Erik Buchmann

Wireless Sensor Network Technologies for the Information Explosion Era

This volume aims to cover both theoretical and practical aspects related to this challenge, and it explores directions for future research to enable efficient utilization of WSNs in the information-explosion era. The book is organized in three main parts that consider (1) technical issues of WSNs, (2) the integration of multiple WSNs, and (3) the development of WSNs systems and testbeds for conducting practical experiments. Each part consists of three chapters.

Towards materialized view selection for distributed databases

Materialized views (MV) can significantly improve the query performance of relational databases. In this paper, we consider MVs to optimize complex scenarios where many heterogeneous nodes with different resource constraints (e.g., CPU, IO and network bandwidth) query and update numerous tables on different nodes. Such problems are typical for large enterprises, e.g., global retailers storing thousands of relations on hundreds of nodes at different subsidiaries. Choosing which views to materialize in a distributed, complex scenario is NP-hard. Furthermore, the solution space is huge, and the large number of input factors results in non-monotonic cost models. This prohibits the straightforward use of brute-force algorithms, greedy approaches or proposals from organic computing. For the same reason, all solutions for choosing MVs we are aware of do not consider either distributed settings or update costs. In this paper we describe an algorithmic framework which restricts the sets of considered MVs so that a genetic algorithm can be applied. In order to let the genetic algorithm converge quickly, we generate initial populations based on knowledge on database tuning, and devise a selection function which restricts the solution space by taking the similarity of MV configurations into account. We evaluate our approach both with artificial settings and a real-world RFID scenario from retail. For a small setting consisting of 24 tables distributed over 9 nodes, an exhaustive search needs 10 hours processing time. Our approach derives a comparable set of MVs within 30 seconds. Our approach scales well: Within 15 minutes it chooses a set of MVs for a real-world scenario consisting of 1,000 relations, 400 hosts, and a workload of 3,000 queries and updates.

Understanding User Preferences and Awareness: Privacy Mechanisms in Location-Based Services

Location based services (LBS) let people retrieve and share information related to their current position. Examples are Google Latitude or Panoramio. Since LBS share user-related content, location information etc., they put user privacy at risk. Literature has proposed various privacy mechanisms for LBS. However, it is unclear which mechanisms humans really find useful, and how they make use of them. We present a user study that addresses these issues. To obtain realistic results, we have implemented a geotagging application on the web and on GPS cellphones, and our study participants use this application in their daily lives. We test five privacy mechanisms that differ in the awareness, mental effort and degree of informedness required from the users. Among other findings, we have observed that in situations where a single simple mechanism does not meet all privacy needs, people want to use simple and sophisticated mechanisms in combination. Further, individuals are concerned about the privacy of others, even when they do not value privacy for themselves.

FairNet – How to Counter Free Riding in Peer-to-Peer Data Structures

Content-Addressable Networks (CAN) manage huge sets of (key, value)-pairs and cope with very high workloads. They follow the peer-to-peer paradigm: They consist of nodes that are autonomous. This means that peers may be uncooperative, i.e., not carrying out their share of the work while trying to benefit from the network. This article deals with this kind of adverse behavior in CAN, e.g., not answering queries and not forwarding messages. It is challenging to design a forwarding protocol for large CAN of more than 100,000 nodes that bypasses and excludes uncooperative nodes. We have designed such a protocol, with the following characteristics: It establishes logical networks of peers within the CAN. Nodes give positive feedback on peers that have performed useful work. Feedback is distributed in a swarm-like fashion and expires after a certain period of time. In extreme situations, the CAN asks nodes to perform a proof of work. Results of experiments with 100,000 peers are positive: In particular, cooperative peers fare significantly better than uncooperative ones.

Towards Efficient Processing of General-Purpose Joins in Sensor Networks

Join processing in wireless sensor networks is difficult: As the tuples can be arbitrarily distributed within the network, matching pairs of tuples is communication intensive and costly in terms of energy. Current solutions only work well with specific placements of the nodes and/or make restrictive assumptions. In this paper, we present SENS-Join, an efficient general-purpose join method for sensor networks. To obtain efficiency, SENS-Join does not ship tuples that do not join, based on a filtering step. Our main contribution is the design of this filtering step which is highly efficient in order not to exhaust the potential savings. We demonstrate the performance of SENS-Join experimentally: The overall energy consumption can be reduced by more than 80%, as compared to the state-of-the-art approach. The per node energy consumption of the most loaded nodes can be reduced by more than an order of magnitude.

