Electric vehicles (EVs) are key to sustainable mobility, yet their lithium-ion batteries (LIBs) degrade more rapidly under prolonged high states of charge (SOC). This can be mitigated by delaying full charging DFC until just before departure, which requires accurate prediction of user departure times. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach represents each day as a TTE sequence by discretizing time into grid-based tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual information to predict departures. Evaluation on a real-world study involving 93 users and passive smartphone data demonstrates that our method effectively captures irregular departure patterns within individual routines, outperforming baseline models. These results highlight the potential for practical deployment of the DFC algorithm and its contribution to sustainable transportation systems.

For the successful engagement of users with incoming interventions, the delivery of Just-In-Time (or opportune moment—OM) interventions is of the utmost importance. This can be accomplished with a machine learning model that utilizes user context data. Smartwatch and smartphone interactions further open the space for further improvement by considering the user’s state and environmental information. In this work, a novel feature engineering approach for predicting smartphone user receptivity to Just-In-Time (JIT) interventions is proposed. Proposed approach utilizes rich information from user-smartphone interactions and smartwatch sensor data to extract contextually filtered features. The superiority of the proposed feature engineering method is demonstrated by developing a machine learning model that predicts a participant’s receptivity prior to administering an Experience Sampling Method (ESM) questionnaire. The proposed approach is evaluated using the KEmoPhone dataset, which contains 3,334 ESM answers collected over the course of one week from 73 participants. To reduce the bias that may result in selecting specific classifier, multiple classifiers are tested and their average ROC-AUC metrics are compared. The results show that the proposed feature engineering method -CFF improves the prediction performance, achieving an ROC-AUC of 56.5% as opposed to 54.7% obtained using conventional features (statistical moments of time series over an 80 min window). Such superior performance is consistent across multiple machine learning classification algorithms. Full research code will be released in jupyter notebooks for facilitating the research in the domain at https://github.com/Jumabek/receptivity upon the publication of this research work.

Recent advances in wearable technology have led to the development of various methods for stress sensing in both controlled laboratory and real-life environments. However, existing methods often rely on specialized or expensive sensors that may not be easily accessible to the general population. In this study, we investigate the feasibility of using off-the-shelf smartwatches for stress detection in real-life scenarios. To achieve this, we propose SOSW, a comprehensive methodology for robust sensor data processing by considering both physiological and contextual data. SOSW employs a two-layer machine learning (ML) architecture. The first-layer ML model is trained and validated using carefully collected data under controlled laboratory conditions. The second-layer ML model is trained and validated using data collected in real-life settings. We conducted evaluations with 26 and 18 participants in controlled laboratory and real-life conditions, respectively. The results indicate that our methodology can successfully detect stressful events with an F-1 score of up to 0.84 in laboratory conditions and 0.71 in real-life scenarios using off-the-shelf smartwatches. The results are comparable to those achieved by the state of the art methods that rely on dedicated wearables.

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user’s everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and universities make every endeavor to enhance the user experience (UX) of WiFi networks where the network dynamics (e.g., traffic load and user mobility) are usually unexpected. To this end, an existing mechanism for WiFi association is client driven, i.e., users associate themselves to the AP with higher signal strength. However, they still incur dissatisfaction due to the insufficient available bandwidth. To cope with this in a centralized manner, we propose DARCAS, a software-defined network (SDN)-enabled WiFi framework for association regulation. DARCAS adopts a notion of bandwidth satisfaction ratio (BSR), which is closely related to UX. It maximizes the aggregated network throughput while satisfying the BSR of each user with sufficient airtime (i.e., channel occupancy time) provision. We use this idea in a metaheuristic genetic algorithm called DARCAS-GA, which effectively finds the suboptimal association distribution of the maximum BSR in polynomial time. We implement the DARCAS system on off-the-shelf wireless routers and an SDN controller. We report real-life experimental results in the considered scenarios and conduct extensive simulations on the NS-3 simulator to examine its performance with scalability. With fine-tuned settings, DARCAS exhibits up to 80% of the BSR gain compared to existing solutions.

Abstract 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.

