet or satellite

Communication between sink node and sensor node→ UDP type protocol, because sensor node has limited memory

Not based on global addressingAttribute-based naming

Open Research IssueUDP type protocol used for communication between sink node and sensor nodePower consumption, scalability and data-centric routing may need different transport handling in sensor networks


Application Layer

Needs Depending on the sensing tasks, different types of application software built and used

Application layer protocolsSMP (Sensor Management Protocol)

System administrators interact with sensor networks using SMPProvides the software operations needed to perform the followingadministrative tasks

Introducing the rules related to data aggregation, attribute-based naming, and clustering to the sensor nodesExchanging data related to the location finding algorithmsTime synchronization of the sensor nodesMoving sensor nodes Turning sensor nodes on / off

TADAP (Task Assignment and Data Advertisement Protocol)SQDDP (Sensor Query and Data Dissemination Protocol)

Standardization Activity

IEEE 1451IEEE 802.15.4ZigBee6LoWPAN


IEEE 1451 Wireless Smart Transducer Interface Standard

A new family of standards for connecting smart transducers to networks

A single communication protocol usable by all sensorsIEEE 1451.1 Network Capable Application Processor (NCAP) Information model for smart transducersIEEE 1451.2 Transducer to Microprocessor Communication Protocols and Transducer Electronic Data Sheet (TEDS) formatsIEEE1451.3 Digital Communication and Transducer Electronics Data Sheet (TEDS) Formats for Distributed Multidrop SystemsIEEE 1451.4 Mixed-mode Communication Protocols and Transducer Electronic Data Sheet (TEDS) FormatsIEEE Instrumentation and Measurement Society


IEEE 802.15.4 Low-Rate WPAN Standard

Key FeatureDefine the PHY and MAC spec. for low data rate wireless connectivity with fixed, portable and moving devices with no battery or very limited battery consumptionData rates: Maximum 250kbps, and minimum 20kbpsSupport for low latency devices, such as wireless keyboards, mice and joysticks, and high latency applications such as environmental sending MAC : A beacon mode for low delay with guaranteed bandwidth, and a non-beacon CSMA/CA mode for direct, mutual communicationSupports both 64-bit and 16-bit addressesSupport Multi-level securityAutomatic network establishment by the coordinatorFully handshaked protocol for transfer reliabilityPower management to ensure low power consumption

3 bands, 27 channels868MHz : 1 channel, Europe, 915MHz : 10 channels, USA2.4GHz : 16 channels, worldwide

IssuesLong battery lifeSelectable latency for controllers, sensors, remote monitoring and portable electronics


ZigBee Protocol Stack

PHY LAYER

MAC LAYER

NETWORK/SECURTIYLAYERS

APPLICATION FRAMEWORK

APPLICATION/PROFILES

IEEE 802.15.4

ZigBee AlliancePlatform

ZigBee or Manufacturer(User Defined)

ZigBee takes full advantage of a powerful physical radio specified by IEEE 802.15.4ZigBee adds logical network, security and application software

ZigBee characteristicsDual physical layer (2.4GHz, 869/915 MHz)Data rates of 250 kbps (@2.4 GHz), 40 kbps (@ 915 MHz), and 20 kbps (@868 MHz) Low power (battery life multi-month to years) Multiple topologies: star, peer-to-peer, mesh Addressing space of up to: -18,450,000,000,000,000,000

devices (64 bit IEEE address)- 65,535 networks

Fully hand-shaked protocol for transfer reliability Range: 50m typical (5-500m based on environment)


IETF 6LoWPAN

Provide IPv6 over Low Power Wireless Personal Area Network (LoWPAN), 2004 Nov., IETF 6LoWPAN WGStandard Documents

IPv6 over LoWPAN networks Goals and Assumptions: OverviewTransmission of IPv6 Packets over IEEE 802.15.4 WPAN Networks

How to transfer IPv6 packet using IEEE 802.15.4IPv6 address configuration method, header Compression, AODV-based Ad-Hoc Routing

IssuesIP adaptation / Packet Formats and interoperability Addressing schemes and address management Network management Routing in dynamically adaptive topologies Security, including set-up and maintenance Application programming interface Discovery (of devices, of services, etc) Implementation considerations

Location and Synchronization Technology in Sensor Network


Location Technology in Sensor Network

Discovery of absolute or relative locationGeographical routing (location attribute based naming and addressing)Tracking of moving objectsContext (location) aware applications

Challenges in Sensor NetworksEnergy constraintHarsh environment with multipathsMinimal infrastructure (Few beacons, No backend computation)

Many techniques for location sensingTDOA (Time Difference Of Arrival)TOA (Time Of Arrival)AOA (Angle Of Arrival)SSR (Signal Strength Ranging)GPS, etc.

