University PI: Shashi Shekhar 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 Marsman

Outline Proposal Summary, Tasks and Progress Next steps Journal Paper Updates

Proposal Summary Ephemeral Networks: Objective: 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.

Proposal Summary: Related Work and Limitations Social Network Analysis Ephemeral 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.

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. 2) Accuracy versus scalability 3) Real-time response, scalability 4)

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 Formally modeling providers fairness Develop measures for a transaction’s promise using historical transactions and routines to predict near-future commerce opportunities 2) Accuracy versus scalability 3) Real-time response, scalability 4)

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 Validate algorithms for predicting promising transactions Validate proposed algorithms using real-datasets and larger synthetic datasets 2) Accuracy versus scalability 3) Real-time response, scalability 4) Order of milliseconds

Next Steps: Short Term Plan 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 -Mar12th, 2nd Intl. Conf. on Smart Data and Smart Cities - Mar 31st) With rendezvous points May also model group matching ( grouping requests for optimizing provider route)

Next Steps: Long Term Plan 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 trajectories

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 umax where umax = Minimize total utilization cost of all providers: (traffic engineering) Min Maximize Shannon’s diversity index (entropy measure): Max

Journal Paper Updates: Optimization Problem Formulation We 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.

Mixed Integer Programming Formulation (1/2) Decision Variables: Notations Variable Description N # consumers M # providers K Number of require propositions per consumer ai Arrival time of Ci di Max travel time constraint of Ci wi Max waiting time constraint of Ci dij Shortest travel time between locations of Ci and Pj cj Service rate (capacity) per hour for Pj ej Earliest available service time at Pj 1

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 1

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 Match O(E): Ratio cut heuristic used in CCAM O(VE2): max flow approach

Greedy Approach: Novel Supply-Demand Ratio Aware Heuristics Least 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 utilization Current 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 heuristics O(E): Ratio cut heuristic used in CCAM O(VE2): max flow approach

“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 1 1 1 1 1 1 1 S D 1 1 1 1 1 1 1 1 Notes: for up to K: if capacity of consumers to D is K, algorithm will treat increasing the propositions of one provider similar to increasing the number of matched consumers

Thank you.