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Indoor localization is the key infrastructure for indoor location-aware applications. In this paper, we consider an emergency scenario, where a team of soldiers or first responders perform time-critical missions in a large and complex building. In particular, we consider the case where infrastructure-based localization is not feasible for various reasons such as installation/management costs, a power outage, and terrorist attacks. We design a novel algorithm called the collaborative indoor positioning scheme (CLIPS), which does not require any pre-existing indoor infrastructure. Given that users are equipped with a signal strength map for the intended area for reference, CLIPS uses this map to compare and extract a set of feasible positions from all positions on the map when the device measures signal strength values at run time. Dead reckoning is then performed to remove invalid candidate coordinates, eventually leading to only correct positions. The main departure from existing peer-assisted localization algorithms is that our approach does not require any infrastructure or manual configuration. We perform testbed experiments and extensive simulations, and our results verify that our proposed scheme converges to an accurate set of positions much faster than existing noncollaborative solutions. [ABSTRACT FROM PUBLISHER]

Cognitive routing for maritime wireless ad hoc networks is proposed in this paper to find a stable path between source and destination. This is ship-to-ship communication where two ships can communicate when they not only have consensus on a common idle channel but also are in the transmission range of each other. We apply belief propagation algorithm for collaborative spectrum sensing. Every user (ship) exchanges its local decisions with its neighbors to compute the final belief about the state of the channel. These beliefs are applied for estimating the link duration for total number of hops between source and destination. Then, a path is selected which maximizes the path duration among all the paths in the network to reach the destination. We apply both flood-based and geographical routing protocols to find a route between source and destination for different scenarios. We simulate our scheme for different ocean settings and evaluate path durations for different number of ships. The results report a significant increase in path duration as the number of nodes increases in the network. In addition, we verify that path duration increases with an increase in probability of primary user being idle via extensive simulations. Hence, our scheme provides stable path selection for communication among users onboard. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In wireless sensor networks (WSNs), hierarchical routing protocol is commonly used for energy efficiency. In particular, the TEEN (Threshold sensitive Energy Efficient sensor Network) protocol is used widely as a basic clustered multihop routing protocol. However, energy efficient routing protocols without proper security suffer from many security vulnerabilities. Hence, in this paper, we propose a hybrid key scheme specially for the TEEN protocol: a symmetric key scheme for the intracluster and a public key scheme for the intercluster. The simulation results show that network lifetime of the proposed hybrid key scheme decreases about 8% than the TEEN protocol and about 4% compared with the TEEN protocol with symmetric key scheme. On the other hand, a hybrid key scheme provides better probability of successful transmission than that of the symmetric key scheme. [ABSTRACT FROM AUTHOR]

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 F 1 score achieved using sensor data features as inputs to machine learning models with the F 1 score obtained using the symptom profile vectors as inputs. Our findings demonstrate that symptom profiling improves the F 1 score by up to 0.09, with an average improvement of 0.05, resulting in a depression severity prediction with an F 1 score as high as 0.86. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There has been extensive research on building energy saving (BES), which aims to reduce energy consumption inside buildings. One of the key solutions for energy saving in buildings is to reduce energy consumption in areas that are not occupied by inhabitants. However, effective monitoring of occupants for energy-saving purposes can be challenging due to unpredictable variations in the indoor environment, such as variations in space size, furniture arrangement, the nature of occupants' activities (e.g. , varied intensities and instances), and penetration losses of walls. Unfortunately, the existing solutions for occupancy monitoring in smart buildings, such as PIR sensors, C O 2 sensors, and cameras, etc., are expensive, require excessive maintenance, and are not adaptable to the complex variations in indoor environments. This paper introduces WiSOM , for occupancy detection that utilizes the channel state information (CSI), of commodity WiFi. The method is self-adaptive and designed to handle complex variations in indoor environments. We conducted a thorough analysis of WiSOM and evaluated it under various indoor conditions, including the impact of multipath effects, the detection of different intensities and instances of activities of daily living (ADL), and the impact of wall absorption in a real-home scenario. Our evaluation demonstrated an average detection rate of 98.25% for multipath effects, 96.5% and 98.1% for different intensities and instances of ADL, and 94.4% for wall absorption. Additionally, we assessed WiSOM 's resilience to temporal variation in the CSI and achieved a false alarm rate of less than 2%. In comparison to recent baselines, WiSOM outperformed, achieving up to a 21% improvement in detection rate within real-house scenarios. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Geographic routing in wireless sensor networks requires sources nodes to be aware of the location information of sinks to send their data. To provide the sink location service, quorum-based schemes have been proposed, which exploit crossing points between a quorum of a sink location announcement (SLA) message from a sink and a quorum of a sink location query (SLQ) message from a source node. For guaranteeing at least one crossing point in irregular sensor networks with void areas or irregular boundaries, the previous schemes however collect and flood the network boundary information or forward a SLA and SLQ message along the whole network boundary. In this paper, we design a novel quorum-based sink location service scheme that exploits circle and line quorums, which does not require the network boundary information and send a SLA and SLQ message along the whole network boundary. In the proposed scheme, a source node sends a SLQ message to the network center and sends another SLQ message to an edge node in the network boundary, thus generating a SLQ line quorum. On the other hand, a sink node sends a SLA message along a circle path whose center is the network center, thus forming a SLQ circle quorum. By this way, it is guaranteed that the SLQ and SLA quorums have at least one crossing point in irregular sensor networks. Both numerical analysis and extensive simulation results verify that the proposed scheme outperforms the existing schemes in terms of the delivery distance, the delivery hop count, and the energy consumption for providing sink location service. [ABSTRACT FROM AUTHOR]

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, physical activity, mood, social activity, sleep, and food intake 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 normal, mildly depressed, moderately depressed, and severely depressed 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). [ABSTRACT FROM AUTHOR]