Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.

A survey of spectrum sensing algorithms for cognitive radio applications

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

/pdf/maui-making-smartphones-last-longer-with-code-offload-go3o1uwp1u.pdf

MAUI: making smartphones last longer with code offload

Capacity improvement is one of the principal challenges in wireless networking. We present a link-layer protocol called Slotted Seeded Channel Hopping, or SSCH, that increases the capacity of an IEEE 802.11 network by utilizing frequency diversity. SSCH can be implemented in software over an IEEE 802.11-compliant wireless card. Each node using SSCH switches across channels in such a manner that nodes desiring to communicate overlap, while disjoint communications mostly do not overlap, and hence do not interfere with each other. To achieve this, SSCH uses a novel scheme for distributed rendezvous and synchronization. Simulation results show that SSCH significantly increases network capacity in several multi-hop and single-hop wireless networking scenarios.

/pdf/ssch-slotted-seeded-channel-hopping-for-capacity-improvement-85v0v477js.pdf

SSCH: slotted seeded channel hopping for capacity improvement in IEEE 802.11 ad-hoc wireless networks

There are a number of scenarios where it is desirable to have a wireless device connect to multiple networks simultaneously. Currently, this is possible only by using multiple wireless network cards in the device. Unfortunately, using multiple wireless cards causes excessive energy drain and consequent reduction of lifetime in battery operated devices. We propose a software based approach, called MultiNet, that facilitates simultaneous connections to multiple networks by virtualizing a single wireless card. The wireless card is virtualized by introducing an intermediate layer below IP, which continuously switches the card across multiple networks. The goal of the switching algorithm is to he transparent to the user who sees her machine as being connected to multiple networks. We present the design, implementation, and performance of the MultiNet system. We analyze and evaluate buffering and switching algorithms in terms of delay and energy consumption. Our system is agnostic of the upper layer protocols, and works well over popular IEEE 802.11 wireless LAN cards.

/pdf/multinet-connecting-to-multiple-ieee-802-11-networks-using-a-3nk5jp93u7.pdf

MultiNet: connecting to multiple IEEE 802.11 networks using a single wireless card

Networking over UHF white spaces is fundamentally different from conventional Wi-Fi along three axes: spatial variation, temporal variation, and fragmentation of the UHF spectrum. Each of these differences gives rise to new challenges for implementing a wireless network in this band. We present the design and implementation of Net7, the first Wi-Fi like system constructed on top of UHF white spaces. Net7 incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. builds on a simple technique, called SIFT, that reduces the time to detect transmissions in variable channel width systems by analyzing raw signals in the time domain. We provide an extensive evaluation of the system in terms of a prototype implementation and detailed experimental and simulation results.

/pdf/white-space-networking-with-wi-fi-like-connectivity-10vrdfonkw.pdf

White space networking with wi-fi like connectivity

Localizing the sources of performance problems in large enterprise networks is extremely challenging. Dependencies are numerous, complex and inherently multi-level, spanning hardware and software components across the network and the computing infrastructure. To exploit these dependencies for fast, accurate problem localization, we introduce an Inference Graph model, which is well-adapted to user-perceptible problems rooted in conditions giving rise to both partial service degradation and hard faults. Further, we introduce the Sherlock system to discover Inference Graphs in the operational enterprise, infer critical attributes, and then leverage the result to automatically detect and localize problems. To illuminate strengths and limitations of the approach, we provide results from a prototype deployment in a large enterprise network, as well as from testbed emulations and simulations. In particular, we find that taking into account multi-level structure leads to a 30% improvement in fault localization, as compared to two-level approaches.

/pdf/towards-highly-reliable-enterprise-network-services-via-6tykdrayp6.pdf

Ranveer Chandra

Papers

MAUI: making smartphones last longer with code offload

SSCH: slotted seeded channel hopping for capacity improvement in IEEE 802.11 ad-hoc wireless networks

MultiNet: connecting to multiple IEEE 802.11 networks using a single wireless card

White space networking with wi-fi like connectivity

Towards highly reliable enterprise network services via inference of multi-level dependencies