Showing papers in "ACM Transactions on Sensor Networks in 2020"

Dynamic Task-based Intermittent Execution for Energy-harvesting Devices

Abstract: Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala’s specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail.

SCANet: Sensor-based Continuous Authentication with Two-stream Convolutional Neural Networks

Abstract: Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this article, we present SCANet, a two-stream convolutional neural network--based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data—frequency domain data and temporal difference domain data—from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features and then performs the principal component analysis to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate on two datasets and takes approximately 3 s for user authentication.

ahoi: Inexpensive, Low-power Communication and Localization for Underwater Sensor Networks and μAUVs

Abstract: The recent development of small, cheap AUVs enables a plethora of underwater near- and inshore applications. Among these are monitoring of wind parks, detection of pollution sources, water-quality inspection, and the support of divers during disaster management. These tasks profit from online reporting, control, and AUV swarm interaction; yet they require underwater communication. Unfortunately, commercial devices are prohibitively expensive and typically closed-source, hampering their application in affordable products and research. Therefore, we developed the open-source ahoi acoustic modem. It is (i) small enough to be carried by micro AUVs, (ii) consumes little enough energy to not diminish operation times of its host, (iii) comes at an attractive unit cost below d600, (iv) can reliably communicate at distances of 150 m and more, and (v) supports ranging without additional hardware. Due to its modular build, the modem can be customized and is suitable as research platform to analyze, e.g., MAC and routing protocols. We conducted extensive real-world studies and present results of communication range, packet reception rate, ranging accuracy, and efficient and reliable self-localization. Finally, we draw conclusions regarding acoustic communication, ranging, and localization with inexpensive and low-power devices that go beyond a particular device. Our study, hence, encompasses general insights, observations, and recommendations.

Smart Sensing, Communication, and Control in Perishable Food Supply Chain

Abstract: Transportation and distribution (T8D) of fresh food products is a substantial and increasing part of the economic activities throughout the world. Unfortunately, fresh food T8D not only suffers from significant spoilage and waste, but also from dismal efficiency due to tight transit timing constraints between the availability of harvested food until its delivery to the retailer. Fresh food is also easily contaminated, and together with deteriorated fresh food is responsible for much of food-borne illnesses.The logistics operations are undergoing rapid transformation on multiple fronts, including infusion of information technology in the logistics operations, automation in the physical product handling, standardization of labeling, addressing and packaging, and shared logistics operations under 3rd party logistics (3PL) and related models. In this article, we discuss how these developments can be exploited to turn fresh food logistics into an intelligent cyberphysical system driven by online monitoring and associated operational control to enhance food freshness and safety, reduce food waste, and increase T8D efficiency. Some of the issues discussed in this context are fresh food quality deterioration processes, food quality/contamination sensing technologies, communication technologies for transmitting sensed data through the challenging fresh food media, intelligent management of the T8D pipeline, and various other operational issues. The purpose of this article is to stimulate further research in this important emerging area that lies at the intersection of computing and logistics.

ACES: Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning

Abstract: Many modern smart building applications are supported by wireless sensors to sense physical parameters, given the flexibility they offer and the reduced cost of deployment. However, most wireless sensors are powered by batteries today, and large deployments are inhibited by the requirement of periodic battery replacement. Energy harvesting sensors provide an attractive alternative, but they need to provide adequate quality of service to applications given the uncertainty of energy availability. We propose ACES, which uses reinforcement learning to maximize sensing quality of energy harvesting sensors for periodic and event-driven indoor sensing with available energy. Our custom-built sensor platform uses a supercapacitor to store energy and Bluetooth Low Energy to relay sensors data. Using simulations and real deployments, we use the data collected to continually adapt the sensing of each node to changing environmental patterns and transfer learning to reduce the training time in real deployments. In our 60-node deployment lasting 2 weeks, nodes stop operations only 0.1% of the time, and collection of data is comparable with current battery-powered nodes. We show that ACES reduces the node duty-cycle period by an average of 33% compared to three prior reinforcement learning techniques while continuously learning environmental changes over time.

Pushing the Limits of Transmission Concurrency for Low Power Wireless Networks

Abstract: Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities.In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Abstract: A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.

