Public displays remain limited to flat, network-dependent media, missing the opportunities for spontaneous immersive user interaction. In this work, we present HoloQRam, a system for real-time, infrastructure-free spatial video delivery using animated QR codes and mobile RGB-D reconstruction. HoloQRam transmits lightweight 128×128 RGB streams encoded into sequential QR symbols on commodity displays, avoiding device pairing or network connectivity. On the receiver side, a genetic algorithm (GA)-optimized filtering pipeline enhances decoding robustness under various QR image capture conditions, while a hybrid Transformer-CNN jointly performs 4× super-resolution and depth estimation to reconstruct high-quality RGB-D videos. Implemented on smartphones and tested across LCD, OLED, and projector displays, HoloQRam achieves up to 70% higher decoding success rates than baselines, maintains >30 fps throughput, and delivers 30.8 dB PSNR with consistent depth accuracy. Our results demonstrate the feasibility of spontaneous, one-to-many spatial video broadcasting, transforming ordinary public screens into rich volumetric media sharing portals.

We propose GPUSched, a foreground graphics aware preemptive scheduling framework for concurrent DNN inference and seamless graphics rendering on mobile GPUs. Given that existing GPU scheduling methods relying on offline slicing are inadequate for dynamic graphics workloads, GPUSched is driven by two key components: (i) Render-State Detection via GPUPing, which leverages lightweight probing of GPU queue latency to accurately identify foreground rendering operations and (ii) Adaptive Operator Chunking, which dynamically fits DNN model chunk sizes within GPU idle times in a GPU frequency-aware manner. By combining these mechanisms GPUSched, coordinates DNN inference with graphics rendering in real time, preventing deadline violations while maximizing GPU utilization. Experiments on two commodity mobile devices show that GPUSched consistently outperforms baseline schedulers, achieving lower deadline miss rates while sustaining high rendering quality even under demanding graphics workload.

Accurate swimming performance monitoring has traditionally relied on wearable sensors, which can disrupt natural technique and are impractical in competitive settings. In this paper, we present a fully vision-based system for automatic swimmer analysis using overhead drone footage, removing the need for any wearable device or underwater equipment. By fine-tuning pose estimation models for aerial aquatic conditions, our approach robustly extracts full-body swimmer skeletons even under challenging scenarios such as splashes and partial occlusions. From these poses, we classify swimming strokes, compute instantaneous speed, estimate lap times, and count individual strokes. Unlike existing methods, our system provides scalable, unobtrusive, and infrastructure-free tracking. Evaluated on real-world drone-captured swimming competition data, our method achieves a median speed estimation error below 4% (under 0.05 m/s), a median lap time error of just 0.03s, and stroke count errors typically under one stroke per lap.

Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs significant environmental, economic, and ethical costs, limiting sustainability and equitable access. Inspired by biological sensory systems, where adaptation occurs dynamically at the input (e.g., adjusting pupil size, refocusing vision)—we advocate for adaptive sensing as a necessary and foundational shift. Adaptive sensing proactively modulates sensor parameters (e.g., exposure, sensitivity, multimodal configurations) at the input level, significantly mitigating covariate shifts and improving efficiency. Empirical evidence from recent studies demonstrates that adaptive sensing enables small models (e.g., EfficientNet-B0) to surpass substantially larger models (e.g., OpenCLIP-H) trained with significantly more data and compute. We (i) outline a roadmap for broadly integrating adaptive sensing into real-world applications spanning humanoid, healthcare, autonomous systems, agriculture, and environmental monitoring, (ii) critically assess technical and ethical integration challenges, and (iii) propose targeted research directions, such as standardized benchmarks, real-time adaptive algorithms, multimodal integration, and privacy-preserving methods. Collectively, these efforts aim to transition the AI community toward sustainable, robust, and equitable artificial intelligence systems.

With the aggressive growth in AI model complexity to achieve higher performance, operating them on mobile platforms becomes more and more challenging. This issue is even more prominent for time-series data, commonly dealt with in mobile/IoT computing scenarios, given their inherent issues such as label imbalance, user and sensor diversity, and out-of-distribution inference data. In this work, we investigate into the efficacy of a k-nearest neighbor classifier enhanced with a lossless compressor gzip, introducing novel sequence tokenization algorithms that show superior performance compared to traditional machine/deep learning classifiers. Our evaluation across three diverse real-world applications with distinct datasets emphasizes the generalization potential of our approach in real-world scenarios, especially in situations with few training samples.

The prevalence of counterfeit infant formulas worldwide poses serious threats to infant health and safety, a concern highlighted by the notorious Melamine Milk Scandal that affected hundreds of thousands of children. The primary challenge in detecting counterfeit formulas lies in their sophisticated adulteration and substitution techniques. Such detection is feasible only in laboratory settings, making it nearly impossible for average consumers to test the formula before feeding their infants. To address this problem, we propose PowDew, a novel and practical system for detecting counterfeit infant formula that utilizes only a commodity smartphone. PowDew operates by capturing and analyzing the interaction of a water droplet with the powdered formula, focusing on the droplet motion, namely its spreading and penetration. Our insight is that the droplet motions are governed by powder-specific properties such as wettability and porosity. PowDew analyzes the subtle differences in droplet motions, and infers the formula's authenticity. To demonstrate PowDew's effectiveness, we implement PowDew and conduct comprehensive real-world experiments under varying conditions with different brands of powdered infant formula and adulterants. Our experiments result in a total of 12,000 minutes of video recordings of the droplet motions on various infant formulas, including authentic and altered. Our experiments demonstrate that PowDew yields an overall detection accuracy of up to 96.1%.

