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.

This paper addresses the vulnerability of LoRa communications against attackers transmitting LoRa packet and proposes an effective anti-jamming technique. Mitigating jamming in a LoRa network is extremely challenging as the devices have low computation power and limited energy. The state-of-the-art work addresses this type of jamming by exploiting Received Signal Strength Indicator (RSSI). It is effective only against a single jammer and is ineffective against multiple jammers transmitting LoRa packets with coordinated timing. The variability in arrival times of jamming LoRa packets results in differing RSSI, rendering the technique impractical against multiple jammers. In this paper, we propose a new technique to handle jamming when multiple attackers transmit LoRa packet simultaneously. Our idea is to implicitly synchronize all the LoRa symbols from different packets to ensure the jammers' energy in FFT bins remain distinguishable. This method is link layer-agnostic, entails no overhead at the LoRa nodes, and enables packet decoding even when facing attacks from a single jammer or multiple jammers on a channel, effectively combating reactive jamming. We have implemented our anti-jamming system at the LoRa gateway and conducted experiments under various jamming scenarios on LoRa nodes. The results show that our anti-jamming technique improves packet reception rate and per packet energy consumption by up to 106.56 and 135.15 times under collaborative jamming.

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.

While LoRa networks typically handle very infrequent communications, they can occasionally encounter sudden data bursts in response to unpredictable events such as a forest fire or volcanic eruption. LoRaWAN, the MAC protocol for LoRa, lacks a collision avoidance mechanism due to the severe energy constraints of the nodes. Burst traffic can result in substantial collisions, retransmissions, and packet loss when dealing with burst traffic, leading to rapid depletion of node batteries, increased network latencies, and significantly degraded throughput. In this paper, we propose Burst-MAC, a low-overhead MAC protocol for LoRa networks to efficiently handle burst traffic. When the network traffic is light and infrequent, Burst-MAC operates just like LoRaWAN. Upon detecting a data burst, it switches to burst mode that functions as follows: (i) nodes with bursty data are organized into virtual groups based on channel and spreading factor, and (ii) nodes within each group transmit in a semi-distributed TDMA fashion with each node determining its transmission time slots using a hash function to eliminate the need for centralized schedule distribution. Hash function collisions are resolved through piggybacking with acknowledgements from the gateway, ensuring bounded latencies for transmitted packets. We evaluate Burst-MAC through both physical experiments on LoRa devices and NS-3 simulations under various burst scenarios. Our results demonstrate that Burst-MAC significantly outperforms LoRaWAN and other baselines in several aspects, including a $4.5 \times$ improvement in packet reception rate, up to $6 \times$ reduction in energy consumption, and up to $2.5 \times$ decrease in latency.

With technological advancement, the leakage power of multicore platforms which correlates exponentially with chip temperature becomes more than dynamic power. Energy-aware hardware solutions use dynamic voltage and frequency scaling (DVFS) to avoid overheating in aggressive performance-boosting scenarios, and software solutions assign high-utilization tasks to different core configurations to conserve processor package power. Since the current energy-aware heuristics are not based on core-by-core frequency monitoring, they do not address overheating when some cores are more active than others. Also, assigning tasks to cores without comprehensive profiling does not address irregular task execution. In this article, we investigate potential of applying an online reinforcement learning method to efficiently allocate tasks based on their profiling data, including makespan, to appropriate core combinations according to their temperature status, aiming to reduce energy consumption. We propose using a hierarchical multi-agent reinforcement learning (HMARL) approach, where one agent selects cores and frequency scaling as a function of profiler results, while another agent selects core combinations as a function of temperature sensor results.

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, the audit and diagnosis of the causal relationships between the events in a trigger-action-based event chain (e.g., why is a light turned on in a smart home?) in the Internet of Things (IoT) platforms are untrustworthy and unreliable. The current IoT platforms lack techniques for transparent and tamper-proof ordering of events due to their device-centric logging mechanism. In this article, we develop a framework that facilitates tamper-proof transparency and event order in an IoT platform by proposing a Blockchain protocol and adopting the vector clock system, both tailored for the resource-constrained heterogeneous IoT devices, respectively. To cope with the unsuited storage (e.g., ledger) and computing power (e.g., proof-of-work puzzle) requirements of the Blockchain in the commercial off-the-shelf IoT devices, we propose a partial consistent cut protocol and engineer a modular arithmetic-based lightweight proof of work puzzle, respectively. To the best of our knowledge, this is the first Blockchain designed for resource-constrained heterogeneous IoT platforms. Our event ordering protocol based on the vector clock system is also novel for the IoT platforms. We implement our framework using an IoT gateway and 30 IoT devices. We experiment with ten concurrent trigger-action-based event chains while each chain involves 20 devices, and each device participates in five different chains. The results show that our framework may order these events in 2.5 s while consuming approximately 140 mJ of energy per device. The results, hence, demonstrate the proposed platform as a practical choice for many IoT applications, such as smart home, traffic monitoring, and crime investigation.