QoS-Aware VNF Placement and Service Chaining for IoT Applications in Multi-Tier Mobile Edge Networks

Abstract: Mobile edge computing and network function virtualization (NFV) paradigms enable new flexibility and possibilities of the deployment of extreme low-latency services for Internet-of-Things (IoT) applications within the proximity of their users. However, this poses great challenges to find optimal placements of virtualized network functions (VNFs) for data processing requests of IoT applications in a multi-tier cloud network, which consists of many small- or medium-scale servers, clusters, or cloudlets deployed within the proximity of IoT nodes and a few large-scale remote data centers with abundant computing and storage resources. In particular, it is challenging to jointly consider VNF instance placement and routing traffic path planning for user requests, as they are not only delay sensitive but also resource hungry. In this article, we consider admissions of NFV-enabled requests of IoT applications in a multi-tier cloud network, where users request network services by issuing service requests with service chain requirements, and the service chain enforces the data traffic of the request to pass through the VNFs in the chain one by one until it reaches its destination. To this end, we first formulate the throughput maximization problem with the aim to maximize the system throughput. We then propose an integer linear program solution if the problem size is small; otherwise, we devise an efficient heuristic that jointly takes into account VNF placements to both cloudlets and data centers and routing path finding for each request. For a special case of the problem with a set of service chains, we propose an approximation algorithm with a provable approximation ratio. Next, we also devise efficient learning-based heuristics for VNF provisioning for IoT applications by incorporating the mobility and energy conservation features of IoT devices. We finally evaluate the performance of the proposed algorithms by simulations. The simulation results show that the performance of the proposed algorithms is promising.

H-DrunkWalk: Collaborative and Adaptive Navigation for Heterogeneous MAV Swarm

Abstract: Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk, a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

PC-RPL: Joint Control of Routing Topology and Transmission Power in Real Low-Power and Lossy Networks

Abstract: 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.

Ultra-wideband Concurrent Ranging

Abstract: We propose a novel concurrent ranging technique for distance estimation with ultra-wideband (UWB) radios. Conventional schemes assume that the necessary packet exchanges occur in isolation to avoid collisions. Concurrent ranging relies on the overlapping of replies from nearby responders to the same ranging request issued by an initiator node. As UWB transmissions rely on short pulses, the individual times of arrival can be discriminated by examining the channel impulse response (CIR) of the initiator transceiver. By ranging against N responders with a single, concurrent exchange, our technique drastically abates network overhead, enabling higher ranging frequency with lower latency and energy consumption w.r.t. conventional schemes.Concurrent ranging can be implemented with a strawman approach requiring minimal changes to standard schemes. Nevertheless, we empirically show that this limits the attainable accuracy, reliability, and therefore applicability. We identify the main challenges in realizing concurrent ranging without dedicated hardware and tackle them by contributing several techniques, used in synergy in our prototype based on the popular DW1000 transceiver. Our evaluation, with static targets and a mobile robot, confirms that concurrent ranging reliably achieves decimeter-level distance and position accuracy, comparable to conventional schemes but at a fraction of the network and energy cost.

Identifying Child Users via Touchscreen Interactions

Abstract: With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch behaviors are related to his or her age. Thus, iCare records the touch behaviors and extracts hand geometry, finger dexterity, and hand stability features that capture the age information. We conduct experiments on 100 people including 62 children (3 to 17 years old) and 38 adults (18 to 59 years old). Results show that iCare can achieve 96.6% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 98.3% with three consecutive swipes.

Smartphone-Based SpO2 Measurement by Exploiting Wavelengths Separation and Chromophore Compensation

Abstract: Patients with respiratory diseases require frequent and accurate blood oxygen level monitoring. Existing techniques, however, either need a dedicated hardware or fail to predict low saturation levels. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO2, using camera and flashlight functions that are readily available on today’s off-the-shelf smartphones. Since the phone’s camera and flashlight were not made for this purpose, utilizing them for oxygen level estimation poses many difficulties. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A near-field-based pressure detection and feedback mechanism are also proposed to mitigate the negative impacts of user’s behavior during the measurement. We also derive a non-linear referencing model with an outlier removal technique that allows PhO2 to accurately estimate the oxygen level from color intensity ratios produced by the smartphone’s camera.An evaluation on COTS smartphone with six subjects shows that PhO2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. In addition, our evaluation in hospitals presents high correlation with ground-truth qualified by the 0.83/1.0 Kendall τ coefficient.

