In agricultural research, early-stage evaluations of new seed lines or plant treatments often face delays due to reliance on human inspections, which can take days for stress symptoms becoming visible. Image-based phenotyping techniques have emerged as a potential solution, but they are often operationally intensive. Invasive sensors attached to plant tissues enable rapid stress detection but may cause tissue damage and potentially affect plant growth. Inspired by the observation that near-canopy carbon dioxide (CO2) exchange fluctuates within minutes, we present CarbonSense, a rapid, non-invasive plant stress detector. CarbonSense exploits near-canopy CO2 to interpret plant behavior and detect stress, enabling quick go/no-go decisions for experimental trials in agricultural research. We have evaluated CarbonSense using our greenhouse testbed across three crop cycles (79 days). The results show that Carbon-Sense achieves an average accuracy of 93.7% in detecting crop stress conditions (100% on 20 stress days and 91.5% on 59 healthy days). Notably, CarbonSense detects stress with an average latency of 7.3 hours after stress onset, which is 41.4 hours sooner than traditional visual-based stress detection methods.

Stealthy attacks manipulate the operation of Industrial Control Systems (ICSs) without being undetected, allowing persistent manipulation of system operation and thus the potential to cause destructive damage. This paper introduces a new vulnerability of ICS that can be exploited to mount stealthy attacks without requiring any domain knowledge. This vulnerability is caused by a common practice in system monitoring, i.e., the SCADA monitors ICS operation at a much lower frequency than system execution, causing a loss of precision when the SCADA tries to cross-validate the issued control commands using the collected sensory data. Exploiting this vulnerability, an attack calledPLC-SAGEis designed to stealthily manipulate the system operation by identifying and injecting malicious control commands that will not be concluded as abnormal by the SCADA. This paper further discusses a preferred ICS engineering practice and an attestation strategy to mitigate the above vulnerability and protect ICS fromPLC-SAGE. BothPLC-SAGEand the proposed mitigations have been experimentally validated on two ICS platforms.

The security risk of semantic attacks to Industrial Control Systems (ICSs) is increasing. Semantic attacks manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables, which is usually and inefficiently achieved via manual examination of system documents and long-term observation of system behavior. In this paper, we designARES, a method thatAutomaticallyReverseEngineers theSemantics of variables in PLC programs without requiring any domain knowledge.ARESis built on the fact that the Supervisory Control And Data Acquisition (SCADA) system monitors the behavior of PLC using a fixed mapping between the variables of program code and data log, and the data log variables are marked with physical semantics. By identifying the mapping between PLC code and SCADA data (i.e., the code-data mapping),ARESreverse engineers the physical semantics of program variables.ARESalso sheds light on the preferred defense strategies in implementing control rules that improve the resistance of PLC programs to semantic attacks, as well as in detecting and responding to semantics attacks in real time. We have experimentally evaluatedARESand the recommended defending practices on two ICS platforms.

The increasing network integration of industrial control systems amplifies the risk of cyberattacks on Programmable Logic Controllers (PLCs). In particular, the weak authentication of industrial communication protocols makes PLC program variables vulnerable to manipulation. Current defensive methods cannot reliably identify manipulated variables, even after PLC program manipulations have been detected. To bridge this gap, we presentADIS, a cross-domain Attack Detection and Identification System designed to detect and identify manipulated PLC program variables. Building on a novel state-aware graph representation of the PLC program,ADISdetects variable manipulations by comparing SCADA monitoring data with the control logic defined by the PLC program.ADISfurther identifies suspiciously manipulated program variables by excluding cascading failures from the detected anomalies and tracking suspicious variables based on the edges of the state-aware dependency graph. We have implemented and evaluatedADISon two platforms. The results demonstrate thatADISdetects attacks with a true positive rate exceeding 99% and a false positive rate of less than$0.04{\unicode {0x2030}}$. Furthermore, it successfully identifies manipulated program variables with up to a 71.3% reduction in suspicious variables compared to a baseline method.

The advancement of Industrial Internet-of-Things (IIoT) magnifies the cyber risk of automated production lines, especially to deception attacks that tamper with the monitoring data to prevent the manipulated operation of production lines from being detected. To address this issue, we propose Stochastic Manufacturing (StoM), a new paradigm of manufacturing that is resistant to deception by design. StoM voids the foundation of deception attacks — i.e., the highly predictable operation data due to the cyclical manufacturing process — by injecting controlled stochasticity into the operation of production lines without degrading manufacturing efficiency or quality. StoM then examines if this stochasticity can be observed from the operation data and triggers an alarm of deception attack if not. We have experimentally evaluated StoM on two production line platforms, showing StoM to detect deception attacks with a detection rate exceeding 99.1%, a false alarm rate below 0.1%, and a latency of less than 1.2 manufacturing cycles. Our empirical analysis also shows that it is highly impractical for attackers to spoof the controlled stochasticity.

Low-power wide-area networks (LPWAN) evolve rapidly with advanced communication primitives (e.g., coding, modulation) being continuously invented. This rapid iteration on LPWAN, however, forms a communication barrier between legacy wireless sensor nodes deployed years ago (e.g., ZigBee-based sensor node) with their latest competitor running a different communication protocol (e.g., LoRa-based IoT node): they work on the same frequency band but share different MAC- and PHY-layer regulations and thus cannot talk to each other directly. To break this barrier, we propose LigBee, a cross-technology communication (CTC) solution that enables symbol-level communication from the latest LPWAN LoRa node to legacy ZIGBEE node. We have implemented LigBee on both software-defined radios and commercial-off-the-shelf (COTS) LoRa and ZigBee nodes, and demonstrated that LigBee builds a reliable CTC link from LoRa node to ZigBee node on both platforms. Our experimental results show that i) LigBee achieves a bit error rate (BER) in the order of 10−3with 70 ∼ 80% frame reception ratio (FRR), ii) the range of LigBee link is over 300m, which is 6 ∼ 7.5× the typical range of legacy ZigBee and state-of-the-art solution, and iii) the throughput of LigBee link is maintained on the order of kbps, which is close to the LoRa’s throughput.

Cyberization is the foundation of vehicle electrification and automation, requiring the deployment of ever-increasing on-board sensing, communication, and computing services. However, vehicle cyberization also introduces new cyber vulnerabilities. In this article, we discuss the opportunities and challenges of using the common 12/24V automotive batteries as sensors and actuators to provide vehicles with three-pronged physical security protection: driver authentication, vehicle access control, and vehicle intrusion detection.

