Recent attacks on federated learning (FL) can introduce malicious model updates that can circumvent widely adopted Euclidean distance-based detection methods. This article proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in FL. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual explanation. The autoencoder processes the LayerCAM heat maps from the local model updates, improving their distinctiveness and increasing the accuracy in spotting anomalous maps and malicious local models. To mitigate the risk of misclassifications in LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several communication rounds. Extensive tests on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with the state-of-the-art ResNet-50 and REGNETY-800MF defense models. The experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and the test accuracy of FL, surpassing both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE .

Accurately recognizing and assessing pain in sheep is key to discern animal health and mitigating harmful situations. However, such accuracy is limited by the ability to manage automatic monitoring of pain in those animals. Facial expression scoring is a widely used and useful method to evaluate pain in both humans and other living beings. Researchers also analyzed the facial expressions of sheep to assess their health state and concluded that facial landmark detection and pain level prediction are essential. For this purpose, we propose a novel weighted graph neural network (WGNN) model to link sheep’s detected facial landmarks and define pain levels. Furthermore, we propose a new sheep facial landmarks dataset that adheres to the parameters of the Sheep Facial Expression Scale (SPFES). Currently, there is no comprehensive performance benchmark that specifically evaluates the use of graph neural networks (GNNs) on sheep facial landmark data to detect and measure pain levels. The YOLOv8n detector architecture achieves a mean average precision ($\mathbf{m A P}$) of $\mathbf{5 9. 3 0 \%}$ with the sheep facial landmarks dataset, among seven other detection models. The WGNN framework has an accuracy of $92.71 \%$ for tracking multiple facial parts expressions with the YOLOv8n lightweight on-board device deployment-capable model.

Recent poisoning attacks on federated learning (FL) generate malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This article proposes a new defense mechanism, namely, GradCAM-AE, against model poisoning attacks on FL, which integrates Gradient-weighted Class Activation Mapping (GradCAM) and autoencoder (AE) to offer a substantially more powerful detection capability compared to existing Euclidean distance-based approaches. Particularly, GradCAM-AE generates a heat map for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. An AE further reprojects the GradCAM heat maps of all local module updates with improved distinguishability, thereby accentuating the hidden features of the heat maps and increasing the success rate of identifying anomalous heat maps and malicious local models. A comprehensive evaluation of the proposed GradCAM-AE framework is conducted using the CIFAR-10 and GTSRB datasets under both Independent and Identically Distributed (IID) and Non-IID settings. The ResNet-18 and MobileNetV3-Large models are tested. The results substantiate that GradCAM-AE offers superior detection rates and test accuracy of FL global model, juxtaposed with contemporary state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/GradCAM-AE .

Advancements in the energy efficiency and computational power of embedded devices allow developers to equip resource-constrained systems with a greater number of features and more complex behavior. As complexity of a system grows, so does the difficulty in demonstrating its overall correctness. Formal methods have been successfully applied in a variety of verification and validation scenarios, but their wide adoption in the industry and academia is still lackluster. Among the explanations listed in the literature for the low adoption of these techniques are the perceived difficulty of getting into formal practices and how formal tools are not usually aimed at practical use cases. Striving to address these issues, we present MARS, an open-source domain-specific language for the safe instrumentation of runtime verification monitors into real-time resourceconstrained distributed systems. Our main objective with MARS is to ease the integration of runtime verification monitors in distributed applications while also providing developers with evidence of their correct instrumentation in the context of systems where dependability and temporal requirements need to be respected even under extreme resource constraints. We present the language syntax, the set of tools embedded into its compiler, its functionalities, and a use case to exemplify its use in a practical distributed application.

Physical layer security is of paramount importance in vehicular cyber-physical systems, as it safeguards not only the privacy of sensitive data exchanged between vehicles and infrastructure but also ensures the integrity and reliability of the entire transportation network. Key generation plays a crucial role in establishing secure communication channels and facilitating the creation of unique cryptographic keys used for encryption, decryption, and authentication purposes. The secret key generation involves deriving secret bits by harnessing the inherent randomness present within the communication channels. The difficulty lies in precisely evaluating the randomness of the channel to achieve unanimous agreement on secure key generation within an unpredictable environment. In this line, we propose a combinatorial approach involving A2C and LSTM to decrease the key disagreement rate. A2C employs policy and value-based strategies to choose quantization levels predicated on the randomness of wireless channels and LSTM includes the partially observable radio channels and improves the environment. Based on our performance evaluation, the proposed A2C-LSTM method substantially accelerates the convergence rate by $\mathbf{5 0 - 6 0 \%}$ and reduces the Key Disagreement Rate (KDR) by $40 \%$.

This paper proposes a new visual explanation-based defense mechanism, namely, FedCAMAE, against model poisoning attacks on federated learning (FL), which integrates Layer Class Activation Mapping (LayerCAM) and autoencoder to offer a scientifically more powerful detection capability compared to existing Euclidean distance-based or machine learning-based approaches. Specially, FedCAMAE generates a fine-grained heat map assisted by Layer-CAM for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. To accentuate the hidden features of the heat maps, autoencoder is seamlessly embedded into the proposed FedCAMAE, which can refine the the heat maps and enhance their distinguishability, thereby increasing the success rate of identifying anomalous heat maps and malicious local models. We test ResNet-50 and REGNETY-800MF deep learning models with SVHN and CIFAR-100 datasets under Non-Independent and Identically Distributed (Non-IID) setting, respectively. The results demonstrate that Fed-CAMAE offers superior test accuracy of FL global model compared to the state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

In multiprocessor-based real-time systems, main memory is identified as a major bottleneck in the worst-case timing analysis of tasks. Phased execution models such as the 3-phase task model, i.e., that divides the execution of tasks into distinct computation and memory phases, have shown to be a good candidate to tackle the memory contention problem. The 3-phase execution model in particular has gained much attention from both academia and industry as it limits when tasks can access main memory to pre-defined phases. Information on when those phases may happen and their length can then be leveraged to build a fine-grained memory contention analysis. However, the existing work that focus on the memory contention analysis for 3-phase tasks may overestimate the memory contention caused by interfering write requests. This yields pessimistic bounds on the total memory contention suffered by tasks which in turn leads to pessimistic worst-case execution time (WCET) and worst-case response time (WCRT) bounds. In this work, we improve the state-of-the-art memory contention analysis for 3-phase tasks by (i) tightly bounding the memory contention that can be suffered due to write requests; and (ii) providing a new memory contention-aware WCET analysis.

Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red–green–blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.

The exploitation of radio channels’ inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and multipath propagation, vary in a complex manner that makes it difficult for external adversaries to predict or replicate. A challenge lies in accurately assessing the channel’s randomness to ensure the generated keys are both secure and consistent across the platooning vehicles, especially in vehicular environments with high mobility and the ever-changing urban landscape. This paper proposes a novel channel-based key generation (DRL-KeyAgree) technique to enhance communication security within vehicular platoons through combinatorial deep reinforcement learning (DRL). DRL-KeyAgree addresses key disagreement among platooning vehicles by training advantage Actor-Critic (A2C), which integrates policy- and value-based strategies to dynamically select optimal quantization intervals adapting to the random wireless channels. Further incorporation of Long Short-Term Memory (LSTM) allows DRL-KeyAgree to capture the characteristics of partially observable radio channels, significantly enhancing the key agreement rate among vehicles. DRL-KeyAgree is rigorously evaluated using the standard National Institute of Standards and Technology (NIST) test suite.

Unmanned Aerial Vehicle (UAV) swarm can be employed to collect time-sensitive data of ground sensors in remote and hostile areas. Inadequate design of UAVs' trajectories and data collection schedule incur delay and negatively impact the information freshness of ground sensors. This paper aims to jointly optimize the trajectories and data collection schedules of multiple UAVs to minimize the average Age of Information (AoI), adapting to the AoI of the ground sensors, and the trajectories of the UAVs. The optimization is formulated as a multi-agent Markov decision process (MMDP), where network states consist of AoI at the ground sensors and the flight trajectories. In practice, a multi-UAV-assisted sensor network contains a large number of network states and actions in MMDP. Exploring the actions of multiple agents in a large state space results in considerable training uncertainties that destabilize the AoI minimization. For stabilizing the formulated MMDP, we propose an onboard Proximal Policy Optimization-based flight resource allocation scheme (PPO-FRAS), which conducts an on-policy learning to optimize the trajectories of the UAVs and data collection schedule of the ground sensors. Numerical results show that the proposed PPO-FRAS achieves 28% and 59% lower AoI than the existing trajectory planning solution based on Deep Q-Network and the greedy algorithm, respectively.

The 3-phase task execution model has shown to be a good candidate to tackle the memory bus contention problem. It divides the execution of tasks into computation and memory phases that enable a fine-grained memory bus contention analysis. However, existing works that focus on the bus contention analysis for 3-phase tasks, neglect the fact that memory bus contention strongly relates to the number of bus/memory requests generated by tasks, which, in turn, depends on the content of the cache memories during the execution of those tasks. These existing works assume that the worst-case number of bus/memory requests will be generated during all the memory phases of all tasks, irrespective of the already existing content in the cache memory. This overestimates the memory bus contention of tasks, leading to pessimistic worst-case response time (WCRT) bounds. This work proposes a holistic approach towards bus contention analysis for 3-phase tasks by (1) deriving an upper bound on the actual cache misses of tasks that lead to bus/memory requests; (2) improving State-of-the-Art (SOTA) bus contention analysis of two bus arbitration schemes that dominate all existing works on the bus contention analysis for 3-phase tasks; and (3) performing an extensive experimental evaluation under different settings to compare the proposed analysis against the SOTA. Results show that incorporating a tighter bound on the number of cache misses of tasks into the bus contention analysis can lead to a significant improvement in the task set schedulability.

Network-on-Chips (NoCs) have demonstrated be a favorable alternative to conventional bus-based communication architectures for interconnecting programming elements (PEs). However, NoCs consist of numerous shared resources such as routers and links leading to traffic contention and hence packet transmission delays. Existing works rely on various mechanisms, e.g., leaky buckets, to regulate the network bandwidth distribution and reduce contention. However, such bandwidth regulation mechanisms rarely use runtime information to decide which PEs can inject packets in the network. In this paper, we propose a traffic injection regulation protocol where PEs can dynamically request other PEs to relinquish some network bandwidth. The proposed solution prevents starvation and excessive communication delays due to PEs being unable to inject their flits on the network. Moreover, since the proposed solution uses runtime information, it does not waste communication bandwidth. Experimental results show that our solution leads to a more equitable distribution of network bandwidth among PEs compared to leaky bucket-based mechanisms.

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.

The Predictable Execution Model (PREM) is useful for mitigating inter-core interference due to shared resources such as the main memory. However, it is cache-agnostic, which makes schedulabulity analysis pessimistic, via overestimation of prefetches and write-backs. In response, we present cache-aware schedulability analysis for PREM tasks on fixed-task-priority partitioned multicores, that bounds the number of cache prefetches and write-backs. Our approach identifies memory blocks loaded in the execution of a previous scheduling interval of each task, that remain in the cache until its next scheduling interval. Doing so, greatly reduces the estimated prefetches and write backs. In experimental evaluations, our analysis improves the schedulability of PREM tasks by up to 55 percentage points.

