LR-FHSS extends LoRa uplink coverage to tens of kilometers, but a fundamental asymmetry persists in practice: LR-FHSS nodes are transmit only, and although they contain LoRa receivers, LoRa downlinks fail far short of LR-FHSS ranges. Our outdoor experiments show that LoRa packet reception rate (PRR) collapses beyond 1–2 km while LR-FHSS maintains more than 79% PRR, making ACK, downlink control, and LR-FHSS to LoRa device communication infeasible at long range. Existing CTC techniques cannot address this gap because LoRa requires precise linear chirps, whereas LR-FHSS emits discrete frequency hopped GMSK bursts with mandatory hop gaps and continuous phase memory. We introduce a CTC technique that repurposes the LR-FHSS physical layer into a waveform synthesizer capable of generating LoRa compatible pseudo chirps using either pure tone emission or continuous phase tones derived from GMSK. At the LoRa receiver, a set of physical layer reconstruction mechanisms correct hop discontinuity, offset drift, and GMSK induced phase distortion so that commodity LoRa hardware can decode the resulting chirps. Implemented on USRP B200 with GNU Radio and evaluated across different outdoor scenarios, our technique improves long range reliability by up to 79% over LoRa and increases end to end LR-FHSS PRR by about 15%, enabling a practical long-range downlink and control path without RF front-end changes, assuming PHY-hook access at both the LR-FHSS gateway and the LoRa receiver.

Of late, there has been an increasing popularity in the adoption of Low-Power Wide-Area Network (LPWAN) technologies for industrial control applications. LoRaWAN, a leading LPWAN technology, offers machine-to-machine communication capabilities making it suitable for managing large-area applications (e.g., oil fields over hundreds of km \({}^{2}\) ) or process plants that are often positioned far from the central operations center, at inconvenient or hazardous locations in difficult terrain or offshore. While recent works have studied real-time communication over LoRaWAN, they have not considered optimizing control performance. To optimize control performance, industrial automation needs a co-design of real-time scheduling and control. Such a co-design, in general, is highly challenging due to complex dependencies between control performance, plant dynamics, and real-time communication. Existing co-design approaches for other wireless domains are not applicable to LoRaWAN network. LoRa nodes are extremely power-constrained hindering frequent communication and scale. In this article, we propose a highly energy-efficient and scalable framework for real-time scheduling and control co-design for a LoRaWAN network. By taking into account LoRaWAN characteristics, the co-design approach entails state-aware communication and control to dynamically update the sampling rates while meeting real-time constraints. To minimize communication and synchronization overhead, the co-design is decomposed through a partitioned scheduling. We consider co-design in each partition of the control loops by developing a new schedulability condition. The proposed scheduling-control co-design solution dynamically determines the sampling rates of the sensors to optimize control performance. Simulations based on NS-3 and a custom control script show that our co-design approach minimizes control cost at least by 80% compared to the baselines.

For hard-real time systems, cache memory increases execution time variability, increasing the complexity of timing analysis. As such, cache memory is often treated exclusively as a detractor to schedulability. Cache-aware co-located scheduling aims at improving schedulability by carefully scheduling threads to share cached values. Cache sharing between threads potentially reduces task execution times and increases schedulability with fewer resources. Antithetically, co-located scheduling may reduce parallelism, decreasing efficiency. Thus, identifying the optimal set of threads to co-locate that minimizes the resources required while ensuring timing constraints is a complex challenge. This work establishes optimal co-location as NP-Hard in the strong sense. It offers an approximation method for the co-located scheduling of Fork-Join tasks named 3-parm-hd . The approximation has a 3-factor guarantee and a resource augmentation bound of 3. The simulated evaluation shows 3-parm-hd increases schedulability compared to an optimal intractable algorithm (without co-location) scheduling 28% more tasks with 30% fewer cores. Simulated results show 3-parm-hd outperforms a 2-factor approximation for traditional makespan, scheduling 45 % more tasks with 41% fewer cores. An experimental RISC-V evaluation running on a QEMU platform confirms the benefits of 3-parm-hd , scheduling and executing tasks deemed unschedulable by a 2-factor makespan approximation without co-location.