MORE: Multi-node Mobile Charging Scheduling for Deadline Constraints

Abstract: Due to the merit without requiring charging cable, wireless power transfer technology has drawn rising attention as a new method to replenish energy for Wireless Rechargeable Sensor Networks. In this article, we study the mobile charger scheduling problem for multi-node recharging with deadline constraints. Our target is to maximize the overall effective charging utility and minimize the traveling time for moving as well. Instead of charging only once over a scheduling cycle, we incorporate the multi-node charging strategy with deadline constraints, where charging spots and tour are jointly optimized. Specifically, we formulate the effective charging utility maximization problem as a monotone submodular function optimization subject to a partition matroid constraint, and we propose a simple but effective ½-approximation greedy algorithm. After that, we derive the result of global scheduling and present the grid-based skip-substitute operation to further save the traveling time, which can increase the charging utility. Finally, we conduct the evaluation for the performance of our scheduling scheme. The simulation and field experiment results show that our algorithm excels in terms of effective charging utility.

iSleep: A Smartphone System for Unobtrusive Sleep Quality Monitoring

Abstract: The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

Latency-efficient Data Collection Scheduling in Battery-free Wireless Sensor Networks

Abstract: The lifetime of battery-powered Wireless Sensor Networks (WSNs) are limited by the batteries equipped in sensors. The appearance of Battery-free Wireless Sensor Networks (BF-WSNs) breaks through this limitation, in which battery-free sensors harvest energy from sustainable but uncontrollable energy sources in ambient environment, such as solar power, wind power, radio frequency signal power, and so on. The energy characteristics of BF-WSNs make it more challenging for data collection scheduling in BF-WSNs. Latency of data collection is a crucial measurement to evaluate the performance of data collection schedules. In this article, we study the problem of generating data collection schedules with minimum latency for BF-WSNs and propose latency-efficient data collection scheduling algorithms for line BF-WSNs and general BF-WSNs, respectively. Theoretical analysis and extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms.

Quality-aware Online Task Assignment in Mobile Crowdsourcing

Abstract: In recent years, mobile crowdsourcing has emerged as a powerful computation paradigm to harness human power to perform spatial tasks such as collecting real-time traffic information and checking product prices in a specific supermarket. A fundamental problem of mobile crowdsourcing is: When both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each worker. Most existing studies focus on efficient assignment algorithms based on bipartite graph matching. However, they overlook an important fact that crowd workers might be unreliable. Thus, their task assignment schemes cannot ensure the overall quality. In this article, we investigate the Quality-aware Online Task Assignment (QAOTA) problem in mobile crowdsourcing. We propose a probabilistic model to measure the quality of tasks and a hitchhiking model to characterize workers’ behavior patterns. We model task assignment as a quality maximization problem and derive a polynomial-time online assignment algorithm. Through rigorous analysis, we prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Finally, we demonstrate the efficiency and effectiveness of our solution through intensive experiments.

Driving Lane Detection on Smartphones using Deep Neural Networks

Abstract: Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle’s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle’s speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this article, we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle’s current lane. We employ a deep learning--based technique to classify the vehicle’s lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real-world datasets collected in developed and developing regions. DeepLane can detect a vehicle’s lane position with an accuracy of over 90%, and we have implemented DeepLane as an Android app.

Securing Cyber-Physical Social Interactions on Wrist-Worn Devices

Abstract: Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this article, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel key generation system, which harvests motion data during user handshaking from the wrist-worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g., exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn’t involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed key generation system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate >99%), and is resilient to different types of attacks including impersonate mimicking attacks, impersonate passive attacks, or eavesdropping attacks. Specifically, for real-time impersonate mimicking attacks, in our experiments, the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed key generation system can be extremely lightweight and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption.

dWatch: A Reliable and Low-Power Drowsiness Detection System for Drivers Based on Mobile Devices

Abstract: Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%.