The sharing of main memory among concurrently executing tasks on a multicore platform results in increasing the execution times of those tasks in a non-deterministic manner. The use of phased execution models that divide the execution of tasks into distinct execution and memory phase(s), e.g., the PRedictable Execution Model (PREM) and the 3-Phase task model, along with Memory Centric Scheduling (MCS) present a promising solution to reduce main memory interference among tasks.Existing works in the state-of-the-art that focus on MCS have considered (i) a TDMA-based memory scheduler, i.e., tasks’ memory requests are served under a static TDMA schedule, and (ii) Processor-Priority (PP) based memory scheduler, i.e., tasks’ memory requests are served depending on the priority of the processor/core on which the task is executing. This paper extends MCS by considering a Task-Priority (TP) based memory scheduler, i.e., tasks’ memory requests are served under a global priority order depending on the priority of the task that issues the requests. We present an analysis to bound the total memory interference that can be suffered by the tasks under the TPbased MCS. In contrast to the recent works on MCS that considers non-preemptive tasks, our analysis considers limited preemptive scheduling. Additionally, we investigate the impact of different preemption points on the memory interference of tasks. Experimental results show that our proposed TP-based MCS can significantly reduce the memory interference that can be suffered by the tasks in comparison to the PP-based MCS.

In deflection-based Network-on-Chips (NoC), when several flits entering a router contend for the same output port, one of the flit is routed to the desired output and the others are deflected to alternatives outputs. The approach reduces power consumption and silicon footprint in comparison to virtual-channels (VCs) based solutions. However, due to the non-deterministic number of deflections that flits may suffer while traversing the network, flits may be received in an out-of-order fashion at their destinations. In this work, we present IPDeN, a novel deflection-based NoC that ensures in-order flit delivery. To avoid the use of costly reordering mechanisms at the destination of each communication flow, we propose a solution based on a single small buffer added to each router to prevents flits from over taking other flits belonging to the same communication flow. We also develop a worst-case traversal time (WCTT) analysis for packets transmitted over IPDeN. We implemented IPDeN in Verilog and synthesized it for an FPGA platform. We show that a router of IPDeN requires ≈3-times less hardware resources than routers that use VCs. Experimental results shown that the worst-case and average packets communication time is reduced in comparison to the state-of-the-art.

Commercial-off-the-shelf (COTS) multicore processors have become a preferable choice for modern systems to meet the increasing functionalities and computational demand of modern applications. However, the adoption of multicore platforms in hard real-time systems, i.e., systems that run applications with stringent timing requirements, is still under scrutiny. The main challenge that hinders the use of COTS multicore platforms in hard real-time systems is their unpredictability, which originates from the sharing of different hardware resources. A task executing on one core of a multicore platform has to compete with other co-running tasks (running on other cores) to access hardware resources such as the last-level cache (LLC), the interconnect (e.g., memory bus), and the main memory. This competition leads to inter-core contention which can significantly impact the Worst-Case Execution Time (WCET) and Worst-Case Response Time (WCRT) of tasks.

Private vehicles are expected to continue representing a large share of the urban traffic requiring intelligent management to provide safe and efficient urban mobility. In this context, it is imperative to mitigate traffic congestion and associated travel delays to improve the quality of life of urban dwellers. This paper explores the global performance of grid networks of independent intersections using different intersection management protocols. We particularly aim to compare the performance achieved when using the intelligent intersection management architecture (IIMA) that relies on the synchronous intersection management protocol (SIMP), against two conventional (Round-robin - RR and trivial traffic light control - TTLC) and two adaptive (Max-pressure control algorithm - MCA and Websters traffic light control - WTLC) intersection management approaches. We consider four-way two-lane intersections with two crossing configurations, namely dedicated and shared left lane, on a 2×2 grid network of intersections. Simulation results with SUMO show that composing intersections with synchronous management considerably improves the network throughput and reduces travel delays.

Conventional CAN bus schedulability analysis as-sumes that all messages with a given identifier have the same worst-case length. In this paper we extend that analysis to a more general model in which messages with a given identifier may have different lengths, that vary according to a known periodic pattern. That is, for some positive integer$s$, we assume that the length of message instances$n$and$n+S$with the same id is the same. By leveraging such patterns, where present, our new analysis allows for a more efficient use of CAN bus bandwidth than the application of conventional analysis, which can be pessimistic. This may be interesting when a given node sends the values of multiple signals with different periods. In such a scenario, the conventional CAN schedulability analysis would require either the use of different ids for different signals (assuming there are enough of them), which leads to a higher bandwidth overhead because of the reduplication of message headers, or using only one id, but pessimistically always assuming the maximum possible length of the message, for safety reasons.

Abstract Designing and developing distributed cyber-physical production systems (CPPS) is a time-consuming, complex, and error-prone process. These systems are typically heterogeneous, i.e., they consist of multiple components implemented with different languages and development tools. One of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s maintainability. Adopting the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design, deployment, and management of CPPS in a model-based engineering methodology. We propose a method for CPPS design based on the IEC 61499 standard. The method allows designers to embed a bio-inspired anomaly-based host intrusion detection system (A-HIDS) in Edge devices. This A-HIDS is based on the incremental Dendritic Cell Algorithm (iDCA) and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’ Edge layer. This study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions, and cost-effective security by design system implementation. According to the experimental data, the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.

