98,866 research outputs found
Proactive Caching for Energy-Efficiency in Wireless Networks: A Markov Decision Process Approach
Content caching in wireless networks provides a substantial opportunity to
trade off low cost memory storage with energy consumption, yet finding the
optimal causal policy with low computational complexity remains a challenge.
This paper models the Joint Pushing and Caching (JPC) problem as a Markov
Decision Process (MDP) and provides a solution to determine the optimal
randomized policy. A novel approach to decouple the influence from buffer
occupancy and user requests is proposed to turn the high-dimensional
optimization problem into three low-dimensional ones. Furthermore, a
non-iterative algorithm to solve one of the sub-problems is presented,
exploiting a structural property we found as \textit{generalized monotonicity},
and hence significantly reduces the computational complexity. The result
attains close performance in comparison with theoretical bounds from
non-practical policies, while benefiting from higher time efficiency than the
unadapted MDP solution.Comment: 6 pages, 6 figures, submitted to IEEE International Conference on
Communications 201
A Conceptual Framework for the Prescriptive Causal Analysis of Construction Waste
An initial step towards a prescriptive theory (a set of concepts) to inform the elimination of waste on construction projects. The ultimate intention is to identify the most important types and causes of waste in construction and outline the principal causal relations between them. This is not a straightforward process: the relationships form a complex network of chains and cycles of waste. Waste is defined as the use of more resources than needed, or an unwanted output from production. A conceptual schema of Previous Production Stage > Production Waste > Effect Waste is proposed and applied to the causal analysis of two major types of waste: material waste and making do
Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination
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