Stress is caused by a variety of events in our daily lives. By anticipating stressful situations, we can prepare and better cope with stressors when they actually occur. However, many past-centric personal informatics (PI) tools focus on capturing events that already happened and analyzing the data. In this work, we examine how anticipation — a future-centric self-tracking practice — could be used to manage daily stress levels. To address this, we built MindForecaster, a calendar- mediated stress anticipation application that allows users to expect stressful events in advance, generates activities to mitigate stress, and evaluates actual stress levels compared to previously estimated stress levels. In a 30-day deployment with 47 users, the users who explicitly planned and executed coping interventions reported reduced stress more than those who only expected stressful events. We suggest design implications for stress management by incorporating the properties of anticipation into current PI models.

Buffering architectures and policies for their efficient management are core ingredients of a network architecture. However, despite strong incentives to experiment with and deploy new policies, opportunities for changing anything beyond minor elements are limited. We introduce a new specification language, OpenQueue, that allows to express virtual buffering architectures and management policies representing a wide variety of economic models. OpenQueue allows users to specify entire buffering architectures and policies conveniently through several comparators and simple functions. We show examples of buffer management policies in OpenQueue and empirically demonstrate its impact on performance in various settings.

In the zettabyte era, per-flow measurement becomes more challenging for the data center owing to the increment of both traffic volumes and the number of flows. Also, the swiftness of detection of anomalies (e.g., congestion, link failure, DDoS attack, and so on) becomes paramount. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in high-speed but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. To verify its practicability, we further build a per-flow measurement system, called InstaMeasure, on an off-the-shelf Atom (lightweight) processor board. We evaluate our proposed system in a large scale real-world experiment (monitoring our campus main gateway router for 113 hours, and capturing 122.3 million flows). We verify that InstaMeasure can detect heavy hitters (HHs) with 99% accuracy and within 10 ms (detection is faster for heavier HHs) while providing the one million flows record with only tens of MB of DRAM memory. InstaMeasure's various performance metrics are further investigated by the packet trace-driven experiment using one-hour CAIDA dataset, where the target of measurement was all the 78 million L4 flows for one-hour.

As the number of wireless device deployments grows, it is desirable to share the highly limited wireless bandwidth efficiently and cooperatively. In WLAN, using a central controller for resource sharing and management is a common practice in mid-size and large-size network. However, in case of small businesses (i.e., restaurants, coffee shops, etc.), business owners cannot afford to obtain the controller. To realize the bandwidth sharing among Access Points (AP) in a distribute manner, seamless handoff of mobile devices (i.e., smartphones and tablets) between small-business owned Access Points (APs) via association control and maintain stable TCP connection are essential but quite challenging. This poster proposes a novel way to cooperatively share wireless resources among the APs. This includes an efficient association control between stations and APs, dedicated virtual access point per station, and tunneling support maintaining existing TCP connection after relocation to another AP.

In recent years, large-scale data collection has become crucial in Human-Computer Interaction (HCI) research. With a sharp climb of the amount of data being gathered due to an increasing number of mobile and wearable devices, real-time maintenance of Data Quality (DQ) of data-collection campaigns has already become an overwhelming task, especially in large-scale experiments. This paper proposes EasyTrack, a platform that collects large-scale data in an automatized manners. We describe how our proposed solution detects and tackles issues in data collection campaigns in an automated manner.

In the zettabyte era, per-flow measurement becomes more challenging owing to the growth of both traffic volumes and the number of flows. Also, swiftness of detection of anomalies becomes paramount. For fast and accurate anomaly detection, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in a very fast but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. We evaluated our system in a large scale real-world experiment. As one key application, FlowRegulator detected heavy hitters with 99.8% accuracy.

Recently, the concept of fog-cloud storage is attracting lots of attentions to overcome the limit of the central cloud storage. A storage audit scheme aims to ensure user that his/her data on the storage is sound. So far, various audit schemes have been introduced for cloud storages. However, compared to a central cloud storage, a distributed fog-cloud storage consists of multiple local fog storages in addition to a global cloud storage and therefore it is not straightforward to directly apply an existing audit scheme for a cloud storage to a fog-cloud storage. To address this issue, this paper introduces a new fog-cloud storage architecture which can achieve much higher throughput compared to the traditional central cloud storage architecture by reducing the traffics at the routers nearby the cloud storage. The proposed architecture provides transparency such that an end user device does not know the existence of fog storages, and only needs to upload its request toward the central cloud. This means that there is no need to make a modification on the existing end user devices. Our system provides a stronger audit scheme which is naturally coupled with the initial data upload process and does not suffer from the replay attack using old proof of data soundness.

