Psychotherapy is crucial for managing cue-induced cravings. However, most research has focused on explicit drug cues that elicit intense cravings, and recreating such high-risk scenarios in practice can inadvertently heighten cravings afterward, making these approaches impractical or ethically problematic in real-world settings. To address this, we developed a cue-exposure technology probe system, VirtualCravingProbe, which integrates VR simulations with real-time biofeedback to enhance self-awareness in clinical drug psychotherapy. We conducted an exploratory study with twelve patients recovering from methamphetamine addiction using the VirtualCravingProbe system, generating design guidelines for future iterations of an integrated VR and biofeedback-assisted therapy tool. Results revealed qualitative evidence that real-time heart-rate monitoring in VR heightened patients’ awareness of triggers and their craving responses. These findings align with the CBT cognitive-triangle framework, which emphasizes the interplay of thoughts, emotions, and behaviors. Moreover, the system demonstrated potential to enrich patient–therapist dialogue and support the adoption of effective coping strategies.

Psychotherapy is vital for identifying and managing craving-triggering cues. In this study, we paired virtual reality (VR) drug-cue scenarios with physiological sensors to capture participants’ real-time responses. Qualitative interviews with nineteen patients and six therapists showed that contextual elements resembling past drug-use experiences reliably provoked cravings, while biofeedback data confirmed that VR effectively elicits measurable physical responses. Our findings further suggest that adjusting the completeness and fidelity of VR scenarios to match a patient’s recovery stage can manage craving intensity and prevent urges from persisting beyond each session. Therapists also see opportunities to integrate virtual reality into clinical practice to address challenges from prior sessions and enhance therapeutic outcomes. This paper offers concrete recommendations for developing clinically deployable VR scenarios and outlines implications for future research and therapeutic applications in drug treatment.

Shopping addiction is characterized by unrestrained repetitive purchasing behavior, negatively impacting one's financial stability and social welfare. This study sought to gain insights into the development of self-monitoring devices and data sharing schemes to assist in dealing with shopping addiction. In this two-phase study, the Bergen Shopping Addiction Scale was first used in the pre-study screening survey to identify individuals with a propensity toward shopping addiction. In the second phase, an online survey was conducted, comprising (1) a study introduction and consent form, (2) the main survey, and (3) follow-up demographic questions. Phase II involved 332 participants meeting this criterion and was conducted to examine behavioral tendencies, evaluate prior intervention experiences, and elicit user preferences pertaining to self-monitoring and data sharing. The majority of respondents expressed a preference for systems that facilitate the tracking of shopping behavior and enable data sharing with intimate partners. Based on these findings, we recommend three directions for the further development of support systems: 1) Reduce the burden of mood tracking; 2) Streamline mechanisms for sharing data with key support figures; and 3) Tailor intervention strategies in accordance with key addiction indicators.

Gambling addiction can have a profound impact on the mental and financial well-being of individuals and their families. This paper presents an in-depth study on the development of technologies aimed at promoting collaborative efforts between patients and family members to deal with gambling addiction. The study performed interviews with ten pairs of gambling-addicted patients with a family member, six patients without family participation, and four treatment experts. Thematic analysis was conducted from the perspectives of patients and family members with the aim of identifying key themes underlying the development of the three antecedents of Planned Behavioral Theory: attitude, subjective norms, and perceived behavioral control. In accordance with the tenets of Prospect Theory, we sought to elucidate the process of attitude formation during editing and evaluation. We also analyzed opportunities and concerns in designing technologies to address gambling addiction. The identified themes provided a basis by which to assess design implications from the perspective of Planned Behavioral Theory. Our analysis revealed three directions for future development: 1) helping patients to make informed gambling decisions through rational editing and evaluation, as suggested by Prospect Theory (attitude level); 2) promoting communication within families to enhance mutual understanding and trust (subjective norm level); and 3) helping patients to develop personal capabilities, while providing a realistic impression of their progress (perceived behavioral control level).