The automotive industry is increasingly deploying software solutions to provide value-added features for vehicles, especially in the era of vehicle electrification and automation. However, the ever-increasing cyber components of vehicles (i.e., computation, communication, and control) incur new risks of anomalies, as evident by the millions of vehicle recalls by different automakers. To mitigate these risks, we design theB-Diag, a battery-based diagnostic system detects engine anomalies with a cyber-physical approach. The core idea ofB-Diagis to diagnose engines using the physically-induced correlations between battery voltage and engine variables, which is captured as a customized 3-layer correlation graph and a set of data-driven norm models describing the edges thereof. The design ofB-Diagis steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life for over three months. Our evaluation showsB-Diagto detect anomalies of 17 engine variables with a$>$$86\%$(up to$100\%$) accuracy.

Auto thieves often exploit the cyber vulnerabilities of existing key/phone-based vehicle immobilizers. To prevent this exploitation of cyber vulnerabilities for auto thefts, we present Battery Sleuth (Bleuth), a novel "physical" vehicle immobilizer which is immune to the common cyber-attack vectors - avoiding the use of wireless communication between key/phone and vehicle as well as in-vehicle networks. Bleuth achieves this by using the common 12V vehicle batteries to authenticate the driver with encrypted power-line communication and then control the battery's output power based on the authentication results, hence (im)mobilizing the vehicle without requiring drivers to carry any additional token. Bleuth is also equipped with four alarms to detect, and respond to, illegitimate operations (i.e., theft attempts), including attempts of unauthorized cranking of the engine, removal/shorting of the authenticator from the battery, abuse of the PLC to drain the car battery, and removal of the dongle from the auxiliary power outlet. Bleuth recharges its power supply automatically to free drivers from the maintenance burden. We have prototyped Bleuth as an add-on module that can be installed on commodity vehicles and evaluated it via field tests on 8 vehicles. We have also demonstrated Bleuth's utility and effectiveness via a survey of 612 car owners.

To enhance anti-theft protection of vehicles, we present Battery Sleuth (Bleuth), a novel "physical" vehicle immobilizer that is immune to the common cyber-attack vectors of car keys/keyfobs. Bleuth uses the common 12V vehicle batteries to authenticate the driver with encrypted power-line communication and then control the vehicle's drivability by regulating the battery output power based on the authentication results, hence (im)mobilizing the vehicle without requiring drivers to carry any additional token.

As the electric (e-) scooter market grows rapidly, e-scooter thefts are becoming a major issue. To mitigate this problem, we design, implement and evaluate BEAS, a Battery-Enabled Authentication System to provide e-scooters with an additional layer of anti-theft protection. Installed on commodity e-scooters as an add-on module, BEAS allows users to customize their passwords for activating e-scooters in the form of pre-defined on/off patterns of the scooter lamp, authenticates users by examining the password based on the thus-generated voltage, and immobilizes e-scooters based on the authentication results by controlling the battery output power. Compared to other existing/potential anti-theft solutions for e-scooters, BEAS has the advantages of being resistant to wireless hacking (i.e., a well-known cyber vulnerability of car keys/keyfobs) and convenient as users are not required to (un)install BEAS every time the e-scooter is parked or carry any additional tokens. We will demonstrate a proof-of-concept prototype of BEAS in this demo.

Semantic attacks have incurred increasing threats to Industrial Control Systems (ICSs), which manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables. This is usually (and inefficiently) achieved via manual examination of system documents and long-term observation of system behavior. In this paper, we design ARES, a method that Automatically Reverse Engineers the Semantics of variables in PLC programs without requiring any domain knowledge. ARES is built on the fact that the Supervisory Control And Data Acquisition (SCADA) system monitors the behavior of PLC using a fixed mapping between the variables of program code and data log, and the data log variables are marked with physical semantics. By identifying the mapping between PLC code and SCADA data (i.e., the code-data mapping), ARES reverse engineers the physical semantics of program variables. ARES also sheds light on the preferred practices in implementing control rules that improve the resistance of PLC programs to semantic attacks. We have experimentally evaluated ARES and the recommended implementation practices on two ICS platforms.

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we designPLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants—i.e., the correlations between sensor readings and the concomitantly triggered PLC commands—that exist pervasively in all control systems. Specifically, taking the system’s supervisory control and data acquisition log as input,PLC-Sleuthabstracts/identifies the system’s control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluatedPLC-Sleuthusing both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show thatPLC-Sleuthcan: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE; 2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE; and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE.

Industrial robots are prototypical cyber-physical systems widely deployed in (smart) manufacturing, which operate according to the operation code uploaded by the human operator and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, calledPIDS(Power-basedIntrusionDetectionSystem), and deliverPIDSas abump-in-the-wiremodule installed at the powerline of commodity robots. The foundation ofPIDSis the physically-induced dependency between the robot movement and the concomitant power consumption, whichPIDScaptures via joint physical analysis and (cyber) data-driven modeling.PIDSthen fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information — a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluatedPIDSusing three models of robots from different vendors — i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots — with over 2,000 operation cycles. Experimental results show thatPIDSdetects replay attacks at an average rate of 96.5 percent (up to 99.9 percent) and a 0.1s latency.

Long Range (LoRa) communication, thanks to its wide network coverage and low energy operation, has attracted extensive attentions from both academia and industry. However, existing LoRa-based Wide Area Network (LoRaWAN) suffers from severe inter-network interference, due to the following two reasons. First, the densely-deployed LoRa ends usually share the same network configurations, such as spreading factor (SF), bandwidth (BW) and carrier frequency (CF), causing interference when operating in the vicinity. Second, LoRa is tailored for low-power devices, which excludes LoRaWAN from using the listen-before-talk (LBT) mechanisms commonly used in wireless communication technologies, such as WiFi and ZigBee -LoRaWAN has to use the duty-cycled medium access policy and thus being incapable of channel sensing or collision avoidance. To mitigate the inter-network interference, we propose a novel solution achieving the online concurrent transmissions at LoRa gateway, called OCT, which recovers collided packets at the gateway and thus improves LoRaWAN's throughput. Moreover, OCT achieves the online concurrent transmission using only LoRa's (de)modulation information, thus can be easily deployed at LoRa gateway. We have implemented and evaluated OCT on USRP platform and commodity LoRa ends, showing OCT achieves: (i) >90% packet reception rate (PRR), (ii) 3 × 10-3bit error rate (BER), (iii) 2x and 3x throughput in the scenarios of two- and three- packet collisions respectively, and (iv) reducing 67% latency compared with state-of-the-art.