Multicore platforms are being increasingly adopted in Cyber-Physical Systems (CPS) due to their advantages over single-core processors, such as raw computing power and energy efficiency. Typically, multicore platforms use a shared memory bus that connects the cores to the off-chip main memory. This sharing of memory bus may cause tasks running on different cores to compete for access to the main memory whenever data/instructions are need to be read/written from/to the main memory. Such competition is problematic, as it may cause variations in the execution time of tasks in a non-deterministic way. To reduce the complexity of analyzing this problem, the 3-phase task model was proposed that divides tasks’ executions into distinct memory and execution phases. The distinctive memory phases are then scheduled to eliminate/minimize main memory contention between concurrently executing tasks. However, 3-phase tasks running on different cores may still compete to access the shared memory bus/main memory in order to execute memory phases. This paper presents a partitioned scheduling-based approach that allows one to derive memory bus contention-aware worst-case response time of tasks that follow the 3-phase task model. In particular, the bus-contention analysis is derived by considering two memory access models, i.e., (i) dedicated memory access model, where a core having allowed to access the main memory via memory bus is permitted to execute more than one memory phase, and (ii) fair memory access model, that restrict each core to execute only one memory phase in its allocated bus access. Both these models represent different system and application requirements, and the resulting bus contention of tasks may vary depending on the considered model. To evaluate the effectiveness of the proposed bus contention analysis, we compare its performance against an existing analysis in the state-of-the-art by performing (i) case-study experiments, using benchmarks from the Mälardalen Benchmark suite, and (ii) empirical evaluation using synthetic task sets. Results show that our proposed analysis can improve task set schedulability of 3-phase tasks by up to 88 percentage points.

Tasks running on microprocessors with cache memories are often subjected to cache related preemption delays (CRPDs). CRPDs may significantly increase task execution times, thereby, affecting their schedulability. Schedulability analysis accounting for the impact of CRPD has been extensively studied over the past two decades for systems with a single level of cache. Yet, the literature on CRPD for multilevel non-inclusive caches is relatively scarce. Two main challenges exist when analyzing multilevel caches: (1) characterization of the indirect effect of preemption, i.e., capturing the increase in cache interference at lower cache levels (e.g., level-two or L2 cache) due to the evictions of cache content from a higher cache level (e.g., level-one or L1 cache), and (2) upper bounding the maximum CRPD suffered by tasks at lower cache levels (e.g., L2 cache), i.e., determining the cache content of tasks that can be evicted from lower cache levels in case of preemptions. Existing analysis that focus on bounding CRPD for multilevel non-inclusive caches overestimate the values of (1) and (2) leading to pessimistic worst-case response time (WCRT) estimations. In this work, we reduce the excessive pessimism of the state-of-the-art CRPD analysis for multilevel non-inclusive caches by (i) introducing the notion of multi-level useful cache blocks, i.e., cache blocks that can cause CRPD at different cache levels, and use it to compute a tighter bound on the indirect effect of preemption of tasks; and (ii) deriving a new analysis to compute tighter bounds on the CRPD of tasks at lower cache levels (e.g., L2 cache). We performed a thorough experimental evaluation using benchmarks to compare the performance of our proposed CRPD analysis against the state-of-the-art CRPD analysis. Experimental results show that our proposed CRPD analysis dominates the existing analysis and improves task set schedulability by up to 20% percentage points.

The growing demand of powerful embedded systems to perform advanced functionalities led to a large increase in the number of computation nodes integrated in Systems-on-chip (SoC). In this context, network-on-chips (NoCs) emerged as a new standard communication infrastructure for multi-processor SoCs (MPSoCs). In this work, we present nDimNoC, a new D-dimensional NoC that provides real-time guarantees for systems implemented upon MPSoCs. Specifically, (1) we propose a new router architecture and a new deflection-based routing policy that use the properties of circulant topologies to ensure bounded worst-case communication delays, and (2) we develop a generic worst-case communication time (WCCT) analysis for packets transmitted over nDimNoC. In our experiments, we show that the WCCT of packets decreases when we increase the dimensionality of the NoC using nDimNoC’s topolgy and routing policy. By implementing nDimNoC in Verilog and synthesizing it for an FPGA platform, we show that a 3D-nDimNoC requires ≈5-times less silicon than routers that use virtual channels (VC). We computed the maximum operating frequency of a 3D-nDimNoC with Xilinx Vivado. Increasing the number dimensions in the NoC improves WCCT at the cost of a more complex routing logic that may result in a reduced operating clock frequency.

Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46 % and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.

Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm.

Autonomous UAV cruising is gaining attention due to its flexible deployment in remote sensing, surveillance, and reconnaissance. A critical challenge in data collection with the autonomous UAV is the buffer overflows at the ground sensors and packet loss due to lossy airborne channels. Trajectory planning of the UAV is vital to alleviate buffer overflows as well as channel fading. In this work, we propose a Deep Deterministic Policy Gradient based Cruise Control (DDPG-CC) to reduce the overall packet loss through online training of headings and cruise velocity of the UAV, as well as the selection of the ground sensors for data collection. Preliminary performance evaluation demonstrates that DDPG-CC reduces the packet loss rate by under 5% when sufficient training is provided to the UAV.

Intersections are critical elements of urban traffic management and are identified as bottlenecks prone to traffic congestion and accidents. Intelligent intersection management plays a significant role in improving traffic efficiency and safety determining, among other metrics, the waiting time that vehicles incur when crossing an intersection. This work presents a preliminary analysis of the worst-case response time of intersection management protocols that handle mixed traffic with autonomous and human-driven vehicles. We deduce theoretical bounds for such time considered as the interval between the injection of a vehicle in the road system and its departure from the intersection, considering different intersection management protocols for mixed traffic, namely the Synchronous Intersection Management Protocol (SIMP) and several configurations of the conventional Round-Robin (RR) policy. Simulation results validate the analytical bounds partially. Ongoing work addresses the queue dynamics and its reliable detection by traffic simulators.

