When a mobile user dials 911, a key to arriving to the emergency scene promptly is knowing the location of the mobile user. This paper presents SkyLoc, a GSM fingerprinting-based localization system that runs on a mobile phone and identifies the current floor of a user in tall multi-floor buildings. Knowing the floor in a tall building significantly reduces the area that emergency service personnel have to canvas to locate the individuals in need. We evaluated our system in three multi-floor buildings located in Washington DC, Seattle and Toronto. Our system identifies the floor correctly in up to 73% of the cases and is within 2 floors in 97% of the cases. The system is robust as it works for different network operators, when the training and testing sets were collected with different hardware and up to one month apart. In addition, we show that feature selection techniques that select a subset of highly relevant radio sources for fingerprint matching nearly double the localization accuracy of our system

When a service is offered by multiple servers in a mobile ad hoc network (MANETs), the manner in which clients and servers are paired together, referred to as service selection, is crucial to network performance. Good service selection groups clients with nearby servers, localizing communication, which in turn reduces inter-node interference and allows for multiple concurrent transmissions in different parts of the network. Although much previous research has concentrated on service discovery in MANETs, not much effort has gone into understanding the effects of service selection. This paper demonstrates that service selection in MANETs has profound implications for network performance. Specifically, we show that effective service selection can improve network throughput by up to 400%. We show that to maximize performance service selection decisions need to be continuously reassessed to offset the effects of topology changes. We argue that effective service selection in MANETs requires a cross-layer approach that integrates service discovery and selection functionality with network ad hoc routing mechanisms. The cross-layer approach leverages existing routing traffic and allows clients to switch to better servers as network topology changes.

We are concerned with the problem of statically certifying (verifying) whether the client of a software component conforms to the component's constraints for correct usage. We show how conformance certification can be efficiently carried out in a staged fashion for certain classes of first-order safety (FOS) specifications, which can express relationship requirements among potentially unbounded collections of runtime objects. In the first stage of the certification process, we systematically derive an abstraction that is used to model the component state during analysis of arbitrary clients. In general, the derived abstraction will utilize first-order predicates, rather than the propositions often used by model checkers. In the second stage, the generated abstraction is incorporated into a static analysis engine to produce a certifier. In the final stage, the resulting certifier is applied to a client to conservatively determine whether the client violates the component's constraints. Unlike verification approaches that analyze a specification and client code together, our technique can take advantage of computationally-intensive symbolic techniques during the abstraction generation phase, without affecting the performance of client analysis. Using as a running example the Concurrent Modification Problem (CMP), which arises when certain classes defined by the Java Collections Framework are misused, we describe several different classes of certifiers with varying time/space/precision tradeoffs. Of particular note are precise, polynomial-time, flow- and context-sensitive certifiers for certain classes of FOS specifications and client programs. Finally, we evaluate a prototype implementation of a certifier for CMP on a variety of test programs. The results of the evaluation show that our approach, though conservative, yields very few "false alarms," with acceptable performance.