As the most widely used outdoor navigation system, GPS can provide accurate localization on the horizontal plane. However, the vertical localization accuracy exhibits a poor performance. Vehicles on the elevated road usually receive wrong navigation instructions since GPS fails to detect vehicles' vertical location. In this paper, we present a vertical location system named VLD for vehicles in metropolises mainly leveraging smartphones' barometers, a low-power sensor found in an increasing number of smart devices. When initially on the ground, VLD combines the height and angle detection algorithms to confirm the vertical location with low complexity. Once vehicles have exited the ground and start to travel on the elevated road, a pressure-height model trained by a novel proposed sensor fusion algorithm is activated to measure vehicles' relative height. Then, it tracks the relative height in real time according to a pressure-temperature model which is calibrated by current weather. Comparing the relative height with the single-level elevated road's height, VLD can determine which level vehicles travel on in many highway interchanges and when they exit the elevated road. Experiments covering one month demonstrate that the detection accuracy of VLD exceeds 99% under different weather conditions, and it shows a more accurate relative height measurement compared to available literature, including Baidu Maps and one barometer based application-Altitude. Finally, VLD demonstrates a high efficiency with respect to both the detection delay and power consumption.

Many users have reported that their smartphones shut off unexpectedly, even when they show >30% remaining battery capacity. After examining the problem from both the user and phone sides, we discovered the cause of these unexpected shutoffs to be a large and dynamic internal voltage drop of the phone battery, which is, in turn, caused by the dynamics of both battery's internal resistance and the phone's discharge current. To fix these unexpected shutoffs, we design a novel Battery-aware Power Management (BPM) middleware that accounts for these dual-dynamics in phone operation. Specifically, BPM profiles the battery's internal resistance --- which varies with battery state-of-charge (SoC), temperature, and aging --- using a novel duty-cycled charging method. BPM then regulates, at run-time, the phone's discharge current based on the constructed battery profile. We have implemented and evaluated BPM on 4 commodity smartphones from different OEMs with the latest battery firmware, demonstrating that BPM prevents unexpected phone shutoffs and extends their operation time by 1.16--2.03X. Our user study, which includes 121 mobile phone users, also corroborates BPM's usefulness/attractiveness.

Industrial robots have been shown to suffer from replay attacks, via which adversaries not only manipulate the robot operation by downloading malicious code, but also prevent the detection of this manipulation by replaying recorded (and normal) movement data to the monitoring system. To protect industrial robots from replay attacks, we design a novel intrusion detection system using the power fingerprint of robots, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant electrical power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information --- a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluated PIDS using three models of robots from different vendors --- i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots --- with over 2, 000 operation cycles. The experimental results show that PIDS detects replay attacks with an average rate of 96.5% (up to 99.9%) and a 0.1s latency.

Modern electric systems, such as electric vehicles, mobile robots, nano satellites, and drones, require to support various power-demand operations for user applications and system maintenance. This, in turn, calls for advanced power management that jointly considers power demand by the operations and power supply from various sources, such as batteries, solar panels, and supercapacitors. In this article, we develop a power scheduling framework for a reliable energy storage system with multiple power-supply sources and multiple power-demand operations. Specifically, we develop offline power-supply guarantee analysis and online power management. The former provides an offline power-supply guarantee such that every power-demand operation completes its execution in time while the sum of power required by individual operations does not exceed the total power supplied by the entire energy storage system at any time; to this end, we develop a plain power-supply analysis as well as its improved version using real-time scheduling techniques. On the other hand, the latter efficiently utilizes the surplus power available at runtime for improving system performance; we propose two approaches, depending on whether future scheduling information of power-demanding tasks is available or not. For evaluation, we perform simulations to evaluate both the plain and improved analyses for offline power guarantee under various synthetic power-demand operations. In addition, we have built a simulation model and demonstrated that the proposed framework with the offline analysis and online management not only guarantees the required power-supply, but also enhances system performance by up to 56.49 percent.

We present mLoRa in this paper, a novel protocol that can decode multiple collided packets simultaneously from different transmitters in LoRa networks. As a recently proposed wireless technology designed for low-power wide-area networks, LoRa has been proverbially employed in many fields, such as smart cities, intelligent agriculture, and environmental monitoring. In LoRa networks, a star-of-stars topology is conventionally implemented, in which thousands of nodes connect to a single gateway. Accordingly, the convergecast scenario becomes common. For example, in intelligent agriculture, multiple sensor nodes send information with respect to the soil temperature and humidity to a LoRa gateway. Regularly, simultaneous transmissions result in the severe collision problem. Meanwhile, the ALOHA protocol is widely applied in LoRa networks, which further aggravates the collision problem. To conquer this challenge, we propose a protocol named mLoRa for multi-packet reception in LoRa networks, leveraging unique features inherent in LoRa’s physical layer including chirp spread spectrum (CSS), M-FSK modulation, and demodulation. In addition, design enhancements are developed to mitigate the noise and frequency offset influence. We implement mLoRa on a six-node testbed with USRPs. Experiment results demonstrate that mLoRa enables up to three concurrent transmissions. Correspondingly, mLoRa based throughput is around 3 times more than the conventional LoRa.

The automotive industry is increasingly employing software- based solutions to provide value-added features on vehicles, especially with the coming era of electric vehicles and autonomous driving. The ever-increasing cyber components of vehicles (i.e., computation, communication, and control), however, incur new risks of anomalies, as demonstrated by the millions of vehicles recalled by different manufactures. To mitigate these risks, we design B-Diag, a battery-based diagnostics system that guards vehicles against anomalies with a cyber-physical approach, and implement B-Diag as an add-on module of commodity vehicles attached to automotive batteries, thus providing vehicles an additional layer of protection. B-Diag is inspired by the fact that the automotive battery operates in strong dependency with many physical components of the vehicle, which is observable as correlations between battery voltage and the vehicle's corresponding operational parameters, e.g., a faster revolutions-per-minute (RPM) of the engine, in general, leads to a higher battery voltage. B-Diag exploits such physically-induced correlations to diagnose vehicles by cross-validating the vehicle information with battery voltage, based on a set of data-driven norm models constructed online. Such a design of B-Diag is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life over 3 months, covering a total mileage of about 1, 400 miles. Besides the Crosstrek, we have also evaluated B-Diag with driving traces of a 2008 Honda Fit, a 2018 Volvo XC60, and a 2017 Volkswagen Passat, showing B-Diag detects vehicle anomalies with >86% (up to 99%) averaged detection rate.