Neurofeedback refers to the process of feeding a sensory representation of brain activity back to users in real time to improve a particular brain function, e.g., their focus and/or attention on a particular task. This study addressed the notable lack of research on methods used to visualize EEG data and their effects on the immersive quality of VR. We developed an algorithm to quantify focus, yielding a focus score. A pre-study with twenty participants confirmed its effectiveness in distinguishing between focused and relaxed mental states. Subsequently, we used this focus score to prototype a VR experience system visualizing the focus score in preconfigured manners, which was utilized in an exploratory study to assess the impact of different neurofeedback visualization methods on user engagement and focus in VR. Among all the visualization methods evaluated, the environmental scheme stood out due to its superior usability during task execution, its ability to evoke positive emotions through the visualization of objects or scenes, and its minimal deviation from user expectations. Additionally, we explored design guidelines based on collected results for future research to further refine the visualization scheme, ensuring effective integration of the focus score within the VR environment. These enhancements are crucial for designing neurofeedback visualization schemes that aim to boost participant focus in VR settings, offering significant insights into the optimization of such technologies.

Depression is the most common mental health problem in older adults; however, a lack of understanding in the interaction between physical and social causes hinders effective treatment. Unique issues such as age-specific increases in comorbid physical problems and alienation from social contact can make it difficult for health providers to identify instances of depression. These also make it difficult for depressed older adults to communicate with their social resources, such as friends, family, and health providers. Integrating technology-assisted collaboration with members of patients' social network to observe and manage multi-dimensional factors in depressed older adults' states is a potential way to improve the quality of practitioners' treatment-planning around these multi-dimensional factors, as well as provide assistance for family and friends' involvement in managing the depression. We conducted an interview study on stakeholders' perceptions of depression and communication to understand the opportunities and challenges involved in implementing such collaborative design. Interviewees included 16 depressed older adult patients, 10 of their family members, and two psychiatrists. Our findings reveal new insights into 1) patients' and families' social values and understandings of patients' condition, as well as 2) how these values and understandings influenced decision-making on communicating with each other and acting on depression. These insights have implications for the consideration of information and communication systems to aid depressed older adults' recovery and engagement with social network members.

Drug addiction is a chronic condition, marked by compulsive drug use. In previous research, cue exposure and biofeedback technologies proved effective in drug psychotherapy sessions; however, the focus has generally been on the awareness of cravings and the identification of cues. There has been relatively little research on methods aimed at facilitating therapist-patient communication, particularly from a user-centered perspective. In this paper, we describe a qualitative technology probe study exploring the means by which patients identify cues and perceive cravings as well as the way that they communicate with therapists. Our analysis considers the difficulties in cue identification and craving perception, the interactions between the two, and the means by which these characteristics could impact the design of VR support systems in the future.

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous researches have demonstrated various approaches to infer user privacy based on mobile devices. In this paper, we study the electromagnetic (EM) emission of a laptop that could be harvested by a commercial-off-the-shelf (COTS) mobile device, e.g., a smartphone. We proposeMagAttack, which exploits the electromagnetic side channel of a laptop to guess user activities, i.e., application launching and application operation. The key insight ofMagAttackis that applications are discrepant in essence due to the different compositions of instructions, which can be reflected on the CPU power consumption, and thus the corresponding EM emissions.MagAttackis challenging since that EM signals are noisy due to the dynamics of applications and the limited sampling rate of the built-in magnetometers in COTS mobile devices. We overcome these challenges and convert noisy coarse-grained EM signals to robust fine-grained features. We implementMagAttackon both an iOS and an Android smartphone without any hardware modification, and evaluate its performance with 30 popular applications, 30 YouTube videos, and 50 top websites in China. The results demonstrate thatMagAttackcan recognize aforementioned 30 applications with an average accuracy of 98.6 percent, and identify which video out of the 30 candidates being played with an average accuracy of 97.5 percent and visiting which website among the 50 candidates with an average accuracy of 90.4 percent.

Smartphone addiction refers to the problematic use of smartphones, which can negatively impact one’s quality of life and even health. We conducted a two-week technology probe study to explore the use of technologies aimed at improving smartphone addiction among seven dyads of adolescents and their parents. Interviews conducted during and after the probe study revealed that manually reporting lifestyle and well-being data could provide motivation to improve one’s lifestyle and well-being by moderating phone use. Sharing smartphone use data with parents was also shown to head off negative communication loops and foster opportunities to overcome the smartphone addiction.

