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This article proposes a new pulsewidth modulation (PWM) strategy to reduce common-mode voltages (CMVs) in grid-connected two-level inverters. Reduction of CMVs is an important issue in the field of distributed energy resources (DERs) because CMVs at a high switching frequency cause ground leakage currents that lead to safety issues in the applications of DERs. In the proposed PWM strategy, each reference vector is split into two vectors during the PWM period. The peak-to-peak magnitude of CMV is reduced by one-third as the split vectors are synthesized without using zero vectors. The influence of the dead-time and its compensation in the proposed method are also described. Our proposed method can be easily implemented with carrier-based PWM. Simulation and experimental results are presented to verify the performance of the proposed strategy.

In this article, we present a client-based multipath TCP (cMPTCP) framework that can cope with path heterogeneity across multiple LTE networks deployed by different operators. cMPTCP is the first work to apply the mobile client's inference based on our observation that an out-of-order queue in an MPTCP receiver (i.e., mobile client) builds up with increasing heterogeneity between different subflows and can significantly degrade throughput performance compared to that achieved by single-path TCP via the best path. For agile and accurate control for the existing MPTCP architecture, we argue that mobile clients are the best network entities to infer the bottleneck state of an end-to-end MPTCP connection. Our proposed cMPTCP consists of two main components: client-initiated multipath selection and subflow-level rate control mechanisms. These mechanisms are compatible with the default MPTCP architecture implemented in the Linux kernel. For performance evaluation, we prototype a complete LTE network testbed consisting of mobile phones, LTE base stations, LTE cores, and application servers. Our experimental results confirm that cMPTCP outperforms existing server-side schedulers in practical dynamic channel conditions.

Video conferencing applications have seen explosive growth both in the number of available applications and their use. However, there have been few studies on the detailed analysis of video conferencing applications with respect to network dynamics, yet understanding these dynamics is essential for network design and improving these applications. In this paper, we carry out an in-depth measurement and modeling study on the rate control algorithms used in six popular commercial video conferencing applications. Based on macroscopic behaviors commonly observed across these applications in our extensive measurements, we construct a unified architecture to model the rate control mechanisms of individual applications. We then reconstruct each application's rate control by inferring key parameters that closely follow its rate control and quality adaptation behaviors. To our knowledge, this is the first work that reverse-engineers rate control algorithms of popular video conferencing applications, which are often unknown or hidden as they are proprietary software. We confirm our analysis and models using an end-to-end testbed that can capture the dynamics of each application under a variety of network conditions. We also show how we can use these models to gain insights into the particular behaviors of an application in two practical scenarios.

This paper presents a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy), which jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation. Based on our observations, we exploit the RSRQ (Reference Signal Received Quality) and SINR as features in a machine learning algorithm to accurately estimate the cellular load. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized backoff-based algorithm that fits practical environments. To evaluate these features, we prototype a complete LTE system testbed consisting of mobile devices, eNodeBs, EPC (Evolved Packet Core) and application servers. Our comprehensive experimental results show that CASTLE's load estimation is up to 91% accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore, we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the United States, prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system, and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using both commercially available hardware as well as modified open source software. Our attack can be performed using a commercially-available software defined radio, and our modifications to the open source NextEPC and srsLTE software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The true impact of such an attack would of course depend on the density of cell phones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cell phone manufacturers. To seed this effort, we also discuss several defenses to address this threat in both the short and long term.

Since the diagnosis of severe bufferbloat in mobile cellular networks, a number of low-latency congestion control algorithms have been proposed. However, due to the need for continuous bandwidth probing in dynamic cellular channels, existing mechanisms are designed to cyclically overload the network. As a result, it is inevitable that their latency deviates from the smallest possible level (i.e., minimum RTT). To tackle this problem, we propose a new low-latency congestion control, ExLL, which can adapt to dynamic cellular channels without overloading the network. To do so, we develop two novel techniques that run on the cellular receiver: 1) cellular bandwidth inference from the downlink packet reception pattern and 2) minimum RTT calibration from the inference on the uplink scheduling interval. Furthermore, we incorporate the control framework of FAST into ExLL's cellular specific inference techniques. Hence, ExLL can precisely control its congestion window to not overload the network unnecessarily. Our implementation of ExLL on Android smartphones demonstrates that ExLL reduces latency much closer to the minimum RTT compared to other low-latency congestion control algorithms in both static and dynamic channels of LTE networks.