Due to the limited battery capacity of mobile devices, various CPU power governors and dynamic frequency adjustment schemes have been proposed to reduce CPU energy consumption. However, most such schemes are app-oblivious, ignoring an important fact that real-world applications often exhibit multiple execution phases that perform different functionality and may request different amounts of hardware resources. Having a unified app-level frequency setting for different phases of an application may not be energy efficient enough and may even violate the desirable latency performance required by certain phases. Motivated by this observation, in this paper, we present PIFA, which is an intelligent Phase Identification and Frequency Adjustment framework for energy-efficient and time-sensitive mobile computing. PIFA addresses two major challenges of fully automatically identifying different execution phases of an application and efficiently integrating the phase identification results for runtime frequency adjustment. We have fully implemented PIFA on the Android platform. An extensive set of experiments using real-world Android applications from multiple app categories demonstrate that PIFA achieves closely better performance than the desired latency requirement specified for each phase, while dramatically reducing energy consumption (e.g., >30% energy reduction for most apps) and incurring rather small runtime overhead (e.g., <;5% overhead for most apps).

ZigBee is a widely used wireless technology in low-power and short-range scenarios such as the Internet of Things, sensor networks, and industrial wireless networks. However, the traditional ZigBee supports only one data rate, 250 Kbps, which thoroughly limits ZigBee's efficiency in dynamic wireless channels. In this paper, we propose Mrs. Z, a novel physical layer design to enable multi-rate selection in ZigBee with lightweight modification on the legacy ZigBee modules. The key idea is to change the single spectrum spreading length to multiple ones. Correspondingly, to support the rate adaptation to the channel variations, we propose a bit-error-based rate selection scheme, which predicts BER by leveraging the physical properties of ZigBee to calculate the confidence for each symbol in transmission. Then, the receiver selects the rate based on the negative impact on throughput incurred by bit errors and gives feedback to the transceiver. We implement Mrs. Z on USRPs and evaluate its performance in different scenarios. Experiment results demonstrate that Mrs. Z achieves about 1.15, 1.2, and 1.8 × average throughput compared to the classic smart pilot, softrate, and the traditional ZigBee.

Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs’ output voltage to those of the loads’ required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance.

Much recent work has been conducted on supporting soft real-time tasks on multiprocessors due to the multicore revolution. While most earlier works focus on the traditional sporadic task model with deterministic worst-case specification, several recent works investigate the stochastic nature of many workloads seen in practice, specifying task execution times using average-case provisioning instead of the worst case. Unfortunately, all the existing work on supporting soft real-time workloads ignores a simple practical fact that the job inter-arrival time (or task period) is also stochastic for many real-world applications. Adopting a fixed worst-case period to model all the arriving pattern is rather pessimistic and may result in significant capacity loss in practice. Based on these observations, we present a general soft real-time multiprocessor schedulability analysis framework in this paper for practical sporadic task systems specified by stochastic period and execution demand, following probability distributions. Our analysis can be generally applied to global tunable priority-based schedulers, which allow any job's priority to be changed dynamically at runtime within a priority window of constant length. We have extensively evaluated the analysis framework using a MPEG video decoding case study and simulation-based experiments. Experimental results demonstrate significant advantages of our analysis, which yields over 200 and 50 percent improvements compared to existing analysis assuming worst-case task periods in terms of schedulability and magnitude of the derived tardiness bound, respectively.

ZigBee, a low-power wireless communication technology, has been used in various applications such as smart health/home/buildings. The proliferation of ZigBee-based applications (and thus devices), however, makes the concurrent transmissions - i.e., multiple transmitters send packets to the same receiver at the same time - common in practice, leading to inevitable collisions. To facilitate the concurrent transmissions of ZigBee, we design Pre/Post-amble based Multi-packet reception (PPM), a method that recovers the collided ZigBee messages by exploiting their collision-free chips and the overlapped chips in their pre/post-ambles. Such a collision recovery of PPM reduces the retransmissions caused due to collisions, facilitating the realization green ZigBee. We have prototyped and evaluated PPM with USRP, showing PPM recovers the collided messages with bit-error-rates in the order of 10-6, which is magnitudes lower than state-of-the-art methods.

Mobile Crowdsensing (MCS) is a promising paradigm that utilizes ubiquitous mobile devices to collect environmental data. Specially, location data is critical among all kinds of data because most MCS applications are location-based. Faulty data and missing values, however, may exist in the collected location data due to various reasons. This brings forth an important issue of detecting faulty location data in the presence of missing values. To address this issue, we propose I(TS, CS), a joint faulty data detection framework that combines TimeSeries and Compressive Sensing techniques. The framework adopts a DETECT-and-CORRECT approach to iteratively detect faulty data and reconstruct the dataset, which bypasses the tradeoff between false positive ratio (Type-I error) and false negative ratio (Type-II error), and thus detects more faulty data without increasing False Positive Rate. We have evaluated the proposed I(TS, CS) framework based on a real trace consisting of the trajectories of 2000 taxies, showing I(TS, CS) dramatically improve the performance of both faulty data detection and data reconstruction.

WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.

The collection of sensory data is crucial for cyber‐physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on‐demand MDC; that is, MAs collect data based on the real‐time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve‐based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.

ZigBee is a widely used wireless technology in low-power and short-range scenarios such as Internet of Things (IoT), sensor networks, and industrial wireless networks. However, the standard ZigBee supports only one data rate, 250Kbps, which thoroughly limits ZigBee's efficiency in dynamic wireless channels. In this paper, we propose Mrs.Z, a novel physical layer design to enable multi-rate selection in ZigBee. The key idea is to change the single spectrum spreading length to multiple ones. Correspondingly, to gracefully adapt to the channel variations, we propose a BER-based rate selection scheme, dividing bit errors into two categories: errors caused by the exceeding despreading threshold, which can be discovered in the physical layer, and caused by incorrect despreading, which is not visible until cyclic redundancy check (CRC) in the media access control (MAC) layer. Then, the receiver selects the rate based on the underlying negative impacts incurred by them and feedbacks to the transceiver. We implement Mrs.Z on USRPs and evaluate its performance in different scenarios. Results demonstrate that Mrs.Z achieves an improvement of 20% and 80% compared to the classic SoftRate and the standard ZigBee.