Technology abuse among adolescents refers to the problematic use of technology devices, and the negative impact it can have on lifestyle and one's physical and mental health. This paper reports on in-depth interviews with 15 dyads of adolescent patients, their parents, and four experts with the objective of unraveling the issue of technology abuse. We conducted qualitative analysis aimed at unpacking the contextual factors affecting technology abuse, and differences between adolescents and their parents pertaining to this issue. Our discussions led us to formulate solutions to technology abuse: (1) motivating adolescents by sending timely reminders and providing interactive micro-incentives; (2) promoting communication between adolescents and their parents by sharing usage data related to device usage; and (3) incorporating social supports to complement parental support, while fulfilling the adolescent's social needs. This paper provides valuable insights into the design of technological solutions aimed at mediating technology abuse.

Maintaining an awareness of one’s well-being and making work-related decisions to achieve work-life balance is critical for flexible long-hour workers. In this study, we propose that social sensing could address bottlenecks in worker’s awareness, interpretation of the informatics, and subsequent behavioral change. We conducted a four-week technology probe study by recruiting flexible long-hour professional drivers (Taxi and Uber drivers) and their significant others to use a social sensing prototype which collects data from the drivers and shares it with their partners as well as incorporates partners’ observations. We interviewed them before and after the probe study and found that while technological sensing was able to increase drivers’ awareness of their well-being status and intention to modify behaviors. The “social sensing” design was able to further shape such awareness or intention into action, highlighting the potential of using the sociotechnical approach in promoting work-life balance among long-hour workers.

Various characteristics of mobile applications (apps) and associated in-app services have been used reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to rigorously restrict access to data related to mobile app usage. This paper outlines a novel approach to the extraction of detailed app usage information based on analysis of the electromagnetic (EM) signals emitted from mobile devices when executing app-related tasks. Note that this type of EM leakage becomes high-complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. This paper proposes a deep learning-based multi-label classification system to identify apps and in-app services based on magnetometer readings. The proposed MAGTHIEF system uses accelerometer and gyroscope data to cancel out the offset in geomagnetic signals followed by an elaborate deep region convolution neural network (DRCNN) to differentiate among multiple apps and the corresponding inapp services. Experiments on 50 apps demonstrated the efficacy of MAGTHIEF in identifying multiple apps and in-app services, achieving high average macro F1 scores of 0.87 and 0.95, respectively. MAGTHIEF also achieved time duration accuracy of 89.5% in recognizing app trajectory in the real-world scene.

On-skin displays are emerging as a wearable form factor for the display of information; however, the perception of using such devices in public could determine whether they are eventually adopted or rejected. This study investigated the means by which on-skin notification displays are perceived by the general public. We adopted a mixed-methods approach to the analysis of results from an online survey (n = 254) and in-lab interviews (n = 36) pertaining to the novel form factor, device materiality, and envisioned use cases. The study was conducted in the US and Taiwan in order to examine cross-cultural attitudes toward device usage. The results of this structured examination provide valuable insights into the design of on-skin notification displays for everyday use across cultures.

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous researches have demonstrated various approaches to infer user privacy based on mobile devices. In this paper, we study a new side channel of a laptop that could be harvested by a commercial-off-the-shelf (COTS) mobile device, eg, a smartphone. We propose MagAttack, which exploits the electromagnetic (EM) side channel of a laptop to infer user activities, i.e., application launching and application operation. The key insight of MagAttack is that applications are discrepant in essence due to the different compositions of instructions, which can be reflected on the CPU power consumption, and thus the corresponding EM emissions. MagAttack is challenging since that EM signals are noisy due to the dynamics of applications and the limited sampling rate of the built-in magnetometers in COTS mobile devices. We overcome these challenges and convert noisy coarse-grained EM signals to robust fine-grained features. We implement MagAttack on both an iOS and an Android smartphone without any hardware modification, and evaluate its performance with 13 popular applications and 50 top websites in China. The results demonstrate that MagAttack can recognize aforementioned 13 applications with an average accuracy of 98.6%, and figure out the visiting operation among 50 websites with an average accuracy of 84.7%.