In this paper, we present a practical secret key management for data relay security of bluetooth-connected drones. Time-varying received signal strengths between the drones and the ground sensing nodes are quantized to generate the secret key pairs, where the quantization interval is adjusted to reduce the number of mismatched secret key bits. To validate the key management performance, a multihop aerial relay system testbed is developed based on the MX400 drone platform and the bluetooth low energy radio transceiver.

Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q-network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.

Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.

Memory bus contention strongly relates to the number of main memory requests generated by tasks running on different cores of a multicore platform, which, in turn, depends on the content of the cache memories during the execution of those tasks. Recent works have shown that due to cache persistence the memory access demand of multiple jobs of a task may not always be equal to its worst-case memory access demand in isolation. Analysis of the variable memory access demand of tasks due to cache persistence leads to significantly tighter worst-case response time (WCRT) of tasks.In this work, we show how the notion of cache persistence can be extended from single-core to multicore systems. In particular, we focus on analyzing the impact of cache persistence on the memory bus contention suffered by tasks executing on a multi-core platform considering both work conserving and non-work conserving bus arbitration policies. Experimental evaluation shows that cache persistence-aware analyses of bus arbitration policies increase the number of task sets deemed schedulable by up to 70 percentage points in comparison to their respective counterparts that do not account for cache persistence.

Cyber-Physical Production Systems (CPPS) are key enablers for industrial and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new revolution towards the Smart Manufacturing oncept and is usually designated as the 4thIndustrial Revolution. Despite the huge interest from the industry to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. Facing this paradigm and, since CPPS have reached a level of complexity, where the human intervention for operation and control is becoming increasingly difficult, Smart Factories require autonomic methodologies for security management and self-healing. This paper presents an Intrusion Detection System (IDS) approach for CPPS, based on the deterministic Dendritic Cell Algorithm (dDCA). To evaluate the dDCA effectiveness, a testing dataset was generated, by implementing and injecting various attacks on a OPC UA based CPPS testbed. The results show that these attacks can be successfully detected using the dDCA.

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.

In Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.

Cache memories have a strong impact on the response time of tasks executed on modern computing platforms. For tasks scheduled under fixed-priority preemptive scheduling (FPPS), the worst-case response time (WCRT) analyses that account for cache persistence between jobs along with cache related preemption delays (CRPDs) have been shown to dominate analyses that only consider CRPDs. Yet, the existing approaches that analyze cache persistence in the context of WCRT analysis can only support direct-mapped caches. In this work, we analyze cache persistence in the context of WCRT analysis for set-associative caches. The main contributions of this work are: (i) to propose a solution to find persistent cache blocks (PCBs) of tasks considering set-associative caches, (ii) to present three different approaches to calculate cache persistence reload overheads (CPROs), i.e., the memory overhead due to eviction of PCBs of tasks, under set-associative caches, and (iii) an experimental evaluation showing that our proposed approaches result in up to 22 percentage points higher task set schedulability than the state-of-the-art approaches.

Multicore platforms are being increasingly adopted in Cyber-Physical Systems (CPS) due to their advantages over single-core processors, such as raw computing power and energy efficiency. Typically, multicore platforms use a shared system bus that connects the cores to the memory hierarchy (including caches and main memory). However, such hierarchy causes tasks running on different cores to compete for access to the shared system bus whenever data reads or writes need to be made. Such competition is problematic as it may cause large variations in the execution time of tasks in a non-deterministic way. This paper presents an analysis that allows one to derive bus contention-aware worst-case response-time of tasks that follow the 3-phase task model executing under partitioned scheduling.

Guaranteeing that safety-critical Cyber-Physical Systems (CPS) do not fail upon deployment is becoming an even more complicated task with the increased use of complex software solutions. To aid in this matter, formal methods (rigorous mathematical and logical techniques) can be used to obtain proofs about the correctness of CPS. In such a context, Runtime Verification has emerged as a promising solution that combines the formal specification of properties to be validated and monitors that perform these validations during runtime. Although helpful, runtime verification solutions introduce an inevitable overhead in the system, which can disrupt its correct functioning if not safely employed. We propose the creation of a Domain Specific Language (DSL) that, given a generic CPS, 1) verifies if its real- time scheduling is guaranteed, even in the presence of coupled monitors, and 2) implements several verification conditions for the correct-by-construction generation of monitoring architectures. To achieve it, we plan to perform statical verifications, derived from the available literature on schedulability analysis, and powered by a set of semi-automatic formal verification tools.

For assisting data communications in human-unfriendly environments, Unmanned Aerial Vehicles (UAVs) are employed to relay data for ground sensors thanks to UAVs' flexible deployment, high mobility, and line-of-sight communications. In UAV relay networks, energy efficient data relay is critical due to limited battery of the ground sensing devices. In this paper, we propose a butter-aware transmission scheduling optimization to minimize the energy consumption of the ground devices under constraints of butter overflows and energy cost fairness on the ground devices. Moreover, we show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. The performance of the proposed algorithm is evaluated in terms of network sizes, packet arrival rates, and fairness of the energy consumption. Numerical results confirm that the proposed scheduling algorithm reduces the energy consumption of the ground devices in a fair fashion, while the butter overflow constraint holds.

The unprecedented pervasiveness of IoT systems is pushing this technology into increasingly stringent domains. Such application scenarios become even more challenging due to the demand for encompassing the interplay between safety and security. The IEEE 802.15.4 DSME MAC behavior aims at addressing such systems by providing additional deterministic, synchronous multi-channel access support. However, despite the several improvements over the previous versions of the protocol, the standard lacks a complete solution to secure communications. In this front, we propose the integration of TAKS, an hybrid cryptography scheme, over a standard DSME network. In this paper, we describe the system architecture for integrating TAKS into DSME with minimum impact to the standard, and we venture into analysing the overhead of having such security solution over application delay and throughput. After a performance analysis, we learn that it is possible to achieve a minor impact of 1% to 14% on top of the expected network delay, depending on the platform used, while still guaranteeing strong security support over the DSME network.