The rise of counterfeit powdered food products, exemplified by notorious incidents such as the Melamine Milk Scandal, poses significant risks to consumers. The primary challenge in identifying these counterfeit products comes from their intricate adulteration and substitution techniques. Currently, such identification methods are only viable in laboratory settings, making average consumers nearly impossible to authenticate their products. To address this limitation, we propose PowDew, a novel system that employs a smartphone to detect counterfeit powdered food products. PowDew utilizes the powder's physical property, namely droplet motion, as a basis for verification. Through real-world experiments, PowDew demonstrate a practicality with achieving an overall detection accuracy of up to 96.1%.

While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients' resources and careful parameter aggregation to accommodate this heterogeneity. We propose HypeMeFed, a novel federated learning framework for supporting client heterogeneity by combining a multi-exit network architecture with hypernetwork-based model weight generation. This approach aligns the feature spaces of heterogeneous model layers and resolves per-layer information disparity during weight aggregation. To practically realize HypeMeFed, we also propose a low-rank factorization approach to minimize computation and memory overhead associated with hypernetworks. Our evaluations on a real-world heterogeneous device testbed indicate that HypeMeFed enhances accuracy by 5.12% over FedAvg, reduces the hypernetwork memory requirements by 98.22%, and accelerates its operations by 1.86X compared to a naive hypernetwork approach. These results demonstrate HypeMeFed's effectiveness in leveraging and engaging heterogeneous clients for federated learning.

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (iii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work summarizes and analyzes state-of-the-art systems research that allows such on-device model training capabilities and provides a survey of on-device training from a systems perspective.

In this demonstration, we introduce the vulnerability of indices in unpooling autoencoders. We show that this small factor can be maliciously exploited by performing man-in-the-middle attacks to eavesdrop on the victim's data, resulting in reconstruction and adversarial attacks. Such attacks especially make systems that integrate collaborative inference operations vulnerable. This demo presentation will empirically show the feasibility of index-based attacks by launching reconstruction and adversarial attacks on embedded/mobile computing platforms.

We introduce Monte-Carlo Attention (MCA), a randomized approximation method for reducing the computational cost of self-attention mechanisms in Transformer architectures. MCA exploits the fact that the importance of each token in an input sequence vary with respect to their attention scores; thus, some degree of error can be tolerable when encoding tokens with low attention. Using approximate matrix multiplication, MCA applies different error bounds to encode input tokens such that those with low attention scores are computed with relaxed precision, whereas errors of salient elements are minimized. MCA can operate in parallel with other attention optimization schemes and does not require model modification. We study the theoretical error bounds and demonstrate that MCA reduces attention complexity (in FLOPS) for various Transformer models by up to 11 in GLUE benchmarks without compromising model accuracy. Source code and appendix: https://github.com/eis-lab/monte-carlo-attention

On-device deep neural network (DNN) training holds the potential to enable a rich set of privacy-aware and infrastructure-independent personalized mobile applications. However, despite advancements in mobile hardware, locally training a complex DNN is still a nontrivial task given its resource demands. In this work, we show that the limited memory resources on mobile devices are the main constraint and propose Sage as a framework for efficiently optimizing memory resources for on-device DNN training. Specifically, Sage configures a flexible computation graph for DNN gradient evaluation and reduces the memory footprint of the graph using operator- and graph-level optimizations. In run-time, Sage employs a hybrid of gradient checkpointing and micro-batching techniques to dynamically adjust its memory use to the available system memory budget. Using implementation on off-the-shelf smartphones, we show that Sage enables local training of complex DNN models by reducing memory use by more than 20-fold compared to a baseline approach. We also show that Sage successfully adapts to run-time memory budget variations, and evaluate its energy consumption to show Sage's practical applicability.

This demo is supplementary to the accepted submission #117 "Memory-efficient DNN Training on Mobile Devices", without an extended abstract. We present (1) federated learning and (2) model fine-tuning demo applications on Android smartphones which exploit the low-memory DNN training scheme featured in our paper.

Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.

Reckless driving is dangerous, and must be monitored, detected, and law-enforced to assure road safety. For this purpose, this work presents an embedded system for monitoring and detecting reckless driving activities on the road autonomously in real-time. Using an embedded GPU (eGPU) platform, a camera, and a combination of light-weight deep learning models, we design a system that can identify abnormal vehicle motions on the road. Our system analyzes discrete per-frame images from vehicle detection algorithms, and creates a continuous trace of a vehicle's motion trajectory. While doing so, a virtual grid is generated on the road to obtain positions of vehicles with less overhead and accurately track a vehicle's movement even with low frame rate (5fps) videos. Vehicle's motion trajectory is then compared against the surrounding to identify abnormal behavior through driving activity classification, which can be provided to law enforcement personnel for final validation. The key challenge is the fundamental resource constraints of embedded platforms, and we design algorithms to overcome their limitations. Evaluation results show that our scheme can wellextract the horizontal and vertical movements of a vehicle (100% recall and 67% precision) and show the potential for truly autonomous reckless driving activity detection systems.

While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce MilliCat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat’s real-time image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19 × improvement) and consequently more frequent image usage (1.8 ×) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience.

Digital contact tracing is an essential countermeasure for an epidemic as a society, and balancing the surveillance resolution and user privacy for contact tracing remains an open challenge. Existing contact tracing schemes are primarily based on proximity tracing, which uses Bluetooth to detect coexistence. Proximity tracing has a strong advantage in anonymizing the users, but shows low epidemiological resolution and lacks the flexibility to be integrated with other data sources. To address this problem, we propose an alternative scheme we phrase as context tracing. Our scheme achieves strong performance in both surveillance resolution and user privacy protection by integrating multi-modal sensor fusion and homomorphic encryption. While this advantage comes at the cost of high computational overhead, we discuss possible optimization strategies for reducing energy consumption on mobile devices.