Processing continuous join queries in sensor networks: a filtering approach

While join processing in wireless sensor networks has received a lot of attention recently, current solutions do not work well for continuous queries. In those networks however, continuous queries are the rule. To minimize the communication costs of join processing, it is important to not ship non-joining tuples. In order to know which tuples do not join, prior work has proposed a precomputation step. For continuous queries however, repeating the precomputation for each execution is unnecessary and leaves aside that data tends to be temporally correlated. In this paper, we present a filtering approach for the processing of continuous join queries. We propose to keep the filters and to maintain them. The problems are determining the sizes of the filters and deciding which filters to update. Simplistic approaches result in bad performance. We show how to compute solutions that are optimal. Experiments on real-world sensor data indicate that our method performs close to a theoretical optimum and consistently outperforms state-of-the-art join approaches.

Software als Institution und ihre Gestaltbarkeit

ZusammenfassungSoftware regelt immer mehr zwischenmenschliche Interaktionen. Üblicherweise werden die Funktionsmechanismen, Wirkungen und Gestaltungsoptionen von Regeln in der Institutionenforschung behandelt. In diesem Artikel soll beleuchtet werden, inwieweit sich Ansätze der Institutionenforschung auf Software anwenden lassen und was sich aus dieser Forschungsperspektive zu den Regelungswirkungen und Gestaltungsoptionen von Software ableiten lässt.

Finding misplaced items in retail by clustering RFID data

In retail, products are organized according to layout plans, so-called planograms. Compliance to planograms is important, since good product placement can significantly increase sales. Currently, retailers are about to implement RFID installations consisting of smart shelves and RFID-tagged items to support in-store logistics and processes. In principle, they can also use these installations to implement planogram compliance verification: Each antenna is supposed to detect all tagged items in one location of the planogram. But due to physical constraints, RFID tags can be identified by more than one RFID antenna. Thus, one cannot decide if an item carrying such a tag complies with the planogram. We propose a new method called RPCV which checks planogram compliance on large databases of items. It is based on the observation that the number of times an antenna identifies each item of a certain product type roughly follows a normal distribution. RPCV represents each item as a two-dimensional vector containing the number of readings both by the right antenna and by wrong ones according to the planogram. It clusters this data, separately for each product type. A cluster then is a set of correctly placed items or of misplaced ones. RPCV produces one order of magnitude less wrong predictions than current state of the art, and it requires less data to yield good predictions. A study with RFID-equipped goods and smart shelves shows that our approach is effective in realistic scenarios.

Tagmark: reliable estimations of RFID tags for business processes

Radio Frequency Identification (RFID) promises optimization of commodity flows in all industry segments. But due to physical constraints, RFID technology cannot detect all RFID tags from an assembly of items. This poses problems when integrating RFID data with enterprise-backend systems for tasks like inventory management or shelf replenishment. In this paper we propose the TagMark method to accomplish this integration. TagMark targets at a retailer scenario, where it estimates the number of tagged items from samples like the sales history or the tags read by smart shelves. The problem is challenging because most existing estimation methods depend on assumptions that do not hold in typical RFID applications, e.g., static item sets, simple random samples, or the availability of samples with user-defined sizes. TagMark adapts mark-recapture-methods in order to provide guarantees for the accuracy of the estimation and bounds for the sample sizes. It can be implemented as a database extension, allowing seamless integration into existing enterprise backend systems. A study with RFID-equipped goods acknowledges that our approach is effective in realistic scenarios, and database experiments with up to 1,000,000 items confirm that it can be efficiently implemented. Finally, we explore a broad range of extreme conditions that might stress TagMark, including a thief who knows the location of unread items.

Free riding-aware forwarding in Content-Addressable Networks

Research on P2P data structures has tacitly assumed that peers readily participate in the work, i.e., are cooperative. But such participation is voluntary, and free riding is the dominant strategy. This article describes a protocol that renders free riding unattractive, for one particular P2P data structure. The protocol is based on feedback that adjacent nodes exchange. This induces transitive logical networks of nodes that rule out uncooperative peers. The protocol uses proofs of work to deter free riding. To show that cooperative behavior dominates, we have come up with a cost model that quantifies the overall cost of peers, depending on their degree of cooperativeness and many other parameters. The cost model tells us that we can achieve a good discrimination against peers that are less cooperative, with moderate additional cost for cooperative peers. Extensive experiments confirm the validity of our approach.