Bluetooth Low Energy (BLE) is a key wireless technology for the Internet of Things (IoT), supporting low-power, multi-device communication. However, managing and scheduling multiple BLE connections under tight timing constraints remains a significant challenge. The Bluetooth specification lacks explicit strategies for efficient multi-device coordination, often leading to unfair resource allocation and connection event interruptions due toresource overlapamong peripherals. To address this problem, we proposeEMBLEM, a novel scheduling scheme that treats multiple connections as a unified resource list and dynamically adjusts allocations based on traffic intensity. By leveraging event preemption and connection subrating,EMBLEMenables flexible resource allocation that accommodates more peripherals while ensuring fairness. To further improve efficiency, we introduce a bitmap-based state tree for memory-efficient resource tracking, and also connection event extension to reduce resource wastage and improve throughput. We evaluateEMBLEMon a 51-node testbed against state-of-the-art scheduling schemes and popular BLE stacks. Experiment results show thatEMBLEMeliminates resource overlap, improves stability, and significantly enhances system fairness, throughput, and scalability under diverse operating conditions.

Recent advances in electric vehicles, robots, and renewable energy have renewed interest in direct current (DC) power line communication (PLC) technology. However, existing DC-PLC systems face from several limitations, including low data rates, lack of support for duplex communication, and the absence of an effective medium access control (MAC) mechanism. To overcome these challenges, we propose D2-PLC, a novel DCPLC system that features a redesigned physical layer, enabling high-speed duplex communication over a single pair of wires supporting simultaneous power and data transmission. D2-PLC introduces voltage polarity modulation (VPM) and current amplitude modulation (CAM) for downlink and uplink communication, respectively. In addition, we develop a custom data link layer and MAC protocols to coordinate communication in a bus topology where multiple slave nodes interact with a single master node (the power source), minimizing the risk of collisions. We implement a fully functional prototype and evaluate on a 5-node testbed as well as via 256-node simulations. Results demonstrate that D2-PLC achieves a maximum data rate of ~100 kbps–260% improvement over existing solutions(Cwhile maintaining 99+% reliability. These findings highlight D2-PLC’s potential to reduce the cost and weight of battery-powered systems such as electric vehicles

LoRa is a low-power long-range radio technology for wide-area IoT connectivity with exceptional receiver sensitivity thanks to its chirp spread spectrum (CSS) modulation. However, insufficient data rate has always been an Achilles’ heel of LoRa. This paper proposes a novel PHY-layer design, BIC-LoRa, that leverages non-linear chirp shapes as pictograph to enhance LoRa’s data rate. It employs multiple non-linear chirps for modulation whose shapes encode additional bits to boost data rate while maintaining resilience to low SINR scenarios. To the best of our knowledge, this is the first attempt to enhance the data rate of LoRa by embedding bits in the chirp shapes. Furthermore, BIC-LoRa fully leverages the characteristics of non-linear chirps, allowing it to deal with packet collisions and increase network throughput. We implement BIC-LoRa in GNURadio and MATLAB, and compare its performance against CurveALOHA and standard LoRaWAN through real experiments on USRP B210 software-defined radios. Evaluation results demonstrate that BICLoRa achieves up to 29.4% improvement in data rate for a single link and 32% improvement in overall network throughput while retaining the low SNR robustness of LoRa in real-world scenarios.

Cross technology communication (CTC) facilitates direct communication between heterogeneous wireless technologies in the overlapped frequencies such as the 2.4 GHz ISM band. In this poster, we propose a novel method named WBMC for direct multicast communication from a Wi-Fi device to multiple BLE transceivers. This is achieved by making a Wi-Fi signal appear like BLE's Gaussian frequency shift keying (GFSK) signal over multiple subcarrier groups of Wi-Fi. Uniqueness of WBMC compared to prior Wi-Fi-to-BLE CTC studies is that one Wi-Fi transmission can deliver distinct data to multiple BLE receivers simultaneously. The potential and feasibility of the newly suggested approach is demonstrated through implementation on GNURadio.

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.