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.

The industrial control systems (ICS) are using Real-Time Ethernet (RTE) protocols for many years. Today, Ethernet based control systems are widely used in industries. The Time Sensitive Networking (TSN) initiative will definitely push their further diffusion. With the introduction of Industry 4.0, production machines and their components have been connected to the Internet. Currently adopted RTE protocols do not require authentication, and hence may exchange data also with potentially malicious partners. In this paper, a selective Denial of Service (DoS) attack is presented. The proposed Selective Queue Saturation Attack (SQSA) is aimed to jam the message queue of the RTE communication stack in selected devices. The SQSA minimizes the chances of being detected by keeping its requirements (in term generated traffic) as low as possible. The SQSA has been applied to a real scenario based on PROFINET. The results of the use case demonstrate: the feasibility of the proposed attack; the reduced footprint compared to known DoS attacks (more than one thousand times less); and the selectivity of the attack, which can disrupt the realtime behavior of even a single target node inside the RTE network.

Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology for Internet-of-Things (IoT) due to their capability of communicating over long distances at very low transmission power. Existing LPWAN technologies, however, face limitations in meeting scalability and covering very wide areas which make their adoption challenging for future IoT applications, especially in infrastructure-limited rural areas. To address this limitation, in this paper, we consider achieving scalability and extended coverage by integrating multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed LPWAN architecture over the TV white spaces, has demonstrated its advantages over existing LPWANs in performance and energy-efficiency. In this paper, we propose to scale up LPWANs through a seamless integration of multiple SNOWs which enables concurrent inter-SNOW and intra-SNOW communications. We then formulate the tradeoff between scalability and inter-SNOW interference as a constrained optimization problem whose objective is to maximize scalability by managing white space spectrum sharing across multiple SNOWs. We also prove the NP-hardness of this problem. To this extent, We propose an intuitive polynomial-time heuristic algorithm for solving the scalability optimization problem which is highly efficient in practice. For the sake of theoretical bound, we also propose a simple polynomial-time 1/2-approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of (25x 15 )km2as well as large scale simulations demonstrate the effectiveness of our algorithms and feasibility of achieving scalability through seamless integration of SNOWs with high reliability, low latency, and energy efficiency.

Energy-efficiency is a critical requirement for computation-intensive real-time applications on multi-core embedded systems. Multi-core processors enable intra-task parallelism, and in this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware real-time scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 67 percent through our proposed approach compared to existing methods.

Wireless sensor-actuator networks enable an efficient and cost-effective approach for industrial sensing and control applications. To satisfy the real-time requirement of such applications, these networks adopt centralized scheduling algorithms to optimize the real-time performance based on global information. Existing centralized algorithms mostly focus on scheduling time-triggered flows. They cannot effectively schedule event-triggered flows due to the dynamics and unpredictability of events. In this paper, we propose three fundamental centralized algorithms that reserve as few resources as possible for event-triggered flows such that the real-time performance of time-triggered flows is not affected. We then analyze their advantages and disadvantages. Based on the analysis, we combine their advantages, including those in terms of their resource requirements, into a centralized algorithm. Finally, we conduct extensive simulations based on both real topologies and random topologies. The simulations indicate that for most test cases the schedulability of our combined algorithm is close to optimal solutions.

With the growth of computation-intensive real-time applications on multi-core embedded systems, energy-efficient real-time scheduling becomes crucial. Multi-core processors enable intra-task parallelism, and there has been much progress on exploiting that, while there has been only a little progress on energy-efficient multi-core real-time scheduling as yet. In this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware realtime scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 57% through our proposed approach compared to existing methods.

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.

