Jie Wu

1
COT 6930 Ad Hoc Networks (Part III)

• Jie Wu
• Department of Computer Science and Engineering
• Florida Atlantic University
• Boca Raton, FL 33431

2
Table of Contents

• Introduction
• Infrastructured networks
• Handoff
• location management (mobile IP)
• channel assignment

3
Table of Contents (contd.)

• Infrastructureless networks
• Wireless MAC (IEEE 802.11 and Bluetooth)
• Security
• Ad Hoc Routing Protocols
• Multicasting and Broadcasting

4
Table of Contents (contd.)

• Infrastructureless networks (contd.)
• Power Optimization
• Applications
• Sensor networks and indoor wireless environments
• Pervasive computing
• Sample on-going projects

5
Security

• Availability
• Survivability of network services despite DoS
  attacks
• Confidentiality
• information is never disclosed to unauthorized
  entities
• Integrity
• Message being transferred is never corrupted
• Authentication
• Enables a node to ensure that the identity of the
  peer node it is communicating with.
• Non-repudiation
• The origin cannot deny having sent the message

6
Security Challenges

• 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

7
Types 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.

8
Sample Security Attacks

• Routing attacks
• Action of advertising routing updates that does
  not follow the specifications
• Examples add/delete a node in the path,
  advertise a route with smaller (larger) distance
  metric (timestamp)
• Packet forwarding attacks
• Packets are not delivered consistently based on
  routing states.
• Examples drop the packet, inject junk packets

9
Security Problems in DSR and AODV

• 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)

10
Security Solutions

• Routing attacks
• Traditional cryptography (preventive)
• message authentication primitives
• secured ad hoc routing
• Challenges cost, key management
• Packet forwarding attacks
• Watchdog (detective)
• Challenges blackmail attacks

11
Sample Solutions

• Property Techniques
• Timeliness Timestamp
• Ordering Sequence Number
• Authenticity Password, Certificate
• Authorization Credential
• Integrity Digest, Digital Signature
• Confidentiality Encryption
• Non-repudiation Chaining of digital signatures

12
Sample Distance Metric

• Hop count hash chain (Hu et al03)
  h0,h1,hn
• hiH(hi-1) and H is a known one-way hash function
• hn is added to the routing message and the ith
  node along a path has hi
• When a node receives an RREQ or RREP with
  (Hop_Count, hx), it checks
• hn Hn-Hop_Count(hx)
• Hm(.) means applying the H function m times

13
(V) Special Challenges

• Survivability
• Ad hoc networks should have a distributed
  architecture with no central entities to achieve
  high survivability
• Scalability
• Security mechanisms should be scalable to handle
  a large network
• Trust
• Because of frequent changes in topology, trust
  relationship among nodes in ad hoc networks also
  changes

14
Sample Survivability Solution

• Threshold cryptography (Zhou and Haas99)
• The public key is known to all whereas the
  private key is divided into n shares
• Decentralized CA to distribute key pairs
• The private key can be constructed with any
  subset of shares of certain sizes
• Proactive security Share refreshing
• Servers compute new shares from old ones in
  collaboration without disclosing the service
  private key to any server

15
Scalable Design

• Partition the network into groups
• Each group group head group members
• Group heads form a dominating set (DS)
• Also an independent set (IS) to guarantee a
  constant bound
• Also connected (CDS) to ensure routing within the
  heads.

16
Scalable Design (Cont)
17
Scalable Design (Cont)

• Resurrecting duckling transition association
  (Stajano and Anderson99) within a group
• A duckling considers the first moving object it
  sees as its mother
• Transient master-slave relationship
• When a node is deactivated, it goes back to the
  pre-birth stage and can be reborn through another
  imprint (resurrection)

18
Trust

• A lesson from 9/11
• Hierarchical trust
• Funds distribution
• How to build trust
• (Zhou Wu03)
• Survivable Multi-level Ad-Hoc Group Operations

19
Trust Building (Zhou and Wu03)

• An ad hoc network cannot succeed without trust
  within
• Nodes are trustworthy if they have
• integrity, and
• proper capability

20
Operation Policy

• Information sharing
• Minimum information was shared to other members
  whose tasks necessitated their knowledge.
• Knowledge of a lower-level task group was a
  subset of that of a higher-level task group.
• Communication
• Confidential and authentic within the group.
• Three type of inter-group communications.
• Redundancy

21
A Terrorist Network

• From Krebs Mapping Networks of Terrorist Cells
  (Connections, 24(3) 43-52, 2002)