In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon’s moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon’s driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads' throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

Context-awareness is a prominently desired feature in computing systems. Smartphones, smart cards or tags, wearables, sensor nodes, and many other devices enable a system to compute context for different users and environment. With ever increasing advances in hardware for such devices, the interactions with users are increasing every day. This enables the collection of a large amount of data about users, systems, and physical environment. With such data available to be leveraged, context-awareness will soon become a necessity. Such type of data collection happens most frequently in sensing applications enabled by wireless sensor network (WSN) devices. This paper discusses the concept of context for sensing applications, specifically related to Cyber Physical Systems (CPS). The paper highlights key aspects of context and its definition. This paper proposes, to the best of the author's knowledge, the first programming approach to build context-aware applications for WSN-based CPS. This paper provides a proof of concept for a framework to detect, manage and deploy context-aware applications.

In a vehicular platoon, the lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates messages to the following automated vehicles in a multi-hop vehicular network. However, due to the broadcast nature of wireless channels, vehicle-to-vehicle (V2V) communications are vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way for V2V communication security. We study a security scheme for platoon-based V2V communications, where the platooning vehicles generate a shared secret key based on the quantized fading channel randomness. To improve conformity of the generated key, the probability of secret key agreement is formulated, and a novel secret key agreement algorithm is proposed to recursively optimize the channel quantization intervals, maximizing the key agreement probability. Numerical evaluations demonstrate that the key agreement probability achieved by our security protocol given different platoon size, channel quality, and number of quantization intervals. Furthermore, by applying our security protocol, it is shown that the probability that the encrypted data being cracked by an eavesdropper is less than 5%.

Privacy preservation is critical for control information dissemination in Platoon-based Vehicular Cyber-Physical Systems (PVCPS). However, the vehicular communication is vulnerable to wireless eavesdropping attack and message modification, due to broadcast nature of radio channels. In this poster, we present a secret key generation testbed for PVCPS security, which is built based on off-the-shelf autonomous robotic vehicles and TelosB wireless transceivers. A cooperative secret key agreement (CoopKey) scheme is demonstrated for encrypting/decrypting the disseminated control messages. To unify the secret key generated by the vehicles, CoopKey explores received signal strength (RSS) measurements and channel estimation on the inter-node radio channel. In addition, a Python-based user interface is also implemented to show real-time bit mismatch rate of CoopKey.

Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation, and to bridge the gap between development and real-word deployment. However, their complexity and cost, often hinders its validation in the real-world. In this paper we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

This paper considers that a legitimate UAV tracks suspicious UAVs' flight for preventing intended crimes and terror attacks. To enhance tracking accuracy, the legitimate UAV proactively eavesdrops suspicious UAVs' communication via sending jamming signals. A tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter's signal. A new co-simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in Matlab) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of tracking accuracy of the suspicious UAVs' trajectory.

Timing analysis of safety-critical real-time embedded systems should be free of both optimistic and pessimistic aspects. The multiframe model was devised to eliminate the pessimism in the schedulability analysis of systems with tasks whose worst-case execution times vary from job to job, according to known patterns. However, this model is optimistic and unsafe for multicores with shared memory controllers, since it ignores memory contention, and existing approaches to stall analysis based on memory regulation are very pessimistic if straightforwardly applied. This paper remedies this by adapting existing stall analyses for memory-regulated systems of conventional Liu-and-Layland tasks to the multiframe model. Experimental evaluations with synthetic task sets (and different task and memory budget assignment heuristics) show up to 85% higher scheduling success ratio for our analysis, compared to the frame-agnostic analysis, enabling higher platform utilisation without compromising safety. We also explore implementation aspects, such as how to speed up the analysis and how to trade off accuracy with tractability.

Urban traffic management (UTM) is responsible for planning and controlling traffic on road infrastructures, including lane closures, full freeway closures, and pedestrian access. An essential element in UTM is the Intersection Management (IM) that deals with traffic control and is vulnerable to traffic congestion and accidents. In this paper, we propose an intelligent intersection management architecture along with the synchronous intersection management protocol (SIMP) instantiated in two versions. Simulation results show the advantages of SIMP-M (one of the versions) over the well known TraCI IM protocol, in terms of both worst-case and average vehicle speed passing through one intersection.

XDense is a novel wired 2D mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g., thousands of sensors per square meter). It was conceived for cyber-physical systems that require real-time sensing and actuation, like active flow control on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed to enable complex feature extraction within bounded time and in a responsive manner. In this article, we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic-shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and present a comparative performance analysis in terms of link utilization, queue size, and execution time. With the proposed traffic-shaping heuristics, we endow XDense with the capabilities required for real-time applications.

In mixed-criticality systems, tasks of different criticality share system resources, mainly to reduce cost. Cost is further reduced by using adaptive mode-based scheduling arrangements, such as Vestal’s model, to improve resource efficiency, while guaranteeing schedulability of critical functionality. To simplify safety certification, servers are often used to provide temporal isolation between tasks. In its simplest form, a server is a periodically recurring time window, in which some tasks are scheduled. A server’s computational requirements may greatly vary in different modes, although state-of-the-art techniques and schedulability tests do not allow different budgets to be used by a server in different modes. This results in a single conservative execution budget for all modes, increasing system cost. The goal of this paper is to reduce the cost of mixed-criticality systems through three main contributions: (i) a scheduling arrangement for uniprocessor systems employing fixed-priority scheduling within periodic servers, whose budgets are dynamically adjusted at run-time in the event of a mode change, (ii) a new schedulability analysis for such systems, and (iii) heuristic algorithms for assigning budgets to servers in different modes and ordering the execution of the servers. Experiments with synthetic task sets demonstrate considerable improvements (up to 52.8%) in scheduling success ratio when using dynamic server budgets vs. static “one-size-fits-all-modes” budgets.