D3-D2 D3-D1

D3D2

D1

Reader 3(x3,y3) (x2,y2)

Reader 2

Reader 1(x1,y1)

tag

(xM,yM)

TDOA Locating Algorithm


Time Synchronization in Sensor Network

Critical at many layers of sensor networkCommunication, localization, distributed DSP, etc.Conventional approaches

GPSNot suitable for indoors, cost, size, energy

NTP (Network Time Protocol)Delay and jitters due to MAC and store-and-forward relayingDiscovery of timer servers (nodes synchronize with one of a pre-specified list of time servers)

Reference-broadcast synchronization (RBS)Very high precision sync with slow radios

Beacons are transmitted, using physical-layer broadcast, to a set of receiversTime synchronization is based on the difference between reception times, do not sync sender with receivers

USN Management Requirements


User Requirements on Wireless Sensor Network

A survey on the characteristics of a wireless sensor network which are most most important to users (2002)1. Data Reliability2. Battery Life3. Cost 4. Transmission Range5. Date Rate6. Data Latency 7. Physical Size8. Date Security


User Requirements on Wireless Sensor Network (Cont.)

A survey on the characteristics of a wireless sensor network which are most most important to users (2002)1. Data Reliability Reliability, Configuration

(Density), Security Management2. Battery Life Power Management3. Cost 4. Transmission Range, 5. Date Rate,6. Data Latency

Performance Management7. Physical Size8. Date Security Security Management


USN Management Requirements

Fault toleranceHandle loss of nodes - Lack of Power, Physical damage, Environmental interference

ScalabilityHandle high density of nodes - The number of sensor nodes is an extreme value of millions

Production costsMake them low cost - Cost of a single node is very important to justify the overall cost of the network

Operating environmentSurvive and maintain communication - The bottom of an ocean, biologically contaminated field, battlefield

Transmission mediaWireless - Radio, infrared, optical media

Hardware constraintsNodes are tiny - Very small size, very light node, limited memory, limited battery

Power consumptionLimited Tx, computation, lifetime - Replenishment of power is impossible

Changing TopologyNodes - Nodes moving, new nodes, loss nodes


Ubiquitous Sensor Network Management

Why isn’t SNMP (Simple Network Management Protocol) adaptable to USN?

Sensor-specific failures are not handledDifficult to find the failed nodesPhysical connections are not utilizedCommonly, there is not a management agentSpecifying nodes is difficultNetwork is self-configured, so that management server doesn’t have all information of sensor nodes

ChallengesPresents many and drastically different challenges. For example:

Deployment of nodes, Discarding of nodes Requires augmentation to (or new approaches over) traditional network and service management techniques Needs to take into account specific characteristics of WSNs (e.g., energy waste)

USN Management Architecture & Functions


Sensor Network Management Functional Areas:

Reliability

Management

Reliability

Management

Context

Management

Context

Management

Topology

Management

Topology

Management

Security

Management

Security

Management

Performance

Management

Performance

Management

Power Management

Context

Management

Context

Management


USN Management Architecture

To

po

log

y

Security ManagementPrivacy Management

Self-Configuration,Configuration Management

Application

Transport

Network

Data Link

Physical Layer

Po

wer

Relia

bility

Secu

rityPower ON/OFF, Power ExhaustingEnergy-efficient ProtocolEssential Management Function

Context-Awareness ManagementTask Management, Location-Based Context Management

Fault / Reliability Management, Performance Management

Co

nte

xt

Related Research on USN Management Architecture


Ad-Hoc Network Management Characteristics

Dynamic Topology ChangesPower Limitation

Power management strategy affects topological configuration, and fault management.The faulty conditions are different from those of wire-line network or wireless networks.

