Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, wide coverage, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling data flows to meet their hard real-time requirements in industrial applications. In this paper, we first leverage a 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantee through time-frequency resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct a schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. For large-scale multi-cell 5G industrial systems with cloud radio access network (C-RAN) architecture, we further introduce a user association algorithm respecting the real-time requirements of individual user equipment (UEs). Extensive experimental studies show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

Reproducing and extending research on TimeSensitive Networking (TSN) scheduling has become increasingly challenging, as most published methods lack open-source implementations. The few available implementations are often scattered across different programming languages and formats, forcing researchers to reimplement algorithms from scratch—a time-consuming and error-prone process that hinders fair comparison of methods and slows research progress. In this work, we present TSNKit, an open-source toolkit designed to address these challenges through: (i) standardized implementations of a broad set of representative scheduling algorithms with unified interfaces for integrating new methods; (ii) an end-to-end pipeline covering test case generation, scheduling, and simulation-based validation; and (iii) comprehensive benchmarking modules for reproducible performance evaluation. TSNKit enables researchers to reproduce published results, extend existing methods, and perform fair comparisons across algorithms. Our ongoing work extends TSNKit to support multiple traffic shapers beyond Time-Aware Shaping (TAS), improve benchmark efficiency through enhanced scheduling heuristics, and incorporate hardware-in-the-loop capabilities for seamless real-world deployment.

A growing trend in large-scale industrial system design is the integration of multiple real-time, mission-critical applications over shared network infrastructures to reduce hardware costs and improve scalability. Recent advances in network resource partitioning techniques provide practical mechanisms for managing these applications hierarchically while maintaining operational isolation. However, as system complexity increases, applications often exhibit multi-state behaviors that challenge the system's ability to meet stringent timing requirements - especially under static resource partitions. While dynamic resource reconfiguration can restore feasibility, it is typically costly and disruptive in industrial environments. To address this challenge, we propose a flexibility-aware network resource partitioning framework that introduces a novel metric - partition flexibility - to quantify how effectively a resource partition supports an application's state transitions. Using this metric, we develop efficient strategies for both static partition allocation and dynamic partition adjustment, with the goal of minimizing reconfiguration overhead. We validate our framework design through a real-world case study involving a NASA extra-terrestrial habitat system deployed on a time-sensitive networking (TSN) testbed. Extensive simulations further demonstrate that the proposed partitioning framework reduces$\mathbf{5 6. 4 \%}$reconfigurations compared to the state-of-the-art methods.

This paper presents Backscattering Through Cable (BTC), a new backscatter side-channel attack designed for low-cost and effective serial data exfiltration. The BTC attack leverages the impedance variations of a serial port when transmits different bits (‘0’ and ‘1’), which in turn creates fluctuations in the amplitude of the backscattered signal. As a consequence, the sensitive serial data leaks to the backscatter side channel. The serial cable, acting as an unintentional antenna, enables this signal to be intercepted remotely. The BTC attack is notable for its minimal requirements: it does not require any modification to the target device's hardware or software nor any prior knowledge of the target devices or serial communication configurations. Experimental validation shows successful data exfiltration over distances up to 14.5 meters in line-of-sight (LOS) setting and 4.5 meters in nonline-of-sight (NLOS) scenario, even with two wall barriers. The attack is effective at high data rates (1 Mbps and beyond) and operates across various cable types, even with lengths as short as 4 cm. To enhance the understanding of its mechanisms and to facilitate the optimization of attack parameters, a full-wave model was further developed to characterize the impacts of target device cable length and carrier frequency on the attack efficacy. Simulation results indicate that BTC can remain effective with cable lengths as short as 1 cm.

This work studies the challenge of scheduling real-time control commands in Networked Control Systems (NCS), where control actions rely on the freshness of data collected from multiple sources. In dynamic environments, ensuring that control commands in an NCS are accurate and frequent is essential for maintaining the system responsiveness. For this aim, we introduce a new metric, Age of Result (AoR), which quantifies the time elapsed since the last control command was generated and executed. This metric reflects the system’s capability to adapt to real-time changes in the operational environment by considering both data freshness and control command frequency. We conduct a detailed analysis of AoR in NCS, paying special attention to the dependencies between sensing and computing phases. We first address computation-intensive and network-intensive scenarios, proposing random sampling (RS)-based approximate algorithms for each case. Subsequently, we develop another RS-based algorithm and a heuristic approach for the general model. Simulation results demonstrate that our approach can effectively minimize AoR and significantly enhance the system performance and real-time adaptability compared to existing strategies.

Wireless Powered Communication Network (WPCN) is a new paradigm to allow low-power wireless devices to exchange data packets and receive stable energy transfer from a power source and thus support autonomous and sustainable network operations without battery replacements. In recent years, we have witnessed the growing deployment of WPCNs in both industrial and consumer IoT systems to support time-triggered and event-triggered monitoring applications. In this article, we present a novel reinforcement learning (RL)-based on-demand path planning framework to plan the trajectory of a Mobile Charger (MC) and schedule the charging sequence of wireless devices to sustain the network operations. A modified Deep Q-learning approach is designed to charge the wireless devices by balancing between their residual energy level and the distance from the MC to the device. This approach minimizes the total distance that the MC travels while ensuring that individual residual energy of a given set of devices is above a designated threshold. Extensive experimental results from both the Gazebo-based high-fidelity simulation and Turtlebot-based physical testbed demonstrate that our approach outperforms the classic scheduling methods (e.g., Nearest Job Next and Earliest Deadline First), state-of-the-art scheduling methods(Extended Particle Swarm Optimization, Enhanced Teaching–Learning-Based Optimization Algorithm and Spatiotemporal Optimization for Charging Scheduling), learning-based methods (e.g., Proximal Policy Optimization and Advantage Actor-Critic) with similar sample sizes for training.

Time-Sensitive Networking (TSN) technology has been increasingly deployed in mission- and safety-critical industrial applications to achieve high throughput and deterministic communications. To provide stringent timing guarantee, TSN requires that network devices follow a predefined communication schedule for real-time end-to-end packet processing, involving both TSN bridges and end stations. Extensive efforts have been devoted on the TSN bridge design in the literature. Achieving TSN compatibility on the end stations (especially on COTS hardware), however is challenging due to their constrained resources. To fill this gap, this work presents a software-based open-source TSN end station design that i) enables ultra-low latency and nanosecond-level transmission accuracy based on DPDK, and ii) employs a novel multi-core scheduling framework to boost the throughput of real-time TSN traffic. Our proposed solution leverages existing COTS hardware and thus is more generic and cost-effective compared to existing hardware-centric solutions. We validate our design by developing a prototype end station and incorporating it in a real-world TSN testbed. Our extensive experiments demonstrate the efficiency and effectiveness of our design compared with other state-of-the-art solutions.

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN's many traffic shapers due to its ability to achieve deterministic timing guarantees. Many scheduling methods for TAS shapers have been recently developed that claim to improve system schedulability. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding of their performance in diverse application scenarios. In this paper, we fill this gap by presenting a systematic review and experimental study on existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these works along with the specific problems they aim to solve, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform an extensive evaluation on 17 representative solutions using both high-fidelity simulations and a real-life TSN testbed, and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these studies, we identify the limitations of individual scheduling methods and highlight several important findings. We expect this work will provide foundational knowledge and performance benchmarks needed for future studies on real-time TSN scheduling.

Mobile devices have been increasingly deployed in large-scale cyber-physical systems (CPS) to traverse the field and retrieve various data measurements from designated physical entities with stringent performance requirements. This work studies the Availability-constrained real-time Fresh Data Retrieval problem in CPS with a Speed Adjustable mobile device (AFDR-SA). The goal is to maintain the temporal validity of the real-time data with different priorities to be retrieved in the system while meeting the data availability constraints imposed by the communication range between the mobile device and the physical entities. The general case of the AFDR-SA problem is proved to be NP-hard. A dynamic programming (DP)-based optimal algorithm is proposed for a special scenario where the retrieval times of individual data items with the same priority are of the same length. For the general case where data items can have arbitrary retrieval times and different priorities, another different DP-based scheme is proposed, which is proved to be optimal given the retrieval order. A fast heuristic with low complexity is also proposed for the general problem to improve the computational efficiency. The experimental results show that the proposed schemes for the general case outperform the state-of-the-art methods and have close performance compared to the optimal solution while incurring much less computational overhead.

5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.

This work studies the non-uniform multi-source data update problem for real-time monitoring systems, where a set of heterogeneous data sources transmit their updates to a Base Station (BS) through wire or wireless channel(s). The performance metric called Age of Information (AoI) - which measures the time elapsed since the last data update of each source received by the BS - is commonly used to quantify the freshness of the data updates. However, most existing work on minimizing AoI of multi-source data updates assume that all sources have a uniform size of data updates which unnecessarily reduces their applicability. This work explores a more general model where individual sources can have non-uniform sizes of data updates, and provides thorough analysis to optimize both peak and average AoI of the target system. Based on these analysis, an optimal scheme to minimize the peak AoI is first developed by guaranteeing the delivery frequency of each source proportional to the function determined by its data size. A$(2+\delta)$-approximation algorithm based on random sampling (RS) and a heuristic called Ratio-driven Maximum Age First (RMAF) are further proposed to minimize the average AoI. Our extensive experiments validate the bound of RS, and show that RMAF can achieve close performance to the lower bound of the minimum time-average AoI and outperforms the state-of-the-art schemes.