DAML: Practical Secure Protocol for Data Aggregation Based on Machine Learning

Abstract: Data aggregation based on machine learning (ML), in mobile edge computing, allows participants to send ephemeral parameter updates of local ML on their private data instead of the exact data to the untrusted aggregator. However, it still enables the untrusted aggregator to reconstruct participants’ private data, although parameter updates contain significantly less information than the private data. Existing work either incurs extremely high overhead or ignores malicious participants dropping out. The latest research deals with the dropouts with desirable cost, but it is vulnerable to malformed message attacks. To this end, we focus on the data aggregation based on ML in a practical setting where malicious participants may send malformed parameter updates to perturb the total parameter updates learned by the aggregator. Moreover, malicious participants may drop out and collude with other participants or the untrusted aggregator. In such a scenario, we propose a scheme named DAML, which to the best of our knowledge is the first attempt toward verifying participants’ submissions in data aggregation based on ML. The main idea is to validate participants’ submissions via SSVP, a novel secret-shared verification protocol, and then aggregate participants’ parameter updates using SDA, a secure data aggregation protocol. Simulation results demonstrate that DAML can protect participants’ data privacy with preferable overhead.

In-Bed Body Motion Detection and Classification System

Abstract: In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.

Charge-Aware Duty Cycling Methods for Wireless Systems under Energy Harvesting Heterogeneity

Abstract: Recent works have designed systems containing tiny devices to communicate with harvested ambient energy, such as the ambient backscatter and renewable sensor networks. These systems often encounter the heterogeneity and randomness of ambient energy. Meanwhile, the energy storage unit, such as the battery or capacitor, has the inherent property of imperfect charge efficiency λ (λ ≤ 1), which is usually low when the power of the ambient energy is weak or variable. These features bring new challenges in using the harvested energy efficiently. This article calls it the stochastic duty cycling problem and studies it under three cases—offline, online, and correlated stochastic duty cycling—to maximize utilization efficiency. We design an offline algorithm1 for the offline case with optimal performance. An approximation algorithm with the ratio 1 − e−γ is designed for the online case. By adding initial negotiation among devices, we present a correlated algorithm and prove its approximation ratio theoretically. Experiment evaluation on our real energy harvesting platform shows that the offline algorithm performs over the other two algorithms. The correlated algorithm may not perform over the online one under the impacts of the three metrics: heterogeneity, charge efficiency, and energy harvesting probability.

Urban Scale Trade Area Characterization for Commercial Districts with Cellular Footprints

Abstract: Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data and social network check-in data, MFRs can model customer mobility patterns comprehensively at urban scale. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on two large-scale cellular network datasets covering 3.5 million and 1.8 million mobile phone users in two metropolis in China, respectively. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

A Prospect Theoretic Approach for Trust Management in IoT Networks Under Manipulation Attacks

Abstract: As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties. In this article, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub—optimistic and conservative. Furthermore, we put forward asymmetric weighted moving average scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.

Distributed Reliable and Energy-Efficient Scheduling for LR-WPANs

Abstract: Pervasiveness of wireless networks drives the heterogeneity and density of devices in a vast diversity of environments. To achieve high reliability and low energy consumption while enabling pervasiveness is inherently a resource allocation problem. In low-rate wireless personal area networks, multi-frequency time-division multiple access methods are identified as compelling solutions to resource allocation via scheduling transmissions in time and frequency. This work presents the TREE (TRaffic-aware Energy Efficient) algorithm, an adaptive and distributed scheduling algorithm, designed to provide high reliability in terms of packet reception ratio while optimizing the energy consumption of each device. This algorithm schedules communications according to the packets in the queue and short-memory performance. Decisions are made locally, and low-interference scheduling emerges at the network level. TREE is an adaptive threshold-based model that allocates more network resources (e.g., timeslots) when the communication queue size crosses a threshold and frees resources if the resource was underutilized. Implemented over IEEE-802.15.4-TSCH and extensively tested in simulation and on real deployments up to 81 devices, the algorithm is compared to MSF, Alice, and Orchestra, the state of the art in time-slotted channel hopping scheduling. Results highlight a high reliability regarding packet reception ratio and a lower energy consumption compared to the state of the art.

