/
Ephemeral Network Broker to Facilitate Future Mobility Mode Ephemeral Network Broker to Facilitate Future Mobility Mode

Ephemeral Network Broker to Facilitate Future Mobility Mode - PowerPoint Presentation

mitsue-stanley
mitsue-stanley . @mitsue-stanley
Follow
413 views
Uploaded On 2017-10-06

Ephemeral Network Broker to Facilitate Future Mobility Mode - PPT Presentation

A collaboration between Ford University Research Program and University of Minnesota University PI Shashi Shekhar Ford PI Shounak Athavale Eric Marsman Outline Proposal Summary Tasks and Progress ID: 593496

service provider requests providers provider service providers requests time demand max future matching opportunities heuristics algorithms approach fairness matched

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "Ephemeral Network Broker to Facilitate F..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Ephemeral Network Broker to Facilitate Future Mobility Models/Transactions

A collaboration between Ford University Research Program and University of Minnesota

University PI: Shashi Shekhar

Ford PI:

Shounak Athavale

Eric MarsmanSlide2

Outline

Proposal Summary, Tasks and ProgressNext stepsJournal Paper UpdatesSlide3

Proposal Summary

Ephemeral Networks: Groups of people, good, and services that encounter each other in the physical world or are in close geographic proximity during routine activities (e.g. commute, shopping, entertainment)

Objective: To

build an ephemeral network broker

to connect

people, goods and services

based on

mobility profiles

(e.g. GPS trajectories),

geographic proximity, and known intents

(e.g. calendars, wish lists, gift registries, shopping lists).

The broker will assist in identifying

real-time and near future

mobile commerce

transaction opportunities

in ephemeral networks

(MCEN) from inception to conclusion in support of new Future Mobility Business models.Slide4

Proposal Summary: Related Work and Limitations

Related work:Social Network AnalysisEphemeral social networks (temporal proximity e.g. at a conference)

Limitations of Related Work:Do not model socio-economic MCEN semantics (e.g. need, desire, readiness for transaction, trust)

Does not provide quantitative measures to distinguish most promising MCEN opportunities (e.g. recurring commute, travel, other trips) and other ESNs (e.g. rare meeting of academic in research conferences).

Do not scale up to megacities.Slide5

Proposal Summary: Research Tasks

Conceptual Modeling:Leverage novel commerce opportunities facilitated by ephemeral networks and routine activities generating trips to define core concepts and taxonomies to facilitate the design of interest measures and scalable algorithms for identifying MCEN opportunities.

Interest Measures:

Develop quantitative interest measures to separate commercially promising MCEN opportunities from other ephemeral social networks.

Scalable Algorithms:

Design scalable techniques for MCEN opportunities leveraging measure from Task 2

Validation:

Do proposed algorithms scale-up to megacities?

How accurately can we predict MCEN opportunities in real-time or near future?

Synthetic datasets can be generated using city simulator or traffic simulator. Real datasets may come from FORD.Slide6

What has been done so far?

Conceptual Modeling:

Assumed that mobile consumers explicitly specify their service type needs and readiness based on spatial proximity In future: May incorporate ratings to model trust (more interesting with mobile providers)

Learning of user preferences from historical trajectories

Interest Measures:

Focused on maximizing

unexpected real-time demand

under different supply-demand ratio scenarios

In future:

Formally modeling providers fairness

Develop measures for a transaction’s promise using historical transactions and routines to

predict

near-future commerce opportunitiesSlide7

What has been done so far?

Scalable Algorithms:

Designed a scalable greedy algorithm with novel provider-centric heuristics In future: Design algorithms with better matching size and load balancing

Design scalable algorithms for other interest measures (e.g. predicting future opportunities from historical trajectories)

Validation:

Designed a city simulated for fixed supply and variable demand proportional to population

Considered generating whole trajectories for incoming requests

Evaluated greedy approach for several supply-demand rations in Minneapolis for 120,00 requests and 120 restaurants

In future:

Validate algorithms for predicting promising transactions

Validate proposed algorithms using real-datasets and larger synthetic datasetsSlide8

Next Steps: Short Term Plan

Short term:Formally modeling provider fairness/load balancing

(Journal Extension)Propose method for improve matching size and provider balance over greedy algorithm (Journal Extension)

Modeling mobile service providers

(SSTD 2017 -Mar12

th

, 2

nd

Intl. Conf. on Smart Data and Smart Cities - Mar 31

st

)

With rendezvous points

May also model group matching ( grouping requests for optimizing provider route)Slide9

Next Steps: Long Term Plan

Long term: Design interest measures and scalable algorithms:

for predicting near-future opportunities from historical trajectories and current intents based on routine activities (pattern mining for online prediction)

Validation:

Incorporate GPS trajectory generation into City simulator (+ real datasets?)

Enriching the model:

Incorporating ratings to model trust

Learning of user preferences from historical trajectoriesSlide10

Journal Paper Updates:

Formally Defining a Provider Fairness objective Function

Assuming matching size is maximized:Possible Alternatives:Minimize max. utilization of all providers: (communication networks)

Min

u

max

where

u

max

=

Minimize total utilization cost of all providers: (traffic engineering)

Min

Maximize Shannon’s diversity index (entropy measure):

Max

Slide11

Journal Paper Updates:

Optimization Problem FormulationWe propose a mixed-integer programming formulation to optimize the matching size for the set of requests available at time t

Methods to combine both objective functions: maximizing matched consumers and maximizing provider fairness:

Maximize matching size (most favorable) where provider fairness is added as a constraint

pros:

Easiest to implement

cons:

difficult to set fairness threshold

Aggregate functions using a weighted linear sum of objectives

Pros:

can set smaller weight for provider fairness

cons:

difficult to find most appropriate weights

Optimize for matching size first (most favorable), then find max fairness function that achieves the max matching.