Constrained Wireless CommunicationLarge Number of Heterogeneous Nodes


ANMP (Ad-Hoc Network Management Protocol)

ANMPA protocol for managing mobile wireless ad-hoc networks

Focuses on data collection, configuration, fault and security management

Uses hierarchical clustering of nodes Helps to reduce exchanges between manager and agents Easier to keep track of roaming nodes

Fully compatible with SNMPv3 Includes enhanced security features

ArchitectureHierarchical3-Level

Manager

Cluster head

Clusters

Agents


ANMP: Data Collection

ANMP ClusteringFormed for management purposes only - different from those formed for routing purposes Dynamic structures – number and composition of nodes change over time Nodes acting as cluster head may also change Two algorithms proposed for clustering:

Graph based clustering (graphic view of the net; each node is nomore than one or two hops away from the cluster head) Geographical clustering (based on spatial density of nodes usinglatitudes and longitude information)

Data CollectionEvery node runs ANMP locally A new MIB, called ANMP MIB added to MIB

anmpMIB

Powerusage(1)

topologyMaintenance(2)

agentsInformation(3)

Lacm(4)

ANMP MIB


Guerrilla Management

Guerrilla ManagementA supervisor/agency architecture for scalable and cooperative management

Uses mobile code techniques for nomadic and active management

Uses a dynamic adaptive protocol for clustering and selecting nomadic managers

Nodes range in functionality and capability SNMP-capable, Probe-capable, and Full-featured


Guerrilla Management Model

SupervisorSupervisor

Nomadic ManagerNomadic Manager

Nomadic ManagerNomadic Manager Nomadic ManagerNomadic Manager

Nomadic ManagerNomadic Manager

Agency

ActiveProbesActiveProbesActive

ProbesActiveProbes

ActiveProbesActiveProbes


ProbesActiveProbes



ProbesActiveProbes


High-Tier

Low-Tier


Classification of Nodes in GuerrilaManagement Architecture

MIBSNMP agent

Communication protocolCommunication protocol

MIBSNMP agent


Probe processingmodule


MIBSNMP agent




GMIBNormadic management module

a) SNMP-capable node b) probe-capable node architecture

c) full-featured node architecture


GMIB: Guerrilla MIB

Nomadic ManagerCollaborates autonomously to manage the entire ad hoc network with minimal help from the supervisor

Role change

Load sharing - cloning itself into another node

Spawning and merging

GMIB (Guerrilla MIB) A data structure equivalent to a SNMP MIB

An aggregation of management information collected from neighbor nodes via probes

Maintained inside NMM

Also includes Management information (e.g., neighbor information) in the probeprocessing modules

Can be accessed by both the NMM and incoming probes Modeled as a branch in a SNMP MIB


MANNA

Wireless Sensor Network (WSN) Functionalities Another abstraction level to include the network functionalitiesUseful in developing various network management models

Identifies a set of WSN-specific Managed Objects mostly derived from OSI

Management ArchitecturesFunctionalInformationPhysical


MANNA Architecture

Functional ArchitectureDescribes the distribution of management functionalities among manager, agent, and management information base (MIB) Covers variety of manager-agent models

MANNA Manager MANNA Agents

Informational ArchitectureTwo Object Class Type

Managed Object Classes Support object classes


Management Functionality Abstraction in Manna

ManagementFunction

FaultConfigurationPerformance

SecurityAccounting

Managementfunctionalareas

Business managementService managementNetwork managementNetwork element managementNetwork element

Managementlevels

Conf

igura

tion

Mainte

nanc

eSe

nsing

Proc

essin

gCo

mmunica

tion

WSN functionalities


USN Management Architecture (MANNA)

• Requirements that characterize a sensor network come from the objectives defined in the business management layer

• Design of WSNs depends on applications

• The larger the number of monitored parameters, the larger the energy consumption and the lower the network lifetime

• Basic USN services are Sensing, Processing, and Dissemination

• Aims to manage a network as a whole, which is typically distributed over an extensive geographical area

• The relationships among sensor nodes are to be considered: collaboration, connectivity, and correlation

• Power management• Mobility management• State management

• Physical resource: power supply, processor, memory, sensor device, and transceiver