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.

In this work, we propose an SDN-based WLAN monitoring and management framework called RFlow+to address WiFi service dissatisfaction caused by the limited view (lack of scalability) of network traffic monitoring and absence of intelligent and timely network treatments. Existing solutions (e.g., OpenFlow and sFlow) have limited view, no generic flow description, and poor trade-off between measurement accuracy and network overhead depending on the selection of the sampling rate. To resolve these issues, we devise a two-level counting mechanism, namely a distributed local counter (on-site and real-time) and central collector (a summation of local counters). With this, we proposed a highly scalable monitoring and management framework to handle immediate actions based on short-term (e.g., 50 ms) monitoring and eventual actions based on long-term (e.g., 1 month) monitoring. The former uses the local view of each access point (AP), and the latter uses the global view of the collector. Experimental results verify that RFlow+can achieve high accuracy (less than 5% standard error for short-term and less than 1% for long-term) and fast detection of flows of interest (within 23 ms) with manageable network overhead. We prove the practicality of RFlow+by showing the effectiveness of a MAC flooding attacker quarantine in a real-world testbed.

In this work, we propose an SDN-based WLAN monitoring and management framework called RFlow+ and devise a two-level counting mechanism, namely a distributed local counter (on-site and real-time) and a central collector (a summation of local counters). Building on that, we proposed a highly scalable monitoring and management framework to handle immediate actions based on short-term (e.g., 50 ms) monitoring and eventual actions based on long-term (e.g., 1 month) monitoring. The former uses the local view of each access point (AP), and the latter uses the global view of the collector.

With the long propagation delay of an acoustic signal in underwater communications systems, relay node selection is one of the key design factors, because it significantly improves end‐to‐end delay, thereby improving overall network performance. To this end, the authors propose orthogonal frequency division multiplexing‐based spectrum‐aware routing (OSAR), a scheme in which spectrum sensing is done by an energy detector, and each sensor node broadcasts its local sensing results to all one‐hop nodes via an extended beacon message. Each sensor node then selects nodes that agree on an idle channel, consequentially forming a set of neighbouring nodes. The selection of a relay node is determined by calculating the transmission delay – the source/relay node selected is the one that has the minimum transmission delay from among all nodes in the neighbouring set. To evaluate OSAR, the authors perform extensive simulations via ns‐MIRACLE for different numbers of channels using a BELLHOP model, and evaluate the average delay for different sensor nodes within the considered network. The results show a substantial decrease in delay as the number of sensor nodes increases in the network. In addition, the authors verify that the packet delivery ratio increases with increases in the number of sensor nodes, and prove better performance in the overhead ratio. The authors' simulation results verify that OSAR outperforms existing solutions.

In wireless sensor networks, an energy‐efficient routing protocol plays a crucial role in extending the lifetime of the network. In order to realise this, an optimal coverage‐preserving scheme (OCoPS) has been proposed by Boukerche et al. in 2005 as an add‐on to the Low‐energy Adaptive Clustering Hierarchy (LEACH) protocol. This scheme operates alongside another coverage‐preserving scheme that excludes redundant nodes if their on‐duty neighbours fully overlap in terms of their sensing ranges, hence saving energy. In this study, the authors propose a central angle decision algorithm that ensures that it does not introduce any coverage hole after applying the coverage‐preserving scheme. They also propose a base station (BS)‐aided clustering routing protocol with a coverage‐preserving scheme (BCoPS) to assess the applicability of the authors’ proposed algorithm to routing protocols and verify the performance gains. In BCoPS, energy‐intensive tasks for deployed sensor nodes are substituted by the BS to ensure longer network lifetime. The performance of the BCoPS was compared to that of LEACH and OCoPS. The results of simulations carried out in considered scenarios showed that BCoPS outperformed OCoPS by >20% in terms of network lifetime in general, and by >30% when the coverage rate was higher than 80%.

