A Fog Computing Architecture for Disaster Response Networks

In the aftermath of a disaster, the impacted communication infrastructure is unable to provide first responders with a reliable medium of communication. Delay tolerant networks that leverage mobility in the area have been proposed as a scalable solution that can be deployed quickly. Such disaster response networks (DRNs) typically have limited capacity due to frequent disconnections in the network, and under-perform when saturated with data. On the other hand, there is a large amount of data being produced and consumed due to the recent popularity of smartphones and the cloud computing paradigm. Fog Computing brings the cloud computing paradigm to the complex environments that DRNs operate in. The proposed architecture addresses the key challenges of ensuring high situational awareness and energy efficiency when such DRNs are saturated with large amounts of data. Situational awareness is increased by providing data reliably, and at a high temporal and spatial resolution. A waypoint placement algorithm places hardware in the disaster struck area such that the aggregate good-put is maximized. The Raven routing framework allows for risk-averse data delivery by allowing the user to control the variance of the packet delivery delay. The Pareto frontier between performance and energy consumption is discovered, and the DRN is made to operate at these Pareto optimal points. The FuzLoc distributed protocol enables mobile self-localization in indoor environments. The architecture has been evaluated in realistic scenarios involving deployments of multiple vehicles and devices

Robust system multiangulation using subspace methods

Sensor location information is a prerequisite to the utility of most sensor networks. In this paper we present a robust and low-complexity algorithm to self-localize and orient sensors in a network based on angle-of-arrival (AOA) information. The proposed non-iterative subspace-based method is robust to missing and noisy measurements and works for cases when sensor orientations are either known or unknown. We show that the computational complexity of the algorithm is O(mn 2), where m is the number of measurements and n is the total number of sensors. Simulation results demonstrate that the error of the proposed subspace algorithm is only marginally greater than an iterative maximum-likelihood estimator (MLE), while the computational complexity is two orders of magnitude less. Additionally, the iterative MLE is prone to converge to local maxima in the likelihood function without accurate initialization. We illustrate that the proposed subspace method can be used to initialize the MLE and obtain near-Cramér-Rao performance for sensor localization. Finally, the scalability of the subspace algorithm is illustrated by demonstrating how clusters within a large network may be individually localized and then merged. Categories and Subject Descriptors C.2.4 [Computer-communication networks]: Distributed systems; C.3 [Special-purpose and application-based systems]: Signal processing system