As interest in autonomous driving and advanced driver-assistance systems (ADAS) has grown, various sensing technologies have been developed to accurately determine the position and situation of surrounding vehicles and objects. In particular, light detection and ranging (LiDAR) sensors have attracted attention and are widely used in autonomous driving and ADAS because of their accuracy and reliability. However, when LiDAR sensors are used on a single vehicle, they can encounter blind spots caused by obstacles, which limits the detection of the environment. To overcome this issue, a method that can register and identify objects using LiDAR data from multiple vehicles in real-time is needed. Conventional artificial intelligence and iterative closest point (ICP) approaches need faster processing speed for practical use. Therefore, this work proposes an object-based single-point ICP (SP-ICP) which enables faster processing while maintaining accuracy using only a single point centered on each of the objects.

Time-slotted communication is used in countless protocols and systems. IEEE 802.15.4e time-slotted channel hopping (TSCH) is one of those examples which has shown remarkable performances in the literature. However, time-slotted systems have one fundamental drawback: a slot is predefined to be sufficiently long enough to accommodate one exchange of a maximum-sized packet and an acknowledgment. If most packets in the system are far smaller than the maximum, a significant amount of residue time within each slot is wasted, leading to corresponding loss in effective data rate. To address this fundamental challenge, we propose utility-based adaptation of slot-size and aggregation of packets (ASAP) which reduces wasted time in slotted systems to improve throughput and latency. ASAP consists of two orthogonal approaches: 1) slot-length adaptation (SLA) dynamically adapts timeslot length to actual packet size distribution and 2) utility-based packet aggregation (UPA) transmits aggregated packets in multiple consecutive slots to maximize slot utility. We case-study ASAP in the context of TSCH. We implement ASAP on real embedded devices and evaluate on large-scale testbeds using state-of-the-art schedulers to demonstrate a$2.21\times $improvement in throughput as well as a 78.7% reduction in latency.

Time-sensitive networking (TSN) strives to provide an ultralow-latency real-time deterministic network for time-critical traffic using the time-aware shaper (TAS) mechanism. For this purpose, methods for routing and scheduling the time-critical flows must be specified. However, this is an NP-hard problem. Although several prior studies have suggested constraint programming (CP)-based approaches, these methods fail to provide a reasonable runtime due to the complexity of the problem. Motivated by this, we propose a TAS co-optimization (TACO) framework that solves the TAS scheduling and routing problem in TSN with a reasonable runtime. As an alternative to CP-based approaches, TACO considers a metaheuristic approach to co-optimize routing, scheduling order, and transmission timing. However, joint optimization through a metaheuristic algorithm is challenging due to the heterogeneous search spaces of the subproblems. Therefore, TACO carefully integrates the search spaces into a single domain and optimizes routing and TAS scheduling jointly with its heuristic algorithm. We evaluate TACO in various industrial networking scenarios to demonstrate that TACO achieves up to an 88% increase in the scheduling success rate with a good convergence rate and an overall low latency/jitter compared to other approaches.

Time-sensitive networking (TSN) Task Group of the IEEE proposed the frame replication and elimination for reliability (FRER) technique to guarantee reliable transmissions in TSN for the emerging Industrial Internet of Things (IIoT). FRER is a technique that manages the replication and elimination of frames of a stream sent through multiple paths as member streams. However, the standard does not specify how to find and select the multiple paths to send the replicated member streams on, nor how the time-aware shaper (TAS) should be scheduled considering frame elimination. Most prior work on routing or TAS scheduling in TSN do not consider FRER. Conversely, studies on FRER do not address the routing and scheduling issues effectively. In this article, we propose multipath routing and TAS scheduling strategies to support FRER in TSN with reduced complexity. We also identify a scheduling deadlock problem due to the unique characteristics of FRER, and propose a solution using topological sorting. Then, we propose two metaheuristic optimizers to increase the TAS scheduling success rate. Through extensive evaluation against state-of-the-art prior work, we show that our strategies can support more flows with reduced utilization and enhanced schedulability while effectively handling the complexity of routing and scheduling problems for FRER.