Safety-critical systems depend on the temporal guarantees provided by schedulability analysis of hard real-time systems. Worst-case execution time analysis (WCET) is a necessary component in schedulability analysis of hard real-time systems. A central goal of WCET analysis is to produce a tight bound, since tighter bounds (generally) increase the schedulability of a system. Cache memory is an impediment to tight WCET analysis due to the variability it introduces into task systems. However, static modification of memory access patterns within mutable objects may increase cache-hits and reduce WCET. Herein, a mechanism for modifying and analyzing hard real-time tasks is proposed. The proposed mechanism leverages existing persistence analysis to identify sets of blocks to retain in cache during execution. Retention guarantees persistence, resulting in tighter WCET analysis.

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL- a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Energy harvesting sources, such as solar, wind, or vibration, combined with rechargeable batteries, are a promising way to power Low-Power Wide-Area Network (LPWAN) devices to reduce the cost and frequency of redeploying single-use batteries. However, being oblivious to the usage of rechargeable batteries can severely reduce their capacity to store energy, which is also known as the battery lifespan. Existing energy-aware protocols mostly focus on network lifetime and pay little attention to maximizing the battery lifespan of network nodes while the latter can directly help reduce battery waste and enhance environmental sustainability. In this paper, we propose the first Media Access Control (MAC) protocol to maximize the minimum battery lifespan among all nodes in an LPWAN based on LoRa. Our approach differs from traditional objectives focusing on min-imizing energy consumption or maximizing network lifetime as they may not necessarily maximize battery lifespan. The proposed MAC protocol leverages the concept of software-defined batteries to regulate the energy stored and consumed by each node's battery based on estimated energy requirements, green energy generation, and changes in data utility. To limit the degradation of battery capacity due to continuous charging/discharging, the underpinning idea is to determine an appropriate time for each transmission considering its impact on battery degradation while also minimizing the impact on data utility. Furthermore, the energy stored in each battery is limited to reduce calendar aging, the natural degradation of battery capacity over time. The proposed protocol is local, online, and asynchronous, and incurs low overhead. We evaluate our approach through experiments on a LoRa network and large-scale simulations in NS-3. The experiments show that the proposed MAC protocol improves battery lifespan by up to 69.7% and data utility by up to 39% in a current LoRa network while incurring a CPU utilization overhead of only 12 % at each LoRa node.

Industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45 mi × 12 mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in such extremely large-area IoT applications, especially in rural or remote areas with no/limited infrastructure, requiring an in-band integration of multiple LPWANs. We consider a seamless integration of multiple SNOW LPWANs. SNOW (Sensor Network Over White spaces) is an LPWAN architecture over the TV white spaces, avoiding overcrowding problems in the limited ISM band and the cost of licensed band and infrastructure. It offers high scalability through concurrent and bi-directional communication between a base station and numerous nodes. Existing integration of multiple SNOW LPWANs does not consider minimizing network latency and is less suitable for delay-sensitive or real-time applications. In this work, we propose thefirst latency-minimizing scalable in-band integration of multiple SNOWs. Considering the impact of bandwidth on latency and base station power dissipation, low-latency integration of multiple SNOWs as a constrained spectrum allocation problem is formulated. A novel greedy latency- and traffic- aware spectrum allocation to allocate each link's bandwidth is proposed, achieving an integrated network. To enable low-latency integration, we propose two medium access control protocols for multiple SNOWs, RI-TDMA and TDMA, and estimate their latency. We have implemented the proposed integration both on SNOW hardware and in NS-3 simulator. The physical experiments show up to 44% reduction in the maximum network latency under our approach compared to existing approach. The simulation results show at least 62.3% reduction of maximum network latency by the proposed approach with RI-TDMA and 86.7% with TDMA.