Resource virtualization is a promising technique that has been increasingly deployed in industrial automation systems to support multiple time-critical applications sharing the same physical resources. Extensive studies have been reported on how to perform real-time virtualization on computing resources. However, when applying virtualization techniques on network resources (especially for real-time wireless networks), node dependency among applications, wireless channel contention and stringent end-to-end timing requirements of the real-time flows in the network pose severe challenges. To address this problem, this paper formulates the network virtualization problem for multi-hop multi-channel real-time wireless networks (RTWNs). We first present a Satisfiability Modulo Theory (SMT)-based exact solution to capture the constraints posted by each application's resource interfaces and node dependency graphs. A novel supply graph (SG)-based partitioning framework, SGP, is then proposed to determine the resource partitions for individual applications. SGP uses supply graph to maintain compliance with the regularity constraints while efficiently allocating resources. Experimental results from both a real-world testbed and extensive simulations show that SGP can achieve comparable success ratio with the SMT-based exact solution but reduce the computational overhead significantly.

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling industrial data flows to meet their hard real-time requirements. In this paper, we first leverage a real-world 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantees through time-frequency-space resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct the schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. To evaluate the performance of 5G-TPS, we present a case study of a motion control panel use case and perform extensive experiments. The results show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

Maintaining real-time data freshness plays a critical role in ensuring system correctness and optimizing the system performance in networked embedded systems (NESs). To quantitatively measure the freshness of the collected real-time data, the concept of Age of Information (AoI) has been extensively studied in recent years. This article explores how to minimize the worst case AoI of real-time data in radio-frequency (RF)-powered NESs. In such systems, one hybrid access point (HAP) transfers wireless power to a set of distributed sensor nodes, and in the meantime, receives the information from these sensor nodes. We utilize the metric of AoI to measure the data freshness and present a comprehensive analysis of the worst case AoI of the real-time data in the target system. Based on the analysis, an optimal energy schedule solution is designed to judiciously determine individual sensor nodes’ energy and time allocation to minimize the worst case AoI. Considering the varying importance of different information and sensor nodes in the target system, we further propose the optimal time and energy allocation scheme for minimizing the weighted worst case AoI. A multinode RF-powered NES testbed is implemented to validate the functional correctness of our solutions. The results show that our solutions significantly outperform the state-of-the-art solutions, reducing the worst case AoI and weighted worst case AoI by 69.3% and 75.1% on average, respectively.

We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among such applications. However, previous work on RRP model only discusses uniform resource environment, where resources are implicitly assumed to be synchronized and clocked at the same frequency. The challenge is that a task utilizing multiple resources may experience unexpected delays in non-uniform environments, where resources are clocked at different frequencies. This paper extends the RRP model to non-uniform multi-resource open system environments to tackle this problem. It first introduces a novel composite resource partition abstraction and then proposes algorithms to construct and reconfigure the composite resource partitions. Specifically, the Acyclic Regular Composite Resource Partition Scheduling (ARCRP-S) algorithm constructs regular composite resource partitions and the Acyclic Regular Composite Resource Partition Dynamic Reconfiguration (ARCRP-DR) algorithm reconfigures the composite resource partitions in the run time upon requests of partition configuration changes. Our experimental results show that compared with state-of-the-art methods, ARCRP-S can prevent unexpected resource supply shortfall and improve the schedulability up to 50%. On the other hand, ARCRP-DR can guarantee the resource supply during the reconfiguration with moderate computational overhead.

In order for real-time wireless networks (RTWNs) to achieve desired Quality of Service (QoS) for real-time sensing and control, effective packet scheduling algorithms play a critical role, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. However, this assumption is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end flows in RTWNs, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. Through judiciously sharing time slots among tasks, RD-PaS can significantly reduce the number of required time slots to meet the system reliability requirement and improve the network throughput. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings.

Industrial wireless networks (IWNs) are being increasingly deployed in the field to serve as the network fabrics for various industrial Internet-of-Things (IIoT) applications. Given that IWNs typically operate in noisy and harsh environments, frequently occurring network dynamics post huge challenges for IWN resource management especially when the network scales up. Existing centralized and distributed network management solutions either suffer from large communication overhead and time delay, or introduce schedule collisions which unnecessarily degrade the system performance. To address these problems, this work proposes a novel HierArchical Resource Partitioning framework (HARP), to provide dynamic resource management in IWNs. By hierarchically partitioning and allocating resources for the links in the network, HARP enables distributed collision-free resource allocation. HARP enables rapid adjustment of the partitions in the presence of network dynamics with modest communication overhead. The effectiveness of HARP is validated and evaluated through both simulation studies and testbed experiments on a 50-node multi-channel multi-hop 6TiSCH network.

Applying high-speed real-time wireless technologies in industrial applications has the great potential to reduce the deployment and maintenance costs compared to their wired counterparts. Wireless technologies enhance the mobility and reduce the communication jitter and delay for mobile industrial equipment, such as mobile collaborative robots. Unfortunately, most existing wireless solutions employed in industrial fields either cannot support the desired high-speed communications or cannot guarantee deterministic, real-time performance. A more recent wireless technology, RT-WiFi, achieves a good balance between high-speed data rates and deterministic communication performance. It is however developed on commercial-of-the-shelf (COTS) hardware, and takes considerable effort and hardware expertise to maintain and upgrade. To address these problems, this paper introduces the software-defined radio (SDR)-based RT-WiFi solution which we call SRT-WiFi. SRT-WiFi provides full-stack configurability for high-speed real-time wireless communications. We present the overall system architecture of SRT-WiFi and discuss its key functions which achieve better timing performance and solve the queue management and rate adaptation issues compared to COTS hardware-based RT-WiFi. To achieve effective network management with rate adaptation in multi-cluster SRT-WiFi, a novel scheduling problem is formulated and an effective algorithm is proposed to solve the problem. A multi-cluster SRT-WiFi testbed is developed to validate the design, and extensive experiments are performed to evaluate the performance at both device and system levels.

Smart factory automation has an ongoing trend to employ high-speed real-time wireless technologies to interconnect heterogeneous industrial assets to perform various sensing and control services, and support mobile equipment to conduct designated tasks in a collaborative fashion. Applications in automation industries usually have stringent requirements on both high data rates and deterministic real-time performance. Existing efforts, however, either cannot meet the performance requirements or are based on commercial-off-the-shelf (COTS) hardware and cannot provide full-stack configurability [1].

With the rapid growth of industrial Internet of Things (IIoT) applications, real-time wireless networks (RTWNs) are playing an increasingly important role in providing realtime, reliable, and secure communication services for these applications. A key challenge in RTWN management is to ensure real-time Quality of Services (QoS), especially in the presence of unexpected external (i.e., application-side) and internal (i.e., network-side) disturbances. This paper presents a novel framework, DS-PaS, to determine the packet transmission schedule for multi-channel multi-hop RTWNs at the data link layer in a distributed and dynamic fashion. DS-PaS is able to (i) handle external disturbances, (ii) support spatial reuse, (iii) meet deadlines of all critical tasks, and (iv) minimize the number of dropped non-critical packets. To avoid transmission collisions when using inconsistent information in a distributed framework, DS-PaS incorporates several key advances in both the data-link layer protocol and algorithm design so that individual nodes can build on-line schedules with only local interference information. Extensive evaluation based on both testbed implementation and simulation validates the correctness of the DS-PaS design and demonstrates its effectiveness compared to the state of the art.

The fifth-generation (5G) cellular networks are increasingly considered for industrial applications, such as factory automation systems. In 5G networks, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC) are two essential services. eMBB services require high data rates with some lower bounds while URLLC traffic is subject to strict latency and reliability requirements. Existing approaches to scheduling coexisting eMBB and URLLC traffic all assume that URLLC traffic preempts eMBB traffic immediately upon arrival, which can adversely impact the achievable eMBB data rates. Furthermore, none of the prior work considers guaranteeing minimum data rate requirements imposed on certain eMBB traffic. This paper proposes a new model to capture the URLLC and eMBB requirements and introduces a novel framework, QoSG-RA, to perform network resource allocation for coexisting eMBB and URLLC traffic. QoSG-RA builds on a hybrid offline/online approach which performs offline resource allocation to ensure the Quality of Service (QoS) requirements of eMBB and URLLC traffic to be satisfied and online resource allocation to maximize fairness on the data rates among eMBB traffic based on runtime information. QoSG-RA is able to (i) meet latency and reliability requirements of URLLC traffic, and (ii) maximize the data rates for eMBB traffic in a fair way while fulfilling their minimum data rate requirements. Experimental results demonstrate the effectiveness of QoSG-RA compared to the state-of-the-art.

Energy harvesting techniques have been increasingly employed in both consumer and industrial applications to provide clean energy supply. Among the many available energy harvesting techniques, ambient energy harvesting (AEH) is a promising one as it harvests free energy from the environment and, thus, is economically efficient. AEH techniques, however, heavily depend on the dynamic environment and are thus uncontrollable and unstable. More recently, the wireless power transfer (WPT) technique has attracted significant attentions due to its highly controllable feature when powering low-cost devices. Unfortunately, WPT faces strict regulatory limitations to provide high power density and requires charging infrastructures installed to perform effective wireless energy transfer. The pros and cons of the two techniques motivate this work to design a hybrid energy harvesting method by charging a device using a combination of AEH and WPT to maximize the throughput of a wireless system. Specifically, this work first proposes an optimal offline charging scheme to maximize the point-to-point data throughput of a wireless system by fully utilizing the ambient energy and providing extra power supply through WPT to determine the transmission rates. An online heuristic algorithm is further proposed to improve the computational efficiency for practical scenarios when the system has the estimation of future AEH patterns. Our experimental results show that the proposed approaches are effective in maximizing the data throughput when compared to the state of the art.

International audience

No abstract available.