Improvements in small sized sensors allow us to easily detect the presence of Volatile Organic Compounds (VOCs) in the air using easy-to-deploy Internet of Things (IoT)-scale devices. However, classifying what VOC exists in the environment still remains as a complex task. Knowing what VOCs are in the air can help us remove the main cause that vents VOC materials in order to maintain clean air quality. In this work, we present VOCkit, an IoT sensor kit for non-chemical experts to easily detect and classify different types of VOCs. VOCkit combines miniature chemically-designed fluorometric sensors for recognizing VOCs with an embedded imaging system for classification. Exposing the fluorometric sensors with various VOCs, result in photophysical property change of each fluorescent compound, which composes the sensors, and the synergistic combination of the changes create unique individual fluorescent color patterns respectively to the VOC material. The fluorescent color change pattern is captured using a camera and the images are processed with machine learning algorithms on the embedded platform for VOC classification. Using 500 fluorometric sensor images collected for five different commonly contactable VOCs, we show the feasibility of performing VOC classification on small-sized IoT devices. For the VOC types of our interest, our results show a classification accuracy of 97%, implying the potential applicability of VOCkit for real-world usage.

This work presents FDTLS, a security framework that combines storage and network/communication-level security on resource limited Internet of Things (IoT) devices using Datagram Transport Layer Security (DTLS). While coalescing the storage and networking security schemes can reduce redundant and unnecessary cryptographic operations, we identify security-and system-level challenges that can occur when applying DTLS towards such concept. FDTLS addresses these challenges by employing an asymmetric key generation scheme, a virtual peer-based handshaking mechanism, and a header size reduction scheme. Our results obtained using Contiki-based implementations on OpenMote devices show that compared to using storage and networking security separately, FDTLS can reduce the network response latency and improve energy savings.

This work presents FDTLS, a security framework that combines storage and network/communication-level security for resource limited Internet of Things (IoT) devices using Datagram Transport Layer Security (DTLS). While coalescing storage and networking security scheme can reduce redundent and unnecessary operations, we identify security- and system-level challenges that can occur when applying DTLS. FDTLS addresses these challenges by employing asymmetric key generation, a virtual peer, and header reduction-based storage optimization. Our results obtained using a Contiki-based implementation on OpenMote platforms show that compared to using storage and networking security separately, FDTLS can reduce the latency of packet transmission responses and also contribute to saving energy.

can enable futuristic applications including many Virtual Reality, Augmented Reality, and cloud gaming applications on resource constraint mobile devices. While RGR requires a high-level of networking bandwidth for seamless servicing, emerging high-speed communication technologies such as IEEE 802.11ax and millimeter wavebased communications are expected to provide high-bandwidth and low-latency networking performance. Thus, they can potentially resolve the transmission latency constraint, which is one of the most tricky obstacles to resolve prior to applying various RGR applications in the real world.

We present LpGL, an OpenGL API compatible Low-power Graphics Library for energy efficient AR headset applications. We first characterize the power consumption patterns of a state of the art AR headset, Magic Leap One, and empirically show that its internal GPU is the most impactful and controllable energy consumer. Based on the preliminary studies, we design LpGL so that it uses the device's gaze/head orientation information and geometry data to infer user perception information, intercepts application-level graphics API calls, and employs frame rate control, mesh simplification, and culling techniques to enhance energy efficiency of AR headsets without detriment of user experience. Results from a comprehen- sive set of controlled in-lab experiments and an IRB-approved user study with 25 participants show that LpGL reduces up to 22% of total energy usage while adding only 46 sec of latency per object with close to no loss in subjective user experience.

We present Reeboc that combines machine learning and k-means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. Instead of simply analyzing a single input sentence, we consider recent sentences exchanged in a conversation. we performed a user study with 17 participants in 8 groups in a realistic mobile chat environment. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than without emoji recommendation).

Electrocardiogram (ECG) signals offer rich information for analyzing and understanding the cardiac activity of a person. The continuous monitoring of ECG can help diagnose cardiac disorders, such as arrhythmia, effectively. While many wearable healthcare platforms offer continuous ECG monitoring, these devices are cumbersome in the fact that they need to be continuously attached to the human body, which causes uncomfortableness, and limits their usage when monitoring a person's ECG throughout the night as they sleep. In this work, we propose a fully non-intrusive sensing system for monitoring the ECG of a person while in bed. Specifically, we present Heartquake, a geophone-based sensing system for extracting ECG patterns using heartbeat vibrations that penetrate through the mattress. The cardiac activity-originated vibration patterns are captured on the geophone and sent to a server, where the data is filtered to remove external noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform extraction. Our experimental results with 21study participants suggest that Heartquake can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of as low as 16 msec when participants are stationary on the bed. With additional noise factors, this error shows an increase, but can be mitigated from model personalization to still be sufficient enough as a screening tool to detect urgent situations.

We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network models to extract both spatial and temporal information of the video to reconstruct a pulse waveform signal that is optimized for estimating IBI. We evaluated VitaMon with a dataset collected from 30 participants under various conditions involving different light intensity levels and motion artifacts. With the 15 fps video input (66.67 ms time resolution), VitaMon can measure IBI with an average error of 14.26 ms and 21.65 ms using personal and general model respectively. HRV features including geometry Poincare plot, time- and frequency-domain features extracted from the IBI measurement all have high correlation with the reference signal.

Certificate-based Public Key Infrastructure (PKI) schemes are used to authenticate the identity of distinct nodes on the Internet. Using certificates for the Internet of Things (IoT) can allow many privacy sensitive applications to be trusted over the larger Internet architecture. However, since IoT devices are typically resource limited, full sized PKI certificates are not suitable for use in the IoT domain. This work outlines our approach in compressing standards-compliant X.509 certificates so that their sizes are reduced and can be effectively used on IoT nodes. Our scheme combines the use of Concise Binary Object Representation (CBOR) and also a scheme that compresses all data that can be implicitly inferenced within the IoT sub-network. Our scheme shows a certificate compression rate of up to ~30%, which allows effective energy reduction when using X.509-based certificates on IoT platforms.

No abstract available.

No abstract available.