For hard-real time systems, cache memory increases execution time variability, increasing the complexity of timing analysis. As such, cache memory is often treated exclusively as a detractor to schedulability. Cache-aware co-located scheduling aims to improve schedulability by carefully scheduling threads to share cached values. Cache sharing between threads potentially reduces task execution times and increases schedulability with fewer resources. Antithetically, co-located scheduling may reduce parallelism, decreasing efficiency. Thus, identifying the optimal set of threads to co-locate that minimizes the resources required while ensuring timing constraints is a complex challenge. This work establishes optimal co-location as NP-Hard in the strong sense. It offers an approximation method for the co-located scheduling of Fork-Join tasks named 3-PARM-HD. The approximation has a 3-factor guarantee and a resource augmentation bound of 3. The simulated evaluation shows 3-PARM-HD increases schedulability compared to an optimal intractable algorithm (without co-location) scheduling 28% more tasks with 30% fewer cores. Simulated results show 3-PARM-HD outperforms a 2-factor approximation for traditional makespan, scheduling 39% more tasks with 44% fewer cores. An experimental RISC-V evaluation running on a QEMU platform confirms the benefits of 3-PARM-HD, scheduling and executing tasks deemed unschedulable by a 2-factor makespan approximation without co-location.

Today, industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45mi×12mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in such extremely large-area IoT applications, specially in rural or remote areas with no/limited infrastructure, requiring an in-band integration of multiple LPWANs. To avoid the crowd in the limited ISM band and the cost of licensed band and infrastructure, SNOW (Sensor Network Over White spaces) is an LPWAN architecture over the TV white spaces. It offers high scalability through concurrent and bi-directional communication between a base station and numerous nodes. We consider a seamless integration of multiple SNOWs. Existing approach does not consider minimizing network latency and is less suitable for delay-sensitive or real-time applications. We propose the first scalable in-band integration of multiple SNOWs that minimizes network latency. By taking into account the impact of bandwidth on latency and base station power dissipation, we formulate lowlatency integration of multiple SNOWs as a constrained spectrum allocation problem. It is solved through a greedy algorithm by analyzing network latency and by adopting a latency- and traffic- aware bandwidth allocation along the links to achieve an integrated network. We have implemented the proposed integration both on SNOW hardware and in NS-3 simulator. Both physical experiments and simulations show a significant reduction (44% and 97%, resp.) in network latency under our approach compared to existing approach.

The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability.

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.

Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach.

Prolonging the network lifetime is a major consideration in many Internet of Things applications. In this paper, we study maximizing the network lifetime of an energy-harvesting LoRa network. Such a network is characterized by heterogeneous recharging capabilities across the nodes that is not taken into account in existing work. We propose a link-layer protocol to achieve a long-lived LoRa network which dynamically enables the nodes with depleting batteries to exploit the superfluous energy of the neighboring nodes with affluent batteries by letting a depleting node offload its packets to an affluent node. By exploiting the LoRa's capability of adjusting multiple transmission parameters, we enable low-cost offloading by depleting nodes instead of high-cost direct forwarding. Such offloading requires synchronization of wake-up times as well as transmission parameters between the two nodes which also need to be selected dynamically. The proposed protocol addresses these challenges and prolongs the lifetime of a LoRa network through three novel techniques. (1) We propose a lightweight medium access control protocol for peer-to-peer communication to enable packet offloading which circumvents the synchronization overhead between the two nodes. (2) We propose an intuitive heuristic method for effective parameter selections for different modes (conventional vs. offloading). (3) We analyze the energy overhead of offloading and, based on it, the protocol dynamically selects affluent and depleting nodes while ensuring that an affluent node is not overwhelmed by the depleting ones. Simulations in NS-3 as well as real experiments show that our protocol can increase the network lifetime up to 4 times while maintaining the same throughput compared to traditional LoRa network.

Multiprocessor scheduling of hard real-time tasks modeled by directed acyclic graphs (DAGs) exploits the inherent parallelism presented by the model. For DAG tasks, a node represents a request to execute an object on one of the available processors. In one DAG task, there may be multiple execution requests for one object, each represented by a distinct node. These distinct execution requests offer an opportunity to reduce their combined cache overhead through coordinated scheduling of objects as threads within a parallel task. The goal of this work is to realize this opportunity by incorporating the cache-aware BUNDLE-scheduling algorithm into federated scheduling of sporadic DAG task sets.This is the first work to incorporate instruction cache sharing into federated scheduling. The result is a modification of the DAG model named the DAG with objects and threads (DAG-OT). Under the DAG-OT model, descriptions of nodes explicitly include their underlying executable object and number of threads. When possible, nodes assigned the same executable object are collapsed into a single node; joining their threads when BUNDLE-scheduled. Compared to the DAG model, the DAG-OT model with cache-aware scheduling reduces the number of cores allocated to individual tasks by approximately 20 percent in the synthetic evaluation and up to 50 percent on a novel parallel computing platform implementation. By reducing the number of allocated cores, the DAG-OT model is able to schedule a subset of previously infeasible task sets.

