Rukhsana Ruby

Simulation and Experimentation Platforms for Underwater Acoustic Sensor Networks: Advancements and Challenges

Ocean and water basically cover the major parts of our planet. To obtain the best utilization of the underlying resources on these parts of the Earth, people have made some research advancements. Specifically, the research on underwater wireless acoustic sensor networks (UWA-SNs) has made great progress. However, wide deployment of UWA-SNs is far from a reality due to several reasons. One important reason is that offshore deployment and field-level experiments of ocean-centric applications are both expensive and labor intensive. Other alternatives to attain this objective are to conduct simulation or experimentation that can reduce cost and accelerate the research activities and their outcomes. However, designing efficient and reliable simulation and experimentation platforms have proven to be more challenging beyond the expectation. In this article, we explore the main techniques (including their pros and cons) and components to develop simulation and experimentation platforms and provide a comprehensive survey report in this area. We classify simulation and experimentation platforms based on some typical criteria and then provide useful guidelines for researchers on choosing suitable platforms in accordance with their requirements. Finally, we address some open and un-resolved issues in this context and provide some suggestions on future research.

Enhanced Uplink Resource Allocation in Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared with other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, the information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared with the downlink ones due to the individual users’ power constraints and the discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios.

Traffic Engineering Enhancement by Progressive Migration to SDN

Software-defined networking (SDN) provides an unprecedented opportunity to enhance the traffic engineering in IP networks. This emerges from the fine-grained centralized control over routing, which enables an immediate response to network dynamics. However, due to the high operational and performance costs of the migration, network operators prefer a multi-period planning approach for the deployment of SDN-enabled devices. In this letter, we present and evaluate an efficient algorithm to seek a beneficial upgrade policy for a network considering the budget constraints. Our proposed algorithm outperforms the most recent work, particularly for large-scale topologies and an increased number of time periods.

Fine-grained access provisioning via joint gateway selection and flow routing on SDN-aware Wi-Fi mesh networks

In recent years, dramatic growth of mobile data traffic has left the operators no choice but to consider Wi-Fi networks as an economic complementary solution. To achieve this, WLANs require to adopt some of the key features of carrier-grade operators, such as centralized resource management. As an emerging paradigm, Software Defined Networking (SDN) can be used to provide salient centralized network solutions for Wi-Fi infrastructures. In fact, applying SDN to different wireless platforms, e.g., Wi-Fi Mesh Networks (WMNs), brings unprecedented opportunities to improve the network performance by employing more sophisticated algorithms at SDN controllers. Moreover, it should be noted that traffic engineering over WMNs incorporates tightly correlated steps including association control, gateway selection and flow routing which are individually NP-hard problems. In this paper, we present an agile and fine-grained access provisioning solution via bridging the cellular and Wi-Fi technologies that empowers us to address the users demand by steering data flows on different tiers of WMNs. In contrast to the prior work, we present a detailed unified formulation for joint gateway selection and flow routing in Multi-Channel Multi-Radio (MC-MR) WMNs that considers the key attributes of wireless networks. The functionality of the presented solution is evaluated through various experiments with extensive numerical results.

Virtual Keyboard for Wearable Wristbands

The wearable devices are small and easy to carry but typically with poor interaction experience. For example, Apple iWatch does not support instant text message input feature because of the lack of keyboard availability on the tiny touch screen. To address this problem, we develop a novel system, termed iKey, which enables users to use the back of one of their hands as virtual keyboard for wearable wristbands. iKey recognizes keystrokes based on a location-based training model via body vibration. We will demonstrate a real time functional prototype of iKey in this demo.

