Dmitri I. Arkhipov

UbiFlow: Mobility management in urban-scale software defined IoT

The growing of Internet of Things (IoT) devices has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for mobile IoT access is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick configuration of network devices, but its application in multinetworks with frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for ubiquitous flow control and mobility management in multinetworks. UbiFlow adopts distributed controllers to divide urban-scale SDN into different geographic partitions. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on the per-device requirement and whole-partition capability. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance in each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks.

Online War-Driving by Compressive Sensing

Roadside units (RSUs) are public and personal wireless access points that can provide communications with infrastructure in ad hoc vehicular networks. We present CLOCS (Counting and Localization using Online Compressive Sensing), a novel system to retrieve both the number and locations of RSUs through war-driving. CLOCS employs online compressive sensing (CS), where received signal strength (RSS) values are recorded at runtime, and the number and location of RSUs are recovered immediately based on limited RSS readings. CLOCS also uses fine retrieval based on an expectation maximization method along the driving route. Extensive simulation results and experiments in a real testbed deployed in the campus of the University of California, Irvine, confirm that CLOCS can successfully reduce the number of measurements for RSU recovery, while maintaining satisfactory counting and localization accuracy. In addition, data dissemination, time cost, and effects of different mobile scenarios using CLOCS are analyzed, and the impact of CLOCS on network connectivity is studied using Microsoft VanLan traces.

Online Data Repository for Statewide Freight Planning and Analysis

Freight transportation has a multifaceted impact on the economy, and the importance of understanding freight demand is increasing. There is a significant need to access a wide array of data sources for freight modeling and analysis. However, current data sources are not always easily accessible even with the availability of the Internet. Among the reasons are differing user interfaces, unavailability of data type definition, data format incompatibility, and inability to assess the scope of data conveniently. The repository developed in this study, the California Freight Data Repository, is a user-centered online tool designed from a systems perspective with several objectives. First, it facilitates convenient access, standardized interface, and a centralized location for obtaining freight data. Data dictionaries and lookup tables are provided for each data source to allow users to understand the scope of the data source and to give a clear definition of terms found in the data. A quality assessment summary is also provided to inform users of the strengths and limitations associated with each data source. Second, the repository is equipped with several geographic information system–based visualization tools intended to allow users to perform preliminary evaluation of data to determine their suitability for specific modeling or analysis needs. Third, the repository is designed with a customized search engine to retrieve web resources specifically associated with freight modeling and analysis. This paper presents the metadata architecture used for identifying data sources, the assessment framework used to evaluate selected data sources, and the system and interface design of the California Freight Data Repository. Several use cases are presented to demonstrate the applicability of this resource.

Faster Converging Global Heuristic for Continuous Network Design Using Radial Basis Functions

In light of the demand for more complex network models and general solution methods, this research introduces a radial basis function-based method as a faster alternative global heuristic to a genetic algorithm method for the continuous network design problem. Two versions of the algorithm are tested against the genetic algorithm in three experiments: the Sioux Falls, South Dakota, network with standard origin–destination flows; the same network with double the flows to test performance under a more congested scenario; and an illustrative experiment with the Anaheim, California, network to compare the scalability of performance. To perform the experiments, parameters for the network design problem were developed for the Anaheim network. The Anaheim test would be the first instance of testing the radial basis function methods on a 31-dimensional network design problem. Results indicate that the multistart local radial basis function method performs notably better than the genetic algorithm in all three experiments and would therefore be an attractive method to apply to more complicated network design models involving larger networks and more complex constraints, objectives, and representations of the time dimension.

DeepOpp: Context-Aware Mobile Access to Social Media Content on Underground Metro Systems

Accessing online social media content on underground metro systems is a challenge due to the fact that passengers often lose connectivity for large parts of their commute. As the oldest metro system in the world, the London underground represents a typical transportation network with intermittent Internet connectivity. To deal with disruption in connectivity along the sub-surface and deep-level underground lines on the London underground, we have designed a context-aware mobile system called DeepOpp that enables efficient offline access to online social media by prefetching and caching content opportunistically when signal availability is detected. DeepOpp can measure, crowdsource and predict signal characteristics such as strength, bandwidth and latency; it can use these predictions of mobile network signal to activate prefetching, and then employ an optimization routine to determine which social content should be cached in the system given real-time network conditions and device capacities. DeepOpp has been implemented as an Android application and tested on the London Underground; it shows significant improvement over existing approaches, e.g. reducing the amount of power needed to prefetch social media items by 2.5 times. While we use the London Underground to test our system, it is equally applicable in New York, Paris, Madrid, Shanghai, or any other urban underground metro system, or indeed in any situation in which users experience long breaks in connectivity.

From Intermittent to Ubiquitous: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization

Accessing online social networks in situations with intermittent Internet connectivity is a challenge. We have designed a context-aware mobile system to enable efficient offline access to online social media by prefetching, caching and disseminating content opportunistically when signal availability is detected. This system can measure, crowdsense and predict network characteristics, and then use these predictions of mobile network signal to schedule cellular access or device-to-device (D2D) communication. We propose several opportunistic optimization schemes to enhance controlled crowdsensing, resource constrained mobile prefetch, and D2D transmissions impacted by individual selfishness. Realistic tests and large-scale trace analysis show our system can achieve a significant improvement over existing approaches in situations where users experience intermittent cellular service or disrupted network connection.

Yield Optimization with Binding Latency Constraints

Programmatic advertising is an actively developing industry and research area. Some of the research in this area concerns the development of optimal or approximately optimal contracts and policies between publishers, advertisers and intermediaries such as ad networks and ad exchanges. Both the development of contracts and the construction of policies governing their implementation are difficult challenges, and different models take different features of the problem into account. In programmatic advertising decisions are made in real time, and time is a scarce resource particularly for publishers who are concerned with content load times. Policies for advertisement placement must execute very quickly once content is requested, this requires policies to either be pre-computed and accessed as needed, or for the policy execution to be very efficient. In this paper we formulate a stochastic optimization problem for per publisher ad sequencing with binding latency constraints. We adopt a well known heuristic optimization technique to this problem and evaluate its performance on real data instances. Our experimental results indicate that our heuristic algorithm is near optimal for instances where an optimality calculation is feasible, and superior to other reasonable approaches for instances when the calculation is not feasible.