3,855 research outputs found
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
Security for the Industrial IoT: The Case for Information-Centric Networking
Industrial production plants traditionally include sensors for monitoring or
documenting processes, and actuators for enabling corrective actions in cases
of misconfigurations, failures, or dangerous events. With the advent of the
IoT, embedded controllers link these `things' to local networks that often are
of low power wireless kind, and are interconnected via gateways to some cloud
from the global Internet. Inter-networked sensors and actuators in the
industrial IoT form a critical subsystem while frequently operating under harsh
conditions. It is currently under debate how to approach inter-networking of
critical industrial components in a safe and secure manner.
In this paper, we analyze the potentials of ICN for providing a secure and
robust networking solution for constrained controllers in industrial safety
systems. We showcase hazardous gas sensing in widespread industrial
environments, such as refineries, and compare with IP-based approaches such as
CoAP and MQTT. Our findings indicate that the content-centric security model,
as well as enhanced DoS resistance are important arguments for deploying
Information Centric Networking in a safety-critical industrial IoT. Evaluation
of the crypto efforts on the RIOT operating system for content security reveal
its feasibility for common deployment scenarios.Comment: To be published at IEEE WF-IoT 201
Cold Storage Data Archives: More Than Just a Bunch of Tapes
The abundance of available sensor and derived data from large scientific
experiments, such as earth observation programs, radio astronomy sky surveys,
and high-energy physics already exceeds the storage hardware globally
fabricated per year. To that end, cold storage data archives are the---often
overlooked---spearheads of modern big data analytics in scientific,
data-intensive application domains. While high-performance data analytics has
received much attention from the research community, the growing number of
problems in designing and deploying cold storage archives has only received
very little attention.
In this paper, we take the first step towards bridging this gap in knowledge
by presenting an analysis of four real-world cold storage archives from three
different application domains. In doing so, we highlight (i) workload
characteristics that differentiate these archives from traditional,
performance-sensitive data analytics, (ii) design trade-offs involved in
building cold storage systems for these archives, and (iii) deployment
trade-offs with respect to migration to the public cloud. Based on our
analysis, we discuss several other important research challenges that need to
be addressed by the data management community
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