This paper proposes an efficient architecture of HEVC in-loop filters (ILFs) with the target of providing effective multicore utilization for ultra-high definition video applications. While HEVC allows for a high level of parallelization, the issue of data dependencies at the ILF leads to inefficient parallel processing performance. The novel memory organization and management techniques address the data dependence-related issues between multiple processing units and enable to filter the flexible area on multicore decoder. In addition, we introduce the adaptive deblocking filtering order (ADFO) to minimize the impact of bus congestion when multiple cores interoperate for processing very large data. Furthermore, we design the deblocking filter with skip mode pipelining to achieve the high performance minimizing the increased cost and the power consumption. For SAO, we apply the window-based parallel SAO filtering scheme. The resource sharing is considered throughout the entire architecture. Based on both experimental and analytical results, our proposed design can achieve more than 1.31 Gpixels/s and less than 2.6 Gpixels/s at maximum frequency 660 MHz in single core, and consumes 56.2 Kgates including 10.6 Kgates for memory management architecture, which supports multicore decoder, and about 20.8 mW power on average when synthesizing with the 28 nm CMOS library. Moreover, the skip modes of DF improve both the performance and the power dissipation. The ADFO improves the performance of ~9.17% when decoding 8 K sequence on octacore at 400 MHz frequency. TpG (Throughput per Gate) is the highest among the related works.

As mobile computing platforms, head-mounted displays (HMDs) for augmented reality (AR) applications, similar to other mobile computing platforms, face challenges in minimizing their energy usage. While not yet as pervasive as smartphones, we can envision that more useful everyday AR applications can arise as we allow HMDs to enjoy longer operation times. This work takes a first step in designing an application- transparent energy management layer within the AR HMD graphics stack. We point out that one aspect that impacts the HMD lifetime is the frame rate at which AR objects are rendered and displayed. Using the Microsoft Hololens platform we propose a scheme to analyze objects' motion dynamics and respectively control the frame rate while meeting a target user perception. We see this work as a preliminary step towards understanding and improving the operational lifetime for AR HMDs.

Recent advances in machine learning based data analytics are opening opportunities for designing effective clinical decision support systems (CDSS) which can become the "third-eye" in the current clinical procedures and diagnosis. However, common patient movements in hospital wards may lead to faulty measurements in physiological sensor readings, and training a CDSS from such noisy data can cause misleading predictions, directly leading to potentially dangerous clinical decisions. In this work, we present MediSense, a system to sense, classify, and identify noise-causing motions and activities that affect physiological signal when made by patients on their hospital beds. Essentially, such a system can be considered as "glasses" for the clinical third eye in correctly observing medical data. MediSense combines wirelessly connected embedded platforms for motion detection with physiological signal data collected from patients to identify faulty physiological signal measurements and filters such noisy data from being used in CDSS training or testing datasets. We deploy our system in real intensive care units (ICUs), and evaluate its performance from real patient traces collected at these ICUs through a 4-month pilot study at the Ajou University Hospital Trauma Center, a major hospital facility located in Suwon, South Korea. Our results show that MediSense successfully classifies patient motions on the bed with >90% accuracy, shows 100% reliability in determining the locations of beds within the ICU, and each bed-attached sensor achieves a lifetime of more than 33 days, which satisfies the application-level requirements suggested by our clinical partners. Furthermore, a simple case-study with arrhythmia patient data shows that MediSense can help improve the clinical diagnosis accuracy.

Recent advances in smartphone processing power have opened the possibilities for them to act as the processing component of software-defined radios (SDRs). For low-power sensor network systems using various communication protocols, this means that smartphones, when equipped with an SDR, can be their system management end-devices, (potentially) without the need for external communication modules. Nevertheless, the high processor and energy usage overhead of SDRs remains a major technical barrier that blocks the practical adoption of smartphone-based SDRs. In this work, we show that implementation flexibility at the software can relax this overhead. Specifically, we show, using an implementation of the low-power listening (LPL) Medium Access Control (MAC), that software improvements have the potential to significantly reduce the operational overhead of SDRs. Moreover, we show that implementing packet reception filters can help further reduce the performance overhead without sacrificing application-level message exchange qualities. Empirical results with a smartphone-based SDR suggest that by combining LPL with packet reception filters, the processing and energy overhead can be reduced by two to three orders of magnitude. We not only see this as a chance to practically realize smartphones as a wireless sensing system controller but also believe that the experiences with practical smartphone-based SDRs can provide guidelines for future wireless protocol and low-power radio designs that are suitable for mobile computing environments.

With the advancement of technology in various domains, many efforts have been made to design advanced classification engines that aid the protection of civilians and their properties in different settings. In this work, we focus on a set of the population which is probably the most vulnerable: children. Specifically, we present ChildSafe , a classification system that exploits ratios of skeletal features extracted from children and adults using a 3D depth camera to classify visual characteristics between the two age groups. Specifically, we combine the ratio information into one bag-of-words feature for each sample, where each word is a histogram of the ratios. ChildSafe analyzes the words that are normalized within and between the two age groups and implements a fuzzy bin-based classification method that represents bin-boundaries using fuzzy sets. We train and evaluate ChildSafe using a large dataset of visual samples collected from 150 elementary school children and 150 adults, ranging in age from 7 to 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 94%, a false-negative rate as low as 1.82%, and a low false-positive rate of 5.14%. We envision this work as a first step, an effective subsystem for designing child safety applications.

Wireless sensor networks, with their capability to capture physical phenomena at micro-scale, have changed how we collect and analyze data from the real world. Especially, using low-power cameras we can design interesting applications that provide us with previously difficult-to-capture information from the real-world. While cameras hold privacy threats in human-residing environments, they can be actively used in natural world analyzing applications. However, the disadvantage of using cameras is that the samples themselves (e.g., images) have large sizes, forcing the system to exchange more data, and in turn, decreasing energy efficiency. Given that camera-based sensor networks are usually deployed in remote locations, the lifetime of individual devices become a major concern. In this work, we target to utilize machine-learning algorithms to characterize the context of images captured from a real-world deployments. Specifically, using images from the James Reserve bird nest deployment [1], we utilize and optimize machine learning algorithms to operate on embedded low-power, resource-limited platforms. Using both a systematic and algorithmic approach, our proposed systems architecture and algorithms hold the potential to reduce the amount of data to be transmitted by as much a eight-fold.

