To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN) By providing computational and storage resources at the edge, MEC can reduce latency for end users Hence, this article investigates MEC for 5G and IoT comprehensively It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

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Global Positioning System Theory And Practice

A survey on computation offloading modeling for edge computing

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.

Accurate Energy-Efficient Localization Algorithm for IoT Sensors

Communication and control

Present-day and future network protocols that include and implement Forward Error Correction are configurable by internal parameters, typically incorporating expert knowledge to set up.We introduce a framework to systematically, objectively and efficiently determine parameters for Random Linear Network Codes (RLNC). Our approach uses an unbiased, consistent simulator in an optimization loop and utilizes a customizable, powerful and extendable parametric loss function. This allows to tailor existing protocols to various use cases, including ultra reliable, low latency communication (URLLC) codes. Successful configurations exploring the search space are under evolutionary pressure and written into a database for instant retrieval. We demonstrate three examples, Full Vector Coding, tail RLNC, and PACE with different focus for each.

/pdf/usecase-driven-evolution-of-network-coding-parameters-4ofky8nd5p.pdf

Usecase Driven Evolution of Network Coding Parameters Enabling Tactile Internet Applications

Next-generation networks are envisioned to be empowered by artificial intelligence with predictive capabilities. Predicting handovers in high mobility scenarios enables networks and applications to adapt ahead of time to improve the Quality of Service (QoS). In this paper, we present a two-step machine learning (ML) method, consisting of a classifier and regressor, that can predict the remaining time until a handover occurs. Our approach is validated on a dataset that was captured in a real cellular network. The results show that upcoming handovers can be detected with a recall above 90% and the timing of handovers with an error smaller than one second. Furthermore, we compare the importance of input features derived from radio conditions and user locations for the ML models and discuss deployment scenarios of our approach. In particular, our results suggest that cell-based models perform better than models trained for larger areas.

Handover Predictions as an Enabler for Anticipatory Service Adaptations in Next-Generation Cellular Networks

Enabling computation intensive tasks with low latency requirements on mobile devices considering their battery life time is one of the challenges in 5G networks. 5G is an era with massive device connectivities, therefore, a realistic offloading scenario should consider a dense and dynamic scenario with users’ movements. Considering the powerful Central Processing Units (CPUs) of current mobile devices and Device-to-Device (D2D) communications, the available resources of adjacent devices can be exploited to fulfil the requirements of recent applications. However, in order to prevent over-exploiting behaviours of some users in a feasible cooperation offloading scenario, defining proper incentive mechanisms is a must. In this work, we propose a fair energy aware offloading framework considering the inter dependency relationships of computation tasks. Our scenario contains a dense small cell with a MEC attached to the base station (BS). In order to solve our mixed integer nonlinear offloading problem (MINLP), a genetic algorithm is employed. The simulation results show a significant reduction in used energy as well as completion of the tasks within the respective deadlines compared to the other benchmarks.

Vincent Latzko

Papers

Accurate Energy-Efficient Localization Algorithm for IoT Sensors

Communication and control

Usecase Driven Evolution of Network Coding Parameters Enabling Tactile Internet Applications

Handover Predictions as an Enabler for Anticipatory Service Adaptations in Next-Generation Cellular Networks

Energy-Aware and Fair Multi-User Multi-Task Computation Offloading