This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our design is rooted in the theory of network coding. Prior work on network coding is mainly theoretical and focuses on multicast traffic. This paper aims to bridge theory with practice; it addresses the common case of unicast traffic, dynamic and potentially bursty flows, and practical issues facing the integration of network coding in the current network stack. We evaluate our design on a 20-node wireless network, and discuss the results of the first testbed deployment of wireless network coding. The results show that using COPE at the forwarding layer, without modifying routing and higher layers, increases network throughput. The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol.

/pdf/xors-in-the-air-practical-wireless-network-coding-17k31b3st4.pdf

XORs in the air: practical wireless network coding

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

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 Internet of Things vision: Key features, applications and open issues

Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective. We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.

MagNet: A Two-Pronged Defense against Adversarial Examples

The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT), our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

Future of IoT Networks: A Survey

In this paper, we develop a model-based frame scheduling scheme, called MFS, to enhance the capacity of IEEE 802.11-operated wireless LANs (WLANs). In MFS each node estimates the current network status by keeping track of the number of collisions it encounters between its two consecutive successful frame transmissions, and, based on the the estimated information, computes the current network utilization. The result is then used to determine a scheduling delay that is introduced (with the objective of avoiding collision) before a node attempts for transmission of its pending frame. In order to accurately calculate the current utilization in WLANs, we develop an analytical model that characterizes data transmission activities in IEEE 802.11-operated WLANs with/without the RTS/CTS mechanism, and validate the model with ns-2 simulation. All the control overhead incurred in the physical and MAC layers, as well as system parameters specified in IEEE 802.11 are figured in.We have conducted a comprehensive simulation study to evaluate MFS in perspective of number of collisions, achievable throughput, and inter-transmission delay. The simulation results indicate that the performance improvement with respect to protocol capacity can be as high as 20% with RTS/CTS and 150% without RTS/CTS, in a WLAN of up to 300 nodes. In addition, the inter-transmission delay in MFS is smaller and exhibits less variation than that in IEEE 802.11.

Improving protocol capacity with model-based frame scheduling in IEEE 802.11-operated WLANs

This paper presents a comprehensive survey on anti-drone systems. After drones were released for non-military usages, drone incidents in the unarmed population are gradually increasing. However, it is unaffordable to construct a military grade anti-drone system for every private or public facility due to installation and operation costs, and regulatory restrictions. We focus on analyzing anti-drone system that does not use military weapons, investigating a wide range of anti-drone technologies, and deriving proper system models for reliable drone defense. We categorized anti-drone technologies into detection, identification, and neutralization, and reviewed numerous studies on each. Then, we propose a hypothetical anti-drone system that presents the guidelines for adaptable and effective drone defense operations. Further, we discuss drone-side safety and security schemes that could nullify current anti-drone methods, and propose future solutions to resolve these challenges.

Survey on Anti-Drone Systems: Components, Designs, and Challenges

As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system’s output shows about 89 percent accuracy.

Drone Detection and Identification System using Artificial Intelligence

Nowadays, we can hear the word “cloud” everywhere. Cloud web storage, cloud-based web applications, and cloud networks, they all indicate that cloud has become more important than it has ever been. Internet service providers (ISP) and internet contents providers (ICP) are providing more and more services with cloud computing, and also in mobile computing environments, a cloud service is drawing much attention from the community. The biggest advantage of cloud computing is clear. As far as the user is connected to the cloud, (s)he can have all the extra computing power and storage no matter what his device is. However, empirical validation of various research projects on a cloud system seems to be ineffective in both cost and time since it requires involving to a huge amount of computing facilities and resources. Even with its importance as commerce and inconvenience of researching, there are not many simulators that can provide a simulation environment for cloud computing and datacenters. Thus, we propose a simulation tool that supports the MapReduce model, the most widely used programming model for big data processing, implemented on CloudSim, an open source simulator for cloud computing and datacenters.

Hwangnam Kim

Papers

Future of IoT Networks: A Survey

Improving protocol capacity with model-based frame scheduling in IEEE 802.11-operated WLANs

Survey on Anti-Drone Systems: Components, Designs, and Challenges

Drone Detection and Identification System using Artificial Intelligence

MR-CloudSim: Designing and implementing MapReduce computing model on CloudSim