Advances in radio access technologies and mobile terminals are fueling the growth of Internet traffic via mobile networks. The upcoming 5G era, with a new 5G radio technology and increased utilization of heterogeneous networking, will further accelerate mobile data usage. One of the pillars of mobile network architecture is IP mobility management, which is currently based on centralized data path management. In this article, we first describe the efficiency issues of the centralized approach. We then discuss multiple dimensions of distributing the mobility functions, and suggest how the mobile terminal can utilize them in orchestration for efficient communication over a 5G flat mobile network.

Experimentation of a wireless network protocol over the air is of significant interest. However, it is more rarely performed than simulation because of the difficulties in coding and debugging as well as lack of scalability and repeatability. In this article, the concept of a code-reuse platform making use of a simulation code directly for real experiments is revisited as an efficient and easy means of experimentation. Furthermore, an architecture and key components of an idealistic code-reuse platform are suggested, and then CommonCode, the most advanced codereuse platform, is proposed. Through extensive simulations and experiments using Common-Code for the same codes, we demonstrate that CommonCode is valid and accurate in terms of protocol performance, and simultaneously fast and easy in terms of protocol development.

Hundreds of papers over the last two decades have studied the theory of distributed scheduling in wireless networks, including a number of them on stability or utility maximizing random access. Several publications in 2008 studied an adaptive CSMA that in theory can approach utility optimality without any message passing under a number of assumptions. This paper reports the results from the first deployment of such random access algorithms through an implementation over conventional 802.11 hardware, an on-going effort that started in summer 2009. It shows both a confirmation that Utility Optimal CSMA may work well in practice even with an implementation over legacy equipment, and a wide array of gaps between theory and practice in the field of wireless scheduling. This paper therefore also brainstorms the discovery of and bridging over these gaps, and the implementation-inspired questions on modeling and analysis of scheduling algorithms.

A wireless mesh network (WMN) is being considered for commercial use in spite of several unaddressed issues. In this paper we focus on one of the most critical issues: the impact of ambient motion of entities like people on the channel characteristics and on the WMN performance. A human body in an electro-magnetic (EM) field acts as an scatterer that absorbs 60% of incident EM energy, thereby shadowing the receiver. This human body model along with the human mobility behavior gives rise to a black body (a group movement) effect that traps the incident EM wave with repetitive internal reflections. The black body theory is verified by simulating the WiSEMesh testbed in picoKAIST, a tool based on deterministic ray tube method. Experimental results show each link exhibiting a unique channel variation pattern in presence of the black body. Based on the pattern we provide several insights in WMN deployment and protocol design.

A Wireless Mesh Network (WMN) is a fast growing network, which is now a popular technology for providing wireless Internet connection to industry as well as community. A WMN is a collection of nodes (usually a computer with one or mint: wireless Network Interface Cards (NICs)) that are connected to one another with single or multiple hop ad hoc links forming a mesh backbone network. Ad hoc links are popular in mesh connectivity as they are self-configuring and self-healing. In this paper, we discuss WMN design and deployment issues with reference to our WiSEMesh testbed. WiSEMesh has 56 nodes deployed in the campus area providing internet connection for over 1000 users. Each node consists of a small form factor computer with three wireless NICs. We developed the WiSEMesh node software stack that contains Unix based operating system, wireless NIC drivers, tools such as DHCP server, NAT etc. WiSEMesh has properties like scalability, open source operating software, heterogeneous hardware support, high performance, globally reachable, and easy to configure network. Hence, it can spread faster as a community wireless network and also provide a flexible platform for testing communication network layers. We also address design issues of a Network Management System (NMS) that can be implemented in any wireless network. Our NMS named WiVi provides platform for software distribution and network configuration with an elaborate data collection and storage facilities. For a large and scalable network like WiSEMesh, a massive database needs to be designed. One of the WiVi design objectives is to provide a robust, flexible and scalable database. WiVi achieves database design goals by implementing a concept "network in database" where network is logically divided into hierarchy of hardware components and each component is treated as an object in the database. The "network in database" implementation guarantees data collection and storage flexibility. By implementing the NMS software distribution platform and the hardware independent WiSEMesh node software stack, our testbed is confirmed to achieve scalability.

This paper describes highly efficient allyl cross-coupling reactions of allylindiums with organic electrophiles such as aryl and vinyl triflates, vinyl halides, dibromoolefin, and alkynyl iodide. The reactions were carried out using 4 mol % Pd(PPh3)4 in the presence of 3 equiv of LiCl in DMF at 100 degrees C under a nitrogen atmosphere. Allylindium, generated from the reaction of 1 equiv of indium with 1.5 equiv of allyl halide, gave the best result as a coupling partner. The present method is mild and simple to apply, and it produces a diverse range of allylic compounds in good to excellent yields.