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

Plug-in hybrid electric vehicles (PHEV) are becoming gradually more attractive than internal combustion engine vehicles, even though the current electrical grid is not potentially able to support the required power demand increase to introduce charging stations. Acknowledging that design and development of charging stations has crucial importance, this paper introduces a candidate PHEV charging station architecture, along with a quantitative stochastic model, that allows us to analyze the performance of the system by using arguments from queuing theory and economics. A relevant component of the proposed architecture is the capability of the charging stations to store excess power obtained from the grid. The goal is to design a general architecture which will be able to sustain grid stability, while providing a required level of quality of service; and to describe a general methodology to analyze the performance of such stations with respect to the traffic characteristics, energy storage size, pricing and cost parameters. Our results indicate that significant gains in net cost/profit and useful insights can be made with the right choice of storage size. Such considerations are crucial in this early stage of designing the smart grid and charging stations of the future.

Local energy storage sizing in plug-in hybrid electric vehicle charging stations under blocking probability constraints

Although employing cooperation in spectrum sensing for cognitive radio (CR) systems improves the detection accuracy by mitigating the shadowing and multi-path fading faced by cognitive users, it increases the energy consumption especially because spectrum sensing is a periodic process. Therefore, for battery-powered terminals, energy efficiency represents a favorable metric in system design. One of the ways to improve the energy efficiency in CR is to optimize the fusion rule (FR) by which the individual results are processed. In this paper, we optimize the well known FR K-out-of-N for maximizing energy efficiency and detection accuracy. Mathematical expressions for the optimal N and K for both objectives are obtained. Simulation and analytical results show that significant improvement in energy efficiency can be achieved through FR optimization while satisfying a predefined threshold on the missed detection probability.

Optimizing the K-out-of-N rule for cooperative spectrum sensing in cognitive radio networks

Providing reliable and efficient routing in presence of relative movement motivates the introduction of movement awareness to improve performance of existing position-based routing schemes in vehicular ad-hoc networks. The proposed algorithm represents a modification of well-known GPSR which exploits information about movement in order to improve the next forwarding node decision. Performance evaluation of the proposed protocol underlines a promising and robust basis for designing a routing strategy suitable for the automotive scenario.

/pdf/enhanced-gpsr-routing-in-multi-hop-vehicular-communications-3ebtb2y5ez.pdf

Enhanced GPSR Routing in Multi-Hop Vehicular Communications through Movement Awareness

Wireless mesh networks (WMN) typically employ mesh routers that are equipped with multiple radio interfaces to improve network capacity. The key aspect is to cleverly assign different channels (i.e., frequency bands) to each radio interface to form a WMN with minimum interference. The channel assignment must obey the constraints that the number of different channels assigned to a mesh router is at most the number of interfaces on the router, and the resultant mesh network is connected. This problem is known to be NP-hard. In this paper we propose a hybrid, interference and traffic aware channel assignment (ITACA) scheme that achieves good multi-hop path performance between every node and the designated gateway nodes in a multi-radio WMN network. ITACA addresses the scalability issue by routing traffic over low-interference, high-capacity links and by assigning operating channels in such a way to reduce both intra-flow and inter-flow interference. The proposed solution has been evaluated by means of both simulations and by implementing it over a real-world WMN testbed. Results demonstrate the validity of the proposed approach with performance increase as high as 111%.

Interference and traffic aware channel assignment in WiFi-based wireless mesh networks

Accurate measurement of energy consumption of practical wireless deployments is vital to the research community to develop pragmatic simulators and analytical models for the synthesis of new energy-aware and energy-efficient protocols and algorithms for wireless networks. However, there is considerable dearth in the availability of affordable and scalable energy consumption monitoring tools for the research community. In this paper, we introduce Energino, a scalable and affordable solution for energy consumption monitoring in wireless networks. The Energino power meter is a standalone plug-load meter based on the Arduino platform providing high resolution and sampling rate capabilities. We evaluate the capabilities and distinctive features of the Energino power meter in a real WLAN network. Results show that our solution is capable of isolating high resolution/frequency dynamics that can not be analyzed using commercially available tools. We also release both the hardware schematics and the software with a permissive license in order to encourage the research community to use and extend it.

https://hal.inria.fr/hal-00767059/document

Fabrizio Granelli

Papers

Local energy storage sizing in plug-in hybrid electric vehicle charging stations under blocking probability constraints

Optimizing the K-out-of-N rule for cooperative spectrum sensing in cognitive radio networks

Enhanced GPSR Routing in Multi-Hop Vehicular Communications through Movement Awareness

Interference and traffic aware channel assignment in WiFi-based wireless mesh networks

Energino: A hardware and software solution for energy consumption monitoring