Mixed-criticality (MC) multicore system design must reconcile safety guarantees and high performance. The interference among cores on shared resources in such systems leads to unpredictable temporal behaviour. Memory bandwidth regulation among different cores can be a useful tool to mitigate the interference when accessing main memory. However, for mixed-criticality systems conforming to the (well-established) Vestal model, the existing schedulability analyses are oblivious to memory stalling effects, including stalls from memory bandwidth regulation. This makes it unsafe. In this paper, we address this issue by formulating a schedulability analysis for mixed-criticality fixed-priority-scheduled multicore systems using per-core memory access regulation. We also propose multiple heuristics for memory bandwidth allocation and task-to-core assignment. We implement our analysis and heuristics in a tool and evaluate them, performance-wise, through extensive experiments. Our experiments show that stall-oblivious schedulability analysis may be optimistic due to contention on shared memory resources.

In multicore architectures, there is potential for contention between cores when accessing shared resources, such as system memory. Such contention scenarios are challenging to accurately analyse, from a worst-case timing perspective. One way of making memory contention in multicores more amenable to timing analysis is the use of memory regulation mechanisms. It restricts the number of accesses performed by any given core over time by using periodically replenished per-core budgets. Typically, this assumes that all cores access memory via a single shared memory controller. However, ever-increasing bandwidth requirements have brought about architectures with multiple memory controllers. These control accesses to different memory regions and are potentially shared among all cores. While this presents an opportunity to satisfy bandwidth requirements, existing analysis designed for a single memory controller are no longer safe. This work formulates a worst-case memory stall analysis for a memory-regulated multicore with two memory controllers. This stall analysis can be integrated into the schedulability analysis of systems under fixed-priority partitioned scheduling. Five heuristics for assigning tasks and memory budgets to cores in a stall-cognisant manner are also proposed. We experimentally quantify the cost in terms of extra stall for letting all cores benefit from the memory space offered by both controllers, and also evaluate the five heuristics for different system characteristics.

In a vehicular platoon, a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control commands to following vehicles based on vehicle-to-vehicle communications. However, reducing command dissemination latency with multiple vehicles while ensuring successful message delivery to the tail vehicle is challenging. We propose a new linear dynamic programming algorithm using backward induction and interchange arguments to minimize the dissemination latency of the vehicles. Furthermore, a closed form of dissemination latency in vehicular platoon is obtained by utilizing Markov chain with M/M/1 queuing model. Simulation results confirm that the proposed dynamic programming algorithm improves the dissemination rate by at least 50.9%, compared to similar algorithms in the literature. Moreover, it also approximates the best performance with the maximum gap of up to 0.2 second in terms of latency.

Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e that supports deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and CAP reduction that help to increase the number of available guaranteed timeslots in a network. However, no tuning of these functionalities in dynamic scenarios is supported in the standard. In this paper, we present an effective multisuperframe tuning technique that also helps to utilize CAP reduction in an effective manner improving flexibility and scalability, while guaranteeing bounded delay.

Mixed-criticality multicore system design must often guarantee both safety and high performance. Memory bandwidth regulation among different cores can be a useful tool for guaranteeing safety, as it mitigates the interference when accessing main memory. The use of mode changes and system models like Vestal's can help provide both safety, for critical functions, and scheduling performance, by efficiently utilising the platform. This work therefore combines per-core memory access regulation with the well-established Vestal model and improves on the state-of-the-art in two respects: 1) We allow the memory access budgets of the cores to be dynamically adjusted, when the system undergoes a mode change, reflecting the different needs in each mode, for better schedulability. 2) We devise memory-regulation-aware and stall-aware schedulability analysis for such systems, based on AMC-max. By comparison, the state-of-the-art offered no option of dynamic adjustment of core budgets, and only offered regulation-aware schedulability analysis based on AMC-rtb, which is inherently more pessimistic. Finally, 3) we consider different task assignment and bandwidth allocation heuristics, to assess the improvement from the dynamic memory budgets and new analysis. Our results show improvements in schedulability ratio of up to 9.1% over the state-of-the-art.

No abstract available.

The design of mixed-criticality systems often involves painful tradeoffs between safety guarantees and performance. However, the use of more detailed architectural models in the design and analysis of scheduling arrangements for mixed-criticality systems can provide greater confidence in the analysis, but also opportunities for better performance. Motivated by this view, we propose an extension of Vestal's model for mixed-criticality multicore systems that (i) accounts for the per-task partitioning of the last-level cache and (ii) supports the dynamic reassignment, for better schedulability, of cache portions initially reserved for lower-criticality tasks to the higher-criticality tasks, when the system switches to high-criticality mode. To this model, we apply partitioned EDF scheduling with Ekberg and Yi's deadline-scaling technique. Our schedulability analysis and scalefactor calculation is cognisant of the cache resources assigned to each task, by using WCET estimates that take into account these resources. It is hence able to leverage the dynamic reconfiguration of the cache partitioning, at mode change, for better performance, in terms of provable schedulability. We also propose heuristics for partitioning the cache in low- and high-criticality mode, that promote schedulability. Our experiments with synthetic task sets, indicate tangible improvements in schedulability compared to a baseline cache-aware arrangement where there is no redistribution of cache resources from low- to high-criticality tasks in the event of a mode change.