Mixed-criticality (MC) scheduling of sequential tasks (with no intra-task parallelism) has been well-explored by the real-time systems community. However, till date, there has been little progress on MC scheduling of parallel tasks. MC scheduling of parallel tasks is highly challenging due to the requirement of various assurances under different criticality levels. In this work, we address the MC scheduling of parallel tasks of gang model that allows workloads to execute on multiple cores simultaneously. Such a workload model represents an efficient mode-based parallel processing scheme with many potential applications. To schedule such task sets, we propose a new technique GEDF-VD, which integrates Global Earliest Deadline First (GEDF) and Earliest Deadline First with Virtual Deadline (EDF-VD). We prove the correctness of GEDF-VD and provide a detailed quantitative evaluation in terms of speedup bound in both the MC and the non-MC cases. Specifically, we show that GEDF provides a speedup bound of 2 for non-MC gang tasks, while the speedup for GEDF-VD considering MC gang tasks is √5 + 1. Experiments on randomly generated gang task sets are conducted to validate our theoretical findings and to demonstrate the effectiveness of the proposed approach.

As the scale and density of data centers continue to grow, cost-effective data center management (DCM) is becoming a significant challenge for enterprises hosting large-scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. The limitations of today’s typical DCM network are many-fold. Primarily, it is a fixed wired network, and hence scaling it for a large number of servers increases its cost. In addition, with server densities increasing over recent years, this network also has to be cabled correctly and the management of this network parallels the complexity of managing a data network, since it needs to be networked with multiple switches and routers. In this article, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed CapNet, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. CapNet employs an efficient event-driven protocol that triggers data collection only on the detection of a potential power capping event. We deploy and evaluate CapNet in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that CapNet can meet the real-time requirements of power capping. CapNet demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

This work studies energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. First, we adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then, we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency. The effectiveness of the proposed approach is evaluated both theoretically via approximation ratio bounds and also experimentally through simulation study. Experimental results on randomly generated workloads show that our algorithms achieve an energy saving of 60% to 68% compared to existing DAG task schedulers.

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 this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers.

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.

Interference between concurrent transmissions causes severe performance degradation in a wireless network. This paper addresses interference cancellation to enable simultaneous packet receptions at a node with a single radio in Wireless Sensor Networks (WSN). Interference cancellation is particularly important for WSN as most of its applications rely on convergecast where all the traffic in the network is delivered to a base station leading to a lot of packet collisions. Existing solutions for collision recovery make simplified assumptions such as the availability of one of the collided packets, repeated collisions of the same packets, and the ability to identify the collided packets before recovering them which do not hold for WSNs and most wireless networks. In this paper, we propose a novel collision recovery method called Reverse and Replace Decoding (RnR) for WSNs. RnR entails a physical-link layer design to exploit the raw samples of the colliding signals. It does not rely on the assumptions made in existing work, and can recover all packets from a single collision. To demonstrate its feasibility, we have implemented RnR using GNU Radio on USRP devices based on IEEE 802.15.4 network. Our experiments on a 6-node testbed demonstrate that RnR can successfully decode packets in 95% cases of collisions, and improves the correctly packet decoding rate up to 97.5% compared to standard decoders in the case of collisions. Also, our simulation based on GNU Radio simulator using 25 nodes shows that RnR achieves 4x higher throughput compared to the state-of-the-art collision recovery mechanisms.

Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.

Wireless sensor networks (WSNs) face significant scalability challenges due to the proliferation of wide-area wireless monitoring and control systems that require thousands of sensors to be connected over long distances. Due to their short communication range, existing WSN technologies such as those based on IEEE 802.15.4 form many-hop mesh networks complicating the protocol design and network deployment. To address this limitation, we propose a scalable sensor network architecture - called Sensor Network Over White Spaces (SNOW) - by exploiting the TV white spaces. Many WSN applications need low data rate, low power operation, and scalability in terms of geographic areas and the number of nodes. The long communication range of white space radios significantly increases the chances of packet collision at the base station. We achieve scalability and energy efficiency by splitting channels into narrowband orthogonal subcarriers and enabling packet receptions on the subcarriers in parallel with a single radio. The physical layer of SNOW is designed through a distributed implementation of OFDM that enables distinct orthogonal signals from distributed nodes. Its MAC protocol handles subcarrier allocation among the nodes and transmission scheduling. We implement SNOW in GNU radio using USRP devices. Experiments demonstrate that it can correctly decode in less than 0.1ms multiple packets received in parallel at different subcarriers, thus drastically enhancing the scalability of WSN.

