5,484 research outputs found
Optimal WiFi Sensing via Dynamic Programming
The problem of finding an optimal sensing schedule for a mobile device that
encounters an intermittent WiFi access opportunity is considered. At any given
time, the WiFi is in any of the two modes, ON or OFF, and the mobile's
incentive is to connect to the WiFi in the ON mode as soon as possible, while
spending as little sensing energy. We introduce a dynamic programming framework
which enables the characterization of an explicit solution for several models,
particularly when the OFF periods are exponentially distributed. While the
problem for non-exponential OFF periods is ill-posed in general, a usual
workaround in literature is to make the mobile device aware if one ON period is
completely missed. In this restricted setting, using the DP framework, the
deterministic nature of the optimal sensing policy is established, and value
iterations are shown to converge to the optimal solution. Finally, we address
the blind situation where the distributions of ON and OFF periods are unknown.
A continuous bandit based learning algorithm that has vanishing regret (loss
compared to the optimal strategy with the knowledge of distributions) is
presented, and comparisons with the optimal schemes are provided for
exponential ON and OFF times
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity
Social sensing services use humans as sensor carriers, sensor operators and
sensors themselves in order to provide situation-awareness to applications.
This promises to provide a multitude of benefits to the users, for example in
the management of natural disasters or in community empowerment. However,
current social sensing services depend on Internet connectivity since the
services are deployed on central Cloud platforms. In many circumstances,
Internet connectivity is constrained, for instance when a natural disaster
causes Internet outages or when people do not have Internet access due to
economical reasons. In this paper, we propose the emerging Fog Computing
infrastructure to become a key-enabler of social sensing services in situations
of constrained Internet connectivity. To this end, we develop a generic
architecture and API of Fog-enabled social sensing services. We exemplify the
usage of the proposed social sensing architecture on a number of concrete use
cases from two different scenarios.Comment: Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore
Ramachandran. 2017. The Fog Makes Sense: Enabling Social Sensing Services
With Limited Internet Connectivity. In Proceedings of The 2nd International
Workshop on Social Sensing, Pittsburgh, PA, USA, April 21 2017
(SocialSens'17), 6 page
Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation
The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions.
In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled.
Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution
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