Low-power embedded platforms are widely used in various data collection scenarios to capture physical world phenomena in micro-scale. Given that many applications require long operation times, it is important for nodes to employ various schemes to maximize their lifetime. Within the sensor network research community, schemes such as low-power listening (LPL) and low-power probing (LPP) have been actively studied and enhanced [1], [2], [3]. Nevertheless, without MAClayer synchronization, for asynchronous sleep-based systems, the wakeup cycles of the low-power nodes were, in most cases, considered to be identical for the entire network. This work takes a different approach and targets to provide a traffic-rate proportional sleep cycle: resulting in an asynchronous sleep cycle in an asynchronous sleep low-power protocol.

It is crucial that cardiovascular disease patients pay close attention to their heartrate since the elevation of his or her heartrate may lead to critical conditions. Many efforts are made to help monitor heartrate by using wearables. However, these devices project inaccurate readings due to motion artifacts. We evaluate the accuracy of different smart watches in measuring heartrate, design our own filtering algorithm, and validate it with real hospital, clinical-scale devices to improve the quality of disease detection on wearable devices. It is important to note that we plan to make this algorithm as a module or component to use in various applications.

With the advancement of technology in various domains, many efforts have been made to design state-of-the-art classification engines using depth cameras. Being inspired by its potential of providing information at the skeleton level using a non-invasive infrared camera, many studies have been done to aid vulnerable people such as children, elderly, and people that are physically or mentally ill. However, most of these studies focus on the algorithms and processing of a single camera, and do not consider issues that are found in practical deployments. We present KinFrame, a framework that considers challenges and requirements of designing practical systems for vulnerable people and allows application developers to easily and efficiently setup large scale, multiple depth camera deployment.

Near Field Communication (NFC) is a wireless communication technology using 13.56 MHz to support 2-way communications between two devices within ~10 cm. Such a short communication range may be considered as a shortcoming, but at the same time, this enables a secure data transfer within the connectivity region when compared to Bluetooth Low Energy (BLE) or Wi-Fi. Accordingly, NFC can be optionally utilized when secure data are transmitted in various services and applications focusing on ID validation. This demo presents a proof-of-concept for an alternative packet transmission method for achieving secure data exchange based on IP-over-NFC technology. For realizing this, there are a number of technical challenges. Utmost, the current NFC Linux kernel driver requires updates for supporting network-layer functionalities. Using such implementations, this demo demonstrates the procedures of transmitting secure data using NFC, while normal packet transmissions occur over WiFi simultaneously.

Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emergency event prediction. Specifically, improvements in machine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emergencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environment, this work focuses on developing a data repository for bio-signals collected from a hospital's intensive care init (ICU). Specifically, we design our data collection system to effectively store data from at-bed patient monitors and also integrate sensing information from bed-embedded sensing platforms, which allow filtering of noisy bio-signal samples caused by motion artifacts.

Smart watches are increasingly being used in various applications to monitor heart rate for exercise and health care purposes. It is crucial that the readings from these devices are accurate so that users can take proper actions according to the intensity of the heart rate. Taking actions from inaccurate readings can negatively impact the health of the user. In this work, we run a preliminary study that verifies the accuracy of wearable platforms by comparing the measurements with a clinically-grade device.

In low-power wireless networks, maintaining multihop connectivity is considered effective in constructing communication routes between individual nodes to a gateway. Since sensor networks are typically used for data collection, multihop routing protocols are designed to find routes optimal in upward directions. As sensor networks become widely applied to diverse applications, efficient downward traffic delivery also becomes important. To achieve this, we consider an asymmetric transmission power-based network (APN), where a power-supplied gateway uses high-power radios to cover the entire network via single-hop transmission, whereas common nodes use low-power transmissions. For effective APN operations, we propose a single-hop downlink protocol (SHDP) that consists of direct downlink transmission, local acknowledgment, neighbor forwarding, and contention resolution among the destination’s neighbors. We evaluate SHDP through mathematical analysis, simulations, and testbed experiments. Our proposal outperforms other competitive multihop routing protocols. Specifically, SHDP shows high packet delivery performance and lowers the duty cycle greatly while reducing the packet transmission overhead by >50%.

The introduction of ubiquitously connected mobile computing platforms acted as a catalyst for accessing and enjoying multimedia/visual contents on a more diverse set of computing environments than ever before. Recently, with the standardization of various protocols, a number of screen sharing services have been introduced, in which the contents of a mobile computing platform's screen is shared with a neighboring device [1]. With these services, data contents collected from various external sensing sources, can be captured at a mobile platform and (visually) shared easily with others using direct wireless connections. However, a previously unconsidered technical issue begins to impact the user experience levels of these multimedia/screen sharing mobile services. Specifically, we begin to notice energy efficiency as an important performance factor to consider when utilizing visual contents on mobile platforms with resource limitations. Actively using computational resources for visual applications lead to device heating and shorter battery life-times, as well as increased use of the wireless bandwidth when downloading or sharing the visual contents.

The commercialization of cheap unmanned aerial vehicles (UAVs) is starting to change the way we, as sensor network system designers, think of data collection. Especially, UAVs provide a third dimension of mobile data collection as we can now traverse the sky with minimal obstacles, rather than rovering the ground with wheeled robots. However, despite UAVs or drones being an interesting platform with the potential to change sensor network deployment topologies, little do we understand on how data collection will perform "in the air". In this work we present a preliminary empirical study on the performance of aerial data collection using an IEEE 802.15.4 radio-equipped drone connecting itself to sensor nodes positioned on the ground. Our results show that a drone-based data collection platform outperforms that of an "at ground-level" data collection unit, despite being at identical distances. Based on this study, we identify the increased data collection height and "easy-to-achieve" line-of-sight as key features that make this possible.