Slide12

Problem Definition: On-Demand Spatial Service Propositions

Input:A set P of service providers

A set R of consumer requests arriving dynamicallyA number of required propositions K

Output:

K service provider(s) propositions for each request

Objective:

Maximize number of matched requests

Secondary Objective:

Maximize provider-balance

Constraints:

Each returned proposition satisfies the consumer’s max. travel time and waiting time constraints and does not violate the provider’s service rate.Slide13

Mixed Integer Programming Formulation (1/2)

Decision Variables:

1

Notations

Variable Description

N

# consumers

M

# providers

K

Number of require propositions

per consumer

a

i

Arrival time of

C

i

d

i

Max travel time constraint of

C

i

w

i

Max waiting time constraint

of

C

i

d

ij

Shortest travel time between locations of

C

i

and

P

jcjService rate (capacity) per hour for PjejEarliest available service time at PjSlide14

Mixed Integer Programming Formulation (2/2)

Objective Function:Constraints:

(1)(2)(3)

(4)

(5)

(6)

Limitations:

Integer programming may not scale for a large number of consumers and providers

O(

MxN

) variables and constraints

However, solution can also serve as an upper bound for max # matches at time t

Maximizes matching and balance based on current requests, but does not adapt to requests that may appear in the future

1Slide15

Reducing Problem Size using Spatial Partitioning

The complexity of the optimization problem can be reduced by dividing it into smaller matching sub-problems:

Using a min-cut graph partitioning approach e.g. O(E), O(VE2)Continue until problem size is below a threshold

Consumer

Provider

Possible MatchSlide16

Greedy Approach:

Novel Supply-Demand Ratio Aware HeuristicsLeast Accepted First (LAF) and Least Appearance as Candidate First (LCF) heuristics only considered total number of previous matches/occurrences of a provider, but do not its service capacity, resulting in the following limitations:

Do not balance utilizationCurrent decisions may not maximize future matching since

Providers with low demand are favored even if they have near full utilization (i.e. low capacity providers)

Does not consider temporal heterogeneity of demand (e.g. changes in day/night population) and thus providers with historically low demand will be favored even during periods of their high demand

Propose New heuristics:

Prioritize least utilized providers

(i.e. least total utilization)

to balance providers utilization

Prioritize providers with highest recent supply-demand ratio over

(i.e. least recent utilization)

using a moving time horizon to leave capacity for future requests

Note: Replacing the

“exactly K propositions”

constraint with “

Up to K propositions

” also requires

non-straightforward

handling of the LAF and LCF heuristicsSlide17

“Up to K” Formulation: An Iterative Max-Flow Approach

Maximum Matching in bi-partite graphs can be solved by finding the max flow in a flow network

However, we cannot guarantee that each consumer is matched to “

exactly K”

providers

For up to K propositions, we may apply an iterative max flow approach (iterations ≤ K). In each iteration:

Add consumer nodes assigned to fewer than K providers

Add provider nodes with remaining service capacity

Connect feasible matches which are not already selected in previous iterations

S

D

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1Slide18

IWCTS 2016

Journal Extension

Problem Definition

Objective:

max. matched requests

Consumers

had 2

constraints

:

Max. travel distance

Max waiting time

Proof of NP-hardness

Add a “Basic Concepts” subsection

Modify

consumer constraints

Formally define

a measure of provider fairness

Proposed Approach

Proposed 2 service-provider

centric heuristics:

Least Accepted First

Least appearance as Candidate First

Provide integer programming-based approach

Propose a spatial partitioning approach for enhancing the scalability

Propose a new provider capacity-aware/supply-demand ratio aware heuristic

(Up to K): Propose an iterative max-flow approach

Add example for each different heuristic

Experimental Evaluation

Four

experiments:

% matched requests

% matched providers

avg. requests/provider

Stdev

of requests/providerExpand experimental evaluation section by adding

more experiments

.

from original 10-page paper

tie-breaker heuristics

Discussion

None

Add discussion section (other related work, choice of simulation framework)Slide19

Thank you.Slide20

Basic Concepts

A Service Provider: A provider registered in the system is defined using its location and service rate per hour over the day (e.g. 15 requests per hour)

A Consumer Request: A request for from a mobile consumer, including the consumer’s current location, max. acceptable travel distance and max. acceptable waiting time before service.

A Service Provider Proposition

: A quadruple (r, p, d, w) where:

r

ϵ

set of available consumer requests

p

ϵ

set of registered service providers

d: distance between r and p

w: waiting time before r is served by p

Example:

(C

1

, P

1

, 2 miles, 5 min)Slide21

IWCTS Paper Contributions

Formally defined the problem of On-demand Spatial Service Propositions.

Proposed new category of service-provider centric heuristics for increasing the number of engaged service providers while meeting the conflicting requirements of the broker, consumers and service providers.

Our experimental results show that our proposed heuristics (Least Accepted First) result in:

a larger number of matched requests for balanced supply and demand scenarios

a larger number of matched service providers with a

more balanced provider assignment

particularly when the available supply exceeds the available demand.