FLoc: Device-free passive indoor localization in complex environments

Localization in complex indoor environments with RF signal is a challenging task. Via this technique, the signal is easily affected by obstacles and environmental noise due to the broadcast nature of RF signal transmission. In this paper, we observe that seismic signal is more oriented than the RF signal, and thus is more suitable for localization in an complex indoor environment with obstacles. Motivated by this observation, we propose a device-free passive indoor localization system, namely Floc, that can fight against environmental impact and achieve high localization accuracy. FLoc is composed of three modules, which are sensing module that collects seismic signal from footsteps, footstep detection module that remove the environmental impact and recover the clean footsteps, and localization module that leverages seismic signal from footsteps for positioning. We implement FLoc on credit-card sized single-board computer Raspberry Pi equipped with geophones. To verify the effectiveness of our system, we conduct extensive experiments for different scenario in a complex indoor environment with the area of 6 × 8 square meter. The experimental results demonstrates that Floc can achieve up to 7cm localiztion accuracy on average, and can outperform the existing acoustic signal-based localization techniques.

Wi-fire: Device-free fire detection using WiFi networks

Conflagration is one of the major disasters that threatens human life and property. If the proper action is not taken in detecting the symptom of conflagration events ahead of time, the number of such disasters will keep increasing. An effective solution in this context will alleviate many fire-related global problems to a great extent. Although fire detectors are not available in many places, WiFi networks are increasingly prevalent nowadays. Motivated by the previous works that used WiFi signals for the purpose of environment monitoring and activity recognition, we make an attempt to use WiFi signals to detect fire. Through several experiments, we find that fire influences the transmission of wireless signals uniquely, and consequently it affects the amplitude and phase of the resultant Channel State Information (CSI). Based on this observation, in this paper, we propose a device-free fire detection system, namely Wi-Fire, using commercial WiFi devices. To the best of our knowledge, this is the first work that leverages CSI of radio frequency (RF) signal to detect fire events using existing wireless infrastructure without requiring any additional device. We implement our proposed system on desktop computers equipped with commercial 802.11n network interface cards (NICs). Comprehensive experiments have been conducted for different scenarios in different environments to verify the effectiveness of our proposed system. The results verify that the fire detection accuracy of this training-based system is up to 96.67% on average.

Accurate Combined Keystrokes Detection Using Acoustic Signals

With the development of acoustic localization schemes using mobile devices, keystroke detection has received tremendous attention from the academia and industry. Currently, most of the existing systems focus on the recognition of a single keystroke and they are limited by several restrictions. In this paper, we consider the idea of signal variation caused by the combination of two combined keystrokes, and propose an acoustic-based scheme that can detect the combined keystrokes effectively. Our system exploits the blind signal separation technique to deal with the mixed signals, resultant from typing two separate keys simultaneously. Then, we apply feature extraction and pattern recognition algorithms to recognize the combined keystrokes. Extensive experiments have been conducted in a laboratory environment with mobile phones equipped with two microphones. Our results show that for several combinations of two keystrokes, on average, we can achieve 78.4% recognition accuracy.

WiHumidity: A Novel CSI-Based Humidity Measurement System

Atmospheric humidity is one of the most important environmental attributes for weather condition. It affects the economy of nature as well as human life. Many environmental processes are affected by this attribute. For example, rice has the most powerful photosynthesis when the atmospheric humidity is in between 50% and 60%. For most of the human being, the humidity in between 20% and 80% is good to have a healthy life. Consequently, humidity measurement methods are urgently required. The existing methods are neither convenient for large scale deployment due to the high cost nor accurate enough to use. Recently, researchers found that humidity has a direct effect on radio propagation. This observation is undoubtedly useful to measure humidity in the environment. However, the humidity estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to multipath effect. Meanwhile, the change of radio signals incurred by RSSI-based systems is not that much obvious when the transmitter and receiver are in close distance. As a result, it is challenging to measure humidity in indoor environments. In this work, we provide a novel system, namely WiHumidity, to tackle this problem. The system utilizes the special diversity of channel state information (CSI) to alleviate multipath effect at the receiver. Extensive experiments have been conducted to verify the effectiveness of WiHumidity. The experimental results verify that on average, WiHumidity can achieve 79% measurement accuracy.