Detection and monitoring of ships in the images captured from satellites or aerial vehicles is a pivotal task in maritime security applications. Recent advancements in aerial communication and computer vision has enabled real-time collection of such images as well as development of robust and precise models for ship detection. However, conventional machine learning (ML) based models are prone to security and privacy issues as the real-time data captured through aerial imagery may be exposed during transfer or after storage in the cloud server. Furthermore, real-time decision making is a challenging task with conventional ML models due to the latency incurred while transmitting large amount of data from maritime aerial network to the cloud. To address the privacy and latency challenges, we propose a privacy-preserving game-theory based federated learning approach for ship detection in aerial images from maritime network. FL improves privacy by allowing raw data to reside at the edges/clients, and game theory helps in optimizing the parameter updates that are sent to the centralized server. Evaluation results prove the efficacy of the proposed model with a prediction accuracy of 96.01%, 92.96% reduction in time complexity and also 8.28% reduction in communication overhead.

Tracking the position and orientation, or pose, of a viewing device enables AR applications to accurately embed virtual content in physical spaces. Mobile OSs track pose by matching device camera images against street-level imagery. Thus, pose tracking is often unavailable at off-street pedestrian locations. UbiPose enables pose tracking at such locations using aerial meshes, generated from satellite imagery, that are likely to be more widely available at these locations. However, matching a camera image against an aerial mesh can be error-prone, even with modern neural matchers. These neural components are also compute-intensive. UbiPose contains a novel pose tracking pipeline that runs entirely on a mobile device using fast-path optimizations designed to accept or reject pose estimates in many cases, without sacrificing accuracy. Experiments on real-world traces show that it achieves tracking accuracy comparable to AR pose tracking in iOS in places where that is available, and is able to track pose accurately in places where it is not.

Bluetooth Low Energy (BLE) is a representative wireless technology for Internet of Things (IoT) that enables concurrent communication with multiple devices at low power. However, the connection establishment mechanism of BLE is susceptible to resource overlap problem among connected peripherals, leading to resource unfairness. To address problem and improve resource utilization, we propose a "Subrating-based Connection Scheduling (SCS)". It schedules and periodically manages connections by considering the service requirements of the connected devices. Preliminary experiments demonstrate that SCS achieves higher throughput and fairness compared to popular commercial BLE stacks while satisfying the requirements of peripherals.

LoRa technology enables low power long range communication thanks to its modulation scheme, the 'Chirp Spread Spectrum (CSS)'. However, it is extremely susceptible and vulnerable to chirp collisions. To resolve this problem, we propose a novel modulation scheme, 'Frequency Hopped Chirp Spread Spectrum (FH-CSS)'. FH-CSS slices a chirp into several sub-chirps and places each to different frequency offsets. This enables demodulated signals of misaligned chirps to spread out in the frequency domain due to the characteristic of CSS. We implement FH-CSS on GNU Radio and compare its performance against standard CSS through real experiments on USRP B200 software defined radios. Preliminary results show that FH-CSS achieves around 90% decoding success rate despite collision while standard CSS has less than 10% on average.

To guarantee real-time performance and quality-of-service (QoS) of time-critical industrial systems, time-aware shaper (TAS) in time-sensitive networking (TSN) controls frame transmission times in a bridged network using a scheduled gate control mechanism. However, most TAS scheduling methods generate schedules based on pre-configured routes without exploring alternatives for better schedulability, and methods that jointly consider routing and scheduling require enormous runtime and computing resources. To address this problem, we propose a TSN Scheduler with Reinforcement Learning-based Routing (TSLR) that identifies improved load balanced routes for higher schedulability with acceptable complexity using distributional reinforcement learning. We evaluate TSLR through TSN simulations and compare it against state-of-the-art algorithms to demonstrate that TSLR effectively improves TAS schedulability and link utilization in TSN with lower complexity. Specifically, TSLR shows a more than 66% increase in schedulability compared to the other algorithms, and TSLR’s scheduling time is reduced by more than 1 h. It also shows flows’ transmission latency is less than 25% of their latency deadline requirement and reduces maximum link utilization by approximately 50%.