22
A Terrorist Network (Cont)
23
A Terrorist Network (Cont)
24
A Terrorist Network (Prior Contacts Meeting
ties shortcuts)
25
A Terrorist Network (Network Neighborhood)
26
Node Cooperation in MANETs

• Nodes are formed without any infrastructure
• Nodes cooperate to complete a routing process
• Route request, route reply, forwarding

27
Trust vs. Reputation

• Reputation (objective)
• What is general said or believe about somebody
  (say B)
• Trust (subjective judgment opinion)
• Trust is the subjective probability by which A
  expects that another B performs a given action
• Psychological factors
• Rumor
• Influence by others opinions
• Motives to gain something extra by extending
  trust

28
To be trusting is to be fooled from time to time.
To be suspicious is to live in constant torment.
29
Trust vs. Reputation (Contd)

• Reputation system to facilitate trust
• eBay (business)
• H-index (academic)
• Trust in multiple disciplines
• Economics, sociology, psychology, biology,
  political science,
• Computer applications
• electronics commerce, peer-to-peer networks, and
  MANETs
• Computational (e.g. reliability model) vs.
  non-computational

30
How to Build Trust?

• First-hand (direct) and second-hand
  (recommendation)
• E.g. watchdog mechanisms in MANETs

31
Compound Trust

• First-hand First-hand/Second-hand
• Compound 1-d a ? b (such as (a, b) and (a b)
  )
• Commutativity, Monotonicity, and Associativity

32
Sequential - Generic ? Formula
t-norm (with 1 as identity element)
TrustCom'09
9/23/2020
32
33
Parallel - Compound 2-d
(trust (t), confidence (c)) solution 1
(t, c) solution 2
34
Compound Trust

• How to compute compound trust (from s to d)?
• Structured (a well-defined sequential and
  parallel operations)
• Unstructured
• Removing weakest links
  Edge splitting

35
Trust Equivalence Graphs

• How to compute compound trust based on an
  arbitrarily complex graph?
• Trust equivalence approach (Wang Wu09)
• Multi-Dimensional Evidence-based Trust
  Management with Multi-Trusted Paths
• Use GraphReduce and GraphAdjust algorithms to
  guarantee that every link will be used exactly
  once.

35
36
GraphReduce

• To find a maximum number of node- or
  link-disjoint paths

Reduced (node-disjoint) 3 paths
Original 6 paths
Reduced (link-disjoint) 4 paths
36
37
Computation Models

• Aggregation rules
• Sequential structure whole is no more than each
  part
• Parallel structure whole is no less than each
  part
• Models
• Reliability model (reliability as trust)
• Resistive model (current as trust)
• Flow model (max-flow as trust)
• Other model (?)

TrustCom'09
9/23/2020
37
38
Uncertainty

• Uncertainty as part of trust
• Sampling size and information asymmetry
  (on-line shopping)
• Direct observation (evidence)
• Reputation (opinion) b, d, u ( 3-d subjective
  logic)
• b d u 1
• b , d and u designate belief, disbelief, and
  uncertainty

39
Uncertainty-aware Reputation System (Li Wu08)

• Beta distribution Beta(a,ß) in the Bayesian
  inference
• Statistical inference observations are used to
  update or to newly infer the prob. that a
  hypothesis may be true
• A simple example Belief Disbelief 0.5
• On the basis of 5 (50) observed successes and 5
  (50) failures.
• Attributes
• Less uncertainty When the evidence for success
  /failure dominates
• Maximum uncertainty When there is little or no
  evidence
• Applications Mobility Reduce Uncertain

40
Uncertainty Definition

• How to evaluate uncertainty behind a, ß
  Beta(a, ß).
• (Uncertainty computation) Let uncertainty be
  the normalized variance of the Beta function

41
Recommendation Integration

• (Recommendation Calculation) Let
  represent node As opinion towards B, and
  represent node Bs opinion
  towards C. A will take Bs recommendation towards
  C as , where

0.50.2
Belief
Belief
Belief
0.50.2
Uncertainty
Uncertainty
Disbelief
Uncertainty
Disbelief
0.50.6
Disbelief
42
Opinion Combination

• (Recommendation Synthesization) Let
  represent node Bis
  recommendation towards node C computed by node A,
  for 1 i n. Then, node A will synthesize these
  recommendations as

• (Opinion Combination) Let ? be a nodes
  character factor. Each node A will combine its
  first-hand and second-hand opinion towards B as
• 
  

43
Components Design

• Information gathering
• First-hand vs. second-hand
• Information modeling
• Single vs. multiple metrics
• Past vs. recent observations
• Updating function

