Post on 01-Jan-2016
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Jie WuDepartment of Computer Science and EngineeringFlorida Atlantic UniversityBoca Raton, FL 33431
COT 6930 Ad Hoc Networks (Part III)
Table of Contents
Introduction Infrastructured networks
Handoff location management (mobile IP) channel assignment
Table of Contents (cont’d.)
Infrastructureless networksWireless MAC (IEEE 802.11 and Bluetooth)SecurityAd Hoc Routing ProtocolsMulticasting and Broadcasting
Table of Contents (cont’d.) Infrastructureless networks (cont’d.)
Power Optimization Applications
Sensor networks and indoor wireless environments
Pervasive computing Sample on-going projects
SecuritySecurity goals (Zhou and Hass, IEEE Network,
1999): Availability
Survivability of network services despite denial of service attacks
Confidentiality Certain information is never disclosed to
unauthorized entities Integrity
Message being transferred is never corrupted
Security Authentication
• Enables a node to ensure that the identity of the peer node it is communicating with.
Non-repudiation• The origin of a message cannot deny
having sent the message
Security Challenges in ad hoc network
security The nodes are constantly mobile The protocols implemented are co-
operative in nature There is a lack of a fixed infrastructure to collect audit data No clear distinction between normalcy
and anomaly in ad hoc networks
Security Type of attack
External attack: An attack caused by nodes that do not belong to the network.
Internal attack: An attack from nodes that belong to the network due to them getting compromised or captured.
Security Some objectives:
Ad hoc networks should have a distributed architecture with no central entities to achieve high survivability
Because of frequent changes in topology, trust relationship among nodes in ad hoc networks also changes.
Security mechanisms should be scalable to handle a large network.
Security
Sample security attacks: Passive eavesdropping Active impersonation Message reply Message distortion
Security Traditional approaches
Authentication protocols Digital signature Encryption
Security Secure key management
Threshold cryptography• The public key is known to all whereas the
private key is divided into n shares.• Decentralized Certification Authority to
distribute key pairs.• The private key can be constructed with
any subset of shares of certain sizes.
Security Proactive security
• Share refreshing: servers compute new shares from old ones in collaboration without disclosing the service private key to any server
Asynchrony• Cannot distinguish a compromised server from a
correct but slow one• Weak consistency: do not require that the correct
servers to be consistent after each operation; instead, only enough correct servers need to be up-to-date.
Security Secure routing
External attack: injecting erroneous routing information or distorting routing information
Internal attack: compromised node advertise incorrect routing information (similar to the Byzantine general problem)
SecuritySecurity problems in AODV and DSR
(Dahill, UM-CS-2001-037) Remote redirection
• Sequence number (AODV)• Hop count (AODV)• Source route (DSR)
Spoofing (impersonation) (AODV and DSR) Fabrication
• Error message (AODV and DSR)• Source route (DSR)
Power Optimization Network Longevity (Wieselthier, Infocom
2002) Time at which first node runs out of energy Time at which first node degrades below an
acceptable level Time until the network becomes disconnected
High throughput volume High total number of bits delivered
Power Optimization
Two related goals (Toh, IEEE Comm. Mag. 2001)
Saving overall energy consumptions in the networks
Prolong life span of each individual node
Power Optimization
Source of Power Consumption (Singh et al, MobiCom 1998) Communication cost
• Transmit• Receive• Standby
Computation cost
Power-Aware Routing Wu et al’s Power-aware marking
process (Wu et al, ICPP 2001) Use energy level as priority in Rule 1
and Rule 2 of marking process Balance the overall energy
consumption and the lifespan of each node
Location-Based Routing Let P(dis) represent the power
consumption of transmitting with distance dis
Stojmenovic et al’s greedy method (Stojmenovic et al, IPDPS 2001) Each node knows the location of destination
and all its neighbors Source s selects a neighbor n to reach
destination d with minimum P(dis(s,n))+P(dis(n,d))
Adjustable Transmission Ranges Power level of a transmission can be
chosen within a given range of values
Transmission cost: where a=2 or 4.
ddisP )(
Uniform Transmission Range
Problem: Use a minimum uniform transmission range to connect a given set of points
Greedy algorithms Binary search Kruskal’s MST (Ramanathan & Rosales-Hain,
ICC 2000) Prim’s MST (Dai & Wu, FAU 2002)
Power Optimization
Kruskal’s MST: Each node is initialized as a separate
connected component Edges are sorted and traversed in non-
decreasing order An edge is added to the MST whenever
it connects any two connected components.
Power Optimization
Prim’s algorithm The approach starts from an arbitrary
root and grow a single tree until it spans all the vertices.
At each step, an edge of lightest possible weight is added.