Charging mobile devices fast alleviates users' impatience in waiting for their devices to be charged. So, fast charging has been the focus of both industry and academia, developing and deploying various technologies, such as Quick Charge by Qualcomm, TurboPower by Motorola, Flash Charge by OPPO, etc. Fast charging, unfortunately, accelerates the capacity fading of device battery because it follows the Constant Current, Constant Voltage (CCCV) charge principle without considering the behavior of how users charge their devices. CCCV charging principle is a two-phase charging process consisting of (i) Constant-Current Charge (CC-Chg) and (ii) Constant-Voltage Charge (CV-Chg) [2] where CV-Chg is usually triggered at the end of charging (e.g., 80-100%) to stabilize the battery condition. However, fast charging technologies are agnostic of users' available charging time, resulting in premature termination of the planned charging if users only have limited time. This, in turn, leads to an incomplete CV-Chg phase or even skipping it completely. From our empirical measurements, we discovered that CV-Chg relaxes the batteries and slows down their capacity fading by up to 80% [1] incomplete CV-Chg shortens the battery life significantly over time!

Charging mobile devices "fast" has been the focus of both industry and academia, leading to the deployment of various fast charging technologies. However, existing fast charging solutions are agnostic of users' available time for charging their devices, causing early termination of the intended/planned charging. This, in turn, accelerates the capacity fading of device battery and thus shortens the device operation. In this paper, we propose a novel user-interactive charging paradigm, called iCharge, that tailors the device charging to the user's real-time availability and need. The core of iCharge is a relaxation-aware (R-Aware) charging algorithm that maximizes the charged capacity within the user's available time and slows down the battery's capacity fading. iCharge also integrates R-Aware with existing fast charging algorithms via a user-interactive interface, allowing users to choose a charging method based on their availability and need. We evaluate iCharge via extensive laboratory experiments and field-tests on Android phones, as well as user studies. R-Aware is shown to slow down the battery fading by more than 36% on average, and up to 60% in extreme cases, when compared to existing fast charging algorithms. This slowdown of capacity fading translates to, for instance, an up to 2-hour extension of the LTE time for a Nexus 5X phone after its use for 2 years, according to our trace-driven analysis of 976 device charging cases of 7 users over 3 months.

Cell imbalance in large battery packs degrades their capacity delivery, especially for cells connected in series where the weakest cell dominates their overall capacity. In this article, we present a case study of exploiting system reconfigurations to mitigate the cell imbalance in battery packs. Specifically, instead of using all the cells in a battery pack to support the load, selectively skipping cells to be discharged may actually enhance the pack’s capacity delivery. Based on this observation, we propose CSR, a Cell Skipping-assisted Reconfiguration algorithm that identifies the system configuration with (near)-optimal capacity delivery. We evaluate CSR using large-scale emulation based on empirically collected discharge traces of 40 lithium-ion cells. CSR achieves close-to-optimal capacity delivery when the cell imbalance in the battery pack is low and improves the capacity delivery by about 20% and up to 1x in the case of a high imbalance.

Significant research has been devoted to reduce the energy consumption of mobile devices, but how to increase their energy supply has received far less attention. Moreover, reducing the energy consumption alone does not always extend the device operation time due to a unique battery property - the capacity it delivers hinges critically upon how it is discharged. In this paper, we propose B-MODS, a novel design of battery-aware mobile data service on mobile devices. B-MODS constructs battery-friendly discharge patterns utilizing the recovery effect so as to increase the capacity delivered from batteries while meeting data service requirements. We implement B-MODS as an application layer library on the Android platform. Our experiments with diverse mobile devices under various application scenarios have shown that B-MODS increases the capacity delivery from the battery by up to 49.5 percent, with which an increase in the user-perceived data service utilities of up to 28.6 percent is observed.

Since modern electric systems require to support various power-demand operations for user applications and system maintenance, they need advanced power management that jointly considers power demand by the operations and power supply from various sources, such as batteries, solar panels, and supercapacitors. In this paper, we develop a power scheduling framework for a reliable energy storage system with multiple power-supply sources and multiple power-demand operations. First, we provide an offline power-supply guarantee such that every power-demand operation completes its execution in time while the sum of power required by individual operations does not exceed the total power supplied by the entire energy storage system at any time. We find similarities between this and a real-time scheduling problem, and make a power-supply guarantee using real-time scheduling techniques. Second, we propose online power management that efficiently utilizes the surplus power (available at run-time) for system performance improvement. Our experimental results on a prototype demonstrate that the proposed framework not only guarantees the required power supply, but also enhances system performance by up to 33.1%.

Wireless rechargeable sensor networks have recently emerged as a promising platform that can effectively solve the power constraint problem suffered by traditional battery powered systems. The problem of determining the best charging routes for maximizing charging efficiency has been studied extensively. However, the task assignment problem, which plays a crucial role in efficiently utilizing the harvested energy and thus minimize the charging delay, has received rather limited attention. In this paper, we study the problem of assigning a given set of tasks in a wireless rechargeable sensor network while maximizing the charger's velocity to minimize the charging delay. We first propose an online task assignment algorithm, namely Lower Bound assignment (LB), that yields a quantifiable lower bound on the charging velocity while guaranteeing a feasible assignment. This algorithm further enables the transformation of our considered task assignment problem into a variation of the classical multiple knapsack problem. We then present a fully polynomial-time approximation scheme with a (2+ε)-approximation ratio, namely ACT, that is built upon an existing greedy algorithm designed for the original knapsack problem. Extensive experimental results presented herein demonstrate that ACT is able to achieve near-optimal performance in most cases, and can achieve more than 15% performance improvement compared to the baseline algorithms.

Recent developments in embedded hardware have empowered human experiences through pervasive computing. While embedded systems are becoming more powerful, they still fall short when faced with users' growing desire for running more resource-demanding applications. To bridge this gap, one solution is to leverage powerful resources residing at remote sites by performing computation offloading. Unfortunately, the state-of-the-art offloading frameworks cannot be applied in many embedded systems supporting applications with soft real-time (SRT) constraints or high delay sensitivity, as they typically optimize response times on a "best-effort" basis using heuristics. This paper establishes a soft real-time offloading framework that optimizes the resource utilization of the embedded system while analytically guaranteeing SRT schedulability. The key idea behind the proposed framework is to view offloading-induced delays as suspensions occurring at the local embedded system side, which allows a task being offloaded to be modelled as a suspending task and thus existing SRT suspension-aware scheduling and analysis techniques to be leveraged. Based on this idea, we propose an offloading algorithm, namely Real-time Offloading Decision-making Algorithm (RODA), to make offloading decisions such that SRT schedulability of the task system can be ensured. The optimality properties of RODA have been proved on both uniprocessors and multiprocessors. We conducted extensive simulations on evaluating schedulability and implemented a case study offloading system on top of real hardware to test runtime response time performance. Results demonstrated that RODA is superior to existing performance-driven offloading algorithms, particularly under heavy workloads.

Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of9,000 m even when up to 50% original data are missing.

This paper presents a novel approach to providing a service for electric-vehicle (EV) battery charge replenishment. This is an alternate system in which the charge replenishment is provided by mobile chargers (MCs). These chargers could have two possible configurations: a mobile plug-in charger (MP) or a mobile battery-swapping station (MS). A queuing-based analytical approach is used to determine the appropriate range of design parameters for such a mobile charging system. An analytical analysis is first developed for an idealized system with a nearest-job-next (NJN) service strategy explored for such a system. In a NJN service strategy, the MC services the next spatially closest EV when it is finished with its current request. An urban environment approximated by Singapore is then analyzed through simulation. Charging requests are simulated through a trip generation model based on Singapore. In such a realistic environment, an updated practical NJN service strategy is proposed. For an MP system in an urban environment such as Singapore, there exists an optimal battery capacity with a threshold battery charge rate. Similarly, the battery swap capacity of an MS system does not need to be large for the system to perform.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging (DMC) problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work, we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

In many real-time systems, tasks may experience suspension delays when they block to access shared resources or interact with external devices such as I/O. It is known that such suspensions delays may negatively impact schedulability. Particularly in hard real-time systems, a few negative results exist on analyzing the schedulability of such systems, even for very restricted suspending task models on a uniprocessor. In this paper, we focus on the particular case of hard real-time suspending task systems with harmonic periods, which is a special case of practical relevance. We propose a new uniprocessor suspension-aware analysis technique for supporting such task systems under rate-monotonic scheduling. Our analysis technique is able to achieve only Theta(1) suspension-related utilization loss on a uniprocessor. Based upon this technique, we further propose a partitioning scheme that supports suspending task systems with harmonic periods on multiprocessors. The resulting schedulability test shows that compared to existing schedulability tests designed for ordinary non-suspending task systems, suspensions only results in Theta(m) additional suspension-related utilization loss, where m is the number of processors. Furthermore, experiments presented herein show that both our uniprocessor and multiprocessor schedulability tests improve upon prior approaches by a significant margin.

In wireless rechargeable sensor networks (WRSN), rechargeable sensor nodes harvest ambient RF energy from power sources such as the RFID readers. However, the simultaneous transmissions may cause severe communication collisions. Different from traditional approaches which mainly resolve such collisions at the MAC layer, in this work we optimize the deployment of RFID reader in order to avoid the communication collisions by exploiting the differences in the time of charge among rechargeable sensor nodes. Specifically, when the reader is able to cover the whole deployment field, an efficient collision-free solution with proved optimality is presented to minimize the communication delay in the network. Simulation results are employed to verify the proposed algorithm.

Link correlation in wireless sensor networks has recently attracted a considerable amount of attention in the research community. Various pioneer works have empirically demonstrated the existence of link correlations and designed novel network protocols to exploit such link correlations. While all existing works focus on the correlated receptions at multiple receivers from a single sender, in this work we empirically demonstrate another type of link correlation, called sender-based link correlation. For sender-based link correlation, we observe wireless links from multiple senders to a single receiver are also correlated. Based on this observation, we design a two-tiered data forwarding scheme for improving the energy efficiency of unicast in the network. At the micro-level, individual nodes reduce their transmission energy consumption by temporarily switching to a new forwarder or suppressing the current transmission with the knowledge of link correlations. At the macro-level, we schedule the ordering of transmission times among neigh boring nodes so that the gains from all link correlation information in the network is maximized. Through trace-driven emulations and large scale simulations, we demonstrate that our design reduces data retransmissions by an average of 12% when compared with already highly energy efficient ETX-based protocols.

Recently, much research effort has been devoted to employing mobile chargers for energy replenishment of the robots in robotic sensor networks. Observing the discrepancy between the charging latency of robots and charger travel distance, we propose a novel tree-based charging schedule for the charger, which minimizes its travel distance without causing the robot energy depletion. We analytically evaluate its performance and show its closeness to the optimal solutions. Furthermore, through a queue-based approach, we provide theoretical guidance on the setting of the remaining energy threshold at which the robots request energy replenishment. This guided setting guarantees the feasibility of the tree-based schedule to return a depletion-free charging schedule. The performance of the tree-based charging schedule is evaluated through extensive simulations. The results show that the charger travel distance can be reduced by around 20%, when compared with the schedule that only considers the robot charging latency.

Nowadays, RFID systems have been widely deployed for applications such as supply chain management and inventory control. One of their most essential operations is to swiftly identify individual tags to distinguish their associated objects. Most existing solutions identify tags sequentially in the temporal dimension to avoid signal collisions, whose performance degrades significantly as the system scale increases. In this paper, we propose aParallel Identification Protocol(PIP) for RFID systems, which achieves the parallel identification paradigm and is compatible with current RFID devices. Uniquely, PIP encodes the tag ID into a specially designed pattern and thus greatly facilitates the reader to correctly and effectively recover them from collisions. Furthermore, we analytically investigate its performance and provide guidance on determining its optimal settings. Extensive simulations show that PIP reduces the identification delay by about 25%-50% when compared with the standard method in EPC C1G2 and the state-of-the-art solutions.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumption rates, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging requests sequence of nodes with their sequence on the TSP tours.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumptions, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging re- quests sequence of nodes with their sequence on the TSP tours. Experiment and simulation demonstrate ESync can reduce charger travel distance and nodes charging delay by about 30% and 40% respectively.

The Wireless Identification and Sensing Platform (WISP) has become a very promising experimental platform of wireless rechargeable sensor networks (WRSN), which integrates the sensing and computation capabilities to the traditional RFID tags. In such kind of networks, the simultaneous transmission may introduce severe communication collisions, which have attracted various research efforts for resolving such collisions at the MAC layer. However, different from existing works, we avoid such communication collisions through proper reader movement by exploiting the differences in the time of charge among rechargeable sensor nodes. We formulate the optimization problem and prove that complexity of the optimal solution is NP-hard, and propose a simple yet effective algorithm to optimize both the reader stop location and stop time for minimizing the total communication delay. Extensive simulation under different system settings show that our design can largely reduce the communication delay and outperform the baseline design by at least 20%.

Smartphones and mobile devices have greatly penetrated the daily lives of many people. While participatory/pervasive sensing has gained wide adoptions by leveraging various onboard sensors on mobile devices, another powerful resource, the computational power on these mobile devices has been less frequently harnessed by researchers and practitioners. To fill this gap, we propose in this work the modeling, analysis, and implementation of participatory computing. Specifically, we propose REPC, a generic randomized task assignment framework for the participatory computing paradigm, which guarantees the overall system performance with close to minimal workload at individual participating devices. To achieve these design objectives, we model the intrinsic relationship between the workload of individual devices and the probability they complete their assigned tasks. Based on our modeling results, we analyze the maximal system capacity for any given participatory computing system and derive the minimal workload for individual participating devices to achieve the overall system performance requirement. We have fully implemented our design on the Android platform and demonstrated its performance through a representative participatory computing application. Extensive experiments and simulation results demonstrate that our design is able to achieve more than 90% task completion ratios with only 10% system overhead in practice.

Flooding service has been investigated extensively in wireless networks to efficiently disseminate network-wide commands, configurations, and code binaries. However, little work has been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of the time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. Starting with an energy-optimal tree structure, probabilistic forwarding decisions are made at each sender based on the delay distribution of next-hop receivers. Only opportunistically early packets are forwarded via links outside the tree to reduce the flooding delay and redundancy in transmission. We further propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We show by extensive simulations and test-bed implementations that Opportunistic Flooding is close to the optimal performance achievable by oracle flooding designs. Compared with Improved Traditional Flooding, our design achieves significantly shorter flooding delay while consuming only 20-60% of the transmission energy.

With advances in energy-harvesting techniques, it is now feasible to build sustainable sensor networks to support long-term applications. Unlike battery-powered sensor networks, the objective of sustainable sensor networks is to effectively utilize a continuous stream of ambient energy. Instead of pushing the limits of energy conservation, we aim to design energy-synchronized schemes that keep energy supplies and demands in balance. Specifically, this work presents Energy-Synchronized Communication (ESC) as a transparent middleware between the network layer and MAC layer that controls the amount and timing of RF activity at receiving nodes. In this work, we first derive a delay model for cross-traffic at individual nodes, which reveals an interesting stair effect . This effect allows us to design a localized energy synchronization control with ℴ( d 3 ) time complexity that shuffles or adjusts the working schedule of a node to optimize cross-traffic delays in the presence of changing duty cycle budgets, where d is the node degree in the network. Under different rates of energy fluctuations, shuffle-based and adjustment-based methods have different influences on logical connectivity and cross-traffic delay , due to the inconsistent views of working schedules among neighboring nodes before schedule updates. We study the trade-off between them and propose methods for updating working schedules efficiently. To evaluate our work, ESC is implemented on MicaZ nodes with two state-of-the-art routing protocols. Both testbed experiment and large-scale simulation results show significant performance improvements over randomized synchronization controls.

Mobility-assisted data collection in sensor networks creates a new dimension to reduce and balance the energy consumption for sensor nodes. However, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to schedule the movement of mobile elements throughout the field is of ultimate importance. In this paper, the on-demand scenario where data collection requests arrive at the mobile element progressively is investigated, and the data collection process is modelled as an$M/G/1/c$-$NJN$queuing system with an intuitive service discipline of nearest-job-next (NJN). Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. NJN is further extended by considering the possible requests combination (NJNC). The simulation results validate our models and offer more insights when compared with the first-come-first-serve (FCFS) discipline. In contrary to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have better performance than FCFS, in both the average and more importantly the worst cases, which offers the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes, as well as other similar scheduling scenarios.

Synchronization is a pre-requisite for many sensor network applications. However, it remains challenging in sensor networks due to both the limited resources and the dynamic environments. In this paper, we propose a new two-phase clock synchronization scheme. The first one is the external clock synchronization phase, during which nodes update their clock by exchanging timestamp messages with the reference clock. Different from the conventional solutions, we propose to directly remove the clock skew during the external synchronization to achieve a higher synchronization accuracy and lower computational complexity. The second one is the clock self-calibration phase, as the accumulated clock skew will make the synchronized clock drift away again, we need to compensate the clock skew to maintain the clock synchronization accuracy. However, the compensation is non-trivial as the clock skew may not be constant due to the changing environment. Thus we propose the temperature-assisted clock self-calibration (TACSC) to dynamically compensate the clock skew according to the working temperature. Extensive simulation demonstrates that the proposed synchronization scheme can achieve a much lower root mean square error in the external synchronization phase. Furthermore, during the clock self-calibration phase, the TACSC scheme can improve the synchronization accuracy by more than one order of magnitude, which is verified by both simulation and testbed experimentation.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than the systems that harvested dynamic energy from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present an example on determining the optimal remaining energy level for individual sensor nodes to send out their recharging requests. Through extensive simulation with real-world system settings, we verify our analysis matches the simulation results well and the system designs based on our analysis are effective.

Large-scale battery packs with hundreds/thousands of battery cells are commonly adopted in many emerging cyber-physical systems such as electric vehicles and smart micro-grids. For many applications, the load requirements on the battery systems are dynamic and could significantly change over time. How to resolve the discrepancies between the output power supplied by the battery system and the input power required by the loads is key to the development of large-scale battery systems. Traditionally, voltage regulators are often adopted to convert the voltage outputs to match loads' required input power. Unfortunately, the efficiency of utilizing such voltage regulators degrades significantly when the difference between supplied and required voltages becomes large or the load becomes light. In this paper, we propose to address this problem via an adaptive reconfiguration framework for the battery system. By abstracting the battery system into a graph representation, we develop two adaptive reconfiguration algorithms to identify the desired system configurations dynamically in accordance with real-time load requirements. We extensively evaluate our design with empirical experiments on a prototype battery system, electric vehicle driving trace-based emulation, and battery discharge trace-based simulations. The evaluation results demonstrate that, depending on the system states, our proposed adaptive reconfiguration algorithms are able to achieve 1× to 5× performance improvement with regard to the system operation time.