44
Components Design (Contd)

• Information sharing
• First-hand info only (OCEAN and pathrater)
• First-hand and second-hand info (CORE and
  CONFIDANT)
• Second-hand info only (DRBTS)
• (Srinivasan, Teitelbaum Wu05) DRBTS
  Distributed Reputation-based Beacon Trust System
• Radical strategy suicide attacks
• Challenges
• False praise
• Bad mouthing

45
Components Design (Contd)

• Information sharing
• Positive vs. negative information
• Positive only (CORE)
• Both positive and negative (with recommenders
  reputation)
• Deviation test A node believes second-hand info
  only if it does not differ too much from the
  nodes reputation value. (DRBTS)
• Dissemination
• Proactive vs. reactive
• Local vs. global (EigenTrust)
• Content raw vs. processed

46
Components Design (Contd)

• Decision making
• Single threshold cooperative/non-cooperative
• Multiple thresholds Anantvalee Wu07
• Selfish node RF lt T(selfish)
• Suspicious node T(selfish) RF lt
  T(cooperative)
• Cooperative node T(cooperative) RF
• Bootstrap
• Start with a low value and move up
• Start with a high value and deteriorate over time
  unless reinforced

47
3. Trust Model Revisited

• Risk attitudes in trust reliability and utility
• Trust The extend to which one is willing to
  depend on somebody even though negative
  consequences are possible
• Best route importance of the package
• Valuable package Fedex (more reliable, costs
  more)
• Regular package Regular mail (less reliable,
  costs less)

48
A Sample Network

• Traditional metrics cost/reliability
• The minimum cost path s ? 1 ? d
• Cost 2 3 5
• Reliability 0.8 0.9 0.72
• The most reliable path s ? 2 ? d
• Cost 4 3 7
• Reliability 0.9 0.9 0.81

49
Utility-Based Routing (LuWu06)

• Each packet is assigned a benefit value, v
• s transmits a packet with benefit v to d
• Transmission cost/reliability c/p
• Utility v c if success, 0 c otherwise
• Expected utility U p(v-c) (1-p)(0-c) pv -
  c
• The best route maximizes U
• s c/p
  d

50
A General Expression

• General form of U for path R s 1, 2, , k-1, d
  k
• PR route stability and CR route cost

51
Prop. 1 Backward Calculation

• How to calculate U?
• Direct
• (1) 0.8 0.920 2 30.810
• Backward calc. ui pi,i1 ui1 - ci,i1
  (virtual s/d)
• (2) 0.920 3 15 (at i)
• 0.815 2 10 (at s)

52
Prop. 2 Benefit-dependent Best Path
Ri Pi Ci
R1 0.72 4.4
R2 0.81 6.7
R3 0.5 5.3
R4 0.57 7.7
Different benefit values may have different best
paths! For v20, R1 10 and R2 9.5 For v30, R1
17.2 and R2 17.6
53
Uncertainty Mitigation (Li et al07)

• Each intermediate node i performs risk analysis
  when selecting a downstream node j
• i monitors j using (b, d, u) (subjective logic)
• An uncertainty threshold T is set based on
  expected utility and cost
• i selects j if u T and yields a high utility

54
Multi-dimensional Model

• Multi-dimensional model (Zhou Wu03)
• I Integrity on a subject (direct)
• C Capability on a subject (direct)
• A Ability to evaluate I or C of other nodes
  (indirect)
• Granularity
• group vs. individual

55
Game Theoretical Model

• Game theory
• Rational economic agents
• Backward induction to maximize private utilities
• Node behavior selfish
• E.g., VCG mechanism
• In reality, people are boundedly rational.
• Reciprocity norms (social strategies)
• Encouraging social cooperation
• Node behavior reciprocal altruism
• Be nice to others who are nice to you
• E.g., nuglets (virtual currency) and barter
  exchange

56
Incentive Compatible Routing

• Nodes are selfish and may give false information
• Without reimbursement, they will not help relay
  packets
• Maximize utility payment cost
• Based on VCG payment scheme
• (enforcing the reporting of correct link
  costs)
• Nodes on the optimal path utility remains the
  same when lying
• Nodes not on the optimal path utility reduces
  when lying
• Integrative neighbor surveillance mechanism
• (enforcing the reporting of correct link
  stability)
• Forwarding status is monitoring by a neighbor
  (monitor)