• Logical resource: communication protocols, application programs, correlation procedures, operating systems, and network services

Business Management

Service Management

Network Management

Element Management

Network Entities


USN Management Functional Area (MANNA)

Security functionalities for USNs are intrinsically difficult to be provided because of their ad-hoc organization, intermittent connectivity, wireless communication and resource limitationsA WSN is subject to different safety threats: internal, external, accidental, and malicious

Security

There is a trade-off to be considered: the higher the number of managed parameters, the higher the energy consumption and the lower the network lifetimeOn the other hand, if enough parameter values are not obtained, it may not be possible to manage the network appropriately

Performance

It includes functions related to the use of resources and corresponding reportsIt establishes metrics, quotas and limits that can be used by functions of other functional areasIt must provide self-sustaining functionalities

Accounting

Self-organization: is the property which the sensor nodes must have to organize themselves to form the networkSelf-configuration: nodes setup and network boot up must occur automatically

Configuration

Faults in USNs are not an exception and tend to occur frequently, thus fault management is a critical function.This is one of the reasons that make WSN management different from traditional network managementSelf-diagnostic: the network monitors itself and find faulty or unavailable nodesSelf-healing: the network prevents disruptions or that acts to recover itself or the node after the self-diagnostic

Fault

Functions

Specific Management Functions of USN

Power ManagementTopology ManagementSecurity ManagementContext Management


Power Management

Manages how a sensor node uses its power

ExampleSensor node may turn off its receiver after receiving a message from one of its neighbors

Avoid getting duplicated messages

When the power level of the sensor node is lowBroadcasts to its neighbor when it is low in power

Cannot participate in routing messages

Reserve the remaining power for sensing

RequirementsUsing batteryLimited PowerExpand the life time of sensor nodeReduce the overhead


Power Management in Protocol Layer

Physical layerLow Power Modulation SchemeTransceiver, Sensor, Processor: Small, Low Power, Low Cost

Data link layerEnergy efficiency MAC protocol

Adaptive duty cycling – SMAC, ASCENT, SPANWake up on-demand – STEM, Wake-on-Wireless

Reduce the collision, signaling, frame overheadPower saving mode (ex. On/Off mode)

Network LayerEnergy-efficiency routingEnergy-efficiency data aggregation algorithmsLocation-based routing

Transport LayerUse UDP message protocol between Sink and Sensor nodeLimited memory and processing power

Application LayerEnergy-efficiency applications


Topology Management

GoalCoordinate the sleep transitions of all nodes, while ensuring adequate network connectivity, such that data can be forwarded efficiently to the data sink

RequirementsHeterogeneous nodeData discovery & data disseminationLimited memory & power constraintApplication requirementsNode mobility

Ad-hoc Self-organization LCA (Linked Cluster Algorithm)LAA (Link Activation Algorithm)DEA (Distributed Evolution Algorithm)


Topology Management (Cont.)

SMACS (Self-Organizing Medium Access Control for Sensor networks)EAR (Eavesdrop And Register)

BI (Broadcast Invite)MI (Mobile Invite)MR (Mobile Response)MD (Mobile Disconnect)

SAR (Sequential Assignment Routing)SWE (Single Winner Election)MWE (Multi Winner Election)


Security Management

RequirementsPeanut CPU (slow computation rate)Battery power: trade-off between security and battery lifeLimited memoryHigh latency: conserve power, turn on periodically

Security Management in USNApplications need security (privacy)Absence of security enables attacks such as spoofing & replay attacks, resulting in DoS or system compromiseIntrusion prevention : First line of defenseIntrusion detection : Second line of defense

Main Security Threats in USNRadio links are insecureSensor nodes are not tamper resistant – it it is compromised, attacker obtains all security information

Attacker typesMote-class: attacker has access to some number of nodes w/ similar characteristicsOutside / inside


Security Management (Cont.)

AttacksPhysical attackDenial-of-serviceBattery exhaustionClock synchronizationLocation discoveryAttacks on routing

spoofed, altered, or replayed routing informationselective forwarding by compromised nodessinkhole attack: pretend to be a sink node (fake sink node)sybil attack: a single node pretending to be in different parts of the networkWormholes: tunnel packets received on one part of the network toanotherHELLO flood attacksacknowledgment spoofing


CountermeasuresLink layer encryption – selective forwardingUsing a counter – Replay attacksLimiting the number of neighbors per node – Inside attacksBi-directionality of the link – HELLO floodGeographical routing – Wormhole attacks

Security Management (Cont.)


