Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS.
Abstract:The telco operators face up to challenges related to the need of ensuring a quality of service to the user in a planning, maintenance and resource allocation in their complex networks. These challenges are directly related with the need to ensure an user's service with a good level of quality in a highly dynamic environment in terms of changes in the radio access technologies, growth in the number of mobile users, technical requirements of the new services and applications, and the possibility to connect to different networks at the same time, among others. In this paper, we address the problem of the user's service allocation into the different feasible networks in order to reduce the network overloading. We present a multihoming load balancing scheme that allows the re-allocation of services according to their QoS requirements and the availability of network resources. We propose a multi-objective optimization model of this problem together with an evolutionary algorithm to solve it. Through simulation in different scenarios, we show that our algorithm is efficient, sensitive, scalable and provides optimal solutions.
Abstract:Regardless of the type of service that a company offers the customer satisfaction is a factor for success, if these services are in a highly competitive environment. This situation encourages companies to develop strategies to improve the Quality of the Experience (QoE) of their users. Strategies include improving their processes, or infrastructure for provisioning the services. Take these kind of decisions is very difficult because they ignore how the Key Performance Indicators (KPI) services are correlated with the information about user experience. This problem is approached from the perspective of mobile telecom operators, who have addressed this challenge through the Quality of Service (QoS) concept. Unfortunately, the QoS is only characterized by technical aspects, the user's criteria are not included. Into a highly competitive environment, the user's loyalty is a key component to be considered in the operator's development plan. Nowadays, the mobile telecom operators focus their efforts to ensure not only the QoS but also the QoE. The aim of this paper was the develop a decision making tool that allows the mobile telco operators support their determinations about the maintenance of network infrastructure, as well as the expansion of the same, specifically for their critical web services; based in a correlated information between QoS and QoE. This tool was developed on the basis of the Pseudo Subjective Quality Assessment (PSQA) methodology.
Due to the growth of the number of intelligent devices and the broadband requirements, between others technical requirements, of the new applications, suppose a new challenge in planning, maintenance and resource allocation in mobile networks for the telecommunication operators. Service providers must ensure a quality of service for users in a new environment based in Heterogeneous Wireless Networks (HWN). A good way to achieve this goal is to prevent the quantity of services of each mobile users being connected to the same access networks and therefore reducing the possibility of overloading it. This paper presents a load balancing optimization scheme that enables operators to make decisions about re-allocation of each of the services in different access networks, keeping the required Quality of Service (QoS). In this paper, we propose 1) a mathematical model addressed as a fairness resource allocation in order to obtain a global load balancing, and 2) a two-step algorithm based on the anchor-adjustment heuristic to solve it. Our algorithm contribute to unload the network with maximum load while at the same time, the other networks are balanced. As a result, we show that our algorithm finds (near)-optimal solutions while keeps low complexity.
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