25,573 research outputs found
Provenance-enabled Packet Path Tracing in the RPL-based Internet of Things
The interconnection of resource-constrained and globally accessible things
with untrusted and unreliable Internet make them vulnerable to attacks
including data forging, false data injection, and packet drop that affects
applications with critical decision-making processes. For data trustworthiness,
reliance on provenance is considered to be an effective mechanism that tracks
both data acquisition and data transmission. However, provenance management for
sensor networks introduces several challenges, such as low energy, bandwidth
consumption, and efficient storage. This paper attempts to identify packet drop
(either maliciously or due to network disruptions) and detect faulty or
misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks
(RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT)
approach. Firstly, a system-level ordered-provenance information encapsulates
the data generating nodes and the forwarding nodes in the data packet.
Secondly, to closely monitor the dropped packets, a node-level provenance in
the form of the packet sequence number is enclosed as a routing entry in the
routing table of each participating node. Lossless in nature, both approaches
conserve the provenance size satisfying processing and storage requirements of
IoT devices. Finally, we evaluate the efficacy of the proposed scheme with
respect to provenance size, provenance generation time, and energy consumption.Comment: 14 pages, 18 Figure
Provenance Management over Linked Data Streams
Provenance describes how results are produced starting from data sources, curation, recovery, intermediate processing, to the final results. Provenance has been applied to solve many problems and in particular to understand how errors are propagated in large-scale environments such as Internet of Things, Smart Cities. In fact, in such environments operations on data are often performed by multiple uncoordinated parties, each potentially introducing or propagating errors. These errors cause uncertainty of the overall data analytics process that is further amplified when many data sources are combined and errors get propagated across multiple parties. The ability to properly identify how such errors influence the results is crucial to assess the quality of the results. This problem becomes even more challenging in the case of Linked Data Streams, where data is dynamic and often incomplete. In this paper, we introduce methods to compute provenance over Linked Data Streams. More specifically, we propose provenance management techniques to compute provenance of continuous queries executed over complete Linked Data streams. Unlike traditional provenance management techniques, which are applied on static data, we focus strictly on the dynamicity and heterogeneity of Linked Data streams. Specifically, in this paper we describe: i) means to deliver a dynamic provenance trace of the results to the user, ii) a system capable to execute queries over dynamic Linked Data and compute provenance of these queries, and iii) an empirical evaluation of our approach using real-world datasets
Where Do Your IoT Ingredients Come From?
The Internet of Things (IoT) is here: smart objects are
pervading our everyday life. Smart devices automatically collect and
exchange data of various kinds, directly gathered from sensors or generated
by aggregations. Suitable coordination primitives and analysis
mechanisms are in order to design and reason about IoT systems, and
to intercept the implied technology shifts. We address these issues by
defining IoT-LySa, a process calculus endowed with a static analysis
that tracks the provenance and the route of IoT data, and detects how
they affect the behaviour of smart objects
Data provenance to audit compliance with privacy policy in the Internet of Things
Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The
first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.Engineering and Applied Science
Tracing where IoT data are collected and aggregated
The Internet of Things (IoT) offers the infrastructure of the information society. It hosts smart objects that automatically collect and exchange data of various kinds, directly gathered from sensors or generated by aggregations. Suitable coordination primitives and analysis mechanisms are in order to design and reason about IoT systems, and to intercept the implied technological shifts. We address these issues from a foundational point of view. To study them, we define IoT-LySa, a process calculus endowed with a static analysis that tracks the provenance and the manipulation of IoT data, and how they flow in the system. The results of the analysis can be used by a designer to check the behaviour of smart objects, in particular to verify non-functional properties, among which security
Interrogating Capabilities of IoT Devices
This research is supported by the UK Research Councils’ Digital Economy IT as a Utility Network+ (EP/K003569/1) and the dot.rural Digital Economy Hub (EP/G066051/1).Postprin
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