Context Management

Gathering the “User Context”Requirement

User intent predictionApplication deployment supportRuntime context serviceReal-time serviceInter-user coordination and collaboration

ContextAny information that can be used to characterize the situation of an entity

Considered relevant to the interaction of an entityConsidered relevant to the interaction between a user and an application, including themselves

Context ModelACTIVITY – behavior, taskENVIRONMENT

physical status (location, time, etc)social surroundings (nearby objects)

SELF – status of device itself

Activity

Environment Self


Context Management (cont’)Key Components

Context discovery and acquisitionUser interfaceContext management and modelingContext composition and gathering

Group Context ManagementEnable syntactic and semantic interoperability between context- aware applicationsEnable seamless integration of various kinds of contexts and make it easy to be inferred

User ContextUser intent predictionApplication development supportRuntime context serviceInter-user coordination and collaboration


ConclusionBrief Overview on Ubiquitous Sensor Network Technology

Identify salient features of USN Introduce sensor network protocols Current standardization activities

Analyze USN Management RequirementsPropose a USN Management ArchitectureIntroduce USN Management Functions

Related Research on USN Management ArchitectureIdentify Specific USN Management Functions

Power ManagementTopology ManagementSecurity ManagementContext Management

Research of sensor network management are at the early stages and a challenging task


References

Sensor Network Projects UC Berkeley: Smart Dust and TinyOS: today.cs.berkeley.edu/tos; robotics.eecs.berkeley.edu/~pister/SmartDust; also www.tinyos.netEyes: http://eyes.eu.org/index.htmCornell: Cougar: http://www.cs.cornell.edu/database/cougar/UCLA: CENS - Center for Embedded Networked Sensing www.cens.ucla.edu/ Northern Arizona: WNRL: www.cet.nau.edu/Projects/WNRL USC: SCADDS: www.isi.edu/scaddsUCLA: WINS: www.janet.ucla.edu/WINS JPL: Sensor Web: sensorwebs.jpl.nasa.gov/ Georgia Tech: SensorSimII: users.ece.gatech.edu/~grimace/research/sensorsimii/index.html

Basics of Sensor NetworkIan F. Akyildiz, W, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks”, Communication Magazine IEEE. vol. 40, no. 8, pp. 102-114, Aug. 2002.A. WADAA, S. OLARIU and L. WILSON, M. ELTOWEISSY, K. JONES “Training a Wireless Sensor Network”Macros Augusto M. Vieira, Claudionor N. Coelho. Jr., Diogenes Cecilio da Silva Junior, Jose M. da Mata, “Survey on Wireless Sensor Network Devices”Chien-Chung Shen, Chavalit Srisathapornphat, Chaiporn Jaikaeo, “Sensor Information Networking Architecture and Applications” IEEE Personal Comm., August, 2001M. Ulema, Wireless Sensor Networks; Applications, Technology, and Management, IEEE NOMS 2004, Tutorial


References (Cont.)Ubiquitous Sensor Network Management

Wang Feng, Tian Qichuan, Gao Quanzue and Pan Quan, “A Study of Sensor Management Based on Sensor Networks”, International Conference on Robotics, Intelligent Systems and Signal Processing, October, 2003Mark A. Perillo, Wendi B. Heinzelman, “Optimal Sensor Management Under Energy and Reliability Constraints,” IEEE, 2003W. Chen, N. Jain, and S. Singh, “Anmp: Ad Hoc Net-work Network Management Protocol,”IEEE JSAC, vol. 17,no. 8, Aug. 1999. C-C. Shen, C. Srisathapornphat, and C. Jaikaeo, “An Adaptive Management Architecture for Ad hoc Networks,” IEEE Communications Magazine, Feb. 2003. I.F.Akyildiz, W.Su , Y.Sankarasubramaniam, E.Cayirci, “Wireless sensor networks: survey,”Computer Networks 38 (2002)393 –422 D. Estrin, “Some Distributed Coordination Schemes for Wireless Sensor Networks,” Talk given at StanfordNov 2000 L. B. Ruiz, J. M. S. Nogueira, and A. A.F. Loureiro, “MANNA: Management Architecture for Wireless Sensor Networks,” , IEEE Communications Magazine, Feb. 2003.