Recent emerging technologies such as artificial intelligence and machine learning have been promising enormous economic and societal benefits. While it is desirable to deploy these technologies to Internet-of-Things (IoT) infrastructures in many applications such as medical, energy, transportation, and industrial automation systems, such deployments present daunting challenges in performance, efficiency, and dependability of scaling-up IoT infrastructure, due to the ever-increasing number of edge devices, ever-increasing levels of device and system heterogeneity, and more stringent requirements of reliability, robustness, and security in mission-critical settings. This position paper elaborates the needs for a cross-layer and full hardware/software stack solution for the design and deployment of scalable, secure, and smart mission-critical IoT systems from four different perspectives and research fields. We present a review of recent studies on such issues and identify the potential challenges and gaps, based on which we highlight some important research directions and future works that can be conducted to tackle such challenges.

Industrial Internet of Things (IIoT) systems aim to interconnect a large number of heterogeneous industrial sensing and actuation devices through both wired and wireless communication technologies and further connect them to the Internet to achieve ubiquitous sensing, computing and control services [1]. As a representative IIoT technology, 6TiSCH [2] targets at gluing together the 802.15.4e data link layer (offering industrial performance in terms of timing, reliability and power consumption) and an IP-enabled upper layer stack to achieve both deterministic network performance and seamless integration with Internet services. In recent years, 6TiSCH has been receiving increasing attentions from both industry and academia. We have witnessed its wide deployment in many industrial domains, including advanced manufacturing, industrial process control, smart grids, and healthcare.

The past decade has witnessed the rapid development of real-time wireless technologies and their wide adoption in various industrial Internet-of-Things (IIoT) applications. Among those wireless technologies, 6TiSCH is a promising candidate as the de facto standard due to its nice feature of gluing a real-time link-layer standard (802.15.4e, for offering deterministic communication performance) together with an IP-enabled upper-layer stack (for seamlessly supporting Internet services). 6TiSCH's built-in random slot selection scheduling algorithm, however, often leads to large and unbounded transmission latency, thus can hardly meet the real-time requirements of IIoT applications. This paper proposes an adaptive partition based scheduling framework, APaS, for 6TiSCH networks. APaS introduces the concept of resource partitioning into 6TiSCH network management. Instead of allocating network resources to individual devices, APaS partitions and assigns network resources to different groups of devices based on their layers in the network so as to guarantee that the transmission latency of any end-toend flow is within one slotframe length. APaS also employs a novel online partition adjustment method to further improve its adaptability to dynamic network topology changes. The effectiveness of APaS is validated through both simulation and testbed experiments on a 122-node multi-hop 6TiSCH network.

Applying emerging wireless powered communication (WPC) technologies in wireless networks has the potential to significantly reduce the maintenance cost of battery replacement and facilitate device deployment in extreme environments. This paper presents the design, implementation and performance evaluation of a real-time wireless powered communication network (RT-WPCN). We present the overall system architecture of RTWPCN and discuss the design challenges of its key hardware and software components, including the wireless powered user device, the beamforming-based hybrid access point (HAP), and the timedivision multiple access (TDMA) based data link layer protocol. We present our solutions to address each of the challenges and describe their implementation details on real hardware. Based on the developed RT-WPCN system, the minimal user throughput maximization problem is formulated and an effective algorithm is proposed to solve it. Extensive expenments are conducted to validate the design principles of RT-WPCN, and thoroughly evaluate its performance at both the component and system levels.

Wireless sensing devices compose the major physical components of Internet-of-Things (IoT) systems which in recent years we have witnessed their rapid deployment in both consumer and industrial applications to support diverse sensing and control services [1]. Most of those sensing devices however are battery powered, and they require significant economic and human resources to replace or charge the batteries frequently to maintain proper operations. To overcome this issue, an ever-growing number of IoT devices are now being powered by harvesting ambient energy sources, which are mostly unreliable and not controllable. By contrast, emerging RF-based energy harvesting technologies [2] provide key benefits in terms of being wireless and controllable to provide sustainable energy supply. The IoT systems relying on RF-based energy harvesting technologies are in general called wireless powered communication networks (WPCN).

Mobile devices have been increasingly deployed in large-scale cyber-physical systems (CPS) to traverse the field and retrieve data measurements from designated physical entities with stringent performance requirements. This work studies the availability-constrained real-time data retrieval problem in CPS with a speed adjustable mobile device (AFDR-SA). The goal is to maintain the temporal validity of the real-time data to be retrieved in the system while meeting the data availability constraints imposed by the communication range between the mobile device and the physical entities. A dynamic programming (DP)-based optimal algorithm is proposed for a special but commonly presented scenario where the retrieval times of individual data items are of the same length. Based on this optimal algorithm, an effective heuristic method is further developed for the general case where data items can have arbitrary retrieval times. The effectiveness of the proposed methods are validated through extensive experiments. Our results demonstrate the optimality of the DP-based algorithm, and show that the heuristic method outperforms the state-of-the-art schemes and performs close to the optimal solution obtained by the exhaustive search with much less computational overhead.

Real-time end-to-end task scheduling in networked control systems (NCSs) requires the joint consideration of both network and computing resources to guarantee the desired quality of service (QoS). This paper introduces a new model for composite resource scheduling (CRS) in real-time networked control systems, which considers a strict execution order of sensing, computing, and actuating segments based on the control loop of the target NCS. We prove that the general CRS problem is NP-hard and study two special cases of the CRS problem. The first case restricts the computing and actuating segments to have unit-size execution time while the second case assumes that both sensing and actuating segments have unit-size execution time. We propose an optimal algorithm to solve the first case by checking the intervals with 100% network resource utilization and modify the deadlines of the tasks within those intervals to prune the search. For the second case, we propose another optimal algorithm based on a novel backtracking strategy to check the time intervals with the network resource utilization larger than 100% and modify the timing parameters of tasks based on these intervals. For the general case, we design a greedy strategy to modify the timing parameters of both network segments and computing segments within the time intervals that have network and computing resource utilization larger than 100%, respectively. The correctness and effectiveness of the proposed algorithms are verified through extensive experiments.

In this article, we present an advanced coherent receiver for the IEEE 802.15.4 offset quadrature phase-shift keying (OQPSK) physical layer and provide an open-source implementation in the GNU Radio framework. Simulation and field test results show that the proposed receiver achieves about 11-dB power gain over an existing GNU Radio receiver, which treats OQPSK as minimum-shift-keying (MSK) for low-complexity processing. While suitable modules can be added to the MSK-based receiver for performance enhancement, the proposed receiver still maintains a 6-dB power gain. The proposed coherent receiver is attractive for IoT applications where a powerful software-defined-radio (SDR)-based gateway is deployed to interact with various sensors.

Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable, and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected disturbances and lossy wireless links. Most prior work takes centralized approaches for handling disturbances, which are slow and subject to single-point failure, and do not scale. To overcome these drawbacks, this article presents a fully distributed packet scheduling framework called FD-PaS . FD-PaS aims to provide guaranteed fast response to unexpected disturbances while achieving minimum performance degradation for meeting the timing and reliability requirements of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances while ensuring the designated QoS requirements.

This paper studies how to maintain real-time data freshness in the emerging wireless powered communication networks (WPCNs). In a WPCN, one or multiple hybrid access points (HAPs) with constant power supply transfer the energy and receive the status update through wireless to and from a set of distributed sensor nodes simultaneously. We utilize the concept of Age of Information (AoI) to quantitatively measure the freshness of the sensor data, and formulate the AoI optimization problem. Based on this problem formulation, we first explore the optimal time allocation method to minimize the average AoI for sensor nodes in single-hop WPCNs. This method is then extended to derive the optimal time allocation for two-hop WPCNs, where some sensor nodes may transmit status update to the HAP through relay nodes. In this more complex scenario, we apply a two-phase method to 1) identify all the two-hop node candidates and their associated relay nodes, and 2) determine the best assignment and the corresponding time allocation to optimize the average AoI. A 5-node WPCN testbed is developed to validate the functional correctness of the proposed methods. Extensive simulations are also conducted for performance evaluation under more comprehensive settings. The experimental results show that the proposed methods can reduce the average AoI by 83.4% on average compared to the state-of-the-art methods.

This article explores how to optimize the freshness of real-time data for energy harvesting (EH)-based networked embedded systems (NESs) with energy constraints. We introduce the concept of age of information (AoI) to quantitatively measure the data freshness and present a comprehensive analysis on the average AoI of the real-time data with stochastic update arrival and energy replenishment patterns for single-source EH-based systems. An optimal offline solution and an effective online solution are designed to select a sequence of real-time data updates (while discarding the remaining ones) and determine their corresponding transmission time to minimize the average AoI. We further extend these findings to multisource EH-based NESs, and present an optimal offline solution and an efficient online solution to schedule updates for each data source to optimize the average AoI. The correctness of the analysis and the effectiveness of the proposed solutions have been validated through extensive experiments by comparing to the state-of-the-art methods. According to the experimental results, the proposed solutions reduce the average AoI by 47.2% and 69.1% on average comparing to the state-of-the-art solutions for single-source and multisource EH-based NESs, respectively, with low harvesting rates.

We consider the problem of clustering graph nodes over large-scale dynamic graphs, such as citation networks, images and web networks, when graph updates such as node/edge insertions/deletions are observed distributively. We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models. Given a graph with n nodes that is observed at s remote sites over time [1,t], the two proposed algorithms have communication costs Õ(ns) and Õ(n + s) (Õ hides a polylogarithmic factor), almost matching their lower bounds, Ω(ns) and Ω(n + s), respectively, in the message passing and the blackboard models. More importantly, we prove that at each time point in [1,t] our algorithms generate clustering quality nearly as good as that of centralizing all updates up to that time and then applying a standard centralized clustering algorithm. We conducted extensive experiments on both synthetic and real-life datasets which confirmed the communication efficiency of our approach over baseline algorithms while achieving comparable clustering results.