Non-uniform transmission range
Wireless multicast advantage (Wieselthier, Infocom 2000):
where is power needed between node i and node j
},max{),,( ijikkji PPP
ijP
Non-uniform transmission range
S broadcasts to two destinations: D1 and D1 (r1=dis(s, D1), and r2=dis(s, D2)). Direct: S broadcasts to both at the
same time Indirect: S sends the packet to D1
which then relays the packet to D2
Non-uniform transmission range Use “direct” if angle between
whererr ,cos21
21 randr
Non-uniform transmission range Broadcast incremental power
algorithm (Wieselthier Infocom 2000) Standard Prim’s algorithm Pair {i, j} that results in the minimum
incremental power for i to reach j is selected, where i is in the tree and j is outside the tree.
Non-uniform transmission range Other algorithms
Broadcast least-unicast-cost algorithm Broadcast link-based MST algorithm
The sweep: removing unnecessary transmissions
Non-uniform transmission range Extensions to directional antennas (Wieselthier, Infocom 2002)
Energy consumption:
Extended power incremental algorithm
300
r
Non-uniform transmission range Possible extensions
Fixed beamwidth Single beam per node Multiple beams per node Limited multiple beams per node Directional receiving antennas
Non-uniform transmission range Incorporation of resource limitation
Bandwidth limitation• Greedy frequency assignment, but cannot
ensure coverage (when running out of frequencies)
Energy limitation
))(
)0(('
tE
EPP
i
iijij
Sensor Networks Sensor networks (Estrin, Mobicom
1999) Information gathering and processing Data centric: data is requested based on
certain attributes Application specific Energy constraint Data aggregation (also data fusion)
Sensor Networks
Military applications: (4C’s) Command, control,
communications, computing Intelligence, surveillance,
reconnaissance Targeting systems
Sensor Networks Health care
• Monitor patients• Assist disabled patients
Commercial applications• Managing inventory• Monitoring product quality• Monitoring disaster areas
Sensor Networks
Design factors (Akyildiz et al, IEEE Comm. Mag. Aug. 2002) Fault Tolerance (sustain functionalities) Scalability (hundreds or thousands) Production Cost (now $10, near future $1) Hardware Constraints Network Topology (pre-, post-, and re-
deployment) Transmission Media (RF (WINS), Infrared
(Bluetooth), and Optical (Smart Dust)) Power Consumption (with < 0.5 Ah, 1.2 V)
Sensor Networks Sample problems
Coverage and exposure problems Data dissemination and gathering
Coverage and Exposure Problems Coverage problem (Meguerdichian, Infocom 2001)
Quality of service (surveillance) that can be provided by a particular sensor network
Related to to Art Gallery Problem (solved optimally in 2D, but NP-hard in 3D)
Exposure problem (Meguerdichian, Mobicom 2001)
A measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of time
Coverage and Exposure Problems Voronoi diagram of a set of points
Partitions the plane into a set of convex polygons with such that all points inside a polygon are closest to only one point.
Coverage and Exposure Problems A sample Voronoi diagram
Coverage and Exposure Problems Delaunay triangulation
Obtained by connecting the sites in the Voronoi diagram whose polygons share a common edge.
It can be used to find the two closest points by considering the shortest edge in the triangulation.
Coverage and Exposure Problems Maximal breach path (worst case
coverage) A path p connecting two end points such that
the distance from p to the closest sensor is maximized
Fact: The maximal breach path must lie on the line segments of the Voronoi diagram.
Solution: binary search + breadth-first search
Coverage and Exposure Problems Maximal Support Path (Best Case
Coverage) A path p with the distance from p to the
closest sensor is minimized The maximal support path must lie on the
lines of the Delaunay triangulation
Coverage and Exposure Problems Exposure problem
Expected average ability of serving a target in the sensor field
General sensing model:
where s is the sensor and p the point.
),(
),(psdis
psS
Coverage and Exposure Problems Exposure problem: integral of the sensing
function
Coverage and Exposure Problems Minimal Exposure Path
Transform the continuous problem domain to a discrete one.
Apply graph-theoretic abstraction. Compute the minimal exposure path using
Dijkstra’s algorithm.
Coverage and Exposure Problems First, second, and third-order generalized 2*2 grid
Data Dissemination and Gathering
Two different approaches Traditional reverse multicast/broadcast tree
with BS as the sink (root). Three-phase protocol: sinks broadcast the
interest, and sensor nodes broadcast an advertisement for the available data and wait for a request from the interested nodes.