57
Second Price Path Auction

• Why doesnt the first price work?
• System objective ? individual nodes objectives
• The solution second price
• Losers utility is 0
• Winner is payment
• lowest cost without i - lowest cost cost of
  node i

58
The Sample Network

• Case 1 nodes on an optimal path lie
• If (s, 1) is changed to 3
• S still gets 7 6 3 4
• (same as 7 5 2 4)
• Case 2 nodes on a non-optimal path lie
• If (2, d) is changed to 1
• 2 gets 5 5 1 1 lt 3
• (utility is negative)

59
Summary of Trust

• Model trust
• Probability, utility, and game theory
• One-dimensional vs. multi-dimensional
• Computational vs. non-computational reliability,
  dependability, honesty, truthfulness, security,
  competence, and timeliness
• Uncertainty integration
• Dimension reduction or threshold?
• Right theory probability, utility, game, rough
  set, fuzzy logic, entropy,

60
Summary of Trust (Contd)

• Web of trust
• Network topology design
• Finding trusted paths
• Topology control
• A cross-disciplinary research topic
• Computer science, economics, psychology,
  sociology, biology, political sciences
• NSF NetSE program for network science?

61
Final Thoughts on Trust

• Robust and Trustworthy Review System
• Build a good review system that we can trust?
• INFOCOM 2011 (Shanghai)
• Challenges bad-mouthing and false-praising
• Direct and indirect collusion
• Score a review (score, confidence)
• Multi-round decision process
• Use of trusted reviewers
• Trust as a finite resource (EigenTrust)?

62
Open Problems and Opportunities

• Can preventive methods (cryptography) provide a
  cost-effective solution?
• Hybrid approach cryptography trust model.
• Multi-fence security solution resiliency-oriented
  design.
• Multi-level approach application, transport,
  network, link, and physical
• (link layer jam-resistant communications using
  spread-spectrum and frequency-hopping)

63
Open Problems and Opportunities (Cont)

• New approach incentive-based approaches (to
  avoid free riders)
• Credit mechanism (micro payment)
• Exchange or barter economy (n-way exchange)
• Game theory (Prisoners Dilemma game)

64
Summary of Security

• Research in secured routing in ad hoc networks is
  still in its early stage.
• Is security in ad hoc networks a problem with no
  technical solution?
• Technical solution
• one that requires a change only in the
  techniques of the natural sciences, demanding
  little or nothing in the way of change in human
  values or ideas of morality.
• From Hardins The Tragedy of the Commons,
  1968

65
Energy Management

• The need of energy management
• Limited energy reserve
• Difficulties in replacing the batteries
• Lack of central coordination
• Constraints on the battery source
• Selection of optimal transmission power

• Three techniques
• Battery management schemes
• Transmission power management schemes
• System power management schemes

66
Battery management

• Device-dependent schemes
• Modeling and shaping of battery discharge
  patterns
• Impact of discharge characteristics on battery
  capacity
• Data link layer
• Lazy packet scheduling
• Minimizing the transmission power
• Increasing the duration of transmission
• Battery-aware MAC protocol
• Network layer
• Battery energy-efficient routing

67
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

68
Power Optimization

• Two related goals (Toh, IEEE Comm. Mag. 2001)
• Saving overall energy consumptions in the
  networks
• Prolong life span of each individual node

69
Power Optimization

• Source of Power Consumption (Singh et al, MobiCom
  1998)
• Communication cost
• Transmit
• Receive
• Standby
• Computation cost

70
Power-Aware Routing

• Wu et als 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

71
Location-Based Routing

• Let P(dis) represent the power consumption of
  transmitting with distance dis
• Stojmenovic et als 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))
  

72
Adjustable Transmission Ranges

• Power level of a transmission can be chosen
  within a given range of values
• Transmission cost
• where a2 or 4.

73
Power Optimization

• Problem Each node selects a minimum transmission
  range subject to a global constraint (i.e.
  network connectivity)
• Heterogeneous most problems are NP-complete
• Homogeneous polynomial solutions exist

74
Uniform Transmission Range

• Problem Use a minimum uniform transmission range
  to connect a given set of points
• Greedy algorithms
• Binary search
• Kruskals MST (Ramanathan Rosales-Hain, ICC
  2000)
• Prims MST (Dai Wu, Cluster Computing 2005)

75
Power Optimization

• Kruskals 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.

76
Power Optimization

• Prims 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.