Topology Management Mirkovic, J.; Venkataramani, G.P.; Lu, S., Zhang, L., “A self-organizing approach to data forwarding in large-scale sensor networks,“ ICC 2001. IEEE International Conference, June 2001Alberto Cerpa and Deborah Estrin, “ASCENT: Adaptive Self-Configuring Sensor Networks Topologies,” in Proceedings of the Twenty First International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002), June, 2002Katayoun Sohrabi, Jay Gao, Vishal Ailawadhi, and Gregory J. Pottie, “Protocols for Self-Organization of Wireless Sensor Network,” IEEE Personal Communications, October, 2000


References (Cont.)Context-Awareness Management

“A Middleware Infrastructure for Active Surroundings”, Pervasive Computing Group, Technical Report No. CSPG-2003 04 028James Powell, “Middleware for Pervasive and Proactive Computing”, 2003William Plymale, James Powell, “Pervasive Computing and Institutional Repositories”

Security Management A. Perrig, R. Szewczyk, VictorWen, D. Culler, and J. D. Tygar. “SPINS: Security protocols for sensor networks.” In Proceedings of Seventh Annual International Conference on Mobile Computing and Networks MobiCom 2001, July 2001.J. Douceur. “The sybil attack.” In Proceedings of the IPTPS 2002, Cambridge, MA, USA, March 2002.Yih-Chun Hu, A. Perrig, and D. B. Johnson. “Wormhole detection in wireless ad hoc networks. Technical report,” Department of Computer Science, Rice University, December 2001.Technical Report TR01–384C. Karlof and D. Wagner, “Summary of “Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures”

CompanyEmber: www.ember.comCrossbow: www.xbow.comMillenial Net: www.millennial.netDust Inc. : www. dust-inc.comLuna iMonitoring: www.lunaimonitoring.comMicroStrain: www.microstrain.comSensoria Corp.: www.sensoria.comXsilogy: www.xsilogy.comZigBee Alliance: www.zigbee.org


References (Cont.)

Other ReferencesA. Bharathidasan and V. Ponduru, “Sensor Networks: An Overview” , Department of Computer Science, University of California, Davis, CA 95616 TinyOS web site: http://www.tinyos.net/ Tim Nieberg , ”Wireless Sensor Networks: The EYES Project,” Ubiquitous Computing Colloquia 2003, Universiteit Twente, Apr. 11, 2003 H. Karl, “Making sensor networks useful: Distributed services,” ESF Workshop Oct. 02 2003. Sohrabi, K, Gao, J., Ailawadhi, V, and Pottie, G., "Protocols for self-organization of a wireless sensor network," IEEE Personal Comm. Magazine, vol. 7, No. 5, pp. 16-27, Oct. 2000L. St. Ville, “An architectural overview of a Distributed Sensor Network with Mobile Sensors,”February 2001A. Mainwaring, J. Polastre, R. Szewczyk, and D. Culler, “Wireless Sensor Networks for Habitat Monitoring,” ACM International Workshop on Wireless Sensor Networks and Applications, 2002. W. R. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks,” Proc. ACM MobiCom ’99, Seattle, WA, 1999 S. Tilak, N. Abu-Ghazaleh, and W. Heinzelman, "A Taxonomy of Wireless Micro-Sensor Network Models", ACM Mobile Computing and Communications Review (MC2R), Volume 6, Number 2, April 2002 D. Estrin, “Sensor Network Protocols Tutorial,’ Mobicom 2002A. Woo, and D. Culler, “A Transmission Control Scheme for Media Access in Sensor Networks,”Proc. ACM MobiCom ’01, Rome, Italy, July 2001, Craig Ulmer, “Wireless Sensor Networks,” Presentation at NASA's Jet Propulsion Lab, August 10, 2000 Mani Srivastava, “Sensor Node Platforms & Energy Issues,” Tutorial, Mobicom 2002 Wei Ye, John Heidemann, Deborah Estrin, ”An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, Infocom 2002


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