Industry 4.0 is a new industry trend which relies on data driven business model to set the productivity requirements of the cyber physical system. To meet this requirement, Industry 4.0 cyber physical systems need to be highly scalable, adaptive, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such cyber physical systems. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach, and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART - a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, and thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability.

WirelessHART networks provide the feasibility of achieving real-time performance over wireless through multichannel and graph routing for process monitoring and control applications. However, real-time scheduling theory for Wireless Sensor-Actuator Network (WSAN) is still not well-developed. Besides, the performance of a WSAN induces a complicated problem involving many interrelated objectives and variables, requiring a scheduling-control codesign. This work aims at addressing these challenges. Specifically, we will develop a realtime schedulability analysis for WSAN, and leverage this result to address multiple key challenging problems in wireless Cyber-Physical Systems in the future. Schedulability analysis remains the cornerstone in any real-time system. In WSAN, it is used to determine whether a set of real-time control loops/flows can meet deadlines. It is also used in various scheduling-control codesign, routing, and priority assignment. In this work, we will develop an analysis based on utilization bound. Because of its extremely low runtime overhead, utilization based analysis has been extensively studied in CPU scheduling. However, no work has been done yet on utilization based analysis for multi-hop wireless network. The key challenge arises from transmission conflict and dynamics in wireless. We will address this by characterizing transmission conflict as task blocking in nonpreemptive CPU scheduling, and then by adopting a hierarchical network structure where we will apply the analysis in each subnetwork.

Wireless Sensor Networks (WSNs) are popular for their usage in various application like environmental monitoring because of their small size and ease of deployment. However, there is a considerable cost of owning and maintaining involved which might impede small to medium scale industries from availing their services. Even for large-scale industries, WSNs deployed and maintained with so much financial investment, when remains under utilized affects their profitability. This challenge with WSNs needs to be addressed in such a manner that small and medium scale industries could use a hassle free on-demand provisioned and de-provisioned service by paying usage fee only. On the other hand, this should result in a better utilization of the installed capacity of WSNs, pruning down their under-utilization to the barest minimum. In order to address this challenge such that everybody benefits by using the services offered by WSNs and as well as building new services, we have proposed Sensor Cloud infrastructure. The proposed Sensor Cloud infrastructure is a cloud of heterogeneous WSNs, where owners of WSNs collaborate to offer sensing-as-a-service. In this paper, we discuss the implementation of our proposed Sensor Cloud infrastructure by building middleware, client centric-layer which creates virtual sensors on top of physical WSNs, and present the results of its usage and operation.

The growing number of Additive Manufacturing Web (AMW) services, offered by different providers over the Internet, makes it challenging for consumers to compare these AMW services to select a service of their choice. In addition, it is even more challenging for consumers to compare these AMW services against their personal preferences. This is because, consumers personal preferences on multiple non-functional attributes such as price, material, accuracy and schedule, should be considered for AMW service selection. The decentralized nature of AMW services coupled by the need to consider consumers personal preferences during AMW service selection, requires a system that will serve as a broker between AMW services and consumers. In this paper, we propose a service broker system for AMW services that provides consumers with a single point of access to a large number of AMW services from many additive manufacturing service providers. This broker system also incorporates the first real application of service selection with fuzzy logic based personalized preferences and trade-off. We develop a method to generate fuzzy membership functions for each non-functional attribute. This makes it easy for consumers to specify their fuzzy membership functions. Finally, we present an application case study to demonstrate the feasibility of brokerage in AMW services and also evaluate our method in terms of performance.