Reparameterizing the Birkhoff Polytope for Variational Permutation Inference

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optimization algorithms may enable efficient point estimation, but fully Bayesian inference poses a severe challenge in this high-dimensional, discrete space. To surmount this challenge, we start with the usual step of relaxing a discrete set (here, of permutation matrices) to its convex hull, which here is the Birkhoff polytope: the set of all doubly-stochastic matrices. We then introduce two novel transformations: first, an invertible and differentiable stick-breaking procedure that maps unconstrained space to the Birkhoff polytope; second, a map that rounds points toward the vertices of the polytope. Both transformations include a temperature parameter that, in the limit, concentrates the densities on permutation matrices. We then exploit these transformations and reparameterization gradients to introduce variational inference over permutation matrices, and we demonstrate its utility in a series of experiments

The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course

Tasking networked CCTV cameras and mobile phones to identify and localize multiple people

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person’s mobile phones. Since a person’s motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones ’ IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a twomeasurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed

Supporting Context-Aware Application Development in Ad Hoc Mobile Networks

Some of the most dynamic systems being built today consist of physically mobile hosts and logically mobile agents. Such systems exhibit frequent configuration changes and a great deal of resource variability. Applications executing under these circumstances need to react continuously and rapidly to changes in operating conditions and must adapt their behavior accordingly. Applications with these capabilities are referred to as context-aware. Much of the current work on context-aware computing relies on information directly available to an application via context sensors on its local host, e.g., user profile, host location, time of day, resource availability, and quality of service measurements. The work reported in this dissertation starts by building a new perspective on context-awareness, in which the context includes, in principle, any information available in the ad hoc network but is restricted, in practice, to specific projections of the overall context. This work reports on the design and implementation of a middleware model that brings this notion of context to the application programmer. Another important aspect of the software engineering process is the ability to reason formally about the programs we create. This dissertation details initial steps to create formal reasoning mechanisms dedicated to the needs of context-aware applications. The results of this work simplify application development in ad hoc mobile networks from a design and implementation perspective and through formal reasoning

Emerging models for the development of social mobile applications: people as a service, and social devices. A proof of concept.

Nowadays mobile devices have gained a great importance in our society and they are present everywhere. These devices have a huge amount of data about their owners and context data of their surroundings, and many communication channels. Nevertheless with current models mobile devices cannot take advantage of this situation. For these reasons, new models for mobile applications have appeared recently. People-as-a-Service (PeaaS) allows interactions between mobile devices in their surroundings, using sociological profiles of their owners to create communication between interested devices. Social Devices is a recent model that implements the PeaaS concept, taking advantage the devices are almost always with the user it creates a new kind of interactions between nearby devices with the main target of improve social relations. For this purpose each device is able to provide services to nearby devices and consume them, keeping the user’s data in the device and sharing it only when it is authorized. This concept could be integrated with technologies like Internet-of-Things and Ubiquitous Computing, allowing devices to connect with nearby objects like elevators, traffic lights or your own vehicle. In particular, there is a new platform that implements both concepts, which is the study object of this work: OrchestratorJS. The platform is still under development and is not too stable yet, furthermore there is no documentation available. The main target of this work is to explore the platform and to create a basic documentation which could be used for anybody interested on the in, acting as a starting point for future works

Location Privacy in the Era of the Internet of Things and Big Data Analytics

Location information is generated in large quantities in the Internet of Things and becomes a major component of the big data phenomenon. This results in privacy issues involving sensing, identification, storage, processing, sharing, and use of this information in technical, social, and legal contexts. These issues must be addressed if the IoT is to be widely adopted and accepted. Theory will need to be developed and tested, and new research questions will need to be investigated. This exploratory research begins to identify, classify, and describe these issues and questions