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

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

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

WirelessHART is a new standard specifically designed for real-time and reliable communication between sensor and actuator devices for industrial process monitoring and control applications. End-to-end communication delay analysis for WirelessHART networks is required to determine the schedulability of real-time data flows from sensors to actuators for the purpose of acceptance test or workload adjustment in response to network dynamics. In this paper, we consider a network model based on WirelessHART, and map the scheduling of real-time periodic data flows in the network to real-time multiprocessor scheduling. We then exploit the response time analysis for multiprocessor scheduling and propose a novel method for the delay analysis that establishes an upper bound of the end-to-end communication delay of each real-time flow in the network. Simulation studies based on both random topologies and real network topologies of a$74$-node physical wireless sensor network testbed demonstrate that our analysis provides safe and reasonably tight upper bounds of the end-to-end delays of real-time flows, and hence enables effective schedulability tests for WirelessHART networks.

This paper considers the scheduling of parallel real-time tasks with implicit deadlines. Each parallel task is characterized as a general directed acyclic graph (DAG). We analyze three different real-time scheduling strategies: two well known algorithms, namely global earliest-deadline-first and global rate-monotonic, and one new algorithm, namely federated scheduling. The federated scheduling algorithm proposed in this paper is a generalization of partitioned scheduling to parallel tasks. In this strategy, each high-utilization task (utilization ≥ 1) is assigned a set of dedicated cores and the remaining low-utilization tasks share the remaining cores. We prove capacity augmentation bounds for all three schedulers. In particular, we show that if on unit-speed cores, a task set has total utilization of at most m and the critical-path length of each task is smaller than its deadline, then federated scheduling can schedule that task set on m cores of speed 2, G-EDF can schedule it with speed 3 + v5/2 2.618, and G-RM can schedule it with speed 2 + v3 3.732. We also provide lower bounds on the speedup and show that the bounds are tight for federated scheduling and G-EDF when m is sufficiently large.

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

Data center management (DCM) is increasingly becoming a significant challenge for enterprises hosting large scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. Existing wired networking solutions for DCM come with high cost. In this paper, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed Cap Net, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. Cap Net employs an efficient event-driven protocol that triggers data collection only upon the detection of a potential power capping event. We deploy and evaluate Cap Net in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that Cap Net can meet the real-time requirements of power capping. Cap Net demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

With the advent of industrial standards such as WirelessHART, process industries are now gravitating towards wireless control systems. Due to limited bandwidth in a wireless network shared by multiple control loops, it is critical to optimize the overall control performance. In this article, we address the scheduling-control co-design problem of determining the optimal sampling rates of feedback control loops sharing a WirelessHART network. The objective is to minimize the overall control cost while ensuring that all data flows meet their end-to-end deadlines. The resulting constrained optimization based on existing delay bounds for WirelessHART networks is challenging since it is nondifferentiable, nonlinear, and not in closed-form. We propose four methods to solve this problem. First, we present a subgradient method for rate selection. Second, we propose a greedy heuristic that usually achieves low control cost while significantly reducing the execution time. Third, we propose a global constrained optimization algorithm using a simulated annealing (SA) based penalty method. We study SA method under both constant factor penalty and adaptive penalty. Finally, we formulate rate selection as a differentiable convex optimization problem that provides a quick solution through a convex optimization technique. This is based on a new delay bound that is convex and differentiable, and hence simplifies the optimization problem. We study both the gradient descent method and the interior point method to solve it. We evaluate all methods through simulations based on topologies of a 74-node wireless sensor network testbed. The subgradient method is disposed to incur the longest execution time as well as the highest control cost among all methods. Among the SA-based constant penalty method, the greedy heuristic, and the gradient descent method, the first two represent the opposite ends of the tradeoff between control cost and execution time, while the third one hits the balance between the two. We further observe that the SA based adaptive penalty method is superior to the constant penalty method, and that the interior point method is superior to the gradient method. Thus, the interior point method and the SA-based adaptive penalty method are the two most effective approaches for rate selection. While both methods are competitive against each other in terms of control cost, the interior point method is significantly faster than the penalty method. As a result, the interior point method upon convex relaxation is more suitable for online rate adaptation than the SA based adaptive penalty method due to their significant difference in run-time efficiency.