Blockchain is a distributed and decentralized ledger for recording transactions. It is maintained and shared among the participating nodes by utilizing cryptographic primitives. A consensus protocol ensures that all nodes agree on a unique order in which records are appended. However, current blockchain solutions are facing scalability issues. Many methods, such as Off-chain and Directed Acyclic Graph (DAG) solutions, have been proposed to address the issue. However, they have inherent drawbacks, e.g., forming parasite chains. Performance, such as throughput and latency, is also important to a blockchain system. Sharding has emerged as a good candidate that can overcome both the scalability and performance problems in blockchain. To date, there is no systematic work that analyzes the sharding protocols. To bridge this gap, this paper provides a systematic and comprehensive review on blockchain sharding techniques. We first present a general design flow of sharding protocols and then discuss key design challenges. For each challenge, we analyze and compare the techniques in state-of-the-art solutions. Finally, we discuss several potential research directions in blockchain sharding.

This paper explores how to optimize the freshness of real-time data in energy harvesting based networked embedded systems. We introduce the concept of Age of Information (AoI) to quantitatively measure the data freshness and present a comprehensive analysis on the average AoI of the real-time data with stochastic update arrival and energy replenishment rates. Both an optimal offline solution and an effective online solution are designed to judiciously select a subset of the real-time data updates and determine their corresponding transmission times to optimize the average AoI subject to energy constraints. Our extensive experiments have validated the effectiveness of the proposed solutions, and showed that these two methods can significantly improve the average AoI by 47.2% comparing to the state-of-the-art solutions for low energy replenishment rate.

Along with the rapid development and deployment of real-time wireless network (RTWN) technologies in a wide range of applications, effective packet scheduling algorithms have been playing a critical role in RTWNs for achieving desired Quality of Service (QoS) for real-time sensing and control, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. Although this assumption simplifies the algorithm design and analysis, it is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end packet transmissions in RTWNs for a given periodic network traffic pattern, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings.

Graph sparsification has been used to improve the computational cost of learning over graphs, e.g., Laplacian-regularized estimation and graph semi-supervised learning (SSL). However, when graphs vary over time, repeated sparsification requires polynomial order computational cost per update. We propose a new type of graph sparsification namely fault-tolerant (FT) sparsification to significantly reduce the cost to only a constant. Then the computational cost of subsequent graph learning tasks can be significantly improved with limited loss in their accuracy. In particular, we give theoretical analyze to upper bound the loss in the accuracy of the subsequent Laplacian-regularized estimation and graph SSL, due to the FT sparsification. In addition, FT spectral sparsification can be generalized to FT cut sparsification, for cut-based graph learning. Extensive experiments have confirmed the computational efficiencies and accuracies of the proposed methods for learning on dynamic graphs.

We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among the applications. However, RRP model does not consider changes in resource requests from the applications at run time. To allow for the run time adaptation to change resource requirements, we consider in this paper the issues in online resource partition reconfiguration, including semantics issues that arise in configuration transitions that may cause application failures. Based on the reconfiguration semantics, we study the online resource reconfigurability problem under the RRP model where the availability factors of resource partitions may be reconfigured during run time. We formalize the Dynamic Partition Reconfiguration (DPR) problem and provide a solution to this problem. Extensive experiments have been conducted to evaluate the performance of the proposed approach in different scenarios. We also present a case study using the autonomous F1/10 model car; the controller of the F1/10 car requires resource adaptation to satisfy the computing needs of its PID controller and vision system under different operating conditions. Our implementation demonstrates the effectiveness and benefit of online resource partition reconfiguration using the DPR approach in a real system.

The adoption of real-time wireless technologies within the ever-growing field of networked industrial control systems is continuously gaining popularity. Widespread sensing and actuation devices based on high-throughput wireless standards allow for an increased system mobility and lower configuration and maintenance costs. The IEEE 802.11 standard, especially in its most recent amendments, pushes performance to a very high level, theoretically approaching those of the most common real-time Ethernet networks. Its nondeterministic communication behavior, however, makes 802.11 unsuitable for mission- and safety-critical applications with high-reliability requirements, at least in its standard form. In this paper, we present several major solutions that tackled this issue to achieve real-time and reliability guarantees in wireless networked control systems, capitalizing on the strong efforts devoted to the adoption of IEEE 802.11 physical layer (PHY) technologies. We first provide a deep analysis of the 802.11 protocols in order to propose guidelines toward smart parameter selection at the data-link layer (DLL). In addition, the design of effective rate selection algorithms is considered as a way of increasing both the timeliness and reliability of data delivery. A further systematic solution is represented by real-time (RT)-WiFi, a new time-division multiple-access (TDMA)-based highly configurable DLL protocol that enables high-speed hard real-time data exchange over 802.11 networks. An important goal of this paper is also to provide a thorough comparison among different discussed solutions, in order to put in evidence their advantages and disadvantages, possibly in relation with systems based on different underlying PHYs. We will finally highlight the open challenges and future directions in this active research field.

Maintaining the temporal validity of real-time data in cyber-physical systems is of critical importance to ensure the correct decision making and appropriate system operation. Most existing work on real-time data retrieval assume that the real-time data under study are always available for retrieval, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity constraints. This assumption, however does not hold in many real-time applications with intermittent data availability. In this paper, we study the Availability-constrained Fresh Data Retrieval (AFDR) problem, which aims to retrieve all required real-time data for a given set of decision tasks on time while taking both the temporal validity and data availability constraints into consideration. We formulate the AFDR problem as an ILP problem and study its complexity under different settings. Given the general case of the AFDR problem is proved to be NP-hard, we focus on the cases that data items have unit-size retrieval time. For the single decision task scenario, we propose a polynomial-time optimal data retrieval algorithm, which consists of a task finish time selection phase and an optimal retrieval schedule construction phase, to solve the AFDR problem. For the multiple decision task scenario, we propose an efficient heuristic algorithm by transforming the temporal validity constraint of a real-time data item to the availability constraint. The effectiveness of the proposed algorithms has been validated through extensive experiments. Our results show that the heuristic algorithm outputs around $1.5\times$1.5× feasible cases compared to that of the state-of-the-art scheme.

Real-time wireless networks (RTWNs) are fundamental to many Internet-of-Things (IoT) applications. RTWNs typically apply time-division multiple access (TDMA)-based media access control mechanisms and often demand deterministic end-to-end packet delivery to meet the given quality of service (QoS) requirements. Packet scheduling in an RTWN thus plays a critical role for achieving the desired performance but is a challenging problem especially when the RTWN is large and must deal with multiple disturbances (i.e., unexpected events causing abrupt workload changes associated with certain sensing tasks) occurring concurrently. This paper introduces a novel distributed dynamic packet scheduling framework, D2-PaS. D2-PaS is capable of processing disturbances and minimizes the number of dropped packets while ensuring that all critical events due to disturbances are handled by their deadlines. As a distributed approach, D2-PaS constructs schedules locally at individual nodes, which significantly reduces the amount of schedule-related information to be broadcast by the gateway. As a dynamic approach, D2-PaS applies a lightweight packet dropping algorithm to determine on-line at the gateway which packets can be dropped in response to disturbances and disseminate this information to the network. D2-PaS has been implemented on a multi-hop RTWN testbed to validate its applicability on hardware and a popular RTWN stack. Both testbed measurements and extensive simulation results demonstrate the effectiveness of D2-PaS.

Applying wireless technologies in cyber-physical systems (CPSs) has received significant attention in recent years. In our previous work, a high-speed and flexible real-time wireless communication protocol called RT-WiFi was designed to support a wide range of CPSs, and we presented an implementation with a single access point (AP). To serve the CPS applications with communication nodes geographically distributed over a large area, multicluster RT-WiFi networks with multiple APs need to be deployed. Although effective scheduling algorithms have been designed to schedule tasks in RT-WiFi networks with a single AP, uncoordinated packet transmissions from multicluster RT-WiFi networks may suffer from cochannel interferences that cause performance degradation. The multicluster RT-WiFi network management problem is to resolve the cochannel interference through channel assignment for clusters and through phasing assignment for communication tasks. In this article, we first derive a conjunctive normal form encoding of the problem and design a TScheduler that searches feasible solutions through the SAT solver. A novel LRTree Scheduler is further designed to solve the problem in chain graphs while keeping the number of used channels small and the network management overhead low. A testbed of the multicluster RT-WiFi network is deployed to validate the design of the multicluster RT-WiFi network and evaluate the performance of the proposed scheduling algorithms compared to the contention-based methods in regular WiFi networks. Performance of these scheduling algorithms in large-scale networks is further evaluated through extensive simulations on both static and dynamic multicluster RT-WiFi networks.

A demonstration of 6TiSCH Industrial IoT network handling network resource management and data collection and analytics.

A demonstration of a field gateway design that can be configured through a web interface to collect data from multiple data sources using different protocols and stream to cloud based analytics platform.

Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected external and internal disturbances. Most prior work takes a centralized approach for handling disturbances, which is slow and subject to single-point failure, and does not scale. To overcome these drawbacks, this paper presents a fully distributed packet scheduling framework called FD-PaS. FD-PaS aims to provide guaranteed fast response to unexpected disturbances while dropping a minimum number of packets for meeting the deadlines of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances.

