Immanuel Schweizer

Noisemap: multi-tier incentive mechanisms for participative urban sensing

Noise pollution is a problem increasingly acknowledged by authorities and governments around the globe. At last years PhoneSense we presented Noisemap, a participating sensing application to accurately measure noise pollution. Noisemap incorporated frequency calibration to overcome the limited microphone hardware. The challenge remaining is how to motivate smartphone users to sacrifice their time and battery on measuring noise. A user study was conducted with 49 users divided into three groups. As expected the average measurements taken per user increased from 402 to 3,357 as the number of incentive schemes increased. Over the course of 7 weeks the users captured more than 85, 000 measurements, measuring for more than six hours on average.

kTC - Robust and Adaptive Wireless Ad-Hoc Topology Control

Topology control for Wireless Sensor Networks (WSN) is a frequently tackled challenge, for which no satisfying general solution for realistic deployments has been found to the current day. Aiding to minimize unnecessary transmissions, it nevertheless represents a crucial function of WSN, in the light of their pursuit of efficiency. kTC is a new WSN topology control that unlike prior art neither relies on location information, nor on complex geometric structures, which could leave doubts about a practical feasibility. Even though location-free approaches have been proposed to circumvent systematic problems, they do not address issues like robustness and adaptability satisfyingly, which may lead to disconnection in real world deployments. kTC is a location-free approach that adapts topologies dynamically in face of changing environmental influences. It is based on a local, pattern-based heuristic, and transmitting only two messages per node to construct the topology it is highly scalable. The graphs kTC creates are symmetric, connected, and planar; they have bounded degree and nodes are θ-separated. Simulative evaluations indicate that kTC outperforms known topology control schemes. A preliminary deployment on a sensor testbed corroborates the obtained results and acts as proof of concept for kTC.

GTNA: a framework for the graph-theoretic network analysis

Concise and reliable graph-theoretic analysis of complex networks today is a cumbersome task, consisting essentially of the adaptation of intricate libraries for each specific problem instance. The growing number of complex metrics that have been proposed in the last years, which mainly gain significance due to the increasing computational capabilities at hand, have led to important new insights in the field. However, they have solely been implemented as single algorithms, each specialized for the purpose of calculating exactly the targeted metric for a selected type of network graph. A comprehensive, extensible tool for the concise evaluation of graphs is currently not available. For this purpose we introduce the Graph-Theoretic Network Analyzer (GTNA), an efficient, Java-based toolkit for the comprehensive analysis of complex network graphs. GTNA, while already including the main metrics that are used to analyze the complex networks in computer science today, is simple to extend through a well defined plugin interface for metrics, network descriptions and network generator models. Throughout the paper we present the design and simple extensibility of GTNA, as well as the network models and metrics that are already implemented and give examples of its scalability and performance.

Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of range-restricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.

Leveraging Network Motifs for the Adaptation of Structured Peer-to-Peer-Networks

Topology adaptation is a vital operation in tech- nological networks. It is frequently implemented as either an external process or a distributed online optimization that relies on gathering knowledge on the overall state of the system. In this work we propose MBO, a novel approach that uses network motifs (a local, stochastic metric) for distributed topology optimization of arbitrary, adaptable networks. In order to give a proof of concept we chose to optimize structured Peer-to-Peer overlays towards a fair load balancing. MBO is parametrized using target motif signatures of networks, which are derived from exemplary, generated topologies with the desired properties - a fair load balancing in the demonstrated case. Extensive simulations indicate that for CAN and Kademlia, two different types of P2P systems, MBO leads to a well balanced load, while being minimally intrusive.

First response communication sandbox

First response communication is tackled by several independent research groups. While there are existing prototypes and simulated results, comparison of first response solutions is hardly possible so far. We have built an universal XML based description format to handle all relevant settings and actions typical for first response scenarios. In addition we implemented a user-friendly movement and environment simulator which interacts with the network simulation on top of the simulated movement. The chosen data structure have proven to be well suited for describing settings and actions found in a first response scenario. The simulator combines movement and network simulation and therefore enables both, fine grained movement models and location aware network models with reciprocal interdependencies. The simulation results of the chosen communication approach are therefore finer grained than using a network or movement simulator separately.

First responder communication in urban environments

Communication is crucial for first responders. Crisis management is nearly impossible without good means of communication. Unfortunately the communication technology used by first responders today does not scale well. Also most of the given infrastructure, such as cell towers, might be destroyed. In recent research ad-hoc and peer-to-peer-based communication has been proposed to solve the problem of resilient communication. Most mobile devices are equipped with wireless transceivers that make them suitable to participate in ad-hoc networks. But node density might be too small for a connected topology. In this paper, we therefore discuss the implications of an emergency switch for privately owned wireless routers. Wireless routers can transition to an emergency mode to create a supportive wireless mesh network. To analyse if such a network would be beneficial and give a resilient topology real data from wireless routers in a city is gathered. We calculate the locations of these routers from GPS traces and the resulting topologies are analysed investigating suitability and resiliency issues.

Kraken.me: multi-device user tracking suite

An in-depths understanding of human activity is a relevant contribution to the design of interactive systems to support human activity. This is of explicit relevance for assistance systems building on prediction and recommendation. However, the understanding of human activity is limited. Albeit the omnispresence of smart phones and computers, the actual execution of complex activities with those devices in relation to context factors is not completely understood. One possible reason is the limited amount of activity related data to perform actual research. In this paper, we present the Kraken.me framework to address this lack of information. Kraken.me is the first tracking suite to offer integrated tools for mobile, social, and desktop tracking. It is also, to our knowledge, the first tool to emphasize the collection of data from both physical and soft sensors. In this paper, we will introduce the overall architecture, system components, and future research ideas for Kraken.me.

Topology control with application constraints

Numerous topology control algorithms for wireless sensor networks exist. Typically, these algorithms optimize general network metrics like the transmission range of sensors. Being of general nature, they suit application-specific requirements insufficiently; e.g., application-specific communication patterns may favor links that the general algorithm will remove. We suggest a new research focus called application-specific topology control and propose a methodic approach for which we provide a first exploration. First, application-specific communication patterns are expressed as overlay graphs. Then, application-specific requirements are specified as constraints on this overlay. Next follows the core of the method, geared toward deriving application-specific topology control algorithms. Applying the method to a data collection application with a many-to-one communication pattern, we propose two topology control algorithms. In a simulation study, we evaluate these algorithms against the existing algorithm kTC and a shortest path tree, showing that the method allows trading off general network optimization against application-specific optimization.

Noisemap: Discussing Scalability in Participatory Sensing

Environmental pollutants are an ever increasing problem in dense urban environments. To assess the effect of these pollutants, an unprecedented density of data is needed for large areas (cities, states, countries). In the past, participatory sensing has been proposed as a mean to acquire large sets of data. Since the smartphone is ubiquitous, scalability seems to be no problem anymore. In reality this far from the truth. Measuring their environment, people need to invest their time. For Android and iOS the application needs to compete with more than 700,000 other applications. Measuring large amounts of data is only possible, if we can attract large amounts of casual users. Since 2011, we have been working with and on Noisemap. Noisemap is one of many applications that uses the microphone to measure sound pressure. It then uploads the captured data to our backend, where the data is processed and visualized. Noisemap is officially available since February 2012, has been downloaded over 2,500 times, and has more than 1,000 registered users, which have collected over 500,000 unique data points in 39 countries and 58 cities. We want to share the current state of Noisemap as a multi-platform tool on Android and iOS, as well as our experience in scaling the application.