Buffering architectures and policies for their efficient management constitute one of the core ingredients of a network architecture. In this work we introduce a new specification language, BASEL, that allows to express virtual buffering architectures and management policies representing a variety of economic models. BASEL does not require the user to implement policies in a high-level language; rather, the entire buffering architecture and its policy are reduced to several comparators and simple functions. We show examples of buffer management policies in BASEL and demonstrate empirically the impact of various settings on performance.

This study analyses performance‐based implications of combining user scheduling and space–time block coding (STBC) (i.e. joint diversity) under channel estimation error. The exact closed‐form expression of joint diversity for the achievable rate is derived. Moreover, the approximate closed‐form expression of joint diversity for the outage achievable rate is derived through Gaussian approximation. The exact closed‐form expression of joint diversity for the outage probability is finally derived, including the quantification of the order of diversity and the signal‐to‐noise ratio (SNR) gain. Using analytical results, it is demonstrated that both the achievable rate and the outage achievable rate of joint diversity improves as the number of user terminals increases through multiuser diversity. The approximate results of the analysis and simulation of the outage achievable rate for joint diversity are well matched as the number of users increases. The outage probability enhances with the number of user terminals through diversity order improvement, whereas the SNR gain is identical to that in conventional non‐scheduling STBC.

Mobile underwater networks with acoustic communications are confronted with several unique challenges such as long propagation delays, high transmission power consumption, and node mobility. In particular, slow signal propagation permits multiple packets to concurrently travel in the underwater channel, which must be exploited to improve the overall throughput. To this end, we propose the delay-aware opportunistic transmission scheduling (DOTS) protocol that uses passively obtained local information (i.e., neighboring nodes' propagation delay map and their expected transmission schedules) to increase the chances of concurrent transmissions while reducing the likelihood of collisions. Our extensive simulation results document that DOTS outperforms existing solutions and provides fair medium access even with node mobility.

Mobile underwater networking is a developing technology for monitoring and exploring the Earth's oceans. For effective underwater exploration, multimedia communications such as sonar images and low resolution videos are becoming increasingly important. Unlike terrestrial RF communication, underwater networks rely on acoustic waves as a means of communication. Unfortunately, acoustic waves incur long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. On the positive side, the long propagation delay permits multiple packets to be “pipelined” concurrently in the underwater channel, improving the overall throughput and enabling applications that require sustained bandwidth. To enable session multiplexing and pipelining, we propose the Multi-session FAMA (M-FAMA) algorithm. M-FAMA leverages passively-acquired local information (i.e., neighboring nodes' propagation delay maps and expected transmission schedules) to launch multiple simultaneous sessions. M-FAMA's greedy behavior is controlled by a Bandwidth Balancing algorithm that guarantees max-min fairness across multiple contending sources. Extensive simulation results show that M-FAMA significantly outperforms existing MAC protocols in representative streaming applications.

Indoor localization has attracted much attention recently due to its potential for realizing indoor location-aware application services. This paper considers a time-critical scenario with a team of soldiers or first responders conducting emergency mission operations in a large building in which infrastructure-based localization is not feasible (e.g., due to management/installation costs, power outage, terrorist attacks). To this end, we design and implement a collaborative indoor positioning scheme (CLIPS) that requires no preexisting indoor infrastructure. We assume that each user has a received signal strength map for the area in reference. This is used by the application to compare and select a set of feasible positions, when the device receives actual signal strength values at run time. Then, dead reckoning is performed to remove invalid candidate coordinates eventually leaving only the correct one which can be shared amongst the team. Our evaluation results from an Android-based testbed show that CLIPS converges to an accurate set of coordinates much faster than existing noncollaborative schemes (more than 50% improvement under the considered scenarios).

