Chaki Ng

Provenance-Aware Sensor Data Storage

Sensor network data has both historical and realtime value. Making historical sensor data useful, in particular, requires storage, naming, and indexing. Sensor data presents new challenges in these areas. Such data is location-specific but also distributed; it is collected in a particular physical location and may be most useful there, but it has additional value when combined with other sensor data collections in a larger distributed system. Thus, arranging location-sensitive peer-to-peer storage is one challenge. Sensor data sets do not have obvious names, so naming them in a globally useful fashion is another challenge. The last challenge arises from the need to index these sensor data sets to make them searchable. The key to sensor data identity is provenance, the full history or lineage of the data. We show how provenance addresses the naming and indexing issues and then present a research agenda for constructing distributed, indexed repositories of sensor data.

Virtual worlds: fast and strategyproof auctions for dynamic resource allocation

We consider the problem of designing fast and strategyproof exchanges for dynamic resource allocation problems in distributed systems. The exchange is implemented as a sequence of auctions, with dynamically arriving requests from agents matched with each auction. Each auction is associated with some consignment of the resources from a single seller. We provide a simple Virtual Worlds (VW) construction, that extends a fast and strategyproof mechanism for a single auction to apply to this sequence-of-auctions setting. Rather than match each buyer with a single auction, the VW mechanism allows buyers to be considered for multiple auctions while retaining strategyproofness.

Addressing strategic behavior in a deployed microeconomic resource allocator

While market-based systems have long been proposed as solutions for distributed resource allocation, few have been deployed for production use in real computer systems. Towards this end, we present our initial experience using Mirage, a microeconomic resource allocation system based on a repeated combinatorial auction. Mirage allocates time on a heavily-used 148-node wireless sensor network testbed. In particular, we focus on observed strategic user behavior over a four-month period in which 312,148 node hours were allocated across 11 research projects. Based on these results, we present a set of key challenges for market-based resource allocation systems based on repeated combinatorial auctions. Finally, we propose refinements to the systems current auction scheme to mitigate the strategies observed to date and also comment on some initial steps toward building an approximately strategyproof repeated combinatorial auction.

Egg: An Extensible and Economics-Inspired Open Grid Computing Platform

Citation Brunelle, John, Peter Hurst, John Huth, Laura Kang, Chaki Ng, David C. Parkes, Margo Seltzer, Jim Shank, and Saul Youssef. 2006. Egg: An extensible and economics-inspired open grid computing platform. In GECON 2006: Proceedings of the 3rd International Workshop on Grid Economics and Business Models, Singapore, 16 May 2006, ed. H. Lee, and S. Miller. Singapore; Hackensack, NJ: World Scientific.

Concatenated codes for deletion channels

We design concatenated codes suitable for the deletion channel. The inner code is a com- bination of a single deletion correcting Varshamov- Tenengolts block code and a marker code. The outer code is a low-density parity-check (LDPC) code. The inner decoder detects the synchronizing points in the received symbol sequence and feeds the outer LDPC decoder with soft information. Our simulation results with regular LDPC outer codes demonstrate that the bit error rates of can be obtained at rate 0.21 when the probability of deletion is 8%. I. CHANNEL MODEL AND CODE STRUCTURE In the memoryless deletion channel model (l), each trans- mitted symbol is independently deleted with probability Pd; otherwise it is transmitted correctly. As the codewords are passed through the deletion channel the location and the size of each codeword become unclear. Our coding scheme is shown in Figure 1. Information bits are first encoded by an outer low-density parity-check (LDPC) encoder (2). We denote the LDPC block length with N. Then the blocks of N encoded bits are broken into blocks of length k. The inner code consists of Varshamov-Tenengolts (VT) code (3) and Marker code (4). The VT encoder encodes k-bit blocks into blocks of length n. The marker code is used to solve the synchronization problem. A marker (header) is a set of bits with specific length (marker length), inserted between a predetermined number of bits in the code sequences encoded by the outer LDPC and inner VT encoder. The VT code VT,(n) is a single-deletion correcting set of length n binary strings = 21 ... xn satisfying n