New technologies are approaching with big footsteps. In the decades ahead it is expected significant changes in technologies regarding to new types of devices, system and functions that they perform. We are expecting new services with various requirements, and new applications such as holographic telepresence, instant data transmission, remote surgery, minimized IoT terminals, autonomous transportation system, etc. Wide adaptation of new applications depends on how good they are supported by the network infrastructure. That is why the estimation of next generation network architecture is required. By the year 2030 the applications of new technologies are expected to generate an enormous amount of traffic. The architecture of the future networks must consider all the gaps of already existing networks in order to ensure the highest robustness, extremely low latency, Ubiquitous coverage as new applications and services require.

Future Networks 2030: Architecture & Requirements

Computation offloading is one of the important application in Internet of Things (IoT) ecosystem. Computational offloading provides assisted means of processing large amounts of data generated by abundant IoT devices, speed up processing of intensive tasks and save battery life. In this paper, we propose a secure computation offloading scheme in Fog-Cloud-IoT environment (SecOFF-FCIoT). Using machine learning strategies, we accomplish efficient, secure offloading in Fog-IoT setting. In particular, we employ Neuro-Fuzzy Model to secure data at the smart gateway, then the IoT device selects an optimal Fog node to which it can offload its workload using Particle Swarm Optimization(PSO) via the smart gateway. If the fog node is not capable of handling the workload, it is forwarded to the cloud after being classified as either sensitive or non-sensitive. Sensitive data is maintained in private cloud. Whereas non-sensitive data is offloaded using dynamic offloading strategy. In PSO, the availability of fog node is computed using two metrics; i) Available Processing Capacity (APC), and ii) Remaining Node Energy (RNE). Selection of cloud is based on Reinforcement Learning. Our proposed approach is implemented for smart city applications using NS-3 simulator with JAVA Programming. We compare our proposed secure computation offloading model with previous approaches which include DTO-SO, FCFS, LOTEC, and CMS-ACO. Simulation results show that our proposed scheme minimizes latency as compared to selected benchmarks.

SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

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A Survey of Security in Cloud, Edge, and Fog Computing

The wearables’ market is rapidly evolving, with applications ranging from healthcare and activity monitoring to emerging domains such as drones and haptic helmets. Wearable-based contact tracing is gaining increased attention in the COVID-19 era for more efficient disease prevention. Therefore, it is of timely relevance to identify the leading existing wireless contact-tracing solutions and their suitability for wearables. Existing trade-offs of contact-tracing applications require a thorough analysis of technical capabilities, such as accuracy, energy consumption, availability, sources of errors when dealing with wireless channels, privacy challenges, and deterrents towards a large-scale adoption on the wearables market. Based on extensive literature research, we conclude that decentralized architectures generally offer a better place in a trade-off in terms of accuracy and user eagerness to adopt them, taking into account privacy considerations, compared to centralized approaches. Our paper provides a brief technical overview of the existing solutions deployed for contact tracing, defines main principles that affect the overall efficacy of digital contact tracing, and presents a discussion on the potential effect of wearables in tackling the spread of a highly contagious virus.

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Technical Perspectives of Contact-Tracing Applications on Wearables for COVID-19 Control

With recent advancements in communication networks, congestion control remains a research focus. Active Queue Management (AQM) schemes are normally used to manage congestion in routers. Random Early Detection (RED) is the most popular AQM scheme. However, RED lacks self-adaptation mechanism and it is sensitive to parameter settings. Many enhancements of RED were proposed and are yet to provide stable performance under different traffic load situations. In this paper, AQM scheme called Flexible Random Early Detection (FXRED) is proposed. Unlike other RED’s enhancements with static drop patterns, FXRED recognizes the state of the current network’s traffic load and auto tune its drop pattern suitable to the observed load situation in order to maintain stable and better performance. Results of the experiments conducted have shown that regardless of traffic load’s fluctuation, FXRED provides optimal performance and efficiently manages the queue.

Flexible Random Early Detection Algorithm for Queue Management in Routers

Today, the number of interconnected Internet of Things (IoT) devices is growing tremendously followed by an increase in the density of cellular base stations. This trend has an adverse effect on the power efficiency of communication, since each new infrastructure node requires a significant amount of energy. Numerous enablers are already in place to offload the scarce cellular spectrum, thus allowing utilization of more energy-efficient short-range radio technologies for user content dissemination, such as moving relay stations and network-assisted direct connectivity. In this work, we contribute a new mathematical framework aimed at analyzing the impact of network offloading on the probabilistic characteristics related to the quality of service and thus helping relieve the energy burden on infrastructure network deployments.

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Mobility-Centric Analysis of Communication Offloading for Heterogeneous Internet of Things Devices

Consideration is given to the queuing system, consisting of one server and a queue of unlimited capacity, with the implemented mechanism of general renovation and feedback. This mechanism, which may be considered as a variant of an active queue management scheme, works as follows. In this short note we show how the main ingredients needed to compute some of the main stationary performance characteristics of the system can be found. The basic methods are transform techniques and methods for the solutions of Volterra integral equations of the second kind with the kernels of convolution type. We concentrate on the stationary distribution of the embedded Markov chain, show how it is related with the stationary distribution of the process, describing the evolution of the total number of customers in the system. Under the two assumptions about the service discipline and the order in which the customers are removed from the queue whenever renovation occurs, we derive expressions for stationary loss probability and the sojourn time distribution in terms of Laplace-Stielties transform.

Stationary Characteristics of the GI/M/1 Queue with General Renovation and Feedback

This work is devoted to some aspects of using the general renovation (the definition and brief overview are given) as the active queue management mechanism (like RED (Random Early Detection) algorithms).

The General Renovation as the Active Queue Management Mechanism. Some Aspects and Results

The article is devoted to the application of Markov modulated arrival processes (Markov modulated Poisson process — MMPP, Markov modulated Bernoulli process — MMBP and Markov modulated fluid flow — MMFF) models to the analysis of Active Queue Management (AQM) algorithms (Random Early Detection (RED) family, for example). The main ideas and properties of Markov modulated arrival processes (MMAP) are presented as the brief description of RED-type AQM algorithms. A review of the main results obtained with the help of MMAP processes in the analysis of AQM algorithms models is made. The authors formulated problems that also can be solved with the help of MMAP processes when analysing the systems with RED-like algorithms.

The Survey on Markov-Modulated Arrival Processes and Their Application to the Analysis of Active Queue Management Algorithms

Recent recommendation RFC 7567 by IETF indicates that the problem of active queue management remains vital for modern communications networks and the development of new active queue management is required. Queueing system with renovation when customers upon service completion pushes-out other customers residing in the queue with a given probability distribution may have potential application as an alternative active queue management. In this paper one presents the analytic method for the computation of the customer’s stationary waiting time distribution in G|M|n|r queue with random renovation under FCFS (and non-preemptive LCFS) scheduling in the terms of Laplace-Stieltjets transform. The method is illustrated by one particular case: stationary waiting time distribution of the customer, which either received service or was pushed-out under FCFS scheduling.

Tatiana Milovanova

Papers

Mobility-Centric Analysis of Communication Offloading for Heterogeneous Internet of Things Devices

Stationary Characteristics of the GI/M/1 Queue with General Renovation and Feedback

The General Renovation as the Active Queue Management Mechanism. Some Aspects and Results

The Survey on Markov-Modulated Arrival Processes and Their Application to the Analysis of Active Queue Management Algorithms

Stationary Waiting Time Distribution in G | M | n | r with Random Renovation Policy