77
Non-uniform transmission range

• Wireless multicast advantage (Wieselthier,
  Infocom 2000)
• where is power needed between node i and
  node j

78
Non-uniform transmission range

• S broadcasts to two destinations D1 and D1
  (r1dis(s, D1), and r2dis(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

79
Non-uniform transmission range

• Use direct if
• angle between

80
Non-uniform transmission range

• Broadcast incremental power algorithm
  (Wieselthier, Infocom 2000)
• Standard Prims 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.

81
Non-uniform transmission range

• Other algorithms
• Broadcast least-unicast-cost algorithm
• Broadcast link-based MST algorithm
• The sweep removing unnecessary transmissions

82
Non-uniform transmission range

• Extensions to directional antennas
• (Wieselthier, Infocom 2002)
• Energy consumption
• Extended power incremental algorithm

83
Non-uniform transmission range

• Possible extensions
• Fixed beamwidth
• Single beam per node
• Multiple beams per node
• Limited multiple beams per node
• Directional receiving antennas

84
Non-uniform transmission range

• Incorporation of resource limitation
• Bandwidth limitation
• Greedy frequency assignment, but cannot ensure
  coverage (when running out of frequencies)
• Energy limitation

85
Hitch-hiking (Agrawal, Cho, Gao, Wu, INFOCOM
2004)

• Full and partial coverage (assuming )

86
Network Coding

• In early 2000.
• XOR network coding (SIGCOMM 2006)
• 3 transmissions instead of 4 using XOR (at
  router)

87
Topology Control (Wu and Dai, TPDS 2006)

• RNG-based protocols
• An edge (u, v) is removed if there exists a third
  node w such that d(u,v) gt d(u,w) and d(u,v) lt
  d(v,w), where d() stands for Euclidean distance.
• Minimum-energy protocols
• An edge (u,v) can be removed if there exists
  another node w such that 2-hop path (w, w,v)
  consumes less energy. It is extensible to k-hop.
• Cone-based protocols (CBTC)
• If a disk centerd at v is divided into k cones,
  the angle of the maximal cone is no more than a.
• When a lt 5?/6, CBTC preserves connectivity, and
  when a lt 2 ?/3, symmetric subgraph is connected.
• MST-based protocls (next page)

88
MST-based Topology Control

• 1-hop information (Li, Hou, and Sha, INFOCOM
  2003)
• Network connectivity if each node connects to
  its neighbors in the local MST (LMST)

1-hop neighborhood
89
Strong and Weak View Consistency

• Strong Consistency (using timestamp)
• Requires a certain degree of synchronization
• Weak Consistency (without using timestamp)
• Max max cost in a view window max1,3,5 5,
  max2,4,6 6
• Min min cost in a view window min1,3,5 1,
  min2,4,62
• MaxMin Max of Min values from all views of a
  node 2
• MinMax Min of Max values from all views of a
  node 5
• Local views are weakly consistency if
• MinMax MaxMin

90
Sampling Strategies (handling mobility)

• Two sampling strategies
• Instantaneous whenever a new Hello is
  transmitted or received.
• Periodical once per Hello interval
• Constructing weakly consistent local views
• Two recent Hello messages for the instantaneous
  model
• Three recent Hello messages for the periodical
  model

91
Framework with Consistent View
92
Framework with Weak Consistent View
93
Topology Control using Hitch-hiking (Cardei, Wu,
Yang, TMC 2006)

• Strong connectivity For any node s sending a
  packet, there should be a path to every other
  node.
• Forwarding rule.
• (a) s has the full packet and (b) only nodes
  that fully received the packet are able to
  forward it.

94
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)

95
Sensor Networks

• Military applications
• (4Cs) Command, control, communications,
  computing
• Intelligence, surveillance, reconnaissance
• Targeting systems

96
Sensor Networks

• Health care
• Monitor patients
• Assist disabled patients
• Commercial applications
• Managing inventory
• Monitoring product quality
• Monitoring disaster areas

97
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 lt 0.5 Ah, 1.2 V)

98
Sensor Networks

• Sample problems
• Coverage and exposure problems
• Data dissemination and gathering

99
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

100
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.

101
Coverage and Exposure Problems

• A sample Voronoi diagram

102
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.

103
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

104
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

105
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.

106
Coverage and Exposure Problems

• Exposure problem integral of the sensing
  function

107
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
  Dijkstras algorithm.

108
Coverage and Exposure Problems

• First, second, and third-order generalized 22
  grid

109
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.