To guarantee stringent real-time requirements of time-critical traffic in industrial systems, the IEEE time-sensitive networking (TSN) task group has standardized time-aware shaping (TAS) in IEEE 802.1Qbv, which schedules precise and periodic transmission times using preassigned traffic information. However, nonperiodic/unexpected but time-critical traffic, such as emergency events or alarms, may occur in real industrial scenarios, and TAS does not provision for performance of traffic that are unknowna priori, nor the impact thereof on prescheduled traffic. Moreover, recalculating the schedule for every sporadic, nonisochronous event traffic is extremely difficult, complex, and costly. To address these challenges, we propose a novel enhancement to TAS, referred to aseTAS, which defines a new scheduling rule for immediate forwarding of emergency traffic to guarantee real-time performance, while dynamically extending the scheduled time windows to protect scheduled time-critical traffic from the interference of emergency traffic. We evaluateeTASthrough extensive simulations on OMNeT++ under an advanced driver assistance system (ADAS) scenario for autonomous driving to show thateTASeffectively allows rapid transmission of event traffic with minimal impact on scheduled traffic, even for highly congested networks.

Most wireless technologies, including WiFi, rely heavily on clear channel assessment (CCA) to avoid collisions, not only among the devices within the same technology but also against cross-technology interference. If the CCA threshold is not configured properly, both the transmission and reception performance will be seriously affected with behaviors unexpected from the protocol's perspective. On the other hand, WiFi uses an adaptive CCA threshold based on a noise floor (NF) calibration algorithm to account for ambient NF changes and slight differences in hardware. However, this turns out to be a serious vulnerability of WiFi. In this work, we show that one can easily generate a wireless signal that can take advantage of the NF calibration algorithm of WiFi to inflate the CCA threshold and degrade performance significantly. The signal can be generated from a commercial off-the-shelf ZigBee device, and if well designed, need not be too long nor strong. We show that WiFi is vulnerable to such attack, and more surprisingly, the network performance does not recover long after the signal disappears. We exemplify and verify our findings through extensive real-world experiments using five types of commercial WiFi NICs and three different WiFi access points to show that this is a critical problem that exists in reality and must be addressed.

As the emerging Internet of Things (IoT) devices and applications flourish, demand for reliable and energy-efficient low-power wireless network protocols is surging. For this purpose, IEEE 802.15.4 standardized time-slotted channel hopping (TSCH), a promising and viable link-layer solution that has shown outstanding performance achieving over 99% reliability with low duty-cycles. However, it lacks one thing, flexibility. It is not adaptable to a wide variety of applications with varying traffic load and unpredictable routing topology due to its static timeslot scheduling. To this end, we propose OST, an On-demand Scheduling scheme for TSCH with traffic-awareness. In OST, each node dynamically self-adjusts the frequency of timeslots at run time according to time-varying traffic intensity. Moreover, it features on-demand resource allocation to handle bursty/queued packets in a timely manner. By doing so, OST aims to minimize its energy consumption while guaranteeing reliable packet delivery. We evaluate OST on a large-scale 72-node testbed, demonstrating that it achieves improvement of 60% in reliability and 52% in energy-efficiency compared to the state of the art.

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.

We present PC-RPL , a transmission power-controlled IPv6 routing protocol for low-power and lossy wireless networks that significantly improves the end-to-end packet delivery performance under heavy traffic compared to the standard RPL. We show through actual design, implementation, and experiments that a multihop wireless network can achieve better throughput and routing stability when transmission power and routing topology are “jointly and adaptively” controlled. Our experiments show that the predominant “fixed and uniform” transmission power strategy with “link quality and hop distance”–based routing topology construction (i.e., RPL) loses significant bandwidth due to hidden terminal and load imbalance problems. We design an adaptive and distributed control mechanism for transmission power and routing topology, named PC-RPL , on top of the standard RPL routing protocol for hidden terminal mitigation and load balancing. We implement PC-RPL on real embedded devices and evaluate its performance on a 49-node multihop testbed. PC-RPL reduces total end-to-end packet losses by approximately sevenfold without increasing hop distance compared to RPL with the highest transmission power, resulting in 17% improvement in aggregate bandwidth and 64% improvement for the worst-case node by successfully alleviating both hidden terminal and load imbalance problems.