Recently, multi-core processors have become mainstream in processor design. To take full advantage of multi-core processing, computation-intensive real-time systems must exploit intra-task parallelism. In this paper, we address the problem of real-time scheduling for a general model of deterministic parallel tasks, where each task is represented as a directed acyclic graph (DAG) with nodes having arbitrary execution requirements. We prove processor-speed augmentation bounds for both preemptive and non-preemptive real-time scheduling for general DAG tasks on multi-core processors. We first decompose each DAG into sequential tasks with their own release times and deadlines. Then we prove that these decomposed tasks can be scheduled using preemptive global EDF with a resource augmentation bound of$4$. This bound is as good as the best known bound for more restrictive models, and is the first for a general DAG model. We also prove that the decomposition has a resource augmentation bound of$4$plus a constant non-preemption overhead for non-preemptive global EDF scheduling. To our knowledge, this is the first resource augmentation bound for non-preemptive scheduling of parallel tasks. Finally, we evaluate our analytical results through simulations that demonstrate that the derived resource augmentation bounds are safe in practice.

Interference between concurrent transmissions can cause severe performance degradation in wireless sensor networks (WSNs). While multiple channels available in WSN technology such as IEEE 802.15.4 can be exploited to mitigate interference, channel allocation can have a significant impact on the performance of multi-channel communication. This paper proposes a set of distributed protocols for channel allocation in WSNs with theoretical bounds. We first consider the problem of minimizing the number of channels needed to remove interference in a WSN, and propose both receiver-based and link-based distributed channel allocation protocols. Then, for WSNs with an insufficient number of channels, we formulate a fair channel allocation problem whose objective is to minimize the maximum interference (MinMax) experienced by any transmission link in the network. We prove that MinMax channel allocation is NP-hard, and propose a distributed link-based MinMax channel allocation protocol. Finally, we propose a distributed protocol for link scheduling based on MinMax channel allocation that creates a conflict-free schedule for transmissions. The proposed decentralized protocols are efficient, scalable, and adaptive to channel condition and network dynamics. Simulations based on the topologies and data traces collected from a WSN testbed of 74 TelosB motes have shown that our channel allocation protocols significantly outperform a state-of-the-art channel allocation protocol.

With the advent of industrial standards such as Wireless Hart, process industries are now gravitating towards wireless control systems. Due to limited bandwidth in a wireless network shared by multiple control loops, it is critical to optimize the overall control performance. In this paper, we address the scheduling-control co-design problem of determining the optimal sampling rates of feedback control loops sharing a Wireless Hart network. The objective is to minimize the overall control cost while ensuring that all data flows meet their end-to-end deadlines. The resulting constrained optimization based on existing delay bounds for Wireless Hart networks is challenging since it is non-differentiable, non-linear, and not in closed-form. We propose four methods to solve this problem. First, we present a sub gradient method for rate selection. Second, we propose a greedy heuristic that usually achieves low control cost while significantly reducing the execution time. Third, we propose a global constrained optimization algorithm using a simulated annealing (SA) based penalty method. Finally, we formulate rate selection as a differentiable convex optimization problem that provides a closed-form solution through a gradient descent method. This is based on a new delay bound that is convex and differentiable, and hence simplifies the optimization problem. We evaluate all methods through simulations based on topologies of a 74-node wireless sensor network testbed. Surprisingly, the sub gradient method is disposed to incur the longest execution time as well as the highest control cost among all methods. SA and the greedy heuristic represent the opposite ends of the trade off between control cost and execution time, while the gradient descent method hits the balance between the two.

WirelessHART is a new wireless sensor-actuator network standard specifically developed for process industries. A key challenge faced by WirelessHART networks is to meet the stringent real-time communication requirements imposed by process monitoring and control applications. Fixed-priority scheduling, a popular scheduling policy for real-time networks, has recently been shown to be an effective real-time transmission scheduling policy in WirelessHART networks. Priority assignment has a major impact on the schedulability of real-time flows in these networks. This paper investigates the open problem of priority assignment for periodic real-time flows in a WirelessHART network. We first propose an optimal priority assignment algorithm based on local search for any given worst case delay analysis. We then propose an efficient heuristic search algorithm for priority assignment. We also identify special cases where the heuristic search is optimal. Simulations based on random networks and the real topology of a physical sensor network test bed showed that the heuristic search algorithm achieved near optimal performance in terms of schedulability, while significantly outperforming traditional priority assignment policies for real-time systems.

The WirelessHART standard has been specifically designed for real-time communication between sensor and actuator devices for industrial process monitoring and control. End-to-end communication delay analysis for WirelessHART networks is required for acceptance test of real-time data flows from sensors to actuators and for workload adjustment in response to network dynamics. In this paper, we map the scheduling of real-time periodic data flows in a WirelessHART network to real-time multiprocessor scheduling. We, then, exploit the response time analysis for multiprocessor scheduling and propose a novel method for the end-to-end delay analysis of the real-time flows that are scheduled using a fixed priority scheduling policy in a WirelessHART network. Simulations based on both random topologies and real network topologies of a physical testbed demonstrate the efficacy of our end-to-end delay analysis in terms of acceptance ratio under various fixed priority scheduling policies.

