Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability

The fifth generation (5G) mobile telecommunication network is expected to support Multi- Access Edge Computing (MEC), which intends to distribute computation tasks and services from the central cloud to the edge clouds. Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services, the current mobile network security architecture should enable a more decentralized approach for authentication and authorization processes. This paper proposes a novel decentralized authentication architecture that supports flexible and low-cost local authentication with the awareness of context information of network elements such as user equipment and virtual network functions. Based on a Markov model for backhaul link quality, as well as a random walk mobility model with mixed mobility classes and traffic scenarios, numerical simulations have demonstrated that the proposed approach is able to achieve a flexible balance between the network operating cost and the MEC reliability.Comment: Accepted by IEEE Access on Feb. 02, 201

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Treatment of palm oil mill secondary effluent (POMSE) using ultrafiltration and nanofiltration membranes

Malaysian palm oil industry has grown rapidly over the last few decades, to becoming the world’s largest producer and exporter of palm oil. This success story however, comes with a greater challenge and equally required more sacrifices in order to maintain the tempo. In the year of 2004, it has been recorded that 26.7 million tons of solid biomass and approximately a 30 million tons of palm oil mill effluent (POME) were generated from 381 palm oil mills in Malaysia [1]. Although different kind of wastes are generated in the palm oil mills, the perceived harmful waste among all the waste generated is the palm oil mill effluent (POME) due to its associated harm if discharged into the environment untreated [2]. POME is a colloidal suspension originating from mixture of sterilizer condensate, separator sludge and hydro cyclone wastewater in a ratio of 9:15:1 respectively [3]. It is a brownish colored, thick liquid that is containing high amount of oil, solids, and grease with high Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) values. Table 15.1 describes the characteristic of POME obtained from Malaysian Palm Oil Board

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

IPv6 Network Mobility

Network Authentication, Authorization, and Accounting has been used since before the days of the Internet as we know it today. Authentication asks the question, “Who or what are you?” Authorization asks, “What are you allowed to do?” And fi nally, accounting wants to know, “What did you do?” These fundamental security building blocks are being used in expanded ways today. The fi rst part of this two-part series focused on the overall concepts of AAA, the elements involved in AAA communications, and highlevel approaches to achieving specifi c AAA goals. It was published in IPJ Volume 10, No. 1[0]. This second part of the series discusses the protocols involved, specifi c applications of AAA, and considerations for the future of AAA