Cross-technology interference in the ISM band is a prevalent and challenging problem. Prior work has proposed to use busytone signaler that can protect Zigbee transmissions from WiFi interference, but only in a single-hop setting. In this work, we propose Queue-size based Busy Tone (QBT) scheme that uses novel multihop busytone scheduling and priority path selection algorithms to protect multihop Zigbee network under heavy WiFi traffic. We adopt the busytone idea from prior work, but extend it to multihop networks where data is collected from and forwarded by a number of low-power nodes. We implement QBT on a lowpower embedded platform, and evaluate through experimental measurements on a real multihop LLN testbed consisting of 30 low-power embedded nodes and one gateway with two radios, the sink and the signaler, respectively. We show that QBT improves the performance of Zigbee network under WiFi interference dramatically in terms of both reliability (up to 66.4%) and energy efficiency (up to 24.4%). We also report our findings on adjacent-channel interference among Zigbee channels, and investigate the effect of busytone on WiFi traffic as well.

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.

As the use of micro-UAV (a.k.a drone) increases, distance measurement and localization of drones becomes important. We propose a real-time audio-based system that uses deep learning for not only detecting but also ranging and localization of a drone. In the proposed system scenario, each node records sound and sends it to the server after processing, and the server receives the data from each node and computes the final location of a drone. To explore the design space and investigate the feasibility of real-time acoustic ranging using deep learning, we first measure the drone detection accuracy and processing latency using two deep learning models (CNN, DNN) on both an embedded and a server-class device. By analyzing the relationship between detection probability and distance measurement, and comparing between binary and multi-class classification, we suggest a system design to range and localize the drone.

No abstract available.

We show that a multihop wireless network can achieve better bandwidth and routing stability when transmission power and routing topology are jointly and adaptively controlled. Our experiments show that the predominant 'fixed and uniform' transmission power strategy with 'link quality and hop distance'-based routing topology construction loses significant bandwidth due to hidden terminal and load imbalance problems. We design an adaptive and distributed control mechanism for transmission power and routing topology, PCRPL, within the standard RPL routing protocol. We implement PC-RPL on real embedded devices and evaluate its performance on a 49-node multihop testbed. PC-RPL reduces total end-to-end packet losses ~7-fold without increasing hop distance compared to RPL with the highest transmission power, resulting in 17% improvement in aggregate bandwidth and 64% for the worst-case node.

Jamming is one of the most famous and powerful attacks in wireless networks, and is advancing to be more stealthy and long-lasting with limited energy. Stealthy attackers transmit short jamming signals to become less detectable with less energy, and yet powerful enough to ruin the entire packet transmission procedures. For this study, we deal with three types of stealthy attacks: 'reactive jamming', 'jamming ACK', and 'fake ACK' attacks. These attacks are fatal to Low-power and Lossy wireless Network (LLN) applications because they not only interfere with communication, but also cause LLN devices to quickly drain their batteries. In this paper, we present Dodge-Jam, a light-weight antijamming technique suitable for LLN environments to address the stealthy jamming attacks with small overhead. It protects ACK exchange by switching the ACK channel calculated based on the content of a data packet. Moreover, by partitioning a packet into multiple small blocks and performing logical shifts of the blocks when retransmitting the packet, it helps the receiver recover the original packet from multiple erroneous packets. We implement Dodge-Jam on practical embedded devices, and evaluate its performance through experiments on a multihop LLN testbed. Our results show that Dodge-Jam successfully avoids many jamming attacks, recovers packets that have been jammed, and improves packet delivery performance of both singlehop and multihop networks significantly.

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.

