Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by ...obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.
A Sensor Equipped Aquatic (SEA) swarm is a sensor cloud that drifts with water currents and enables 4-D (space and time) monitoring of local underwater events such as contaminants, marine life, and ...intruders. The swarm is escorted on the surface by drifting sonobuoys that collect data from the underwater sensors via acoustic modems and report it in real time via radio to a monitoring center. The goal of this study is to design an efficient anycast routing algorithm for reliable underwater sensor event reporting to any surface sonobuoy. Major challenges are the ocean current and limited resources (bandwidth and energy). In this paper, these challenges are addressed, and HydroCast, which is a hydraulic-pressure-based anycast routing protocol that exploits the measured pressure levels to route data to the surface sonobuoys, is proposed. This paper makes the following contributions: a novel opportunistic routing mechanism to select the subset of forwarders that maximizes the greedy progress yet limits cochannel interference and an efficient underwater dead end recovery method that outperforms the recently proposed approaches. The proposed routing protocols are validated through extensive simulations.
It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, ...which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely,
,
,
,
, and
and extracted features related to each symptom cluster from mobile devices' sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are
,
,
, and
and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).
With the growing dependence on smartphones for everyday activities, a large number of pedestrians nowadays are constantly fixated on their smartphone screens, and hence are susceptible to walking off ...pavements or colliding with other pedestrians. Reduced attention and situational awareness can render smartphone-occupied users, or smombies, oblivious to potential risks when using their smartphones while walking or driving. In this paper, we introduce a smartphone application, called Smombie Guardian, that detects obstacles and alerts smombies as they walk while viewing their smartphone screens to prevent potential collisions. Based on a user study with 74 participants who used Smombie Guardian in a real-life scenario, we highlight the effectiveness, usefulness, and unobtrusiveness of the algorithm and Smombie Guardian in helping users to avoid potential obstacles.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In this letter, we present a performance analysis of scheduled transmit antenna selection with maximal ratio combining (TAS-MRC) in the presence of non-identical channel estimation errors. This ...letter derives the probability density function (PDF) of the instantaneous effective signal-to-noise ratio for the scheduled TAS-MRC over Rayleigh fading channels with non-identical channel estimation errors. Using this PDF, we derive exact closed-form expressions for the scheduled TAS-MRC symbol error rate in the presence of non-identical channel estimation errors. The analysis results show that the order of diversity of scheduled TAS-MRC is improved in terms of transmit spatial diversity and multiuser diversity, whereas the received diversity is diminished by non-identical channel estimation errors, regardless of the modulation type.
Increasing the size of memory in network devices leads to the problem of a persistently full buffer (a.k.a, bufferbloat). The objective of this study is to compare the recently introduced Controlled ...Delay (CoDel) scheme with the traditional method of active queue management, such as Random Early Detection (RED) algorithms over TCP variants. To explore the potential of CoDel over RED, TCP variants have been assessed at three settings: variable congestion and fixed payload (VCFP), variable payload and fixed congestion (VPFC), and high congestion and high payload (HCHP). We assessed the CoDel and RED schemes for active queue management (AQM) using three performance metrics: link utilization, drop rate, and queuing delay. The analytical results show that CoDel outperformed RED in most aspects over variants of TCP because of its auto‐tuning and auto‐adjustment features. However, RED outperformed CoDel in a few cases. In the VCFP setting, RED recorded a lower drop rate overall TCP variants. Moreover, in the VPFC setting, RED with a payload of 500–1000 bytes performed better in terms of drop rate. Finally, in the HPHC setting, there were two cases where RED, over TCP NewReno and Vegas, performed well in terms of drop rate.
Depression is a significant mental health issue that profoundly impacts people’s lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become ...cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement. This study contributes to the detection of depression severity and depressive symptoms using mobile devices. Our proposed approach aims to distinguish between different patterns of depression and improve prediction accuracy. We conducted an experiment involving 381 participants over a period of at least three months, during which we collected comprehensive passive sensor data and Patient Health Questionnaire (PHQ-9) self-reports. To enhance the accuracy of predicting depression severity levels (classified as none/mild, moderate, or severe), we introduce a novel approach called symptom profiling. The symptom profile vector represents nine depressive symptoms and indicates both the probability of each symptom being present and its significance for an individual. We evaluated the effectiveness of the symptom-profiling method by comparing the F1 score achieved using sensor data features as inputs to machine learning models with the F1 score obtained using the symptom profile vectors as inputs. Our findings demonstrate that symptom profiling improves the F1 score by up to 0.09, with an average improvement of 0.05, resulting in a depression severity prediction with an F1 score as high as 0.86.