Data Dissemination and Gathering
Energy-efficient route (Akyildiz, 2002) Maximum total available energy route Minimum energy consumption route Minimum hop route Maximum minimum available energy node
route
Data Dissemination and Gathering
Sample data aggregation protocols SMECN (Li and Halpern, ICC’01) SPIN (Heinzelman et al, MobiCom’99) SAR (Sohrabi, IEEE Pers. Comm., Oct. 2000) Directed Diffusion (Intanagonwiwat et al,
MobiCom’00) Linear Chain* (Lidsey and Raghavendra, IEEE
TPDS, Sept. 2002) LEACH * (Heinzelman et al, Hawaii Conf. 2000)
Data Dissemination and Gathering
SMECN Create a subgraph of the sensor network that
contains the minimum energy path SPIN
Sends data to sensor nodes only if they are interested; has three types of messages (ADV, REQ, and DATA)
SAR Creates multiple trees where the root of each tree
is one hop neighbor from the sink; select a tree for data to be routed back to the sink according to the energy resources and additive QoS metric
Data Dissemination and Gathering
Directed diffusion Sets up gradients for data to flow from source
to sink during interest dissemination (initiated from the sink)
Linear Chain A linear chain with a rotating gathering point.
LEACH Clusters with clusterheads as gathering
points; again clusterheads are rotated to balance energy consumption
Data Dissemination and Gathering
Sequential gathering in a linear chain
Data Dissemination and Gathering
Parallel gathering (recursive double)
Data Dissemination and Gathering
Enhancement Multiple chain Better linear chain formation
• New node always the new head of the linear chain• New node can be inserted into the existing chain
Data Dissemination and Gathering
Multiple Chains
Data Dissemination and Gathering
Simple chain (new node as head of chain)
Data Dissemination and Gathering
Simple chain (new node inserted in the chain)
Data Dissemination and Gathering
LEACH
Data Dissemination and Gathering
Extended LEACH (energy-based)
Indoor Environments Three popular technologies
Wireless LANs (IEEE 802.11 standard) HomeRF (http://www.homerf.org/tech/,
Negus et al, IEEE Personal Comm. Feb. 2000) Bluetooth (http://www.bluetooth.com/)
Indoor Environments
Network topology Straightforward for 802.11WLAN and
HomeRF (e.g., In TDMA-based MAC protocol, a central entity is used to assign slots to the stations).
The Bluetooth topology poses interesting challenges.
Bluetooth
Bluetooth Special Interest Group (formed in July 1997 with now 1200 companies).
Major technology for short-range wireless networks and wireless personal area network.
An enabling technology for multi-hop ad hoc networks.
Low cost of Bluetooth chips (about $5 per chip).
Bluetooth
Basic facts Operates in the unlicensed Industrial-Science-
Medical (ISM) band at 2.45 GHz. Adopts frequency-hop transceivers to combat
interference and fading. The nominal radio range: 10 meters with a
transmit power of 0 dBm. The extended radio range: 100 meters with
amplified transmit power of 20 dBm.
Bluetooth: Basic Structure
Piconet A simple on-hop star-like network A master unit Up to 7 active slave units Unlimited number of passive slave units.
Scatternet A group of connected piconets A unit serves as a bridge between the
overlapping piconets in proximity.
Bluetooth: Basic Structure
Open problem: a method for forming an efficient scatternet under a practical networking scenario.
Two methods: Bluetree and Bluenet
Bluetree (Zaruba, ICC 2001)
Blueroot Grown Bluetrees The blueroot starts paging its neighbors one
by one. If a paged node is not part of any piconet, it
accepts the page (thus becoming the slave of the paging node).
Once a node has been assigned the role of slave in a piconet, it initiates paging all its neighbors one by one, and so on.
Bluetree (Zaruba, ICC 2001)
Blueroot Grown Bluetrees (sample)
Bluetree (Zaruba, ICC 2001)
Limiting the number of slaves Observations: if a node has more than five
neighbors, then there are at least two nodes that are neighbors themselves.
The paging number obtains the neighbor set of each neighbor.
Balanced Bluetree (Wu and ?, 2003) Using neighbors’ neighbor sets. Using neighbor locations.
Bluetree (Zaruba, ICC 2001)
Distributed Bluetrees Speed up the scatternet formation process by
selecting more than one root (phase 1). Then by merging the trees generated by each
root (phase 2).
Bluetree (Zaruba, ICC 2001)
Phase 1 Each slave will be informed about the root of
the tree. When paging nodes are in the tree,
information of respective roots are exchanged.
Each node having roles from the set {M, S, (MS)}, where M for master and S for slave.
Bluetree (Zaruba, ICC 2001)
Phase 2 Merge bluetrees (pairwise) Each node can only receive at most one
additional M, S, or MS. Each node having roles from the set:
{M, S, (MS), (SS), (MSS)} (note that (MM)=M).
