5,816 research outputs found
Sensor networks security based on sensitive robots agents. A conceptual model
Multi-agent systems are currently applied to solve complex problems. The
security of networks is an eloquent example of a complex and difficult problem.
A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion
Detection is introduced in the current paper. The proposed technique could be
used with machine learning based intrusion detection techniques. The new model
uses the reaction of virtual sensitive robots to different stigmergic variables
in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges
The Internet of Things (IoT) refers to a network of connected devices
collecting and exchanging data over the Internet. These things can be
artificial or natural, and interact as autonomous agents forming a complex
system. In turn, Business Process Management (BPM) was established to analyze,
discover, design, implement, execute, monitor and evolve collaborative business
processes within and across organizations. While the IoT and BPM have been
regarded as separate topics in research and practice, we strongly believe that
the management of IoT applications will strongly benefit from BPM concepts,
methods and technologies on the one hand; on the other one, the IoT poses
challenges that will require enhancements and extensions of the current
state-of-the-art in the BPM field. In this paper, we question to what extent
these two paradigms can be combined and we discuss the emerging challenges
Designing Human-Centered Collective Intelligence
Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the âCloudâ age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence
NASA space station automation: AI-based technology review
Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures
Averting Robot Eyes
Home robots will cause privacy harms. At the same time, they can provide beneficial servicesâas long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms.
We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology
Security, privacy and safety evaluation of dynamic and static fleets of drones
Inter-connected objects, either via public or private networks are the near
future of modern societies. Such inter-connected objects are referred to as
Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of
such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such
vehicles are prophesied to take on multiple roles involving mundane to
high-sensitive, such as, prompt pizza or shopping deliveries to your homes to
battlefield deployment for reconnaissance and combat missions. Drones, as we
refer to UAVs in this paper, either can operate individually (solo missions) or
part of a fleet (group missions), with and without constant connection with the
base station. The base station acts as the command centre to manage the
activities of the drones. However, an independent, localised and effective
fleet control is required, potentially based on swarm intelligence, for the
reasons: 1) increase in the number of drone fleets, 2) number of drones in a
fleet might be multiple of tens, 3) time-criticality in making decisions by
such fleets in the wild, 4) potential communication congestions/lag, and 5) in
some cases working in challenging terrains that hinders or mandates-limited
communication with control centre (i.e., operations spanning long period of
times or military usage of such fleets in enemy territory). This self-ware,
mission-focused and independent fleet of drones that potential utilises swarm
intelligence for a) air-traffic and/or flight control management, b) obstacle
avoidance, c) self-preservation while maintaining the mission criteria, d)
collaboration with other fleets in the wild (autonomously) and e) assuring the
security, privacy and safety of physical (drones itself) and virtual (data,
software) assets. In this paper, we investigate the challenges faced by fleet
of drones and propose a potential course of action on how to overcome them.Comment: 12 Pages, 7 Figures, Conference, The 36th IEEE/AIAA Digital Avionics
Systems Conference (DASC'17
- âŠ