4,401 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Life Cycle Costing and Food Systems: Concepts, Trends, and Challenges of Impact Valuation
Our global food systems create pervasive environmental, social, and health impacts. Impact valuation is an emerging concept that aims to quantify all environmental, social, and health costs of food systems in an attempt to make the true cost of food more transparent. It also is designed to facilitate the transformation of global food systems. The concept of impact valuation is emerging at the same time as, and partly as a response to, calls for the development of legal mechanisms to address environmental, social, and health concerns. Information has long been understood both as a necessary precursor for regulation and as a regulatory tool in and of itself. With global supply chains and widespread impacts, data necessary to produce robust and complete impact valuation requires participation and cooperation from a variety of food system actors. New costing methods, beyond basic accounting, are necessary to incorporate the scope of impacts and stakeholders. Furthermore, there are a range of unanswered questions surrounding realizations of impact valuation methods, e.g. data sharing, international privacy, corporate transparency, limitations on valuation itself, and data collection standardization. Because of the proliferation of calls for costing tools, this article steps back and assesses the current development of impact valuation methods. In this article, we review current methods and initiatives for the implementation of food system impact valuation. We conclude that in some instances, calls for the implementation of costing have outpaced available and reliable data collection and current costing techniques. Many existing initiatives are being developed without adequate consideration of the legal challenges that hinder implementation. Finally, we conclude with a reminder that although impact valuation tools are most often sought and implemented in service of market-based tools for reform, they can also serve as a basis for robust public policies
Beyond the Hype: On Using Blockchains in Trust Management for Authentication
Trust Management (TM) systems for authentication are vital to the security of
online interactions, which are ubiquitous in our everyday lives. Various
systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage
trust in this setting. In recent years, blockchain technology has been
introduced as a panacea to our security problems, including that of
authentication, without sufficient reasoning, as to its merits.In this work, we
investigate the merits of using open distributed ledgers (ODLs), such as the
one implemented by blockchain technology, for securing TM systems for
authentication. We formally model such systems, and explore how blockchain can
help mitigate attacks against them. After formal argumentation, we conclude
that in the context of Trust Management for authentication, blockchain
technology, and ODLs in general, can offer considerable advantages compared to
previous approaches. Our analysis is, to the best of our knowledge, the first
to formally model and argue about the security of TM systems for
authentication, based on blockchain technology. To achieve this result, we
first provide an abstract model for TM systems for authentication. Then, we
show how this model can be conceptually encoded in a blockchain, by expressing
it as a series of state transitions. As a next step, we examine five prevalent
attacks on TM systems, and provide evidence that blockchain-based solutions can
be beneficial to the security of such systems, by mitigating, or completely
negating such attacks.Comment: A version of this paper was published in IEEE Trustcom.
http://ieeexplore.ieee.org/document/8029486
Actor-based Risk Analysis for Blockchains in Smart Mobility
Blockchain technology is a crypto-based secure ledger for data storage and
transfer through decentralized, trustless peer-to-peer systems. Despite its
advantages, previous studies have shown that the technology is not completely
secure against cyber attacks. Thus, it is crucial to perform domain specific
risk analysis to measure how viable the attacks are on the system, their impact
and consequently the risk exposure. Specifically, in this paper, we carry out
an analysis in terms of quantifying the risk associated to an operational
multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We
conduct an actor-based analysis to determine the impact of the attacks. The
analysis identified five attack goals and five types of attackers that violate
the security of the blockchain system. In the case study of the public
permissioned BSMD, we highlight the highest risk factors according to their
impact on the victims in terms of monetary, privacy, integrity and trust. Four
attack goals represent a risk in terms of economic losses and one attack goal
contains many threats that represent a risk that is either unacceptable or
undesirable.Comment: arXiv admin note: text overlap with arXiv:1904.11908 by other author
Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations
We empirically verify that the market capitalisations of coins and tokens in
the cryptocurrency universe follow power-law distributions with significantly
different values, with the tail exponent falling between 0.5 and 0.7 for coins,
and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a
simple proportional growth with birth & death model previously employed to
describe the size distribution of firms, cities, webpages, etc. We empirically
validate the model and its main predictions, in terms of proportional growth
(Gibrat's law) of the coins and tokens. Estimating the main parameters of the
model, the theoretical predictions for the power-law exponents of coin and
token distributions are in remarkable agreement with the empirical estimations,
given the simplicity of the model. Our results clearly characterize coins as
being "entrenched incumbents" and tokens as an "explosive immature ecosystem",
largely due to massive and exuberant Initial Coin Offering activity in the
token space. The theory predicts that the exponent for tokens should converge
to 1 in the future, reflecting a more reasonable rate of new entrants
associated with genuine technological innovations
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