Underwater mobile sensor networks have recently been proposed as a way to explore and observe the ocean, providing 4D (space and time) monitoring of underwater environments. We consider a specialized geographic routing problem called pressure routing that directs a packet to any sonobuoy on the surface based on depth information available from on-board pressure gauges. The main challenge of pressure routing in sparse underwater networks has been the efficient handling of 3D voids. In this respect, it was recently proven that the greedy stateless perimeter routing method, very popular in 2D networks, cannot be extended to void recovery in 3D networks. Available heuristics for 3D void recovery require expensive flooding. In this paper, we propose a Void-Aware Pressure Routing (VAPR) protocol that uses sequence number, hop count and depth information embedded in periodic beacons to set up next-hop direction and to build a directional trail to the closest sonobuoy. Using this trail, opportunistic directional forwarding can be efficiently performed even in the presence of voids. The contribution of this paper is twofold: a robust soft-state routing protocol that supports opportunistic directional forwarding; and a new framework to attain loop freedom in static and mobile underwater networks to guarantee packet delivery. Extensive simulation results show that VAPR outperforms existing solutions.

Underwater Acoustic Sensor Networks (UW-ASNs) use acoustic links as a means of communications and are accordingly confronted with long propagation delays, low bandwidth, and high transmission power consumption. This unique situation, however, permits multiple packets to concurrently propagate in the underwater channel, which must be exploited in order to improve the overall throughput. To this end, we propose the Delay-aware Opportunistic Transmission Scheduling (DOTS) algorithm that uses passively obtained local information (i.e., neighboring nodes' propagation delay map and their expected transmission schedules) to increase the chances of concurrent transmissions while reducing the likelihood of collisions. Our extensive simulation results document that DOTS outperforms existing solutions and provides fair medium access.

A SEA Swarm (Sensor Equipped Aquatic Swarm) is a sensor "cloud" that drifts with water currents and enables 4D (space and time) monitoring of local underwater events such as contaminants, marine life and intruders. The swarm is escorted at the surface by drifting sonobuoys that collect the data from 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 one of the surface sonobuoys. Major challenges are the ocean current and the limited resources (bandwidth and energy). In this paper, we address these challenges and propose HydroCast, a hydraulic pressure based anycast routing protocol that exploits the measured pressure levels to route data to surface buoys. The paper makes the following contributions: a novel opportunistic routing mechanism to select the subset of forwarders that maximizes greedy progress yet limiting co-channel interference; and an efficient underwater "dead end" recovery method that outperforms recently proposed approaches. The proposed routing protocols are validated via extensive simulations.

In wireless sensor networks, an energy-efficient routing protocol is a key design factor to prolong network lifetime. Recently, Optimal Coverage-Preserving Scheme (OCoPS) is proposed in [11] as an extension of the Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol with the coverage-preserving scheme which saves energy consumption through excluding redundant nodes of which sensing ranges are fully overlapped by their on-duty neighbors. In this paper, we propose a basestation-aided clustering-based routing protocol, namely, the Basestation-aided clustering routing protocol with the Coverage-Preserving Scheme (BCoPS). In BCoPS, the base station substitutes energy intensive tasks for deployed sensor nodes to prolong network lifetime. The performance of BCoPS is compared with the LEACH and OCoPS. The extensive simulation results show that BCoPS outperforms OCoPS by more than 20% on network lifetime and by more than 30% network lifetime until the coverage rate is higher than 80%.

In wireless sensor networks, energy efficiency is a key design factor to prolong the network lifetime. Recently, optimal coverage-preserving scheme (OCoPS) is proposed in the work of Boukerche et al., (2005) as an extension of the low energy adaptive clustering hierarchy (LEACH) protocol with the coverage-preserving scheme which saves energy consumption through excluding redundant nodes of which sensing ranges are fully overlapped by their on-duty neighbors. Nevertheless, in some stringent applications such as battlefield surveillance, fire detection, and toxic liquid leaking detection, the higher network coverage quality is also strictly required. In this paper, we propose the central angle decision algorithm (CADA) which guarantees no coverage-hole during the coverage-preserving scheme. To evaluate applicability of our proposed algorithm to routing protocols and its performance, we extend the OCoPS routing protocol with CADA, namely, the optimal coverage-preserving scheme with the central angel decision algorithm (OCoPS_CADA). Extensive simulations show that the OCoPS_CADA outperforms the OCoPS by initially guaranteeing 100% of the network coverage.