Updating price tags in a large-scale market is a recurrent task, still performed manually in most markets. Given that human-errors can easily lead to customer complaints and accounting inaccuracies, the ability to autonomously reconfigure price tags can be of significant benefit. With the introduction of low-power display techniques such as electronic ink, applications of enabling electronic, wirelessly reconfigurable price tags show potential for future deployment. In this work, we examine networking architectures that can be applied in such scenarios. Through a series of preliminary pilot studies in an actual supermarket, we show that the performance of existing protocols are not ready to overcome the unique challenges of busy market environments. We identify underlying technical challenges and propose MarketNet, an asymmetric transmission power-based system designed for densely populated, obstacle-rich, downwards traffic-oriented environments. We evaluate MarketNet in a large indoor mar- ket visited by 5000+ customers per day. Our results show that MarketNet addresses the challenges of the target application and environment, while achieving higher packet delivery performance with noticeably lower radio duty-cycles than existing protocols such as RPL and SHDP.

Most vehicles today inter-connect their electronic (sensing) components using wired communications such as CAN, Diognistic-CAN, or LIN. However, despite its simple design in connecting various automotive components, the cost and weight of installing wired connectors can increase quickly with the increasing number of electronic control units (ECUs) a vehicle supports. This work looks into the option of enabling wireless communications for intra-vehicle services, especially for less safety-critical applications. We present a wireless communication hardware platform that is used to design a vehicle's body controller / gateway device, which interconnects ECUs of heterogenous communication mediums (e.g., CAN, LIN, etc.). We show using this demo, in which we present prototype implementations, that the latency and packet delivery performance of wireless links can be suitable for supporting various vehicular applications that relate less towards passenger safety.

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports bidirectional IPv6 communication to and from the mesh devices by providing both upward and downward routing over the routing tree. In this article, we focus on the interoperability of downward routing and supporting its two modes of operations (MOPs) defined in the RPL standard (RFC 6550). Specifically, we show that there exists a serious connectivity problem in RPL protocol when two MOPs are mixed within a single network, even for standard-compliant implementations, which may result in network partitions. To address this problem, this article proposes DualMOP-RPL , an enhanced version of RPL, which supports nodes with different MOPs for downward routing to communicate gracefully in a single RPL network while preserving the high bidirectional data delivery performance. DualMOP-RPL allows multiple overlapping RPL networks in the same geographical regions to cooperate as a single densely connected network even if those networks are using different MOPs. This will not only improve the link qualities and routing performances of the networks but also allow for network migrations and alternate routing in the case of LBR failures. We evaluate DualMOP-RPL through extensive simulations and testbed experiments and show that our proposal eliminates all the problems we have identified.

The capability to extract human activity context in a room environment can be used as meaningful feedback for various wireless indoor application systems. Being able to do so with easily installable resource-limited sensing components can even further increase the system's applicability for various purposes. This paper introduces our efforts to design a system consisting of heterogeneous low-cost, resource-limited, wireless sensing platforms for accurately extracting the human activity context from an indoor environment. Specifically, we introduce Resource Limited Sensor-based activity Context Extraction (ReLiSCE), a system consisting of microphone array, passive infra-red (PIR), and illumination sensors that effectively detect the activities that occur in an office (meeting room) environment. The signal processing schemes used in ReLiSCE are designed so that their size and complexity is suitable for the resource limitations that many embedded computing platforms introduce. Using empirical evaluations with a prototype system, we show that despite the simplicity of its data processing schemes, ReLiSCE successfully classifies human activity states in various meeting scenarios. Furthermore, we show that high accuracy is achieved by combining results from heterogeneous sensors. We foresee this paper as a sub-system that interconnects with various application systems for autonomously configuring people's everyday living environments in a more comfortable and energy-efficient manner.

In this work, we present ChildSafe, a classification system which exploits human skeletal features collected using a 3D depth camera to classify visual characteristics between children and adults. ChildSafe analyzes the histograms of training samples and implements a bin-boundary-based classifier. We train and evaluate ChildSafe using a large dataset of visual samples collected from 150 elementary school children and 43 adults, ranging in the ages of 7 and 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 97%, a false negative rate of as low as 1.82%, and a low false positive rate of 1.46%. We envision this work as an effective sub-system for designing various child protection applications.

In this demo, we showcase the first software defined radio (SDR) implementation that runs on today's smartphones. Using the SDR, the smartphones are shown to talk to real IEEE 802.15.4 devices such as sensor motes or Phillips Hue (TM) lightbulbs that otherwise they cannot natively communicate with. Furthermore, we make the SDR available as downloadable software ("app") on Google app store, so that anyone may freely download, use, and purge on his or her smartphone, independently of OS upgrades. Detaching SDR from OS allows almost immediate deployment of wireless rotocols, and only on the phones that do need them. By enabling smartphones to speak non-native protocols in this manner, smartphone vendors will be able to support a wide range of wireless protocols without investing separate hardware and real estate in their devices. A promising application area for this concept could be Internet of Things (IoT), where protocols are diverse, typically low-speed, and not well supported by today's smartphones. Therefore, we believe that the smartphones with SDR will become a strong enabler or facilitator of many interesting applications in the IoT environment. We also believe that the concept of wireless protocol as a smartphone app will accelerate wireless protocol design-test-deployment cycle, catalyzing wireless protocol evolutions on smartphones.