Real-time task scheduling for wireless networked control systems provides guarantees for the quality of service. This paper introduces a new model for joint network and computing resource scheduling (JNCRS) in real-time wireless networked control systems. This new end-to-end real-time task model considers a strict execution order of segments including the sensing, the computing and the actuating segment based on the control loop of WNCSs. The general JNCRS problem is proved to be a NP-hard problem. After dividing the JNCRS problem into four subproblems, we propose a polynomial-time optimal algorithm to solve the first subproblem where each segment has unit execution time, by checking the intervals with 100% network resource utilization and modify the deadlines of tasks. To solve the second subproblem where the computing segment is larger than one unit execution time, we define the new timing parameters of each network segment by taking into account the scheduling of the computing segments. We propose a polynomial-time optimal algorithm to check the intervals with the network resource utilization larger than or equal to 100% and modify the timing parameters of tasks based on these intervals.

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach.

With the ever-growing interests in applying wireless technologies for networked embedded systems to serve as the communication fabric, many real-time wireless technologies have been recently developed to support time-critical sensing and control applications. We proposed in previous work the RT-WiFi protocol that provides real-time high-speed predictable data delivery and enables designs to meet time-critical industrial needs. However, without explicit reliability enforcement mechanisms, our previous RT-WiFi design is either subject to uncontrolled packet loss due to noise and other interferences or may suffer from inefficient communication channel usage. In this article, we explicitly consider interference from both Wi-Fi and non-Wi-Fi based interference sources and propose two sets of effective solutions for reliable data transmissions in RT-WiFi-based networked embedded systems. To improve reliability against general non-Wi-Fi based interference, based on rate adaptation and retransmission techniques, we present an optimal real-time rate adaption algorithm together with a communication link scheduler that has low network management overhead. A novel technique called overbooking is introduced to further improve the schedulability of the communication link scheduler while maintaining the required communication reliability. For Wi-Fi-based interference, we present mechanisms that utilize virtual carrier sensing to provide reliable data transmission while co-existing with regular Wi-Fi networks. We have implemented the proposed algorithms in the RT-WiFi network management framework and demonstrated the system performance with a series of experiments.

The papers in this special section examine the deploying of industrial wireless sensor networks as it applies to the industrial Internet of Things. Industrial networks connect sensors and actuators in various industrial facilities, such as oil and gas production facilities, paper plants, car manufactories, and underground mines. Industrial Internet of Things (IIoT) is a paradigm that involves a network of physical objects containing embedded technologies to collect, communicate, sense, and interact with their internal states or the external environment through wireless or wired connections brilliant machines, advanced analytics, and people at work and deliver valuable new insights like never before. These insights can then help drive smarter, faster business decisions for industrial companies. Since the Internet is designed for best effort services, there is a fundamental challenge to design and support applications in the industrial automation domain that demands real-time performance at different levels.

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

In order to maintain a healthy living both physiologically and psychologically, it is important for patients with mild cognitive impairment (MCI) to maintain active in daily life. In this paper, we demonstrate how to use simple motion sensors, e.g., accelerometers, gyroscopes and magnetometers, to design and develop a system, called ActiveLife, for effective tracking of the daily living activities of MCI patients within their living rooms. In order to simplify the activity detection process, in ActiveLife, we adopt the context-based approach to model the common activities performed by the user within a day. Since the accelerometer and gyroscope are tri-axial sensors, the sensor data for different axes can be used to predict the current posture of the user while he is performing an activity. Combining with the heading direction of the posture obtained from the magnetometer and distance travelled during the transition of activities, we can estimate the current activity of the user. To further improve the estimation accuracy, we have designed an algorithm using the machine-learning technique, i.e., support vector machines (SVM), for activity classification.

6TiSCH network architecture has recently been introduced to combine the high reliability and low-power consumption of the TSCH (Time-Slotted Channel Hopping) mode of IEEE 802.15.4e MAC with the ease of integration offered by the IP-enabled upper layer protocols. 6TiSCH network uses RPL (Routing Protocol for Low Power and Lossy Networks) as its routing protocol to manage the network layer functionalities. RPL however is vulnerable to internal routing attacks such as Rank attack where a malicious node multicasts a fake position (Rank) or a fake path cost toward the sink node to lure nearby nodes to forward their packets through it. In this paper, we propose a hybrid specification-based intrusion detection system (IDS) that consists of centralized and distributed modules installed on the sink and RPL nodes respectively to prevent nodes from selecting an intruder as their successors. The proposed method also eliminates intruders' chances of becoming a time source and disrupt the synchronization of 6TiSCH networks. The results from our extensive simulations show that compared with existing countermeasures, the proposed IDS can effectively protect RPL topologies while only incurring limited network management overhead.

We designed a crossdevice testing and reporting system, called cross-device testing and reporting system (CD-TRS), to facilitate the functional validation of protocol and application design in large-scale RTWNs. CD-TRS leverages the nice property of RTWNs that all devices in the network are fully synchronized. By specifying and retrieving events and device status from multiple devices in the runtime simultaneously, CD-TRS can further assemble them into a network-wide report on the detailed system behavior by aligning the records according to their associated network timestamps (or absolute slot number (ASN) in most RTWNs). By comparing this runtime network behavior report with the required protocol and application specifications, abnormal device/network behavior can be observed and their root cause(s) can be effectively located. This thus can significantly reduce the complexity of RTWN testing and reporting. In the following, we first describe the overall architecture of CD-TRS, and then demonstrate how CD-TRS helps with the functional validation of D2-PaS, a distributed and dynamic packet scheduling framework we recently developed for handling disturbances in real-time wireless networks.

Real-time wireless networks (RTWNs) are fundamental to many Internet-of-Things (IoT) applications. Packet scheduling in an RTWN plays a critical role for achieving desired performance but is a challenging problem especially when the RTWN is large and subject to unexpected disturbances from the environment. Few solutions exist to tackle this challenge but they suffer serious limitations. This paper introduces a novel distributed dynamic packet scheduling framework, D2-PaS. D2-PaS aims to minimize the number of dropped packets while ensuring that all critical events due to disturbances are handled by their deadlines. D2-PaS builds on a number of observations that help reduce the scheduling overhead, and thus is efficient and scalable. Besides extensive simulation, D2-PaS has been implemented on an RTWN testbed to validate its applicability on real hardware. Both testbed measurements and simulation results confirm the effectiveness of D2-PaS. Compared to the best known work, D2-PaS reduces packet drop rates by 65% and 90% on average and in the best case, respectively, and also achieves 100% success for all the randomly generated task sets.

In open systems, no global scheduler has knowledge of the complete resource requirements from all the applications. Each application has its own task group and can generate tasks on demand at run time. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically allocate resource in such environments. However, when applying the RRP model to multi-resource environments, end-to-end tasks could experience unexpected delay and miss the deadlines. The tasks might arrive at non-resource-slice boundaries because the resource slice sizes of different physical resource may vary in such non-uniform environments. This paper extends the RRP model to non-uniform multi-resource open systems. It introduces a novel composite resource partition abstraction, identifies the feasible conditions for hierarchical regular composite resource partitioning and proposes an acyclic regular composite resource partition scheduling (ARCRPS) algorithm. Simulation results show that compared with the state-of-the-art approach, ARCRPS improves the acceptance ratio by 20% and 25% in uniform and non-uniform multi-resource environments, respectively. A multi-resource scheduling framework jointly considering the CPU and network resources is also designed and implemented to evaluate the feasibility of this theoretical model in practice.

Real-time wireless sensor and actuator networks (WSANs) have experienced widespread adoption across many industries due to their great advantages of enhanced mobility, easier deployment as well as reduced configuration and maintenance costs. To compensate for the dynamic and unpredictable nature of wireless networks, extensive research efforts have been devoted on algorithm design for efficient network resource management to both meet robustness requirements and optimize the overall network for latency, power, and throughput. Very limited results, however have been reported for the network performance in real field deployments. This paper presents an in-depth exploration of a WirelessHART mesh network deployed in a separations research facility. To understand how the network performs under different scenarios it is restarted and data traces are collected at three points in time with different experiment settings. We examine the impact of initial neighbor selection and observe the network manager adapting the network. The mesh network performance is reported in terms of its primary functions, which are to report process measurements and maintain the network operations.

Wireless sensing and networking technologies have taken a strong foothold in the process control industry. The focus has now shifted towards applying control with wireless technology. As such, the current industrial wireless sensor network architectures are under increased scrutiny about their real-time, reliability, and security performances. In this paper, we take a renewed look at network synchronization, the basic building block to support real-time activities. We present our analysis, key observations, and recommendations. We also get into the depth of WirelessHART, the most deployed industrial wireless network architecture, and affirm that, with good practice, we could achieve highly reliable synchronization to provide guaranteed packet deliveries. The correctness of the time synchronization analysis in both line and mesh network topologies is verified through extensive simulation experiments.

Maintaining the freshness of real-time data is one of the crucial design issues in cyber-physical systems (CPS). Past studies have focused on designing update algorithms to minimize the workload imposed by a fixed set of update tasks while ensuring the temporal validity of data. In this paper, we revisit this problem in dynamic cyber-physical systems (DCPS) which may exhibit multi-modal behavior. Any solution to this problem must recognize that: (1) different update algorithms may be needed in different modes according to the workload in each mode, and (2) temporal validity of data must be maintained not only in each mode but also during the mode switch. To strike a balance between data freshness and system schedulability, we propose a utilization-based scheduling selection (UBSS) strategy. We first introduce two synchronous mode switch algorithms, named search-based switch (SBS) and adjustment-based switch (ABS) to search for the proper switch point online and execute all update tasks in the new mode synchronously. SBS checks for temporal validity at the beginning time slot of each idle period in the schedule, while ABS relaxes this restriction through schedule adjustment. To support immediate mode switch, we propose an asynchronous switch algorithm named instant switch (IS) to reduce the switch delay. IS schedules outstanding jobs from the old mode together with the jobs in the new mode using the least-available-laxity-first scheduling policy. Our experimental results demonstrate the effectiveness of these three algorithms. They also show that UBSS strategy can significantly outperform a single fixed update algorithm in terms of maintaining better data freshness while incurring only limited online switch overhead.

