Abstract: Large-scale sensor networks, monitoring an environment at close range with high spatial and temporal resolutions are expected to play an important role in various applications, e.g., assessing the ``health'' of machines; environmental, medical, food-safety, and habitat monitoring; inventory control, building automation, etc. Ensuring the security of these complex and yet resource-constrained systems has emerged as one of the most pressing challenges for researchers. In this paper (i) we present the major threats and some characteristic countermeasures, (ii) we propose a way to classify existing systems for intrusion detection in wireless sensor networks and (iii) we present a new approach for decentralized energy efficient intrusion detection that can be used to improve security from both external and internal adversaries.
Abstract: Efficient query processing in traditional database
management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statisticsmanagement still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured
overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge.
To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation
of these tools for distributed statistical synopses,
and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability.
Abstract: The peer-to-peer computing paradigm is an intriguing alternative to Google-style search
engines for querying and ranking Web content. In a network with many thousands or
millions of peers the storage and access load requirements per peer are much lighter
than for a centralized Google-like server farm; thus more powerful techniques from information
retrieval, statistical learning, computational linguistics, and ontological reasoning
can be employed on each peerĘs local search engine for boosting the quality
of search results. In addition, peers can dynamically collaborate on advanced and particularly
difficult queries. Moroever, a peer-to-peer setting is ideally suited to capture
local user behavior, like query logs and click streams, and disseminate and aggregate
this information in the network, at the discretion of the corresponding user, in order to
incorporate richer cognitive models.
This paper gives an overview of ongoing work in the EU Integrated Project DELIS
that aims to develop foundations for a peer-to-peer search engine with Google-or-better
scale, functionality, and quality, which will operate in a completely decentralized and
self-organizing manner. The paper presents the architecture of such a system and the
Minerva prototype testbed, and it discusses various core pieces of the approach: efficient
execution of top-k ranking queries, strategies for query routing when a search request
needs to be forwarded to other peers, maintaining a self-organizing semantic overlay
network, and exploiting and coping with user and community behavior.