49,026 research outputs found
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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
Load-Aware Modeling and Analysis of Heterogeneous Cellular Networks
Random spatial models are attractive for modeling heterogeneous cellular
networks (HCNs) due to their realism, tractability, and scalability. A major
limitation of such models to date in the context of HCNs is the neglect of
network traffic and load: all base stations (BSs) have typically been assumed
to always be transmitting. Small cells in particular will have a lighter load
than macrocells, and so their contribution to the network interference may be
significantly overstated in a fully loaded model. This paper incorporates a
flexible notion of BS load by introducing a new idea of conditionally thinning
the interference field. For a K-tier HCN where BSs across tiers differ in terms
of transmit power, supported data rate, deployment density, and now load, we
derive the coverage probability for a typical mobile, which connects to the
strongest BS signal. Conditioned on this connection, the interfering BSs of the
tier are assumed to transmit independently with probability ,
which models the load. Assuming - reasonably - that smaller cells are more
lightly loaded than macrocells, the analysis shows that adding such access
points to the network always increases the coverage probability. We also
observe that fully loaded models are quite pessimistic in terms of coverage.Comment: to appear, IEEE Transactions on Wireless Communication
- …