In this paper, we introduce SmartMind, an activity tracking and monitoring system to help Alzheimer's diseases (AD) patients to live independently within their living rooms while providing emergent help and support when necessary. Allowing AD patients to handle their daily activities not only can release some of the burdens on their families and caregivers, but also is highly important to help them regain confidence towards a healthy life and reduce the degeneration rates of their memories. The daily activities of a patient captured from SmartMind can also serve as important indicators to describe his/her normal living habit (NLH). By checking with NLH, the patient's current health status can be estimated on a daily basis.

Wireless networked control systems (WNCSs) are widely used in many applications. A key challenge in WNCSs is to develop efficient data link layer (DLL) scheduling algorithms to achieve reliable end-to-end real-time communication. Previous research typically assumes that the network communication schedule of WNCS, once constructed and distributed, stays unchanged, thus cannot handle dynamic variations which are unavoidable in many WNCSs. This paper considers the DLL scheduling problem in WNCSs, where external disturbances occur sporadically. An on-line framework, employing a rhythmic task model, is introduced to adjust the static schedule to respond to external disturbances. The approach determines the time duration to apply a dynamic schedule and generates the schedule for that time duration to minimize the impact of network dynamics on existing network flows. The effectiveness and efficiency of the proposed algorithm are validated by extensive simulation. Results indicate that the proposed approach outperforms existing work both in terms of the number of feasible task sets (between 11% and 41% increase on average) and the number of feasible periodic packets (between 122% and 128% increase on average).

Most existing studies on Delay/Disruption Tolerant Networks (DTNs) consider pair-wise node encountering that assumes nodes only meet in pairs. In many mobile wireless networks, a group of nodes, instead of only a pair of nodes, may meet each other. In this paper, we study how to effectively transmit a set of packets from a source to a destination in such group meeting scenarios. The optimization goal is to minimize the delay for the packets to reach the destination while limiting the energy consumption. We first assume that node encountering is known beforehand, and develop an algorithm to obtain the minimum delay. We then develop two practical network coding based schemes. Both schemes use a token technique to limit the total number of transmissions, and only incur signaling at the beginning of a group meeting. One scheme requires nodes in a group to exchange their encoding matrices with each other, while the other only requires exchanging rank information. Simulation results demonstrate that both schemes achieve delays close to the minimum delay for moderate number of tokens. They present different tradeoffs in the number of transmissions and the signaling overhead.

The ability to estimate performance metrics such as latency (delay) at an early stage of final implementation in any embedded system is essential for efficient design specially realtime systems. Constructing performance models and evaluation techniques of a given system requires a significant effort. This paper presents a mapping scheme between a Functional Modeling Approach such as FSM, UML etc and an Analytical (Mathematical) Modeling Approach such as Hierarchical Performance Modeling (HPM) as a technique to find the expected average delay time for different layers of abstractions. A generic FSM is proposed to be used in order to estimate the expected average delay and to find a bottleneck of a system. A case study is presented to illustrate the concepts of the mapping scheme to estimate the delay and to determine the bottleneck(s).

This paper describes SmartMood, a mood tracking and analysis system designed for patients with mania. By analyzing the voice data captured from a smartphone while the user is having a conversation, statistics are generated for each behavioral factor to quantitatively describe his/her mood status. By comparing the newly generated statistics with those under normal mood, SmartMood tries to identify any new manic episodes so that appropriate consultation and medication actions can be taken. The daily behavioral statistics may serve as important references for psychiatrists to show the effectiveness of treatments. To reduce the probability of false alarms, we propose an adaptive running range method to estimate the normal mood range for each behavioral factor, and study methods to minimize the effects of background noise on the generated statistics. The preliminary experimental results on SmartMood show that a method using the pitch of a voice data sample to identify silent periods can better differentiate the voice of a normal or manic user in a call session than other methods. The results from the limited proof of concept testing indicate that moving to clinical testing is warranted.

In this paper, we study how mobile computing and wireless technologies can be explored to provide effective ubiquitous healthcare services. Instead of reinventing the wheels, we make use of smartphones, off-the-shelf components, and existing technologies in ubiquitous computing (i.e. wireless and mobile positioning technologies, and data acquisition techniques and processing via sensors) to develop a middleware, and tools for the development of systems and applications to provide effective ubiquitous healthcare services. Two main tasks to be studied are: 1) Developing a framework, called SmartHealth, to provide the infrastructure and architectural support for realizing ubiquitous healthcare services, and 2) Designing and developing ubiquitous healthcare applications by utilizing the SmartHealth framework to let users experience and benefit from the provided services. We use scenarios to illustrate how mobile/wireless and sensor technologies can enable ubiquitous healthcare services in Smart Health. Some of the examples included in Smart Health are: location tracking, vital signs and well-being data acquisition and analysis, fall detection and behavior monitoring, and sleep analysis. As a start, based on the Smart Health framework, we introduce a smartphone app, called Smart Mood, for tracking the mood of patients who are suffering mood disorder (i.e., manic and depression) to demonstrate how Smart Health can effectively enable ubiquitous healthcare services.

Wireless networked control systems have received significant attention due to their great advantages in enhanced system mobility, and reduced deployment and maintenance cost. To support a wide range of high-speed wireless control applications, we presented in our prior work the design and implementation of a flexible real-time high-speed wireless communication platform called RT-WiFi. RT-WiFi currently provides up to 6kHz sampling rate and deterministic timing guarantee on packet delivery. While guaranteed delivery latency is essential for networked control, control performance is also impacted by communication jitter and other QoS parameters. To reduce jitter, a flexible network manager is needed to control network-wide scheduling of packet transportation. In this paper, we present an RT-WiFi network manager design and propose efficient solutions for two fundamental RT-WiFi network management problems. To improve control performance in networked control systems, our RT-WiFi network manager is designed to generate data link layer communication schedule with minimum jitter under both static and dynamic network topologies. In order to minimize network management overhead, an efficient data structure called S-tree is invented to manage the communication requests to deal with network dynamics. We have implemented the RT-WiFi network manager, and validated its network and control performance through extensive experiments with a real application.

Although the deferrable scheduling algorithm for fixed priority transactions ( DS-FP) has been shown to provide a better performance compared with the More-Less (ML) method, there is still a lack of any comprehensive studies on the necessary and sufficient conditions for the schedulability of DS-FP. In this paper, we first analyze the necessary and sufficient schedulability conditions for DS-FP, and then propose a schedulability test algorithm for DS-FP by exploiting the fact that there always exists a repeating pattern in a DS-FP schedule. To resolve the limitation of fixed priority scheduling in DS-FP, we then extend the deferrable scheduling to a dynamic priority scheduling algorithm called DS-EDF by applying the earliest deadline first (EDF) policy to schedule update jobs. We also propose a schedulability test for DS-EDF and compare its performance with DS-FP and ML through extensive simulation experiments. The results show that the schedulability tests are effective. Although the schedulability of DS-EDF is lower than DS-FP and the repeating patterns in DS-EDF schedules are longer than those in DS-FP due to the use of dynamic priority scheduling, the performance of DS-EDF is better than both DS-FP and ML in terms of CPU utilization and impact on lower priority application transactions.

Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., indoor environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this article, we present the design and implementation of ColLoc: a collaborative location and tracking system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, ColLoc adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, ColLoc employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. ColLoc adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI presurvey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes, integrating Extended Kalman Filter (EKF) with trilateration to track moving objects. Our implementation of ColLoc can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete ColLoc system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.

The Internet of Things (IoT) is considered to be the biggest challenge and opportunity for the Internet today. The Internet of Things makes it possible to connect embedded devices in physical environments to the Internet and interact with those devices through both IP and web interfaces. As a subset of the Internet of Things, the wireless embedded Internet targets at enabling resource-limited wireless devices with IP functions and connecting them to the Internet through low-power and low-bandwidth wireless networks. In this paper, we describe our design of the network infrastructure of wireless embedded Internet for industrial automation, and present the implementation and demonstration of a prototype system which integrates WirelessHART mesh networks into the Internet and supports web-based monitoring and control services.

Applying wireless technologies in control systems can significantly enhance the system mobility and reduce the deployment and maintenance cost. Existing wireless technology standards, however either cannot provide real-time guarantee on packet delivery or are not fast enough to support high-speed control systems which typically require 1kHz or higher sampling rate. Nondeterministic packet transmission and insufficiently high sampling rate will severely hurt the control performance. To address this problem, in this paper, we present our design and implementation of a real-time high-speed wireless communication protocol called RT-WiFi. RT-WiFi is a TDMA data link layer protocol based on IEEE 802.11 physical layer to provide deterministic timing guarantee on packet delivery and high sampling rate up to 6kHz. It incorporates configurable components for adjusting design trade-offs including sampling rate, latency variance, reliability, and compatibility to existing Wi-Fi networks, thus can serve as an ideal communication platform for supporting a wide range of high-speed wireless control systems. We implemented RT-WiFi on commercial off-the-shelf hardware and integrated it into a mobile gait rehabilitation system. Our extensive experiments demonstrate the effectiveness of RT-WiFi in providing deterministic packet delivery in both data link layer and application layer, which further eases the controller design and significantly improve the control performance.