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, industrial and environmental monitoring, and wireless sensor networks. RPL allows bidirectional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this article, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of the packet losses under heavy traffic are due to congestion, and a serious load balancing problem appears in RPL in terms of routing parent selection. To overcome this problem, this article proposes a simple yet effective queue utilization based RPL (QU-RPL) that achieves load balancing and significantly improves the end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QURPL is very effective in lowering queue losses and increasing the packet delivery ratio. We implement QU-RPL on a low-power embedded platform, and verify all of our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4. We present the impact of each design element of QU-RPL on performance in detail, and also show that QU-RPL reduces the queue loss by up to 84 percent and improves the packet delivery ratio by up to 147 percent compared to the standard RPL.

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.

We present a transmission power control scheme for RPL, the IPv6 Routing Protocol for Low-power lossy network [9], that controls the routing topology to achieve load balancing in a low-power multihop wireless network. We show that higher-than-required transmission power results in congestion and load balancing problems under heavy traffic, and transmission power cannot be optimized for reliable packet delivery when equal power is used by all nodes. To address these issues, we propose an adaptive and non-uniform transmission power controlled RPL, called PC-RPL, that significantly improves the end-to-end packet delivery performance compared to the standard RPL.

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, industrial and environmental monitoring, and wireless sensor networks. RPL allows bi-directional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this paper, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of packet losses under heavy traffic are due to congestion, and a serious load balancing problem exists in RPL in terms of routing parent selection. To overcome this problem, this paper proposes a simple yet effective queue utilization based RPL (QU-RPL) that significantly improves end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QU-RPL is very effective in lowering the queue losses and increasing the packet delivery ratio. We verify all our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4.

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.

Low-power and lossy networks (LLNs) comprised of thousands of embedded networking devices connected to the larger Internet architecture can be used in a variety of applications, leading to the emerging concept of the Internet of Things (IoT). Access control and key management in such LLN is a big challenge since authentication, authorization, and key management process involves several handshakes of large size packets over low bandwidth and high latency links through a bottlenecked LLN border router (LBR). This may result in significant wasted bandwidth and slow authentication completion time. The goal of this work is to propose a “fast and adaptive node authentication mechanism for low-power lossy networks,” called FINALLY, that solves these problems and significantly improves the efficiency and speed of mesh authentication process. We evaluate FINALLY through simulations and show that it allows devices to authenticate quickly without repeated authentication failures, reduces unnecessarily wasted transmissions, and improves the overall completion time of the authentication process.

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.

Many emerging location-aware applications require position information. However, these applications rarely use celltower-based localization because of its inaccuracy, preferring instead to use the more energy-hungry GPS.In this paper, we present CAPS, a Cell-ID Aided Positioning System. CAPS leverages near-continuous mobility and the position history of a user to achieve significantly better accuracy than the celltower-based approach, while keeping energy overhead low. CAPS is designed based on the insight that users exhibit consistency in routes traveled, and that cell-ID transition points that the user experiences can, on a frequently traveled route, uniquely identify position. To this end, CAPS uses a cell-ID sequence matching technique to estimate current position based on the history of cell-ID and GPS position sequences that match the current cell-ID sequence. We have implemented CAPS on Android-based smartphones and have extensively evaluated it at different locations, and for different platforms and carriers. Our evaluation results show that CAPS can save more than 90% of the energy spent by the positioning system compared to the case where GPS is always used, while providing reasonably accurate position information with errors less than 20% of the celltower-based scheme.

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy.

Many applications are enabled by the ability to capture videos on a smartphone and to have these videos uploaded to an Internet-connected server. This capability requires the transfer of large volumes of data from the phone to the infrastructure. Smartphones have multiple wireless interfaces -- 3G/EDGE and WiFi -- for data transfer, but there is considerable variability in the availability and achievable data transfer rate for these networks. Moreover, the energy costs for transmitting a given amount of data on these wireless interfaces can differ by an order of magnitude. On the other hand, many of these applications are often naturally delay-tolerant, so that it is possible to delay data transfers until a lower-energy WiFi connection becomes available. In this paper, we present a principled approach for designing an optimal online algorithm for this energy-delay tradeoff using the Lyapunov optimization framework. Our algorithm, called SALSA, can automatically adapt to channel conditions and requires only local information to decide whether and when to defer a transmission. We evaluate SALSA using real-world traces as well as experiments using a prototype implementation on a modern smartphone. Our results show that SALSA can be tuned to achieve a broad spectrum of energy-delay tradeoffs, is closer to an empirically-determined optimal than any of the alternatives we compare it to, and, can save 10-40% of battery capacity for some workloads.