Tightly synchronizing transmissions of the same packet from different sources theoretically results in constructive interference. Exploiting this property potentially speeds up network-wide packet propagation with minimal latencies. Our empirical results suggest the timing constraints can be relaxed in the real world, especially for radios using lower frequencies such as the IEEE 802.15.4 radios at 900 MHz. Based on these observations we propose PEASST, a topology-free protocol that leverages synchronized transmissions to lower the cost of end-to-end data transfers, and enables multiple traffic flows. In addition, PEASST integrates a receiver-initiated duty-cycling mechanism to further reduce node energy consumption. Results from both our Matlab-based simulations and indoor testbed reveal that PEASST can achieve a packet delivery latency matching the current state-of-the-art schemes that also leverages synchronized transmissions. In addition, PEASST reduces the radio duty-cycling by three-fold. Furthermore, comparisons with a multi-hop routing protocol shows that PEASST effectively reduces the per-packet control overhead. This translates to a ~10% higher packet delivery performance with a duty cycle of less than half.

The capability to interconnect directly with neighboring wireless devices coupled with improvements in high-speed wireless connections, and the wide distribution of high-quality multimedia has led to the design of standards such as the WiFi-based Miracast [1], which allows handheld mobile devices to share their screen contents with larger-sized display devices (e.g., smart TVs). Such screen sharing capabilities allow various multimedia files to be easily accessed through mobile platforms, and played (in real-time) through larger screens; thus, has the potential to enable a variety of attractive entertainment applications. While widely available on all Android-based mobile devices (4.2 or recent), the Miracast standards introduce a significant level of inefficiency as it deals with different types of multimedia contents.

Designing and distributing effective vehicular safety applications can help significantly reduce the number of car accidents and assure the safety of many precious lives. However, despite the efforts from standardization bodies and industrial manufacturers, many studies suggest that it will take more than a decade for full deployment. We start this work with the hypothesis that smartphones may be suitable platforms for catalyzing the distribution of vehicular safety systems. Specifically, smartphones connected to their respective cellular networks can report sensing data to back-end application servers and exchange safety-related messages. This paper first evaluates the performance of the vehicular ad-hoc networking standards and the hardware platforms that implement them. Next, we perform empirical evaluations on the performance of cellular networks to confirm their applicability in vehicular networking. Based on our observations, we present the VoCell application development framework. VoCell, comprehends a set of components that eases the development of smartphone applications for vehicular networking applications. Using VoCell, developers can easily access internal and external sensing components and share this data to servers. We present a number of example applications developed using VoCell and evaluate their effectiveness in local and highway environments using a pilot deployment. We envision that VoCell can act as a building block for enabling new smartphone-based systems for vehicular networking applications.

By interacting with external sensors, smartphones can gather high-fidelity data on the surrounding environment to develop various environment-aware, personalized applications. In this work we introduce the sensor virtualization module (SVM) which virtualizes external sensors so that smartphone applications can easily utilize a large number of sensing resources. Implemented on the Android platform, our SVM simplifies the management of external sensors by abstracting them as virtual sensors to provide the capability of resolving conflicting data requests from multiple applications and also mashing-up sensing data from different sensors to create new customized sensors. We envision our SVM to open the possibilities of designing novel personalized smartphone applications

ABSTRACT With recent advances in wireless networking and in low‐power sensor technology, wireless sensor networks (WSNs) have taken significant roles in various applications. Whereas some WSNs only require minimal bandwidth, newer applications operate with a noticeably larger amount of data. One way to deal with these applications is to maximize the available capacity by utilizing multiple wireless channels. We propose DynaChannAl, a distributed dynamic wireless channel allocation algorithm that effectively distributes nodes to multiple wireless channels in WSNs. Specifically, DynaChannAl targets applications where mobile nodes connect to preexisting wireless backbones and takes the expected end‐to‐end queuing delay as its core metric. We used the link quality indicator values provided by 802.15.4 radios to whitelist high‐quality links and evaluate these links with the aggregated queuing latency, making it useful for applications that require minimal end‐to‐end delay (i.e., health care). DynaChannAl is a lightweight and adoptable scheme that can be incorporated easily with predeveloped systems. As the first study to consider end‐to‐end latency as the core metric for channel allocation in WSNs, we evaluate DynaChannAl on a 45 node test bed and show that DynaChannAl successfully distributes source nodes to different channels and enables them to select channels and links that minimizes the end‐to‐end latency. Copyright © 2012 John Wiley & Sons, Ltd.

Standardization has driven interoperability at multiple layers of the stack, such as the routing and application layers, standardization of radio duty cycling mechanisms have not yet reached the same maturity. In this work, we pitch the two de facto standard flavors of sender-initiated radio duty cycling mechanisms against each other: ContikiMAC and TinyOS LPL. Our aim is to explore pragmatic interoperability mechanisms at the radio duty cycling layer. This will lead to better understanding of interoperability problems moving forward, as radio duty cycling mechanisms get standardized. Our results show that the two flavors can be configured to operate together but that parameter configuration may severely hurt performance.

In this work we point out the issue of the IETF RPL routing protocol's two different downwards routing schemes not being able to interoperate with each other. This problem is less of an issue when low-power and lossy networks (LLNs) are deployed homogeneously but with the industrial kickoff and large scale deployments, the interoperability of heterogeneous, standards-compliant implementations will become a significant issue. To address this, we suggest two major changes to IETF RPL (RFC 6550). First we suggest that all storing mode nodes should hold the capability to understand and attach source routing headers that the non-storing mode nodes require to forward packets. Next, we suggest that RPL's non-storing mode nodes should send their destination advertisement messages hop-by-hop, rather than the current end-to-end approach. We show, with two different IPv6 implementations in TinyOS and NanoQplus, that our suggestions high achieve high interoperability performance among different implementations for downwards traffic patterns.

As the number of sensors increase in wireless sensing applications, it is important for nodes to provide meaningful summary reports of the original data to the gateway. In doing so, given the resource constraints of the sensing devices, we need a light weight, yet, effective scheme to minimize the number of reports at the sensors while preserving the accuracy of the original data. However, we show in this work that unlike outdoors environments where various sensors may show a similar phenomena (e.g., high spatial correlation), this may not be true for sensors deployed in a typical indoors environment. To resolve this issue, we introduce a data summarizing scheme for such indoor applications that combines two techniques. First, our scheme detects events in a data stream by comparing the short term mean of the recent measurements with the most recent report sent to the gateway. Second, we include an exponentially increasing/decreasing timer that triggers additional reports where the timer's interval is reconfigured dynamically with respect to the result of our event detection method. Evaluations with temperature and humidity data collected in an indoors environment indicate that our scheme significantly reduces the number of transmissions while maintaining a mean error as low as ~0.07°C and ~0.08%RH.