Wearable devices have evolved towards intrinsic human augmentation, unlocking the human skin as an interface for seamless interaction. However, the non-traditional form factor of these on-skin interfaces, as well as the gestural interactions performed on them may raise concerns for public wear. These perceptions will influence whether a new form of technology will eventually be accepted, or rejected by society. Therefore, it is essential for researchers to consider the societal implications of device design. In this paper, we investigate the third person perceptions of a user's interactions with an on-skin touch sensor. Specifically, we examine social perceptions towards the placement of the on-skin interface in different body locations, as well as gestural interactions performed on the device. The study was conducted in the United States and Taiwan to examine cross-cultural attitudes towards device usage. The results of this structured examination offer insight into the design of on-skin interfaces for public use.

Our current project involves the development of a wristband-mounted sensor that is meant to function as an alcohol use monitoring system. This paper focuses on the degree to which physical activity influences ethanol concentrations in the vapor secreted from the skin through collecting data from seven recruited participants when they conducting one designated activity, which could presumably affect the accuracy of detection results. We proposes a preliminary design of building a personal alcohol tracking system that can improve the reliability and affordability of current transdermal ethanol tracking devices to accommodate potential interferences presented in daily life and be intuitive to be used to raise the awareness of alcohol use.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we addressed this issue by developing a novel QR code (called mQRCode), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQRCode was > 98.6% within 10.2 frames via a multi-frame decryption method. The decryption rate for cameras positioned 20° off axis or > 10cm away from the designated location dropped to 0%, indicating that mQRCode is robust against attacks.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we address this issue by developing a novel QR code (called mQR code), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moiré pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQR codes is $> 98%$. The decryption rate for cameras positioned $20\degree$ off axis or $> 10cm$ from the designated location drops to $0%$, indicating that any attackers will be unable to steal a usable image.

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Data pertaining to the availability of parking slots is crucial to the efficient operation of systems designed to monitor the state of parking spaces. Outdoor parking systems have been developed using wireless sensors, Internet of Things (IoT) technology, and cameras. Unfortunately, interference from electromagnetic fields complicates the tuning of parameters for detection algorithms and limits accuracy to only 90 percent. In this study, we investigated these problems by collecting data from magnetic sensors, light sensors, and LoRa wireless modules used in the detection transient events (car arrivals and departures) over a period of 13 months. This led to the design an adaptive occupancy detection system using a variety of sensors, which can be deployed with only minimal calibration.

Ketamine is an addictive drug that has been shown to inflict considerable physical and mental damage on users. Due in part to its low cost, ketamine has become one of the most popular club drugs among young adults and teenagers in Southeast Asia. This paper proposes a phone-based support system (KeDiary) with Bluetooth-enabled device for the screening of saliva, as a means of assisting ketamine-dependent patients to self-monitor their ketamine use following acute withdrawal treatment. We also conducted a practical experiment to evaluate the feasibility of the proposed system, wherein three ketamine-dependent patients self-administered tests at least once per day over a period of three weeks. Follow-up interviews with the same users helped in the further refinement of the proposed self-monitoring system.

Alcohol dependence is a chronic disorder associated with severe harm in multiple areas, and relapsing is easy, despite treatment. This study proposes SoberDiary, a phone-based support system that enables alcohol-dependent patients to self-monitor and -manage their own alcohol behavior, and remain sober in their daily lives. We tested SoberDiary in a real-life 12-week user study involving 27 clinical patients. The quantitative and qualitative results revealed that SoberDiary helped patients self-monitor and -manage their alcohol-use behavior, and reduced their total alcohol consumption as well as the number of heavy drinking days. Compared with patients who received standard treatment alone, this study demonstrated SoberDiary successfully complemented current alcohol treatment in reducing patients' alcoholic cravings and dropout rate over 3-month study period. Follow-up interviews further revealed the sophisticated use practices and value of SoberDiary.

The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.

To facilitate the collection of patient biosignals, designing extensible sensing devices in which sensor management is simplified is essential. This paper presents BioScope, an extensible sensing system that facilitates collecting data used in nursing assessments. We conducted experiments to demonstrate the potential of the system. The results obtained in this study can be applied in improving the design, thus enabling BioScope to facilitate data collection in numerous potential applications.

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.