Maintaining sensor data validity while exercising timely control is crucial in real-time sensing and control systems. The goal of scheduling algorithms deployed in such systems is to maintain the validity of real-time sensor data so as to maximize the schedulability of update transactions with minimum update workload so that control actions occur on time. In this paper, we first propose a dynamic scheduling algorithm, called Deferrable Scheduling with Least Actual Laxity First (DS-LALF). DS-LALF is designed by extending the deferrable scheduling algorithm, DS-FP which is designed for fixed priority systems. We develop a schedulability test algorithm for DS-LALF based on pattern analysis and a pattern search algorithm to find the shortest and earliest pattern in the schedule. Then, based on DS-LALF, a co-scheduling algorithm called Co-LALF-to schedule update transactions and control transactions in a real-time sensing and control system together-is developed 1) to meet the deadlines of all the control transactions and 2) to maximize the quality of data (QoD) utilized by the control transactions. Co-LALFschedules the jobs in the ascending order of their actual laxities and defers the release times of update jobs as long as the corresponding sensor data are maintained within the required quality. Experimental results show that DS-LALF incurs lower update workload compared with DS-FP and ML, and its schedulability is close to DS-FP but is much better than ML and DS-EDF. The experimental results also show that Co-LALF is effective in improving the overall performance by ensuring better QoD for the real-time data while meeting the deadline constraints of all the control transactions.

In a real-time database system for detection of critical events,On co-scheduling of periodic update and application transactions with fixed priority assignment for real-time monitoring meeting the deadlines of the application transactions and maintaining the quality of the real-time data objects are two critical issues in ensuring the effectiveness of performing the real-time monitoring tasks. Unfortunately, these two goals conflict with each other and are difficult to be achieved at the same time. To address this update and application transaction co-scheduling problem, in this paper, we propose a fixed priority scheduling algorithm called Periodic Co-Scheduling (PCS). PCS uses periodic update transactions to maintain the temporal validity of real-time data objects. It judiciously decides the priority order among all the update and application transactions so that the constructed co-schedule can satisfy the deadline constraints of all the application transactions while maximizing the qualities of the real-time data objects. The effectiveness of the PCS algorithm is validated through our extensive simulation experiments.

WirelessHART™ was released in September 2007 and became an IEC standard in April 2010 (IEC 62591). It is the first open wireless communication standard specifically designed for process measurement and control applications deployed in harsh and noisy environments. WirelessHART distinguishes itself from other public standards by maintaining a central Network Manager. The Network Manager is responsible for maintaining up-to-date routes and communication schedules for the network, thus guaranteeing the reliable and real-time network communications. To deal with the intensive computation requirement in a centralized WirelessHART Network Manager, particularly of middle or large scale network sizes, in this article, we utilize parallelization techniques to implement the Network Manager on embedded multicore architectures. By leveraging the multicore capabilities of the AMD Embedded G-Series Dual-Core processor, we utilize the Texas Multicore Technologies' (TMT) SequenceL™ language and runtime environment to parallelize the algorithms proposed in our previous work for constructing reliable routing graphs and real-time communication schedule. Our experiments show that AMD Embedded G-Series is an ideal platform for medium to large size WirelessHART network and SequenceL™ language can help significantly reduce the development cycle and further improve the algorithm efficiency and system performance in embedded multicore architectures.

Wireless applications in process automation started in areas where wireless sensors provide rich process information to the automation systems. Shortly after WirelessHART became the international standard, both ISA100.11a and WirelessHART claimed that their respective wireless technology could be applied to control as well. Although both standards provide provisions for supporting control over wireless, actual installations of wireless control systems have been slow to be adopted. Due to people's wariness of using wireless in combination with the serious nature of control, what is needed is a demonstration of performance and reliability of control when being executed across wireless media. A good starting point is a comparison between a wireless control system and its wired counterpart. In this paper we first study if we can establish a WirelessHART control loop the same way as a wired one; then we study if our WirelessHART control loop could achieve the same execution rates and reliability as a wired Foundation Fieldbus control loop. The experiment affirms that, in most likely cases, control over a WirelessHART network could be as good as control over a wired fieldbus.

Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., in-door environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as Wireless Hart provides an alternative. In this paper, we present the design and implementation of Roaming Hart: a collaborative localization system on Wireless Hart as an industrially viable solution. This solution is built upon several technological advances. First, Roaming Hart adds the roaming functionality to Wireless Hart and thus provides a means for keeping mobile Wireless Hart devices connected to the network. Second, Roaming Hart employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. Roaming Hart adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI pre-survey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and on line updating the signal propagation models in the anchor nodes. Our implementation of Roaming Hart can be applied to any Wireless Hart-conforming network because no modification is needed on the Wireless Hart field devices. We have implemented a complete Roaming Hart system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the Wireless Hart network while moving around, with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.

The traditional sampling method in process control systems is based on a periodic task model. This is because controllers are executed in a strictly periodic manner. Sensors sample the process data and send it periodically to the appropriate controllers through a communication system such as the field bus. Since the field bus is shared by multiple sensors, there is some delay (control loop latency)between the sampling and control actions. In order to minimize the control loop latency, a higher than necessary sampling frequency is typically adopted, which results in unnecessary waste of energy. In this paper, we propose Min Max: a sampling interval control algorithm for tackling this problem. In Min Max, sampling tasks are not periodic but have both maximum and minimum distance constraints. This sampling model has advantages that are especially important in the domain of wireless control for industrial automation. We shall then discuss the jitter property of sampling schemes under this model and propose algorithms for controlling the sampling intervals of sensors in terms of the Min Max problem (UMin Max) which we shall introduce. Though this problem is NP-hard in general, even for special case of unit-time tasks, we show how to reduce Min Max to well-studied scheduling models such as Liu and Layland-type periodic models and pinwheel models, at the expense of some loss of schedulability. These reductions allow us to derive efficient schedulability tests that can be used to solve the sampling interval control problem in practice. Simulations are used to compare the performance of different UMin Max schedulers in two key figures of merit: the acceptance ratio and the jitter ratio. Simulation of a process control system model also shows that UMin Max can reduce about 40% of the traffic load on the communication system which is especially important for energy-aware wireless process control applications.

In this paper, we report on the design and implementation of MBStar, a higher-frequency, real-time, reliable, secure protocol for wireless body area networks (WBAN). As in most proposals for body sensor networks, MBStar adopts the star topology for communication, and is designed to support a message rate as high as 400 Hz, which to the best of our knowledge, is the highest among low-power wireless communication protocols implemented at the present time. The physical layer of MBStar utilizes 802.15.4 DSSS compatible radio for which a higher-frequency, reliable, TDMA MAC layer is built. There is a simple application layer designed for security on top of it. MBStar utilizes public/private key encryption for provisioning devices and does not involve any human configuration before device join. Considering the resource limit of most embedded systems, the TDMA requirement of computing a shared global communication schedule presents a practical problem since it may not be feasible for all the devices to communicate in a long hyper-period while the communication schedule between devices is being created or modified as devices depart and rejoin. We solve this problem by keeping only the global hyper-period schedule on the gateway side, with each device being configured with a shorter, local period. Then, retransmission is employed to resolve any conflicts between the devices. Our strategy has the property that, given any fixed task set, the minimal average number of retransmissions is independent of any communication scheduling algorithm, and the EDF (Earliest Deadline First) is optimal for our communication architecture. Finally, we present experimental results that demonstrate that MBStar is an effective protocol for wireless body area networks.

Typical real-time sensing and control systems consist of a set of update tasks for installing sensor measurements from the operation environment and a set of control tasks to access to these measurements for making control decisions. Although configuring the sensors with higher sampling rates could improve the accuracy of the measurements and control quality in general, scheduling high frequent update jobs may seriously affect the schedulability of the control tasks. Missing or delaying the control tasks may severely degrade the overall control performance of the system. In this paper, instead of using the traditional periodic update model, we adopt the a periodic update model in generating update jobs for maintaining data validity. We propose an adaptive co-scheduling algorithm called Least Idle Slot First (LISF) to schedule the update tasks and control tasks with the purposes to meet the deadlines of the control tasks and maximize the quality of control (QoC) offered by the control tasks. LISF schedules the jobs in the ascending order of the number of available idle slots before their deadlines and defers the release times of update jobs as long as the corresponding data objects are maintained within the required quality. The experiment results show that LISF can effectively improve the system schedulability and the control performance in the real-time sensing and control systems.

Industrial wireless mesh networks are deployed in harsh and noisy environments for process measurement and control applications. Compared with wireless community networks, they have more stringent requirements on communication reliability and real-time performance. Missing or delaying of the process data by the network may severely degrade the overall control performance. In this paper, we abstract the primary reliability requirements in typical industrial wireless mesh networks and define three types of reliable routing graphs for different communication purposes. We present efficient algorithms to construct them and describe the recovery mechanisms in the event of component failures. Based on these graphs, data link layer communication schedules are generated to achieve end-to-end real-time performance. We demonstrate through extensive experimental results that our algorithms can achieve highly reliable routing, improved communication latency and stable real-time communication in large-scale networks at the cost of modest overhead in device configuration.

This paper presents the design and analyzes the performance of a reliable communication scheme for the traffic control system built upon a wireless process control protocol, aiming at enhancing the robustness and timeliness of the safety-critical control applications. By exploiting the slot-based predictable access and the grid topology of urban area road networks, the proposed scheme establishes one primary and secondary route from the controller to each node and allocates the time slots accordingly. This is facilitated by a split-merge operation that makes a sender node sense the primary channel(channel to the primary receiver) and take the secondary channel only if the first one is not free in a single slot, while making the receiver first listen to the primary sender and switch to the secondary sender. Our scheme also finds the path with the lowest error rate by modeling the split-merge operation as a single virtual link and applying shortest path algorithm. The experimental results show that the proposed scheme greatly enhances the transmission success ratio for the grid-style traffic control network and the improvement scales up with the network size. In addition, the routing scheme can further find the path that can improve the delivery ratio of control messages compared with the traditional grid routing scheme.