110
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

111
Data Dissemination and Gathering

• Sample data aggregation protocols
• SMECN (Li and Halpern, ICC01)
• SPIN (Heinzelman et al, MobiCom99)
• SAR (Sohrabi, IEEE Pers. Comm., Oct. 2000)
• Directed Diffusion(Intanagonwiwat et al,
  MobiCom00)
• Linear Chain (Lidsey and Raghavendra, IEEE TPDS,
  Sept. 2002)
• LEACH (Heinzelman et al, Hawaii Conf. 2000)

112
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

113
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

114
Data Dissemination and Gathering

• Directed diffusion with several elements
  interests, data messages, gradients, and
  reinforcements
• Interests a query (what a user wants)
• Gradients a direction state created in each node
  that receives an interests
• Events flow towards the originator's of interests
  along multiple gradient paths
• The sensor network reinforces one, or a small
  number of these paths.

115
Data Dissemination and Gathering

• SPIN (Sensor Protocols for Information via
  Negotiation) efficient dissemination of
  information among sensors
• ADV new data advertisement containing meta-data
• REQ request for data when a node wishes to
  receive some actual data.
• DATA actual sensor data with a meta-data header

116
Data Dissemination and Gathering

• Sequential gathering in a linear chain

117
Data Dissemination and Gathering

• Parallel gathering (recursive double)

118
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

119
Data Dissemination and Gathering

• Multiple Chains

120
Data Dissemination and Gathering

• Simple chain (new node as head of chain)

121
Data Dissemination and Gathering

• Simple chain (new node inserted in the chain)

122
Data Dissemination and Gathering

• LEACH

123
Data Dissemination and Gathering

• Extended LEACH (energy-based)

124
Sensor Coverage

• How well do the sensors observe the physical
  space
• Sensor deployment random vs. deterministic
• Sensor coverage point vs. area
• Coverage algorithms centralized, distributed, or
  localized
• Sensing communication range
• Additional requirements energy-efficiency and
  connectivity
• Objective maximum network lifetime or minimum
  number of sensors

125
Sensor Coverage

• Area (point)-dominating set
• A small subset of sensor nodes that covers the
  monitored area (targets)
• Nodes not belonging to this set do not
  participate in the monitoring they sleep
• Localized solutions
• With and without neighborhood information

126
Area-dominating set

• With neighborhood info (Tian and Geoganas, 2002)
• Each node knows all its neighbors positions.
• Each node selects a random timeout interval.
• At timeout, if a node sees that neighbors who
  have not yet sent any messages together cover its
  area, it transmits a withdrawal and goes to
  sleep
• Otherwise, the node remains active but does not
  transmit any message

127
Point-dominating set

• With neighborhood info based on Dai and Wus Rule
  k (Carle and Simplot-Ryl, 2004)
• Each node knows either 2- or 3-hop neighborhood
  topology information
• A node u is fully covered by a subset S of its
  neighbors iff three conditions hold
• The subset S is connected.
• Any neighbor of u is a neighbor of S.
• All nodes in S have higher priority than u.

128
Coverage without neighborhood info

• PEAS probabilistic approach (F. Ye et al, 2003)
• A node sleeps for a while (the period is
  adjustable) and decides to be active iff there
  are no active nodes closer than r.
• When a node is active, it remain active until it
  fails or runs out of battery.
• The probability of full coverage is close to 1 if
  
• r (1 ) r
• where r is the sensing (transmission) range

129
3. Mobility as a Friend

• Movement-Assisted Routing
• Views node movement as a desirable feature
• Store
• Carry
• Forward

130
Challenged Networks

• Assumptions in the TCP/IP Model are Violated
• Limited End-to-End Connectivity
• Due to mobility, power saving, or unreliable
  networks
• DTN
• Delay-Tolerant Networks
• Disruption-Tolerant Networks
• Activities
• IRTFs DTRNRG (Delay Tolerant Net. Research
  Group)
• EUs Haggle project

131
Two Paradigms

• Random Mobility
• E.g., epidemic routing
• Sightseeing cars (random movement)
• Controlled Mobility
• E.g., message ferrying
• Taxi (destination-oriented)
• Public transportation (fixed route)
• Mobility pattern affects the spread of
  information

132
Epidemic Routing (Vahdat Becker 00)

• Nodes store data and exchange them when they meet
• Data is replicated throughout the network through
  a random talk

133
Message Ferrying (Zhao Ammar 03)

• Special nodes (ferries) have completely
  predictable routes through the geographic area

134
Mobility-Assisted Routing

• Replication
• Single copy vs. multiple copy
• E.g., spray-and-wait and spray-and-focus
• Knowledge
• Global vs. local information
• Deterministic vs. probabilistic information
• E.g., MaxProp
• (Predict-and-relay Quan, Cardei, and Wu,
• ACM MobiHoc 2009)

135
Mobility-Assisted Routing (contd)

• Closeness (to dest.)
• Location information (of contacts and dest.)
• Similarity (between intermediate nodes and dest.)
• E.g., logarithmic (and polylogarithmic) contacts
• Mobility
• Random vs. control
• Predictable
• E.g., cyclic MobiSpace

• (More information Wu and Yang IEEE MASS 2007
  and IEEE TPDS 2007 Liu and Wu ACM MobiHoc 2007
  and 2008)?