Spy cameras planted in various private places, such as motels, hotels, homestays ( i.e., Airbnb), and restrooms, have raised immense privacy concerns. Wi-Fi spy cameras are used extensively by ...various adversaries because of easy installability, followed by size reduction. To prevent invasions of privacy, most studies have detected wireless cameras based on video traffic analysis and require additional synchronous data from external sensors or stimulus hardware to confirm the user's motion. Such supplements make the users uncomfortable, requiring extra effort and time for setting. This paper proposes an effective spy camera detection system called DeepDeSpy to detect the recording of a spy camera with no effort from the user. The core idea is using the channel state information (CSI) and the network traffic from the camera to detect whether the wireless camera records the movements of the user. The CSI signal is prone to motion, and detecting motion from an enormous amount of CSI data in real-time is challenging. This was handled by leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep learning methods. Such synergistic CNN and BiLSTM deep learning models enable instant and accurate detection by automatically extracting meaningful features from the sequential raw CSI data. The feasibility of DeepDeSpy was verified by implementing it on both a PC and a smartphone and evaluating it in real-life scenarios (e.g., various room sizes and user physical activities). The average accuracy achieved in different real-life settings was approximately 96%, reaching 98.9% with intensive physical activity in the large-size room. Moreover, the ability to achieve instant detection on a smartphone within only a one-second response time makes it workable for real-time applications.
Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features which are obtained from flow exporting protocols ( i.e., NetFlow). Recent success of ML and Deep Learning (DL) ...based NIDS solutions assume such flow information ( e.g., avg. packet size) is obtained from all packets of the flow. However, often in practice flow exporter is deployed on commodity devices where packet sampling is inevitable. As a result, applicability of such ML based NIDS solutions in the presence of sampling ( i.e., when flow information is obtained from sampled set of packets instead of full traffic) is an open question. In this study, we explore the impact of packet sampling on the performance and efficiency of ML-based NIDSs. Unlike previous work, our proposed evaluation procedure is immune to different settings of flow export stage. Hence, it can provide a robust evaluation of NIDS even in the presence of sampling. Through sampling experiments we established that malicious flows with shorter size ( i.e., number of packets) are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100. Next, using the proposed evaluation procedure we investigated the impact of various sampling techniques on NIDS detection rate and false alarm rate. Detection rate and false alarm rate is computed for three sampling rates ( i.e., 1/10, 1/100, 1/1000), for four different sampling techniques and for three (two tree-based, one deep learning based) classifiers. Experimental results show that systematic linear sampler - SketFlow performs better compared to non-linear samplers such as Sketch Guided and Fast Filtered sampling. We also found that random forest classifier with SketchFlow sampling was a better combination. The combination showed higher detection rate and lower false alarm rate across multiple sampling rates compared to other sampler-classifier combinations. Our results are consistent in multiple sampling rates, exceptional case is observed for Sketch Guided Sampling (SGS) as it caused a drastic performance drop when sampling rate was changed from 1/100 to 1/1000. Our results provide valuable insights for network practitioners and researchers regarding on how packet sampling effects ML-based NIDS performance. In this regard full source code for sampling and ML experiments has been released: github.com/Jumabek/sampledFlowMeter and github.com/Jumabek/nids-with-sampling
The deployment of underwater networks allows researchers to collect explorative and monitoring data on underwater ecosystems.The acoustic medium has been widely adopted in current research and ...commercial uses,while the optical medium remains experimental only.According to our survey onthe properties of acoustic and optical communicationsand preliminary simulation results have shown significant trade-offs between bandwidth,propagation delay,power consumption,and effective communication range.We propose a hybrid solution that combines the use of acoustic and optical communication in order to overcome the bandwidth limitation of the acoustic channel by enabling optical communicationwith the help of acoustic-assisted alignment between optical transmitters and receivers.
PDF