A Query Processing Framework for Efficient Network Resource Utilization in Shared Sensor Networks

Abstract: Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes, and sensing time duration. This overlapping results in redundant sensing tasks generation leading to the increased overall network traffic and energy consumption. Existing approaches operate on data sharing among various tasks to minimize the upstream traffic. However, no existing work attempts to prevent the redundant task generation to reduce the downstream traffic. Moreover, the allocation of suitable sensor nodes to meet the Quality of Service (QoS) requirements of the queries is still an open issue. This article proposes an end-to-end query processing framework (named, QueryPM) that first, calculates the functional requirements similarity among queries to prevent the redundant task generation. Then, it takes the QoS and functional requirements into account while allocating the tasks on the sensor nodes. Extensive simulations on the proposed approach show that downstream traffic, upstream traffic, and energy consumption reduced to 60%, 20--40%, and 40%, respectively, as compared to state-of-the-art mechanisms.

QA-Share: Toward an Efficient QoS-Aware Dispatching Approach for Urban Taxi-Sharing

Abstract: Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, it becomes more difficult to strike the balance between a driver’s profit and a passenger’s quality of service (QoS). In this article, we propose QA-Share, a QoS-aware taxi-sharing system, by addressing two important challenges. First, QA-Share maximizes driver profit and user experience at the same time. Second, QA-Share optimizes these two metrics by dynamically adapting its schedule as new requests arrive. To address these two challenges, we formulated the optimization problem using integer linear programming and derived the optimal solution under a small system scale. Moreover, we also designed a heuristic algorithm to deal with the situation where more passenger requests for taxi service come at the same time. We evaluate our approach with a real-world dataset in a Chinese city—Zhenjiang—that contains the GPS traces recorded by more than 3,000 taxis during a period of 3 months. The results show that both QoS and profit increase by 38% compared to the current schemes. Moreover, as the first study that has conducted simulations with real traces with a population of 3 million and 3,000 taxis, we prove that taxi-sharing is a viable approach in a medium-size city.

Energy-efficient Dual-codebook–based Backscatter Communications for Wireless Powered Networks

Abstract: The common backscatter communications (ComBC), widely applied in wireless powered networks such as the RFID systems, exhibit the shortcoming that only a few bits are backscattered at a time due to energy limitation. It is significant to improve energy efficiency in backscatter communications so more data can be delivered within one backscatter. In this article, the energy-efficient dual-codebook based backscatter communications (DBBC) is proposed, which adopts two prefix codebooks shared by the sender and the receiver over a backscatter communication link. With the DBBC, the sender backscatters codewords from which the receiver decodes the original data. Using Energy Consumption Disparity (ECD) between backscattering bits 1 and 0 in the existing backscatter communications, we formulate the optimization problem minimizing energy consumption over the link for the DBBC. Mapping a prefix codebook into a binary tree and performing pruning and expanding operation (PEO) on binary trees, we obtain the solution to the optimization problem, which includes the two energy-efficient codebooks and the other two key parameters for the DBBC. The experiments on wireless identification sensing platform (WISP) show that, under the proposed DBBC, the sender can backscatter data 83% more than the ComBC with the same energy without sacrificing throughput.

Dynamic Edge-coded Protocols for Low-power, Device-to-device Communication

Abstract: Clock and Data Recovery (CDR) has been a foundational receiver component in serial communications. Yet this component is known to add significant design complexity to the receiver and to consume significant resources in area and power. In the resource-limited world of constrained IoT nodes, the need of including CDR in the communication link is being re-assessed and new techniques for achieving reliable serial transmission without CDR have been emerging. These new techniques are distinguished by their use of transition edges rather than bit times for coding and detection. This article presents the design, implementation, and testing of a novel CDR-less transmission protocol that achieves significant improvements in data rate, reliability, packet security, and power efficiency with respect to state-of-the-art CDR-less techniques. The new protocol further tolerates significant jitters and clock discrepancies between transmitter and receiver. An FPGA and an ASIC (65 nm technology) implementation of the protocol have shown it to consume around 19μ W of power at a clock rate of 25 MHz, and to have a small footprint with a gate count of approximately 2,098 gates. In particular, the new protocol reduces area by more than 87% and power by more than 78% in comparison with CDR-based serial bit transfer protocols. Furthermore, the new protocol is shown to be versatile in its applications to available communication media, including wired, wireless, infrared, and human-body channels, under a variety of digital modulation schemes.