Multi-core processors offer a significant performance increase over single core processors. Therefore, they have the potential to enable computation-intensive real-time applications with stringent timing constraints that cannot be met on traditional single-core processors. However, most results in traditional multiprocessor real-time scheduling are limited to sequential programming models and ignore intra-task parallelism. In this paper, we address the problem of scheduling periodic parallel tasks with implicit deadlines on multi-core processors. We first consider a synchronous task model where each task consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. We propose a new task decomposition method that decomposes each parallel task into a set of sequential tasks. We prove that our task decomposition achieves a resource augmentation bound of 2.62 and 3.42 when the decomposed tasks are scheduled using global EDF and partitioned deadline monotonic scheduling, respectively. Finally, we extend our analysis to directed a cyclic graph tasks. We show how these tasks can be converted into synchronous tasks such that the same transformation can be applied and the same augmentation bounds hold.

Recent years have witnessed the emergence of shared sensor networks as integrated infrastructure for multiple applications. It is important to allocate multiple applications in a shared sensor network, in order to maximize the overall Quality of Monitoring (QoM) subject to resource constraints (e.g., in terms of memory and network bandwidth). The resulting constrained optimization problem is a difficult and open problem since it is discrete, nonlinear, and not in closed-form. This paper makes several important contributions towards optimal multi-application allocation in shared sensor networks. (1) We formulate the optimal application allocation problem for a common class of distributed sensing applications whose QoM can be modeled as variance reduction functions. (2) We prove key theoretical properties of the optimization problem, including the monotonicity and submodularity of the variance reduction functions and the multiple knapsack structure of constraints; (3) By exploiting these properties, we propose a local search algorithm, which is efficient and has a good approximation bound, for application allocation in shared sensor networks. Simulations based on both real-world datasets and randomly generated networks demonstrate that our algorithm is competitive against simulated annealing in term of QoM, with up to three orders of magnitude reduction in execution times, making it a practical solution towards multi-application allocation in shared sensor networks.

Wireless sensor networks are evolving from dedicated application-specific platforms to integrated infrastructure shared by multiple applications. Shared sensor networks offer inherent advantages in terms of flexibility and cost since they allow dynamic resource sharing and allocation among multiple applications. Such shared systems face the critical need for allocation of nodes to contending applications to enhance the overall Quality of Monitoring (QoM) under resource constraints. To address this need, this paper presents Utility-based Multi-application Allocation and Deployment Environment (UMADE), an integrated application deployment system for shared sensor networks. In sharp contrast to traditional approaches that allocate applications based on cyber metrics (e.g., computing resource utilization), UMADE adopts a cyber-physical system approach that dynamically allocates nodes to applications based on their QoM of the physical phenomena. The key novelty of UMADE is that it is designed to deal with the inter-node QoM dependencies typical in cyber-physical applications. Furthermore, UMADE provides an integrated system solution that supports the end-to-end process of (1) QoM specification for applications, (2) QoM-aware application allocation, (3) application deployment over multi-hop wireless networks, and (4) adaptive reallocation of applications in response to network dynamics. UMADE has been implemented on TinyOS and Agilla virtual machine for Telos motes. The feasibility and efficacy of UMADE have been demonstrated on a 28-node wireless sensor network testbed in the context of building automation applications.

WirelessHART is an open wireless sensor-actuator network standard for industrial process monitoring and control that requires real-time data communication between sensor and actuator devices. Salient features of a WirelessHART network include a centralized network management architecture, multi-channel TDMA transmission, redundant routes, and avoidance of spatial reuse of channels for enhanced reliability and real-time performance. This paper makes several key contributions to real-time transmission scheduling in WirelessHART networks: (1) formulation of the end-to-end real-time transmission scheduling problem based on the characteristics of WirelessHART, (2) proof of NP-hardness of the problem, (3) an optimal branch-and-bound scheduling algorithm based on a necessary condition for schedulability, and (4) an efficient and practical heuristic-based scheduling algorithm called Conflict-aware Least Laxity First (C-LLF). Extensive simulations based on both random topologies and real network topologies of a physical testbed demonstrate that C-LLF is highly effective in meeting end-to-end deadlines in WirelessHART networks, and significantly outperforms common real-time scheduling policies.