136
Routing in a Cyclic MobiSpace

• Challenges
• How to perform efficient routing in probabilistic
  time-space graphs
• Definition (ti,p)
• p is the contact probability of two nodes in ti .

137
Probabilistic Time-Space Graph

• A common motion cycle T (60)

138
Probabilistic state-space graph

• Remove time dimension
• Links are labeled d / pmax (delay/max transition
  probability)

139
Iterative Process

• Iterative steps
• Step t1 based on step t
• Ordering of neighbors
• pi pimax and ?i pi 1
• vst1 ? minp1, p2, p3 p1?(d1 vs1t) p2?(d2
  vs2t) p3?(d3 vs3t)

140
Expected Minimum Delay (EMD)

• Using EMD as the delivery probability metrics
• Optimal single-copy forwarding Liu and Wu
  MobiHoc 2008
• Optimal prob. forwarding with hop constraints
• Single copy Liu and Wu MobiHoc 2009
• Multiple copy Liu and Wu MASS 2009

141
Simulation

• Real traces
• NUS student contact trace
• UMassDieselNet trace (sub-shift based)
• Synthetic bus trace
• Miami
• Madrid

142
Other Challenges

• Intermittent connectivity
• Node mobility
• Unstable wireless links
• Scheduled on/off sensor nodes

• Mobility
• Connectivity
• Complexity
• Bandwidth
• Latency
• Robustness
• Storage
• Security

143
Connectivity

• (u,v) - connectivity under time-space view
• Exist i, (u(i), v(i))
• All i, (u(i), v(i))
• Exist i, j, (u(i), v(j))
• All i, j, (u(i), v(j))

u
v
144
Complexity

• Managing complexity of time-space graphs
• Lossless translation method
• Time-space to state-space (state explosion
  issue)
• Lossy comprehension method
• Removing time using averaging in hierarchical
  routing
• E.g. contact information compression
• (Liu Wu Scalable Routing in Delay Tolerant
  Networks,
• ACM MobiHoc 2007)

145
Opportunities

• Increasing system performance
• Routing capability
• Network capacity
• Security
• Sensor coverage
• Information dissemination (mobile pub/sub)
• Reducing uncertainty in reputation systems
• (Li and Wu, IEEE INFOCOM 2007)

146
Evolving Graph and Its Extensions

• Time sequnence t1, t2, ..., tL
• Gi (Vi, Ei) subgraph during ti, ti-1)
• Evolving graph
• (V, E), where (u,v) i (u, v) ? Ei.

• Weighted evolving graph
• E (i, wi) (u, v) ? Ei
• where wi can be bandwidth,
• reliability, or latency

147
Several Optimization Problems

• Optimization
• Earliest-completion
• Fastest
• Minimum-hop
• Maximum-bandwidth
• Maximum-reliability

148
Dijkstras Shortest Path Algorithm

• Dijkstras algorithm (Dijk) on (s, d)
• Initially s is black and all others are white.
• White nodes are colored gray if it has a black
  neighbor.
• Select best gray node (w.r.t to s) and color
  it black (i.e., relax adjust its best metric).
• Repeat the above steps until d becomes black.

149
Challenges

• Optimal greedy optimal prefix principle
• Proposed solutions
• Slicing
• Partition G into G1, G2, , Gi
• Select the best among i solutions for Gi
• Virtualization
• Enlarge G to G through virtualization
• Solve G which includes a solution for G

150
Journey

• Journey
• Selection of non-decreasing link labels along a
  path.
• E.g. (2, 4), (2, 5), (4,5)
• Earliest journey
• A journal with the smallest last label.