We present CarSafe, a new driver safety app for Android phones that detects and alerts drivers to dangerous driving conditions and behavior. It uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front-facing camera while at the same time tracking road conditions using the rear-facing camera. Today's smartphones do not, however, have the capability to process video streams from both the front and rear cameras simultaneously. In response, CarSafe uses acontext-aware algorithm that switches between the two cameras while processing the data in real-time with the goal of minimizing missed events inside (e.g., drowsy driving) and outside of the car (e.g., tailgating). Camera switching means that CarSafe technically has a "blind spot" in the front or rear at any given time. To address this, CarSafe uses other embedded sensors on the phone (i.e., inertial sensors) to generate soft hints regarding potential blind spot dangers. We present the design and implementation of CarSafe and discuss its evaluation using results from a 12-driver field trial. Results from the CarSafe deployment are promising -- CarSafe can infer a common set of dangerous driving behaviors and road conditions with an overall precision and recall of 83% and 75%, respectively. CarSafe is the first dual-camera sensing app for smartphones and represents a new disruptive technology because it provides similar advanced safety features otherwise only found in expensive top-end cars.

We present CarSafe, the first driver safety application that uses dual cameras on smartphones to detect and alert drivers to dangerous driving conditions. CarSafe fuses events detected from cameras and readings from embedded sensors on the phone -- such as the GPS, accelerometer and gyroscope -- to detect and alert the driver of dangerous driving behavior in and outside of the car. Results from a 12-driver field trial show CarSafe can infer five of the most commonly occurring dangerous driving conditions with an overall precision and recall of 83% and 75%, respectively.

We propose MobileQueue, a mobile queue-card management system that offers more freedom to customers by enabling image-based queue-card retrieving and service-information querying actions using mobile phones. MobileQueue interacts with cloud services allowing customers to query summary description and availability (e.g., available seats) of services provided by stores. MobileQueue also offers suggestions to waiting customers such as potentially interesting substitute activities and stores.

We demonstrate CarSafe, a driver safety application for Android phones that fuses information from both front and back cameras and others embedded sensors on the phone to detect and alert drivers to dangerous driving conditions in and outside of the car. In this demonstration, we set up an emulated driving environment to show how CarSafe works.

Driving while being tired or distracted is dangerous. We are developing the CafeSafe app for Android phones, which fuses information from both front and back cameras and others embedded sensors on the phone to detect and alert drivers to dangerous driving conditions in and outside of the car. CarSafe uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front camera while at the same time tracking road conditions using the back camera. CarSafe is the first dual-camera application for smart-phones.

The prevalence of augmented reality devices in our daily lives offers increasing opportunities for users to navigate the real world. However, as users move, on-screen targets move unpredictably, and eventually disappear from the screen in mobile navigation scenarios. The changing target movement pattern creates difficulty for users in selecting the targets on time before targets escape from the screen. This study proposes a novel target selecting technique, AttachedShock, for easing target selection tasks on augmented reality devices by crossing a naturally expanding wave pattern that is attached to targets. We evaluated the effectiveness of the proposed technique by conducting comparative studies on measuring the performance of four techniques under various mobile navigation scenarios. The results indicate that the proposed technique assists users in selecting moving targets to improve the error rate substantially, by a minimum of 61.75%, and incurs acceptable distractions to users, compared to other techniques.

To promote energy-saving behavior, disaggregating electricity usage is critical for increasing consumer awareness of energy usage behavior. This study proposes HeatProbe, a thermal-based power meter system that uses thermal imaging to track disaggregated appliance usage. We have designed, prototyped, and tested the HeatProbe system. Results show that HeatProbe successfully senses individual appliance operating durations with an average error of 125.03 seconds, achieving 80.2% appliance power accounting accuracy in different appliance usage scenarios.

Boundary detection is a form of location-aware services that aims at detecting targets crossing certain critical regions. Typically, a lower location sampling rate contributes to a lower level of energy consumption but, in the meantime, delays the detection of boundary crossing events. Opting to enable energy-efficient boundary detection services, we propose a mobility-aware mechanism that adapts the location sampling rate to the target mobility. Results from our simulations and live experiments confirm that the proposed adaptive sampling mechanism is effective. In particular, when experimented with realistic errors measured from a live radio-frequency-based localization system, the energy consumption can be reduced significantly to 20 percent.

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption