LoRaWAN is widely used for large-scale Internet of Things (IoT) deployments, but real-world reliability is often affected by high packet error rate. Existing optimization methods, such as heuristics or supervised learning, cannot fully capture the effects of environment, spatial layout, and network dynamics, which limits their adaptability. In this paper, we present LORADOCTOR, the first framework that leverages Large Language Models (LLMs) to optimize LoRaWAN. LORADOCTOR performs causal analysis, generates adaptive transmission policies, and predicts network performance in an interpretable way. We perform simulation-based evaluations using a year-long dataset collected from eight sensors deployed in East London. The results show that LORADOCTOR can significantly reduce packet error rates compared to both the default LoRaWAN settings and standard machine learning methods. Our evaluation also identifies distance-based path loss, temperature effects, and human activity as the main causes of packet error rate, showing its potential to support more reliable and adaptive LoRaWAN deployments in future urban environments.

Environmental sensing is essential for many applications. Many existing efforts rely on the readings provided by the weather stations maintained by federal, regional, or local government agencies. While the accuracy of the readings provided by those weather stations is high, the ability of such data to reflect the temperature variability experienced by urban populations is generally low. Therefore, recent studies have proposed to deploy new infrastructures with low-power communication and energy harvesting capabilities to provide fine-scale measurements. Recently, there has been an increasing interest in deploying environmental sensing systems with LoRa radios and solar panels. However, there have been very few studies looking into the reliability of solar power in the LoRa-based environmental sensing settings. In this paper, we present an empirical study that investigates how well solar energy powers an environmental sensing platform. Our study shows that solar energy generation forecasting plays an important role in the performance of the sensing platform. To address the challenges, we develop a novel solution that leverages LoRa, energy harvesting, and domain adaptation to enable reliable environmental sensing. Experimental results show that our solution outperforms the baselines and effectively supports end devices to perform environmental sensing operations without interruptions.

Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating positive examples that assist the model in learning robust and discriminative representations is a crucial stage in contrastive learning approaches. Usually, preset human intuition directs the selection of relevant data augmentations. Due to patterns that are easily recognized by humans, this rule of thumb works well in the vision and language domains. However, it is impractical to visually inspect the temporal structures in time series. The diversity of time series augmentations at both the dataset and instance levels makes it difficult to choose meaningful augmentations on the fly. Thus, although prevalent, contrastive learning with data augmentation has been less studied in the time series domain. In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format. We then propose a parametric augmentation method, AutoTCL, which can be adaptively employed to support time series representation learning. The proposed approach is encoder-agnostic, allowing it to be seamlessly integrated with different backbone encoders. Experiments on univariate forecasting tasks demonstrate the highly competitive results of our method, with an average 6.5\% reduction in MSE and 4.7\% in MAE over the leading baselines. In classification tasks, AutoTCL achieves a $1.2\%$ increase in average accuracy.

Today, wireless mesh networks (WMNs) are deployed globally to support various applications, such as industrial automation, military operations, and smart energy. Significant efforts have been made in the literature to facilitate their deployments and optimize their performance. However, configuring a WMN well is challenging because the network configuration is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Our study shows that the models for network configuration prediction learned from simulations may not work well in physical networks because of the simulation-to-reality gap. In this paper, we employ deep learning-based domain adaptation to close the gap and leverage a teacher-student neural network and a physical sampling method to transfer the network configuration knowledge learned from a simulated network to its corresponding physical network. Experimental results show that our method effectively closes the gap and increases the accuracy of predicting a good network configuration that allows the network to meet performance requirements from 30.10% to 70.24% by learning robust machine learning models from a large amount of inexpensive simulation data and a few costly field testing measurements.

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a ME ta-learning based R untime A daptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. We also develop a data sampling method to reduce the measurements required by MERA at runtime without sacrificing its performance. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a MEta-learning based Runtime Adaptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.

As a leading industrial wireless standard, WirelessHART has been widely implemented to build wireless sensor-actuator networks (WSANs) in industrial facilities, such as oil refineries, chemical plants, and factories. For instance, 54,835 WSANs that implement the WirelessHART standard have been deployed globally by Emerson process management, a WirelessHART network supplier, to support process automation. While the existing research to improve industrial WSANs focuses mainly on enhancing network performance, the security aspects have not been given enough attention. We have identified a new threat to WirelessHART networks, namely smart selective jamming attacks, where the attacker first cracks the channel usage, routes, and parameter configuration of the victim network and then jams the transmissions of interest on their specific communication channels in their specific time slots, which makes the attacks energy efficient and hardly detectable. In this paper, we present this severe, stealthy threat by demonstrating the step-by-step attack process on a 50-node network that runs a publicly accessible WirelessHART implementation. Experimental results show that the smart selective jamming attacks significantly reduce the network reliability without triggering network updates.

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards make a set of specific design choices, such as employing the Time-Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks, which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this article, we introduce a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

Today more and more bus companies are providing real-time bus locations to their riders to improve passenger experience and increase ridership. Most of the existing bus localization systems rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS). However, it is costly to install GNSS receivers and retrofit existing buses to power them, which prevents them to be adopted by those bus operators with tight budgets. There has been increasing interest in developing GPS-free localization schemes that leverage the wireless signals transmitted by the buses to localize them. Such schemes often require the received signal strength (RSS) measured at multiple base stations and therefore are not applicable to a small transportation service with a single base station, such as the shuttle service for a university campus. This paper presents a novel approach that leverages the LoRa link characteristics measured by a single base station and deep learning to localize a campus shuttle when it approaches a stop. Experimental results show that our solution provides a detection accuracy of no less than 92.07% and significantly outperforms all baselines without requiring new hardware and introducing additional communication overhead.

IEEE 802.15.4-based wireless sensor-actuator networks have been widely adopted by process industries in recent years because of their significant role in improving industrial efficiency and reducing operating costs. Today, industrial wireless sensor-actuator networks are becoming tremendously larger and more complex than before. However, a large, complex mesh network is hard to manage and inelastic to change once the network is deployed. In addition, flooding-based time synchronization and information dissemination introduce significant communication overhead to the network. More importantly, the deliveries of urgent and critical information such as emergency alarms suffer long delays, because those messages must go through the hop-by-hop transport. A promising solution to overcome those limitations is to enable the direct messaging from a long-range radio to an IEEE 802.15.4 radio. Then messages can be delivered to all field devices in a single-hop fashion. This article presents our study on enabling the cross-technology communication from LoRa to ZigBee using the energy emission of the LoRa radio as the carrier to deliver information. Experimental results show that our cross-technology communication approach provides reliable communication from LoRa to ZigBee with the throughput of up to 576.80 bps and the bit error rate of up to 5.23% in the 2.4 GHz band.

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards, such as WirelessHART, ISA100, WIA-FA, and 6TiSCH, make a set of specific design choices, such as employing the Time Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks (WSNs), which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this paper, we introduce ATRIA, a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

As a leading industrial wireless standard, WirelessHART has been widely implemented to build wireless sensor-actuator networks (WSANs) in industrial facilities, such as oil refineries, chemical plants, and factories. For instance, 54,835 WSANs that implement the WirelessHART standard have been deployed globally by Emerson process management, a WirelessHART network supplier, to support process automation. While the existing research to improve industrial WSANs focuses mainly on enhancing network performance, the security aspects have not been given enough attention. We have identified a new threat to WirelessHART networks, namely smart selective jamming attacks, where the attacker first cracks the channel usage, routes, and parameter configuration of the victim network and then jams the transmissions of interest on their specific communication channels in their specific time slots, which makes the attacks energy efficient and hardly detectable. In this paper, we present this severe, stealthy threat by demonstrating the step-by-step attack process on a 50-node network that runs a publicly accessible WirelessHART implementation. Experimental results show that the smart selective jamming attacks significantly reduce the network reliability without triggering network updates.

Traditionally, satellite and cellular technologies have been used in establishing the long-distance links that collect real-time data from running vehicles to the base station. However, the systems that implement those technologies are often too costly for use in small communities, such as monitoring shuttles that circle a university campus. Recently, LoRa has been used as a low-cost alternative that provides the capability of long-range data collection for low data rate applications. In this paper, we present a low-cost LoRa-based wireless network that collects real-time data from six shuttles circling our university campus and has operated in the real world for more than a year. The selection of the LoRa Spreading Factor (SF) poses a significant challenge because of its effects on two conflicting network performance metrics. A larger SF provides higher network reliability at the cost of lower throughput. To address this challenge, we develop a runtime SF control solution that employs the K-Nearest Neighbors (KNN) algorithm to adapt the SF configuration based on the current link condition. Experimental results show that our approach significantly increases the data collection throughput while meeting the application reliability requirement compared to the state of the art.

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as manufacturing plants, steel mills, and oil refineries. Although the typical industrial Internet of Things (IoT) applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15.4-based wireless sensor-actuator networks (WSANs) technology is appealing for use to construct industrial networks because it does not require wired infrastructure and can be manufactured inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cables for communication and power. To address the stringent real-time and reliability requirements, WSANs made a set of unique design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology. These designs distinguish WSANs from traditional wireless sensor networks (WSNs) that require only best effort services. The function-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our study shows that an attacker can reverse engineer the channel hopping sequences and graph routes by silently observing the transmission activities and put the network in danger of selective jamming attacks. The cracked knowledge on the channel hopping sequences and graph routes is an important prerequisite for launching selective jamming attacks to TSCH networks. To our knowledge, this article represents the first systematic study that investigates the security vulnerability of TSCH channel hopping and graph routing under realistic settings. In this article, we demonstrate the cracking process, present two case studies using publicly accessible implementations (developed for Orchestra and WirelessHART), and provide a set of insights.

Wireless sensor-actuator network (WSAN) technology is gaining rapid adoption by industrial Internet of Things applications in recent years. A WSAN typically connects sensors, actuators, and controllers in industrial facilities, such as steel mills, oil refineries, chemical plants, and infrastructures implementing complex monitoring and control processes. IEEE 802.15.4–based WSANs operate at low power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Recent studies have shown that the selection of network parameters has a significant effect on network performance. However, the current practice of parameter selection is largely based on experience and rules of thumb involving a coarse-grained analysis of expected network load and dynamics or measurements during a few field trials, resulting in non-optimal decisions in many cases. In this work, we develop P-SAFE (Parameter Selection and Adaptation FramEwork), which optimally selects the network parameters based on the application quality-of-service demands and adapts the parameter configuration at runtime to consistently satisfy the dynamic requirements. We implement P-SAFE and evaluate it on three physical testbeds. Experimental results show that our solution can significantly better meet the application quality-of-service demand compared to the state of the art.

As a key response to the Fourth Industrial Revolution, IEEE 802.15.4-based wireless sensor-actuator network (WSAN) technology is gaining rapid adoption in process industries because of its advantage in lowering deployment and maintenance cost and effort in industrial facilities, such as steel mills, oil refineries, and chemical plants. Although most industrial applications operate at low data rates, they often require their underlying networks to provide real-time and reliable data deliveries in harsh industrial environments. IEEE 802.15.4-based WSANs are appealing for use in industrial networks, since they operate at low-power and can be manufactured inexpensively. To meet the stringent real-time and reliability requirements, WSANs, such as WirelessHART networks, make a set of unique design choices such as employing the Time Slotted Channel Hopping (TSCH) and graph routing that distinguish themselves from traditional wireless sensor networks designed for best effort services. However, the security aspects of this increasingly important class of wireless networks are insufficiently investigated in the literature. Our recent work shows that an attacker can reverse engineer the TSCH channel hopping sequences by silently observing the channel activities and put the network in danger of selective jamming attacks, where the attacker jams only the transmission of interest on its specific communication channel in its specific time slot, which makes the attacks energy-efficient and hardly detectable. A critical step for an attacker to launch selective jamming is to identify the routing paths. Our study shows that an attacker can crack the routes used by the graph routing in WirelessHART networks by silently observing the packet transmission activities. In this poster proposal, we present a vulnerability analysis and our case study performed on a 50-device physical testbed using a publicly accessible WirelessHART implementation.

The importance of real-time wireless data transfer is rapidly increasing for Internet of Things (IoT) applications. For example, smart glasses worn by a doctor need to transmit real-time data to a hospital information system, which performs face detection and recognition, for real-time interaction with recognized patients within a certain deadline, which is ideally a few hundred milliseconds. Other emerging IoT applications, e.g., structural health monitoring, clinical monitoring, and industrial process automation, also require real-time wireless data transfer. Those applications have critical demands for real-time and energy-efficient communication through wireless medium. However, it is very challenging to support stringent timing constraints energy-efficiently through wireless medium due to its inherent unreliability and timing-unpredictability. Fortunately, heterogeneous radios are becoming increasingly available in modern embedded devices, offering new opportunities to use multiple wireless technologies to accommodate the needs of real-time applications. In this paper, we first formulate the runtime radio selection and data partitioning for real-time IoT applications as an Integer Linear Programming (ILP) problem and then present (1) an optimal algorithm that makes quick and optimal decisions when selecting between two radios and (2) a heuristic algorithm for the platforms with more radios. Experimental results show that our heuristic algorithm provides optimal selections to 94.4% of the cases and makes the decisions 336~1412 times faster than an ILP problem solver.

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network (LPWAN) radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this paper, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength (RSS). Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61bps in the Sub-1 GHz bands.

Wireless Sensor-Actuator Network (WSAN) technology is gaining rapid adoption in process industries in recent years. A WSAN typically connects sensors, actuators, and controllers in industrial facilities, such as steel mills, oil refineries, chemical plants, and infrastructures implementing complex monitoring and control processes. IEEE 802.15.4 based WSANs operate at low-power and can be manufactured inexpensively, which make them ideal where battery lifetime and costs are important. Recent studies have shown that the selection of network parameters has a significant effect on the network performance. However, the current practice of parameter selection is largely based on experience and rules of thumb involving a coarse-grained analysis of expected network load and dynamics or measurements during a few field trials, resulting in non-optimal decisions in many cases. In this work, we develop the Parameter Selection and Adaptation FramEwork (P-SAFE) that optimally configures the network parameters based on the application Quality of Service (QoS) demand and adapts the configuration at runtime to consistently satisfy the dynamic requirements. We implement P-SAFE and evaluate it on three physical testbeds. Experimental results show our solution can significantly better meet the application QoS demand compared to the state of the art.

Internet of Things (IoT) has emerged as a new computing paradigm that promises to offer a fully connected “smart” world. However, due to the open nature of wireless medium, the information sensed, collected, and transmitted by IoT devices can be easily intercepted by adversaries, which becomes a serious concern in most IoT applications requiring sensitive data. In practice, cooperative communication approaches can effectively improve the security level for wireless communication under the presence of eavesdroppers with unbounded computational ability. In this paper, we apply the amplify-and-forward (AF) cooperative communication to increase the secrecy capacity of IoT systems by incentivizing relay IoT devices. Specifically, a Stackelberg game is designed to motivate the participation of the relay IoT devices for security enhancement. Extensive experimental results have demonstrated the feasibility and security of the proposed mechanism under both unknown and known channel state information (CSI) models.

Wireless sensor-actuator networks (WSANs) technology is appealing for use in the industrial Internet of Things (IoT) applications because it does not require wired infrastructure. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in the industrial facilities without running cabling for communication and power. The IEEE 802.15.4-based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Almost, a decade of realworld deployments of WirelessHART standard has demonstrated the feasibility of using its core techniques including reliable graph routing and time slotted channel hopping (TSCH) to achieve reliable low-power wireless communication in the industrial facilities. Today, we are facing the fourth Industrial Revolution as proclaimed by political statements related to the Industry 4.0 Initiative of the German Government. There exists an emerging demand for deploying a large number of field devices in an industrial facility and connecting them through the WSAN. However, a major limitation of current WSAN standards is their limited scalability due to their centralized routing and scheduling that enhance the predictability and visibility of network operations at the cost of scalability. This paper decentralizes the network management in WirelessHART and presents the first Distributed Graph routing and autonomous Scheduling (DiGS) solution that allows the field devices to compute their own graph routes and transmission schedules. The experimental results from two physical testbeds and a simulation study shows our approaches can significantly improve the network reliability, latency, and energy efficiency under dynamics.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS , an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as steel mills and oil refineries. Although the typical industrial applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15.4 based Wireless Sensor-Actuator Networks (WSANs) technology is appealing for use to construct industrial networks because it can be deployed and maintained inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cabling for communication and power. To address the stringent real-time and reliability requirements, WSANs adopt a set of novel design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology that distinguish themselves from traditional Wireless Sensor Networks (WSNs) that require only best effort services. The equation-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our case study shows that an attacker can reverse engineer the channel hopping sequence by silently observing the channel activities and then perform smart collision attacks. In this poster proposal, we describe our target problem and present our case study based on a publicly accessible implementation of TSCH.

Wireless Sensor-Actuator Networks (WSANs) technology is appealing for use in industrial IoT applications because it does not require wired infrastructure. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cabling for communication and power. IEEE 802.15.4 based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Almost a decade of real-world deployments of WirelessHART standard has demonstrated the feasibility of using its core techniques including reliable graph routing and Time Slotted Channel Hopping (TSCH) to achieve reliable low-power wireless communication in industrial facilities. Today we are facing the 4th Industrial Revolution as proclaimed by political statements related to the Industry 4.0 Initiative of the German Government. There exists an emerging demand for deploying a large number of field devices in an industrial facility and connecting them through a WSAN. However, a major limitation of current WSAN standards is their limited scalability due to their centralized routing and scheduling that enhance the predictability and visibility of network operations at the cost of scalability. This paper decentralizes the network management in WirelessHART and presents the first Distributed Graph routing and autonomous Scheduling (DiGS) solution that allows the field devices to compute their own graph routes and transmission schedules. Experimental results from two physical testbeds and a simulation study show our approaches can significantly improve the network reliability, latency, and energy efficiency under dynamics.

60 GHz millimeter-wave networks have emerged as a potential candidate for designing the next generation of multi-gigabit WLANs. Since the 60 GHz links suffer from frequent outages due to blockages caused by human mobility, deploying 60 GHz WLANs that can provide robust coverage in presence of blockages is a challenging problem. In this paper, we study blockage-aware coverage and deployment of 60 GHz WLANs. We first show that the reflection profile of an indoor environment can be sensed using a few measurements. A novel coverage metric (angular spread coverage) which captures the number of available paths and their spatial diversity is proposed. Additionally, it is shown that using relays can extend the coverage of the AP at a lower cost and provide added spatial diversity in the available paths. We propose a heuristic algorithm that determines the AP and relay locations while maximizing the angular spread coverage metric for the clients. Our testbed-based evaluation shows that for five different rooms, our proposed deployment can guarantee an average connectivity of 91.7%, 83.9%, and 74.1% of client locations in the presence of 1, 3 and 5 concurrent human blockages respectively, substantially increasing the robustness of 60 GHz links against blockages.

Industrial automation has emerged as an important application of wireless sensor-actuator networks (WSANs). To meet stringent reliability requirements of industrial applications, industrial standards such as WirelessHART adopt Time Slotted Channel Hopping (TSCH) as its MAC protocol. Since every link hops through all the channels used in TSCH, a straightforward policy to ensure reliability is to retain a link in the network topology only if it is reliable in all channels used. However, this policy has surprising side effects. While using more channels may enhance reliability due to channel diversity, more channels may also reduce the number of links and route diversity in the network topology. We empirically analyze the impact of channel selection on network topology, routing, and scheduling on a 52-node WSAN testbed. We observe inherent tradeoff between channel diversity and route diversity in channel selection, where using an excessive number of channels may negatively impact routing and scheduling. We propose novel channel and link selection strategies to improve route diversity and network schedulability. Experimental results on two different testbeds show that our algorithms can drastically improve routing and scheduling of industrial WSANs.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various Internet of Things (IoT) applications. However, none of the existing technologies delivers optimal performance across all critical quality of service (QoS) dimensions under varying environmental conditions. Using a single wireless technology therefore cannot meet the demands of varying workloads or changing environmental conditions. This problem is exacerbated with the increasing interest in placing embedded devices on the user's body or other mobile objects in mobile IoT applications. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an adaptive radio and transmission power selection system, which makes available multiple wireless technologies at runtime and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability.

Wireless sensor–actuator networks (WSANs) offer an appealing communication technology for process automation applications to incorporate the Internet of Things (IoT). In contrast to other IoT applications, process automation poses unique challenges for industrial WSAN due to its critical demands on reliable and real-time communication. While industrial WSANs have received increasing attention in the research community recently, most published results to date have focused on the theoretical aspects and were evaluated based on simulations. There is a critical need for experimental research on this important class of WSANs. We developed an experimental testbed by implementing several key network protocols of WirelessHART, an open standard for WSANs that has been widely adopted in the process industries based on the HART. We then performed a series of empirical studies showing that graph routing leads to significant improvement over source routing in terms of worst-case reliability, but at the cost of longer latency and higher energy consumption. It is therefore important to employ graph routing algorithms specifically designed to optimize latency and energy efficiency. Our studies also suggest that channel hopping can mitigate the burstiness of transmission failures; a larger channel distance can reduce consecutive transmission failures over links sharing a common receiver. Based on these insights, we developed a novel channel hopping algorithm that utilizes far away channels for transmissions. Furthermore, it prevents links sharing the same destination from using channels with strong correlations. Our experimental results demonstrate that our algorithm can significantly improve network reliability and energy efficiency.

With recent adoption of wireless sensor-actuator networks (WSANs) in industrial automation, industrial wireless control systems have emerged as a frontier of cyber-physical systems. Despite their success in industrial monitoring applications, existing WSAN technologies face significant challenges in supporting control systems due to their lack of real-time performance and dynamic wireless conditions in industrial plants. This article reviews a series of recent advances in real-time WSANs for industrial control systems: 1) real-time scheduling algorithms and analyses for WSANs; 2) implementation and experimentation of industrial WSAN protocols; 3) cyber-physical codesign of wireless control systems that integrate wireless and control designs; and 4) a wireless cyber-physical simulator for codesign and evaluation of wireless control systems. This article concludes by highlighting research directions in industrial cyber-physical systems.

Wireless sensor-actuator networks are gaining ground as the communication infrastructure for process monitoring and control. Industrial applications demand a high degree of reliability and real-time guarantees in communication. Because wireless communication is susceptible to transmission failures in industrial environments, industrial wireless standards such as WirelessHART adopt reliable graph routing to handle transmission failures through retransmissions and route diversity. While these mechanisms are critical for reliable communication, they introduce substantial challenges in analyzing the schedulability of real-time flows. This paper presents the first worst-case end-to-end delay analysis for periodic real-time flows under reliable graph routing. The proposed analysis can be used to quickly assess the schedulability of real-time flows with stringent requirements on both reliability and latency. We have evaluated our schedulability analysis against experimental results on a wireless testbed of 69 nodes as well as simulations. Both experimental results and simulations show that our delay bounds are safe and enable effective schedulability tests under reliable graph routing.

Smart energy in buildings is an important research area of Internet of Things (IoT). As important parts of the smart grids, the energy efficiency of buildings is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect 1-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be “green” by design. Inspired by “energy proportional computing” in modern computers, we propose an IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud-computing technologies to enable multiscale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments, which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.

The largest source of energy consumption in buildings is heating, ventilation, and air conditioning (HVAC). For an HVAC system to provide comfort and minimize energy consumption, it is crucial to understand the spatiotemporal thermal dynamics, especially in large open spaces. To optimize HVAC control, it is important to establish accurate dynamic thermal models. For this purpose, we constructed a real-world test bed by instrumenting an HVAC-controller auditorium using multiple types of sensors. Based on the dataset, we develop and evaluate a novel data-driven approach to model the complex thermal dynamics in a large space through a combination of data clustering and system identification techniques. Real-world data shows that our approach achieves low estimation errors. Our modeling approach therefore provides a practical foundation for HVAC control and optimization for large open spaces.

Industry is adopting Wireless Sensor-Actuator Networks (WSANs) as the communication infrastructure for process control applications. To meet the stringent real-time performance requirements of control systems, there is a critical need for fast end-to-end delay analysis for real-time flows that can be used for online admission control. This paper presents a new end-to-end delay analysis for periodic flows whose transmissions are scheduled based on the Earliest Deadline First (EDF) policy. Our analysis comprises novel techniques to bound the communication delays caused by channel contention and transmission conflicts in a WSAN. Furthermore, we propose a technique to reduce the pessimism in admission control by iteratively tightening the delay bounds for flows with short deadlines. Experiments on a WSAN testbed and simulations demonstrate the effectiveness of our analysis for online admission control of real-time flows.

Low Power Listening (LPL) is a common MAC-layer technique for reducing energy consumption in wireless sensor networks, where nodes periodically wakeup to sample the wireless channel to detect activity. However, LPL is highly susceptible to false wakeups caused by environmental noise being detected as activity on the channel, causing nodes to spuriously wakeup in order to receive nonexistent transmissions. In empirical studies in residential environments, we observe that the false wakeup problem can significantly increase a node's duty cycle, compromising the benefit of LPL. We also find that the energy-level threshold used by the Clear Channel Assessment (CCA) mechanism to detect channel activity has a significant impact on the false wakeup rate. We then design AEDP, an adaptive energy detection protocol for LPL, which dynamically adjusts a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. Empirical experiments in both controlled tests and real-world environments showed AEDP can effectively mitigate the impact of noise on radio duty cycles, while maintaining satisfactory link reliability.

The integration of wireless sensors with mobile phones is gaining momentum as an enabling platform for numerous emerging applications. These mobile systems face dynamic environments where both application requirements and ambient wireless conditions change frequently. Despite the existence of many MAC protocols, none can provide optimal characteristics along multiple dimensions, especially when the conditions are frequently changing. Instead of pursuing a one-MAC-fit-all approach we present the Self-Adapting MAC Layer (SAML) that dynamically selects and switches MAC protocols to gain the desired characteristics in response to changes in ambient conditions and application requirements. SAML comprises (1) a Reconfigurable MAC Architecture (RMA) that can switch to different MAC protocols at run time and (2) a learning-based MAC Selection Engine that selects the protocol most suitable for the current condition and requirements. To the application SAML appears as a traditional MAC layer and realizes its benefits through a simple API for the mobile applications. We have implemented SAML in TinyOS 2.x and built three prototypes containing up to five MACs. We evaluate the system in controlled tests and real-world environments using a new gateway device that integrates a 802.15.4 radio with Android phones. Our experimental results show that SAML can effectively adapt MAC layer behavior to meet varying application requirements in dynamic environments through judicious selection and efficient switching of MAC protocols.

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy. These applications impose unique network management constraints, requiring low data rates but high network reliability in the face of unpredictable wireless environments. This paper presents two in-depth empirical studies on wireless channels in real homes, providing key design guidelines for meeting the network management constraints of HAN applications. The spectrum study analyzes spectrum usage in the 2.4 GHz band where HANs based on the IEEE 802.15.4 standard must coexist with existing wireless devices. We characterize the ambient wireless environment in six apartments through passive spectrum analysis across the entire 2.4 GHz band over seven days in each apartment. We find that the wireless conditions in these residential environments are much more complex and varied than in a typical office environment. Moreover, while 802.11 signals play a significant role in spectrum usage, there also exists non-negligible noise from non-802.11 devices. The multi-channel link study measures the reliability of different 802.15.4 channels through active probing with motes in ten apartments. We find that there is not always a persistently reliable channel over 24 hours, and that link reliability does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through infrequent channel hopping, suggesting dynamic channel hopping as a key tool for meeting the network management requirements of HAN applications. Our empirical studies provide important guidelines and insights in designing HANs for residential environments.

With the rapid permeation of smartphones and wireless sensors in our society, smartphones are poised to become personal hubs connecting wireless sensors with users and the Internet. Due to frequent changes to applications and network conditions, wireless networks connecting a personal hub and wireless sensors must meet time-varying QoS requirements at minimal energy cost. This paper presents the architecture of a novel Protocol Service System (PSS) towards the vision of protocol service over the air. In contrast to traditional wireless networks where a single MAC protocol is statically selected a priori, PSS switches among multiple MAC protocols at run time to dynamically optimize power efficiency subject and meet current QoS requirements. To meet the memory constraint on wireless sensors PSS employs a component-based reconfigurable MAC architecture to support multiple MAC protocols at significantly reduced memory footprint through component sharing. The feasibility of PSS has been demonstrated through a proof-of-concept implementation of the PSS architecture and the development of a personal hub prototype running the Android OS.

Transmission power control (TPC) has the potential to reduce power consumption in Wireless Sensor Networks (WSNs). However, despite a multitude of existing protocols, they still face significant challenges in real-world deployments. A practical TPC protocol must be robust against complex and dynamic wireless properties, and efficient for resource-constrained sensors. This paper presents P-TPC, a practical TPC protocol designed on control-theoretic techniques. P-TPC features a highly efficient controller designed on a dynamic model that combines a theoretical link model with online parameter estimation. P-TPC's robustness and energy savings are demonstrated through trace-driven simulations and real-world experiments in a campus building and residential environments.

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy. These applications impose unique QoS constraints, requiring low data rates but high network reliability in the face of unpredictable wireless environments. This paper presents two in-depth empirical studies on wireless channels in real homes, providing key design guidelines for meeting the QoS constraints of HAN applications. The spectrum study analyzes spectrum usage in the 2.4 GHz band where HANs based on the IEEE 802.15.4 standard must coexist with existing wireless devices. We characterize the ambient wireless environment in six apartments through passive spectrum analysis across the entire 2.4 GHz band over seven days in each apartment. We find that the wireless conditions in these residential environments are much more complex and varied than in a typical office environment. Moreover, while 802.11 signals play a significant role in spectrum usage, there also exists non-negligible noise from non-802.11 devices. The multichannel link study measures the reliability of different 802.15.4 channels through active probing with motes in ten apartments. We find that there is not always a persistently reliable channel over 24 hours, and that link reliability does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through infrequent channel hopping, suggesting dynamic channel hopping as a key tool for meeting the QoS requirements of HAN applications. Our empirical studies provide important guidelines and insights in designing HANs for residential environments.

Home area networks (HANs) promise to enable sophisticated home automation applications such as smart energy usage and assisted living. However, recent empirical study of HAN reliability in real-world residential environments revealed significant challenges to achieving reliable performance in the face of significant and variable interference from a multitude of coexisting wireless devices. We propose the Adaptive and Robust Channel Hopping (ARCH) protocol: a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. ARCH has several key features. First, ARCH is an adaptive protocol that channel-hops based on changes in channel quality observed in real time. Second, ARCH is a distributed protocol that selects channels on a per-link basis, due to the large link-to-link variations in channel quality observed under empirical study. Third, ARCH is designed to be robust and lightweight. ARCH uses a practical handshaking approach to handle channel desynchronization and an efficient sliding-window scheme that does not involve expensive calculations or modeling, and can be reasonably implemented on memory-constrained wireless sensor platforms. Fourth, ARCH introduces minimal communication overhead for applications where packet acknowledgements are already enabled. We evaluate our approach through real deployment in real-life apartments with residents' daily activity. Our results demonstrate that ARCH can reduce packet retransmissions by a median of 42.3% compared to using a single, fixed wireless channel, and can enable up to a 2.2X improvement in delivery rate on the most unreliable links in our experiment. Under a multi-hop routing scenario, ARCH reduced radio usage by 31.6% on average, by reducing the ETX of each link by up to 83.6%. Due to ARCH's lightweight reactive design, most links achieve this improvement in reliability with 10 or fewer channel hops per day.