151
Earliest Completion Path

• Earliest completion path for G
• Dijk (G) with best being the earliest journey
  of a path.
• Complexity
• O(V log (LE)) using a heap
• O(V log V LE) using a Fibonacci heap

152
Fastest

• Start time is i at s
• Apply Dijk(G(i)) for earliest completion time
• Suppose completion time for d is fi, then time
  span is si fi i
• Fastest minsi
• Complexity L times of Dijk

153
Minimum Hops

• G(l i) a subgraph with labels i
• Dijk(G(l 1))
• Dijk(G(l 2)) on above results by relaxing only
  links with label 2
• Dijk(G(l i)) on above results by relaxing only
  links with label i
• Result is minimum hop count to d after Dijk(G(l
  L))
• Complexity L times of Dijk

154
Maximum Bandwidth

• Round i (starting i largest)
• Dijk(G(Bi)) / subgraph of labels with bandwidth
  i, but exact bandwidth is removed /
• Stop if d is reachable and bandwidth is i
• Otherwise, repeat the above for i i-1
• Complexity log L times of Dijk

155
Maximum Reliability

• Virtual Graph (G)
• For a node v in (u, v) with
• labels l1, l2, , lL
• L virtual nodes are used
• (u, li, v) for each v.
• Dijk(G), where G(V, E) V ?LV and E
  ?L2E

156
Final Notes

• Different applications
• Classic Dijkstras algorithm
• Using sliding and virtualization
• Other optimization problems
• E.g., transmission delay
• Other solutions
• E.g., min-hop by iteratively increased hop count
  and max-bandwidth by applying Kruskals solution
  on G(Bi)
• Open problems
• Problem complexity
• Optimal solutions

157
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/)

158
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.

159
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).

160
Bluetooth

• Basic facts
• Operates in the unlicensed Industrial-Science-Medi
  cal (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.

161
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.

162
Bluetooth Basic Structure

• Open problem a method for forming an efficient
  scatternet under a practical networking scenario.
• Two methods Bluetree and Bluenet

163
Bluetooth Basic Structure

• Scatternet formation
• Connected scatternet
• Resilience to disconnections in the network
• Routing robustness (multiple paths)
• Limited route length
• Selection of gateway slaves (a salve being a
  neighbor of two maters)
• Small number of roles per node
• Self-healing (converge to a new scatternet after
  a topology change)

164
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.

165
Bluetree (Zaruba, ICC 2001)

• Blueroot Grown Bluetrees (sample)

166
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 (Dong and Wu, 2003)
• Using neighbors neighbor sets.
• Using neighbor locations.

167
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).

168
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.

169
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).

170
Bluetree (Zaruba, ICC 2001)

• Distributed bluetree (sample)

171
Bluetree (Zaruba, ICC 2001)

• Overflow problem (Wu)
• Solution slot reservation (up to 6 slaves)

172
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)

173
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
  acquire some number of slaves (not too many or
  too few).

174
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?

175
BlueStars (Petrioli et al, IEEE TR 2003)

• BlueStars (i.e., piconet) formation phase
• Clustering-based approach for master selection
• The formation of disjoint piconets
• Selection of gateway devices to connect multiple
  piconets
• Yao construction phase
• Yao procedure is used to ensuring the max number
  of node degree by removing links without losing
  connectivity
• BlueStars over the Yao topology

176
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).

177
What are P2P networks?

• Definition
• A distributed system in which peers employ
  distributed resources to perform a critical
  function in a decentralized fashion
• Characteristics
• Peer-to-Peer (P2P) equal node roles
• Application-level overlay networks
• Distributed and decentralized
• Nodes join and leave freely

178
What are P2P networks?
Peer-to-peer network
Client-server network
Peer-to-peer network overlay network
179
What are P2P networks?

• Benefits of P2P networks
• No special administration or financial
  arrangement
• Can gather and harness computation and storage
  resources on the edge of the Internet
• Self-organized and adaptive
• File-sharing P2P networks
• Commercial - Napster, Gnutella, BitTorrent,
  Kazaa, eMule, iMesh, Morpheus, Freenet, etc.
• Research-oriented - Chord, Pastry, Tapestry, CAN,
  Symphony, PlanetLab, etc.

180
What are P2P networks?
File-sharing peer-to-peer networks
181
Classification of P2P networks
1
n1
n2
3
12
n4
n3
6
n6
10
n5
9
Loosely structured e.g. Freenet ( based on hints )
Unstructured e.g. Gnutella ( arbitrary )
Structured e.g. Chord ( well defined)
182
Structured P2P-Chord

• Nodes in a network are organized in a circle
• Each node and each key have assigned identifiers
  (distributed harsh table DHT)
• Node ID SHA1(IP address)
• Key ID SHA1(key itself)
• Each key is assigned to its
• Successor

183
Chord Finger Table

• The info. Stored in the Finger Table is used for
  scalable node localization

Slide 184