Mechanism design for spatio-temporal request satisfaction in mobile networks

Mobile agents participating in geo-presence-capable crowdsourcing applications should be presumed rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we design a mechanism that takes into consideration this rationality for request satisfaction in such applications. We propose the Geo-temporal Request Satisfaction (GRS) problem to be that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. The objective of the GRS problem is to maximize the total profit of the system subject to our rationality assumptions. We define the problem formally, prove that it is NP-Complete, and present a practical solution mechanism, which we prove to be convergent, and which we evaluate experimentally.National Science Foundation (1012798, 0952145, 0820138, 0720604, 0735974

Visualizing Sensor Network Coverage with Location Uncertainty

We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks

Distributed Algorithms for Maximizing the Lifetime of Wireless Sensor Networks

Wireless sensor networks (WSNs) are emerging as a key enabling technology for applications domains such as military, homeland security, and environment. However, a major constraint of these sensors is their limited battery. In this dissertation we examine the problem of maximizing the duration of time for which the network meets its coverage objective. Since these networks are very dense, only a subset of sensors need to be in sense or on mode at any given time to meet the coverage objective, while others can go into a power conserving sleep mode. This active set of sensors is known as a cover. The lifetime of the network can be extended by shuffling the cover set over time. In this dissertation, we introduce the concept of a local lifetime dependency graph consisting of the cover sets as nodes with any two nodes connected if the corresponding covers intersect, to capture the interdependencies among the covers. We present heuristics based on some simple properties of this graph and show how they improve over existing algorithms. We also present heuristics based on other properties of this graph, new models for dealing with the solution space and a generalization of our approach to other graph problems