In this article, we study the critical sensor density for partial connectivity of a large area sensor network. We assume that sensor deployment follows the Poisson distribution. For a given partial connectivity requirement ρ, 0.5 < ρ < 1, we prove that there exists a critical sensor density λ 0 , around which the probability that at least a fraction ρ of sensors are connected in the network increases sharply from ε to 1-ε within a short interval of sensor density λ. The length of this interval is in the order of O (-log ε/log A ) as A → ∞, where A is the area of the sensor field, and the location of λ 0 is at the sensor density where the aforesaid probability is about 1/2. We prove the preceding theoretical results in the hexagonal model. We also extend our results to the disk model that models transmission range of sensors as disks. Simulations are performed to confirm the analytical results.

Interference modeling is crucial for the performance of numerous WSN protocols such as congestion control, link/channel scheduling, and reliable routing. In particular, understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for many data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies suggested that the physical interference model (i.e., PRR-SINR model) is significantly more accurate than existing interference models. However, existing approaches to physical interference modeling exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this paper, we propose the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM exploits the spatiotemporal diversity of data traffic for radio performance profiling and only needs to gather a small amount of statistics about the network. We evaluate the efficiency of PIM through extensive experiments on both a 13-node and a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with the active measurement approach.

Assume sensor deployment follows the Poisson distribution. For a given partial connectivity requirement ¿, 0.50, around which the probability that at least 100¿% of sensors are connected in the network increases sharply from ¿ to 1-¿ within a short interval of sensor density ¿. The location of ¿0is at the sensor density where the above probability is about 1/2. We also extend the results to the disk model. Simulations are conducted to confirm the theoretical results.

This paper presents C-MAC, a new MAC protocol designed to achieve high-throughput bulk communication for data-intensive sensing applications. C-MAC exploits concurrent wireless channel access based on empirical power control and physical interference models. Nodes running C-MAC estimate the level of interference based on the physical signal-to-interference-plus-noise-ratio (SINR) model and adjust the transmission power accordingly for concurrent channel access. C-MAC employs a block-based communication mode that not only amortizes the overhead of channel assessment, but also improves the probability that multiple nodes within the interference range of each other can transmit concurrently. C-MAC has been implemented in TinyOS-1.x and extensively evaluated on Tmote nodes. Our experiments show that C-MAC significantly outperforms the state-of-art CSMA protocol in TinyOS with respect to system throughput, delay and energy consumption.

Multi-channel design has received significant attention for low-power wireless networks (LWNs), such as 802.15.4-based wireless sensor networks, due to its potential of mitigating interference and improving network capacity. However, recent studies reveal that the number of orthogonal channels available on commodity wireless platforms is small, which significantly hinders the performance of existing multi-channel protocols. A promising solution is to explore the use of partially overlapping channels for communications. However, this approach faces several key challenges such as increased inter-channel interference and significantly higher overhead of channel measurement. In this paper, we systematically study the inter-channel interference and its impact on link capacity and the performance of multi-channel protocols in LWNs. First, we develop empirical models for characterizing inter-channel signal attenuation based on experiments on TelosB motes. We then propose a novel measurement algorithm which can significantly reduce the overhead of multi-channel interference measurement by exploiting the spectral power density (SPD) of the transmitter. Finally, we apply our interference models to both link capacity analysis and channel assignment protocols. Our extensive experiments on a testbed of 30 TelosB motes show that our interference measurement algorithm has an average error of 2.95%. Our results also demonstrate that multi-channel protocols for LWNs can significantly benefit from using overlapping channels.

Abstract Many wireless sensor networks must sustain long lifetimes on limited energy resources. Two major approaches, transmission power control and sleep scheduling, have been proposed to reduce the radio power consumption in the transmission state and the idle state, respectively. In this paper, we first review existing transmission power control and sleep scheduling approaches and then describe a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in wireless sensor networks. It has two key components: (1) a novel optimization approach called Minimum Power Configuration that minimizes the aggregate radio power consumption of all ratio states and (2) a Unified Power Management Architecture (UPMA) that aims to support the flexible cross‐layer integration of different power management strategies. A novel feature of UPMA is that it enables cross‐layer coordination and joint optimization of different power management strategies that exist at multiple network layers. Copyright © 2008 John Wiley & Sons, Ltd.

Cyber-physical systems (CPS) is a new generation of engineered systems that integrate physical systems with the capability of networked computing and control. Real-time video capture and communication is expected to be an important function in many cyber-physical systems that involve camera-equipped mobile phones. In this paper, we present AnySense, a network architecture that supports video communication between 3G phones and Internet hosts in cyber-physical systems. AnySense implements transcoding of video streams between the Internet and circuit-switched 3G cellular networks, and is transparent to 3G service providers. AnySense can support a class of ubiquitous cyber-physical systems that require video-based information collection and sharing. A prototype of AnySense has been built and a video demo is available at http://www.anyserver.org/.