Bluetree (Zaruba, ICC 2001)
Distributed bluetree (sample)
Bluetree (Zaruba, ICC 2001)
Overflow problem (Wu)
Solution: slot reservation (up to 6 slaves)
u v
Bluenet (Wang et al, Hawaii Conf. 2002)
Drawbacks of bluetrees Lacks of reliability Lacks of efficient routing Parents nodes are likely to become
communication “bottleneck”. Three types of nods in Bluenet
Master (M), Slave (S), Bridge (M/S or S/S)
Bluenet (Wang et al, Hawaii Conf. 2002)
Rule 1: Avoid forming further piconets inside a piconet.
Rule 2: For a bridge node, avoid setting up more than one connections to the same piconet.
Rule 3: Inside a piconet, the master tries to aquire some number of slaves (not too many or too few).
Bluenet (Wang et al, Hawaii Conf. 2002)
Phase 1: Initial piconets formed with some separate Bluetooth nodes left.
Phase 2: Separate Bluetooth nodes get connected to initial piconets.
Phase 3: Piconets get connected to form a scatternet (slaves set up outgoing links).
Dominating-set-based bluenet?
NeuRFon (Motorola Research Lab., ICCCN 2002)
Build a reverse shortest path tree (w.r.t. a given root) through paging.
Self-healing: find a new parent with a lowest-level number (cloested to the root).
On-going projects
Internet P2P applications (http://www.p2pwg.org) Distributed systems in which nodes of equal
roles and capabilities exchanges information and services directly with each other.
Servant for both server/client. Major issue: efficient techniques for search
and retrieval of data. Sample systems: Gnutella, Napster, and
Morpheus.
On-going projects Basics of P2P protocols
Searching: query-source sends “query-send” with file id through controlled flooding
Network dynamic: A peer joins the network through “broadcast-send” to select “logical neighbors” (neighborhood with short session duration, 2 hours per day on average).
Transferring files: The query-source servant establishes the end-to-end communication with the file-source (datagram transmission after the file is fragmented in small pieces).
On-going projects Basics of P2P protocols (cont’d)
Controlled flooding: caches (query-id, query-source) to avoid duplicate query processing and uses TTL to prevents a message being forwarded infinitely.
Neighborhood control: uses the “ping-pong” protocol for maintaining up-to-date neighbors and issues “broadcast-send” to find another neighbor when the current one is lost.
On-going projects Sample P2P search protocols (ICDCS
2002) Iterative deepening: multiple breadth-first
searches with successively large depth limits. Directed BFS: sending query messages to
just a subset of its neighbors. Local indices: each node maintaining an
index over the data of all nodes. Mobile agents: swarm intelligence – the
collection of simple ants achieve “intelligent” collective behavior.
On-going projects Sensor nodes
Smart dust (http://robotics.eecs.berkeley.edu/~pister/SmartDust)• Autonomous sensing and communication in
a cubic millimeter• Macro motes: 20 meter comm. range, one
week lifetime in continuous op. and 2 years with 1% duty cycling.
On-going projects Sensor nodes
Smart dust (http://robotics.eecs.berkeley.edu/~pister/SmartDust)
• Autonomous sensing and communication in a cubic millimeter
• Macro motes: 20 meter comm. range, one week lifetime in continuous op. and 2 years with 1% duty cycling.
PicoRadio (http://bwrc.eecs.berkeley.edu/Research/Pico_Radio/PN3/)
On-going projects Power-Aware Ad Hoc and Sensor
Networks μAMPS (μ-Adaptive Multi-domain Power
aware Sensors) (http://www-mtl.mitedu/research/icsystems/uamps)• Innovative energy-optimized solution at all
levels of the system hiearchy PACMAN (http://pacman.usc.edu)
On-going projects Sensor Networks
WINS (Wireless Integrated Network Sensors) (http://www.janet.ucla/WINS)• Distributed network and internet access to
sensors, controls, and processors that are deeply embedded in equipment.
SensoNet (http://www.ece.gatech.edu/research/labs/bwn)
On-going projects Distributed Algorithms
SCADDS (Scalable Coordination Architectures for Deeply Distributed Systems) (http://www.isi.edu/scadds)• Directed diffusion, adaptive fidelity,
localization, time synchronization, self-configuration, and sensor-MAC
On-going projects Power conservation algorithms
Span (Chen et al, MIT). PAMAS (Power Aware Multi Access
protocol with Signaling for Ad Hoc Net works) (Singh, SIGCOMM, 1999).
On-going projects Distributed query processing
COUGAR device database project (http://www.cs.cornell.edu/database/cougar/index.htm)
Database (http://cs.rutgers.edu/dataman/)
On-going projects Security for Sensor Networks
SPINS (Security Protocols for Sensor Networks) (http://www.ece.cmu.edu/~adrian/project.html)