Unlike wired networks, the configuration and the behavior of wireless networks are heavily dependent on many environmental factors. While this makes it more difficult to build a wireless network testbed whose behavior is controllable, it also makes the availability of such a testbed all the more desirable. Indeed, the difficulties in building a good wireless mesh testbed belie the fact that most research work on mesh network performance is backed up only by computer simulations. In this paper, we present a practical design philosophy in which a realistic and controllable wireless mesh network testbed is built with a relatively small deployment of physical equipments by exploiting the idea of a virtual network. Specifically, we simulate a virtual network within the testbed and use one physical wireless transceiver to simulate multiple virtual devices. An observer who only reads the transmitted wireless messages will not be able to tell the difference from a real wireless mesh. Our approach has been adopted in a recent industry-standard testing suite, namely, Wi-HTest. In this paper, we shall discuss our experience in building Wi-HTest by applying the virtual network approach.

WirelessHART was released in September 2007 and is the first open wireless communication standard specifically designed for real-time process control applications. It is designed to the same standards as its wired counterpart for reliability and interoperability. To ensure the compliance with the HART Communication Protocol and the adherence to its strict timing requirements, all WirelessHART devices must be thoroughly tested and registered with the HART Communication Foundation (HCF). In this paper, we present Wi-HTest, the test suite designed to exercise WirelessHART devices, thus facilitating compliance assessment. We discuss the detailed architecture of Wi-HTest and highlight several critical features like packet handling with accurate timing control and fault data injection. We also describe a sniffer called Wi-Analys for capturing WirelessHART packets along with their timing information and a post process suite for analyzing the packets. These three tools together provide the complete compliance verification environment for WirelessHART. Based on the test specification developed by HCF, a representative test case is conducted for the purpose of demonstration. This test case in turn shows that Wi-HTest is a novel and efficient test suite for verifying the compliance of real-time WirelessHART devices.

WirelessHART is an emerging wireless communication standard that is targeted at the real-time process control industry.An example application of wireless communication in an industrial process control plant is the location of field engineers. The capability to locate personnel is a safety critical issue in process control plants because of high risks posed by toxic chemicals and other hazards. This paper presents the design, implementation and evaluation of a location-aware application built upon WirelessHART. The aim of this application is to locate a mobile device (and thus the person carrying the device) via the deployed WirelessHART network. The application is a software-based - no device modifications are required. Consequently, it is applicable to any WirelessHART network. In this application, both the mobile device (a handheld device or a badge carried by a worker) and field devices (attached to the plant process) periodically send health reports of their neighbors to the network manager. The network manager analyzes these reports and discards the untrustworthy pairs through comparison. Next, the network manager feeds the average received signal indications to a well-trained radio propagation model to derive the location. To evaluation our solution, several preliminary experiments are carried out and the results are very promising, with a median error less than 4 meters, which is good enough for the industrial requirement. To the best of our knowledge, this is the first attempt to develop location-aware application in WirelessHART networks.

Maintaining the temporal validity of real-time data is one of the crucial issues in a real-time database system. Past studies focus on designing algorithms to minimize imposed workload by a fixed set of update transactions while maintaining data freshness within validity intervals. In this paper we revisit this problem by investigating the cost of data freshness maintenance and online scheduling overhead in the presence of mode changes in real-time systems. We propose to apply periodic scheduling policies when the imposed update workload is low to maintain high data freshness. When the update workload becomes high, we propose to switch to more sophisticated algorithms to improve schedulability. In the latter case, not only each scheduling policy must be able to schedule the task set in the corresponding mode, temporal validity must also be maintained during the mode changes. To address this problem, two algorithms, named search-based switch (SBS) and adjustment-based switch (ABS) are proposed to search for the proper switch point online. SBS checks the temporal validity at the beginning time slot of each idle period while ABS further relaxes this restriction through schedule adjustment. Our experimental results demonstrate the correctness and efficiency of these two algorithms. Our results also show that scheduling switch according to the runtime processor workload can significantly outperform a single fixed scheduling policy in terms of data freshness while incurring only limited online switch overhead.

The schedulability testing for the deferrable scheduling algorithm for fixed priority transactions (DS-FP) remainsan open problem since its introduction. In this paper, wetake the first step towards investigating necessary and sufficient conditions for the DS-FP schedulability. We propose a necessary and sufficient schedulability condition for the algorithm in discrete time systems, and prove its correctness. Based on this condition, we propose a schedulability test algorithm that is more accurate than the existing test that is only based on a sufficient condition. Our algorithm exploits the fact that there is always a repeating pattern in a DS-FP schedule in discrete time systems. We demonstrate through examples that our schedulability test algorithm outperforms the existing algorithm in terms of accuracy.

Abstract — To overcome the inherent lossy property of wireless links and increase network throughput, many multi-path routing protocols have been proposed to improve the reliability and latency of packet delivery in wireless networks. Multi-path routing protocols, however, do not take advantage of existing coding opportunities to maximize network throughput. In this paper, we propose a novel coding-aware multi-path routing protocol (CAMP), which forwards packets over multiple paths dynamically based on path reliability and coding opportunity. CAMP employs a route discovery mechanism which returns to the source multiple paths along with ETX (Expected Transmission Count) of all links on each path. Using a novel forwarding mechanism, CAMP splits the traffic among multiple paths and actively creates instead of passively waiting for coding opportunity by switching its path to maximize the switching gain. Experimental results demonstrate that CAMP can achieve much higher throughput than comparable schemes for delivering packets in wireless networks. I.

Wireless technology has been regarded as a paradigm shifter in the process industry. The first open wireless communication standard specifically designed for process measurement and control applications, WirelessHART was officially released in September 2007 (as a part of the HART 7 Specification). WirelessHART is a secure and TDMA-based wireless mesh networking technology operating in the 2.4GHz ISM radio band. In this paper, we give an introduction to the architecture of WirelessHART and share our first-hand experience in building a prototype for this specification. We describe several challenges we had to tackle during the implementation, such as the design of the timer, network wide synchronization, communication security, reliable mesh networking, and the central network manager. For each challenge, we provide a detailed analysis and propose our solution. Based on the prototype implementation, a simple WirelessHART network has been built for the purpose of demonstration. The demonstration network in turn validates our design. To the best of our knowledge, this is the first reported effort to build a WirelessHART protocol stack.

WirelessHART was released in September 2007 and is the first open wireless communication standard specifically designed for process control applications. As an optional part of the HART® Communication Protocol, WirelessHART is designed to the same standards for reliability and interoperability. To ensure the compliance and adherence to the high level of interoperability defined by the HART Communication Foundation (HCF), all WirelessHART devices must be thoroughly tested and registered with the HCF. In this paper, we present Wi-HTest, the test engine designed to exercise WirelessHART devices, thus facilitating compliance assessment. We discuss the detailed architecture of Wi-HTest and highlight several critical features like virtual devices and fault data injection.

WirelessHART is the first open wireless standard for the process control industry. Previously we demonstrated a three-node prototype network based on an early release of the protocol stack. In this demonstration we build a fully operational WirelessHART sensor network of multiple nodes. We show the creation of the network and the execution of process monitoring applications on the network. This new demonstration network serves as a proof of concept for the revised WirelessHART standard and as a platform for our future research and experiments.

The periodic update transaction model has been used to maintain the freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus of past studies, such as the More-Less scheme [25], in which update transactions are guaranteed by the Deadline Monotonic scheduling algorithm [16] to complete by their deadlines. In this paper, we propose a deferrable scheduling algorithm for fixed-priority transactions, a novel approach for minimizing update workload while maintaining the temporal validity of real-time data. In contrast to prior work on maintaining data freshness periodically, update transactions follow an aperiodic task model in the deferrable scheduling algorithm. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical estimation of its processor utilization and a sufficient condition for its schedulability. Our experimental results verify the theoretical estimation of the processor utilization. We demonstrate through the experiments that the deferrable scheduling algorithm is an effective approach and it significantly outperforms the More-Less scheme in terms of reducing processor workload.

The deferrable scheduling algorithm for fixed priority transactions (DS-FP) is demonstrated to be a very effective approach for minimizing real-time update transaction workload while maintaining temporal validity of real-time data. This paper examines the schedulability of deferrable scheduling and presents a sufficient condition. While its online scheduling overhead is a concern, we propose a hyperperiod based DS-FP approach satisfying the temporal validity constraint with low overhead. The algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Adjustment (DESH-SA), adjusts the DS-FP schedule in an interval so that the adjusted schedule in the interval can be repeated infinitely. Our experiments demonstrate that DESH-SA can reduce scheduling overhead of DS-FP and also achieve update workload near optimal

An approach to improve the reliability of query results based on error-prone sensors is to use redundant sensors. However, this approach is expensive; moreover, some sensors may malfunction and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensors to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensors, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for aggregate queries. Our algorithm is demonstrated in simulation experiments to provide accurate and robust query results.

Periodic update transaction model has been used to maintain freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus in the past studies such as the more-less scheme by Xiong and Ramamrithan (2004) in which update transactions are guaranteed by the deadline monotonic scheduling algorithm by Leung and Whitehead (1982) to complete by their deadlines. In this paper, we propose a novel algorithm, namely deferrable scheduling, for minimizing imposed workload while maintaining temporal validity of real-time data. In contrast to previous work, update transactions scheduled by the deferrable scheduling algorithm follow a sporadic task model. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical analysis of its processor utilization, which is verified in our experiments. Our experimental results also demonstrate that the deferrable scheduling algorithm is a very effective approach, and it significantly outperforms the more-less scheme in terms of reducing processor workload