Participatory Computing is a promising solution to fully utilize the wasted computation resources of mobile devices such as smartphones. In this demo abstract, we present our design and implementation of an participatory computing enhanced pervasive surveillance system. Our evaluation results show that the proposed system can effectively utilize the computation capability of mobile devices while guaranteeing the service reliability even with the intermittent nature of participatory computing.

The introduction of mobile elements has created a new dimension to reduce and balance the energy consumption in wireless sensor networks. However, data collection latency may become higher due to the relatively slow travel speed of mobile elements. Thus, the scheduling of mobile elements, i.e., how they traverse through the sensing field and when they collect data from which sensor, is of ultimate importance and has attracted increasing attention from the research community. Formulated as the traveling salesman problem with neighborhoods (TSPN) and due to its NP-hardness, so far only approximation and heuristic algorithms have appeared in the literature, but the former only have theoretical value now due to their large approximation factors. In this paper, following a progressive optimization approach, we first propose a combine-skip-substitute (CSS) scheme, which is shown to be able to obtain solutions within a small range of the lower bound of the optimal solution. We then take the realistic multirate features of wireless communications into account, which have been ignored by most existing work, to further reduce the data collection latency with the multirate CSS (MR-CSS) scheme. Besides the correctness proof and performance analysis of the proposed schemes, we also show their efficiency and potentials for further extensions through extensive simulation.

Using mobile elements (MEs) as mechanical carriers to collect data brings many opportunities to wireless sensor networks, such as improving the energy efficiency of sensor nodes and prolonging network lifetime. However, the limited travel speed of MEs leads to a higher data transfer latency, which in turn degrades the performance of the data collection task. The optimal use of the limited mobility of MEs is thus critical to the overall performance optimization. Considering the data-rate constraints of wireless communications, and by following a progressive optimization approach, we propose a sweeping tour optimization scheme with active skipping (SAS) in this paper. The performance of the proposed scheme is evaluated by investigating the tour length and the data collection latency. Through extensive simulations, our scheme is shown to outperform the best known heuristic algorithms in terms of the data collection latency.

Mobility-assisted data collection in wireless sensor networks brings in new opportunities to improve the energy efficiency of sensor nodes. However, it also introduces new challenges such as large data collection latency. The optimal usage of the limited mobility of mobile elements in the network is of great importance to reduce this latency, and a lot of research efforts have been devoted to it. In this paper, focusing on the scenario where a mobile sink is available to carry out the data collection, a simple and efficient Partition-based Nearest Job Next data collection scheme is proposed, which schedules the travel of the mobile sink based on a clustered structure of the network. Corresponding geometrical probability-based analysis is also presented to shed light on the performance of the scheme. The efficiency of the scheme, along with the accuracy of the analysis, is verified through extensive simulation.

The introduction of mobile elements in wireless sensor networks creates a new dimension to reduce and balance the energy consumption for resource-constrained sensor nodes; however, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to arrange and schedule the movement of mobile elements throughout the sensing field is of ultimate importance. In this paper, the online scenario where data collection requests arrive progressively is investigated, and the data collection process is modeled as an M/G/1/c-NJN queuing system, where NJN stands for nearest-job-next, a simple and intuitive service discipline. Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. The NJN discipline is further extended by considering the possibility of requests combination (NJNC). The simulation results validate our analytical models and give more insights when comparing with the first-come-first-serve (FCFS) discipline. In contrast to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have a better performance than FCFS, in both the average and more importantly the worst cases, which gives the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes for mobile elements in wireless ad hoc sensor networks, as well as many other similar scheduling application scenarios.

In this paper, we investigate the mobile charging in rechargeable sensor networks. By utilizing the historic charging information, the mobile charger can prefetch the charging requests from sensor nodes before they are sent out, and thus both the charging latency of sensor nodes and the travel distance of mobile charger can be reduced. Furthermore, by controlling the amount of energy to charge to nodes, the energy synchronization of sensor nodes can be achieved, which proactively adjust the arrival order of charging requests at the mobile charger. Combining these ideas, a novel charging scheme with verified efficiency is proposed.

Exploiting mobile elements to conduct data collection in wireless sensor networks offers a new approach to reducing and balancing the energy consumption of sensor nodes; however, the resultant data collection latency may be large due to the limited travel speed. Many research efforts have been made on reducing the data collection latency with the scenario where a single mobile element is available. A potential problem with this scenario is the scalability, and a straightforward solution is to employ multiple mobile elements to collect data collaboratively. In this paper, the network where multiple homogeneous mobile elements are available is modeled as an M/G/c queuing system, and insights on the data collection performance are obtained through theoretically analyzing the measures of the queue. In addition, a heuristic formula to determine the optimal number of mobile elements is proposed based on this model. The accuracy of our modeling and analysis, along with the performance evaluation of the proposed heuristic formula, is verified through extensive simulation.

Data collection with mobile elements in wireless sensor networks brings in new opportunities to reduce and balance the energy consumption of sensor nodes, however, it may result in high data collection latency. The optimal scheduling of mobile element's limited mobility is of great importance to reduce this latency, and a lot of research efforts have been made on it. Focusing on the on-demand data collection scenario, where data collection requests appear progressively, and based on the fact that several nearby requests can be combined and served from the same collection site, we propose a simplified combine-skip-substitute (CSS) scheme, which is shown to be able to reduce the data collection latency greatly. We also analyze the probability for the combination to happen, and how many requests can be combined, to gain more insights in its performance. The efficacy of the proposed scheme and the accuracy of the analytical results are verified through extensive simulation.

Exploring mobility to accomplish the data collection in wireless sensor networks (WSNs) has become the focus of recent studies, which can improve the energy efficiency of sensor nodes by shifting the data forwarding task from them to mobile elements (MEs). However, the data collection latency in this case can be much higher. We consider an on-demand data collection scenario in this paper, in which sensor nodes broadcast service requests when their buffer is about to be full. On receiving such requests, the ME moves toward the sensor nodes to collect data, and uploads the data to the sink when possible. An M/G/1 queue-based analytical model is presented, and analytical results on several important system performance metrics are derived. Furthermore, we propose an improved service scheme, which combines requests whenever they are in proximity. The work is evaluated through extensive simulations, which validate the accuracy of our model. The efficacy of the proposed service scheme to improve the system performance is also verified.