There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Table Of Integrals Series And Products

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

The main objective of this special is to offer the interested audience an overview of the most recent advancements in information and communication technologies that can enable the “New Space” vision, turning it from a conceptual design philosophy to real system implementation. 

Foreword to the Special Section on Information and Communication Technologies (ICT) for a New Space Vision

The most commonly used technique to evaluate novel solutions is to leverage on simulation studies which are largely based on a simplified model of the system behavior. Such an approach provides an approximate evaluation of the system’s performances, which can be potentially far away from the behavior on a real deployment. Another solution is to exploit real world prototypes and testbeds. Such testbeds are generally based on a limited number of nodes and, due to their experimental purposes, they only partially present the challenges of a real operational environment with hundred or thousand users. Furthermore, the limited number of nodes limits the possibility to study scalability issues.

/pdf/emulating-multi-hop-wireless-networks-over-a-planetary-scale-14b6kck7k6.pdf

Emulating Multi–hop Wireless Networks over a Planetary Scale Testbed

In this paper, we propose an energy-efficient solution for implementing Network Coding (NC) in wireless networks based on the IEEE 802.11 Standard. The proposed mechanism, called GreenCode, allows nodes to duty cycle by switching to a low-power (sleep) state when they overhear coded packet transmissions that will not provide any new information for them. To facilitate the sleep operation, bidirectional transmissions involving both coded and non-coded packets between pairs of sender-receiver nodes are integrated into the operation of GreenCode. Both analytical and simulation results presented in this paper show the high energy efficiency of GreenCode with gains of up to 360% when compared to the existing mechanisms based on the IEEE 802.11 Standard. ∗

Wireless Networks with Bidirectional Transmissions and Sleeping Periods

As mobile systems for private use are gaining momentum, the area of network management automation is bound to attract renewed attention from standardization organizations and vendors. Prominent examples of tasks that would benefit from network automation tools are provisioning, diagnosing, and healing. Nevertheless, due to the various network and service providers as well as stakeholders involved in the deployment of a non-public mobile system, the success of such automation heavily depends on a smooth and effective interoperability among the components of the overall system. In this article, we review the state of the art of network operations, administration, and management in the context of mobile systems for non-public use, highlighting the differences with respect to traditional public networks. Then, we provide insights about the automated provisioning of an entire core network and a network slice subnet, both for private use, performed on a research testbed under continuous integration. Lastly, we propose a list of future challenges in this research area.

Autonomous Private Mobile Networks: State of the Art and Future Challenges

This chapter focuses on the management of the communications resources and on the access to those resources: the medium access control (MAC). Nanocommunications imply the exchange of information at the nanoscale level, on the basis of any wired or wireless interconnection of nanomachines in a nanonetwork. Effective MAC strategies are well-known to networking and communication experts and are highly dependent on the properties of the communication channel and on the characteristics of communication in environment. In channel partitioning schemes, MAC is able to guarantee a constant performance level by partitioning the channel resources. A nanomachine is an integrated device, with sizes ranging from one to a few hundred nanometers, that is able to pursuit simple tasks such as sensing, plain computation, communication, and local actuation. Physical-layer-aware schemes are MAC protocols that are designed considering the specific features of the physical channel in nanocommunications.

Fabrizio Granelli

Papers

Foreword to the Special Section on Information and Communication Technologies (ICT) for a New Space Vision

Emulating Multi–hop Wireless Networks over a Planetary Scale Testbed

Wireless Networks with Bidirectional Transmissions and Sleeping Periods

Autonomous Private Mobile Networks: State of the Art and Future Challenges

Multiple Access Control Strategies for Nanoscale Communications and Networking