Interoperability is essential for the commercial adoption of wireless sensor networks. However, existing sensor network architectures have been developed in isolation and thus interoperability has not been a concern. Recently, IP has been proposed as a solution to the interoperability problem of low-power and lossy networks (LLNs), considering its open and standards-based architecture at the network, transport, and application layers. We present two complete and interoperable implementations of the IPv6 protocol stack for LLNs, one for Contiki and one for TinyOS, and show that the cost of interoperability is low: their performance and overhead is on par with state-of-the-art protocol stacks custom built for the two platforms. At the same time, extensive testbed results show that the ensemble performance of a mixed network with nodes running the two interoperable stacks depends heavily on implementation decisions and parameters set at multiple protocol layers. In turn, these results argue that the current industry practice of interoperability testing does not cover the crucial topic of the performance and motivate the need for generic techniques that quantify the performance of such networks and configure their run-time behavior.

Interoperability is key to widespread adoption of sensor network technology, but interoperable systems have traditionally been difficult to develop and test. We demonstrate an interoperable system development and performance diagnosis environment in which different systems, different software, and different hardware can be simulated in a single network configuration. This allows both development, verification, and performance diagnosis of interoperable systems. Estimating the performance is important since even when systems interoperate, the performance can be sub-optimal, as shown in our companion paper that has been conditionally accepted for SenSys 2011.

Techniques for controlling the transmission power of mobile devices have been widely studied in MANETs and cellular networks. However, as mobile applications for WSNs emerge, the unique characteristics of WSNs, such as severe resource constraints, suggest that transmission power control should be revisited from a WSN perspective. In this work, we take an experimental approach to examine the effectiveness of transmission power control for WSNs that involve mobility at human walking speeds. We propose two lightweight transmission power control schemes to improve energy efficiency and spatial reuse. The first is an active probing based scheme that adjusts transmission power based on (the lack of) packet losses and applies to all low-power radios, while the second scheme requires radios that offer link quality indicators (LQI) to estimate the proximity between the transmitter and receiver. We evaluate both schemes using mobile nodes in an indoor and an outdoor environment. Results show that the energy efficiency of the proposed transmission power control schemes can be very close to that of the optimal offline strategy and our schemes significantly reduce the interference for spatial reuse. To our knowledge, this is the first work that evaluates the effect of transmission power control in mobile WSNs.

We introduce the Egs mote platform based on the Cortex M3 microcontroller that focuses on medical sensing applications. Egs uses an Atmel SAM3U microcontroller that runs up to 96 MHz and has up to 52 KB of RAM and 256 KB of Flash. Egs combines this microcontroller with two radios (802.15.4 and Bluetooth), external flash, on board sensors, and a LCD touchscreen to enable a rich set of wireless sensing applications.

Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high quality care. At the same time, there is a growing concern about hospitals' ability to provide effective care during disaster events. For these reasons, tools that automate patient monitoring have the potential to greatly improve efficiency and quality of health care. Towards this goal, we have developed MEDiSN , a wireless sensor network for monitoring patients' physiological data in hospitals and during disaster events. MEDiSN comprises Physiological Monitors (PMs), which are custom-built, patient-worn motes that sample, encrypt, and sign physiological data and Relay Points (RPs) that self-organize into a multi-hop wireless backbone for carrying physiological data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to authenticated GUI clients. The combination of MEDiSN's two-tier architecture and optimized rate control protocols allows it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements. Results from extensive simulations, testbed experiments, and multiple pilot hospital deployments show that MEDiSN can scale from tens to at least five hundred PMs, effectively protect application packets from congestive and corruptive losses, and deliver medically actionable data.

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high-quality care. Moreover, there is a growing concern about the ability of hospitals to provide effective care during disaster events. Tools that automate patient monitoring would greatly improve efficiency, quality of care, and the volume of patients treated. Towards this goal, we have developed MEDiSN, a wireless sensor network for monitoring patients' vital signs in hospitals and disaster events. MEDiSN consists of Patient Monitors which are custom-built, patient-worn motes that sample, compress and secure medical data, and Relay Points that form a static multi-hop wireless backbone for carrying patient data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to multiple GUI clients. MEDiSN's heterogeneous architecture enables it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements.

The IEEE 802.15.4 medium access control (MAC) protocol is designed for low data rate, short distance and low power communication applications such as wireless sensor networks (WSN). However, in the standard 802.15.4 MAC, if the remaining number of backoff periods in the current superframe are not enough to complete data transmission procedure, the sensor nodes hold the transmission until the next superframe. When two or more sensor nodes hold data transmission and restart the transmission procedure simultaneously in the next superframe, it causes a collision of data packets and waste of the channel utilization. Therefore, the MAC design is inadequate to deal with high contention environments such as densely deployed sensor networks. In this paper, we propose a data fragmentation scheme to increase channel utilization and avoid inevitable collision. Our proposed scheme outperforms the standard IEEE 802.15.4 MAC in terms of collision probability and aggregate throughput. The proposed scheme is easily adapted to the standard IEEE 802.15.4 MAC without any additional message types

In this paper, we have applied diversity technologies to the IEEE 802.11g to use the IEEE 802.11g in moving channel environments. After analyzing the measurement data, it is shown that the diversity gain is 14 dB at the probability of 1%. The reliable communication range is 63 m when transmitting power is 10 dBm, while the range increases to 163 m when the diversity technique is used. Also the communication range increases from 122 m to 306 m by using diversity technologies when the transmitting power is 20 dBm