Emerging high-rate applications (imaging, structural monitoring, acoustic localization) will need to transport large volumes of data concurrently from several sensors. These applications are also loss-intolerant. A key requirement for such applications, then, is a protocol that reliably transports sensor data from many sources to one or more sinks without incurring congestion collapse. In this article, we discuss RCRT, a rate-controlled reliable transport protocol suitable for constrained sensor nodes. RCRT uses end-to-end explicit loss recovery, but places all the congestion detection and rate adaptation functionality in the sinks. This has two important advantages: efficiency and flexibility. Because sinks make rate allocation decisions, they are able to achieve greater efficiency since they have a more comprehensive view of network behavior. For the same reason, it is possible to alter the rate allocation decisions (for example, from one that ensures that all nodes get the same rate, to one that ensures that nodes get rates in proportion to their demands), without modifying sensor code at all. We evaluate RCRT extensively on a 40-node wireless sensor network testbed and show that RCRT achieves 1.7 times the rate achieved by IFRC and 1.4 times that of WRCP, two recently proposed interference-aware distributed rate-control protocols. We also present results from a 3-month-long 19-node real world deployment of RCRT in an imaging application and show that RCRT works well in real long-term deployments.

Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes . While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered , consisting of motes in the lower tier and masters , relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data. 1

Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code.

We investigate the throughput improvement that ZIGZAG decoding (Gollakota and Katabi (2008)) can achieve in multi-user random access systems. ZIGZAG is a recently proposed 802.11 receiver design that allows successful reception of packets despite collision. Thus, the maximum achievable throughput of a wireless LAN can be significantly improved by using ZIGZAG decoding. We analyze the throughput bounds in three different idealized slotted multi-access system models for the case when ZIGZAG decoding is used. We also provide results for the Aloha and CSMA models where exact closed form solutions are infeasible to calculate. Our analysis and simulation results show that ZIGZAG decoding can significantly improve the maximum throughput of the random access system.

Emerging high-rate applications (imaging, structural monitoring, acoustic localization) will need to transport large volumes of data concurrently from several sensors. These applications are also loss-intolerant. A key requirement for such applications, then, is a protocol that reliably transport sensor data from many sources to one or more sinks without incurring congestion collapse. In this paper, we discuss RCRT, a rate-controlled reliable transport protocol suitable for constrained sensor nodes. RCRT uses end-to-end explicit loss recovery, but places all the congestion detection and rate adaptation functionality in the sinks. This has two important advantages: efficiency and flexibility. Because sinks make rate allocation decisions, they are able to achieve greater efficiency since they have a more comprehensive view of network behavior. For the same reason, it is possible to alter the rate allocation decisions (for example, from one that ensures that all nodes get the same rate, to one that ensures that nodes get rates in proportion to their demands), without modifying sensor code at all. We evaluate RCRT extensively on a 40-node wireless sensor network testbed and show that RCRT achieves more than twice the rate achieved by a recently proposed interference-aware distributed rate-control protocol, IFRC [23].

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

With the advent of miniaturized sensing technology, it has become possible to envision smart structures containing millions of sensors embedded in concrete for autonomously detecting and locating incipient damage. Where are we today in our march towards this vision of autonomous structural health monitoring (SHM) using networked embedded sensing? In this paper, we summarize some of the systems we have developed towards this vision. Wisden is a wireless sensor network that allows continuous monitoring of structures and NetSHM is a programmable system that allows civil engineers to implement and deploy SHM techniques without having to understand the intricacies of wireless sensor networking. We highlight our experiences in developing these systems, and discuss the implications of our experiences on the achievability of the overall vision.