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

In this paper, we introduce a novel queue management technique for the buffer space at the base station of infrastructure 802.11 networks that considers mobile user's receiving characteristics. The maximum amount of the base station buffer that can be used by a given flow is updated proportionally to RTT, measured at the mobile nodes and sent to the base station my the mean of link layer acknowledgements. In this way, the proposed scheme remains transparent to high-level protocols. The proposed approach makes possible the implementation of algorithms able to provide RTT-fairness in wired-cum-wireless networks. Results show the advantage of the proposed queue management scheme when compared to that of traditional drop-tail queue management.

Receiver-Driven Queue Management for Achieving RTT-Fairness in Wi-Fi Networks

Network evolution towards self-aware autonomous adaptive networking aims at reducing the burden of configuring and managing networks, which leads to performance degradation. In order to optimize network operations, the introduction of self-awareness, self-management, and self-healing into the network was proposed, leading to a novel paradigm in networking - known as cognitive networking. This paper surveys the state-of-the-art, in cognitive networking - identifying fundamental techniques and basic building blocks enabling cognition. Following an overview of existing methods for cross-layer signaling, the paper identifies the most appropriate approaches to be used for cognitive network implementation outlining advantages, drawbacks, and provides guidance for usage of each signaling method.

/pdf/survey-on-signaling-techniques-for-cognitive-networks-2pbuvjhaby.pdf

Survey on signaling techniques for cognitive networks

https://orbilu.uni.lu/bitstream/10993/9848/1/whycross-layer.pdf

Introduction: Why Cross-Layer? Its Advantages and Disadvantages

In this paper, the problem of transmitting embedded bitstreams over wireless packet networks is considered. In particular, the authors address the problem of optimal energy allocation in video streams employing fixed-size packets, and analyze the performance under different modulation schemes. Results obtained for the transmission of MPEG-4 FGS bitstreams show the impact of the proposed method in different modulation schemes.

Optimal energy distribution in embedded packet video transmission over wireless channels

This paper proposes a combination of the Reverse Direction Protocol (RDP) defined in the IEEE 802.11n and the Network Coding (NC) technique to improve the energy efficiency of wireless networks based on the IEEE 802.11. The aim of the combined approach, named BidCode, is to allow intermediate nodes to combine several received packets into coded packets and immediately forward them upon successful reception of data. The energy efficiency of BidCode is analyzed in this paper and compared to those of the Distributed Coordination Function (DCF) and an important NC protocol based on IEEE 802.11, called COPE. Both analytical and simulation results show the high energy efficiency of BidCode with gains of up to 350% and 130% when compared to DCF and COPE, respectively.

/pdf/reverse-direction-transmissions-and-network-coding-for-3tf86frkyx.pdf

Fabrizio Granelli

Papers

Receiver-Driven Queue Management for Achieving RTT-Fairness in Wi-Fi Networks

Survey on signaling techniques for cognitive networks

Introduction: Why Cross-Layer? Its Advantages and Disadvantages

Optimal energy distribution in embedded packet video transmission over wireless channels

Reverse direction transmissions and network coding for energy-efficient Wi-Fi networks