We consider a wireless information surveillance in UAV network, where a legitimate unmanned aerial vehicle (UAV) proactively eavesdrops communication between two suspicious UAVs. However, challenges arise due to lossy airborne channels and limited power of the UAV. In this paper, we study an emerging legitimate eavesdropping paradigm that the legitimate UAV improves the eavesdropping performance via jamming the suspicious communication. Moreover, a power efficient legitimate eavesdropping scheme, PELE, is proposed to maximize the number of eavesdropped packets from the legitimate UAV while maintaining a target signal to interference plus noise ratio at the suspicious link. Numerical results are shown to validate the performance of PELE. Additionally, four typical fading channel models are applied to the network so as to investigate their impact on PELE.

XDense is a novel wired 2D-mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g. thousands of sensors per square meter). It was conceived for closed-loop cyber-physical systems (CPS) that require real-time actuation, like active flow control (AFC) on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed such that they enable to extract complex features within bounded time and in a responsive manner. In this paper we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and demonstrate the effectiveness of running real-time applications supported on XDense.

Schedulability analysis for tasks running on micro- processors with cache memory is incomplete without a treatment of Cache Related Preemption Delays (CRPD) and Cache Persistence Reload Overheads (CPRO). State-of-the-art analyses compute CRPD and CPRO independently, which might result in counting the same overhead more than once. In this paper, we analyze the pessimism associated with the independent calculation of CRPD and CPRO in comparison to an integrated approach. We answer two main questions: (1) Is it benecial to integrate the calculation of CRPD and CPRO? (2) When and to what extent can we gain in terms of schedulability by integrating the calculation of CRPD and CPRO? To achieve this, we (i) identify situations where considering CRPD and CPRO separately might result in overestimating the total memory overhead suffered by tasks, (ii) derive new analyses that integrate the calculation of CRPD and CPRO; and (iii) perform a thorough experimental evaluation using benchmarks to compare the performance of the integrated analysis against the separate calculation of CRPD and CPRO.

This paper addresses the performance analysis of an adaptive wireless link with one antenna transmitter and a multiple antenna maximum-ratio combining (MRC) receiver. Two main assumptions are used in this paper: ( 1 ) Rayleigh fading correlated channels (i.e., MRC branch correlation) and ( 2 ) imperfect (outdated) channel state information at the transmitter (CSIT) side. The main contribution of this work lies in the derivation of analytic expressions (in terms of a series expansion) of the statistics of correct packet reception conditional on the decisions made by the transmitter based on outdated CSIT. The novelty of this derivation is the joint modelling of spatially correlated branches, imperfect CSIT, and adaptive modulation based on threshold-trigger decision. Contrary to common belief, the results presented here suggest that spatial correlation not always affects the performance of the MRC receiver: at low signal-to-noise ratio (SNR), correlation can improve performance rather than degrading it. In contrast, at high SNR, correlation is found to always degrade performance. At high SNR, correlation tends to worse the degrading effects of imperfect CSIT, particularly when the number of antennas increases. Imperfect CSIT causes errors in the assignment of MCSs, thus reducing throughput performance. These errors become more evident in the high SNR regime, particularly when the values of branch correlation and the number of antennas increase.

A task can be preempted by several jobs of higherpriority tasks during its response time. Assuming the worst-casememory demand for each of these jobs leads to pessimistic worst-case response time (WCRT) estimations. Indeed, there is a bigchance that a large portion of the instructions and data associatedwith the preempting task Tj are still available in the cache when Tj releases its next jobs. Accounting for this observation allowsthe pessimism of WCRT analysis to be significantly reduced, which is not considered by existing work. The four main contributions of this paper are: 1) The conceptof persistent cache blocks is introduced in the context of WCRTanalysis, which allows re-use of cache blocks to be captured,2) A cache-persistence-aware WCRT analysis for fixed-prioritypreemptive systems exploiting the PCBs to reduce the WCRTbound, 3) A multi-set extension of the analysis that furtherimproves the WCRT bound and 4) An evaluation showing thatour cache-persistence-aware WCRT analysis results in up to 10%higher schedulability than state-of-the-art approaches.

This paper presents the design of an active flow control (AFC) system for commercial aircraft based on a dense wired/wireless sensor and actuator network. The goal is to track gradients of pressure across the surface of the fuselage of commercial aircraft. This collected information will be used to activate a set of actuators that will attempt to reduce the skin drag effect produced by the separation between laminar and turbulent flows. This will be translated into increased lift-off forces, higher speeds, longer ranges and reduced fuel consumption. The paper describes the architecture of the system in the context of the European research project DEWI (dependable embedded wireless infrastructure) using the concept of the DEWI Bubble. A simulator architecture is also proposed to model each process of the AFC system and the DEWI Bubble. To the best of our knowledge this is the first approach towards the use of wireless sensor technologies in the field of active flow control.

This paper presents a demo of an extension developed to support an existing programming abstraction for IoT: mT-Res. mT-Res is an extension of the T-Res programming abstraction, which allows users to write applications using a web framework independent of resources. The paper describes an automated mechanism for allocate resources to such applications and adapt to changes in those resources.

Wireless Sensor Networks have been enabling an ever increasing span of applications and usages in the industrial, domestic and commercial domains. Recent advancements in information and communication technologies have been fueling the increasing pervasiveness and ubiquity of this infrastructures, making them an obvious candidate to support the future Internet of Things. Among the prospective applications, however, there are those which present strict requirements in terms of timeliness and reliability, specially in the industrial domain. To address these, the IEEE 802.15.4 standard functionalities were recently enhanced by the IEEE 802.15.4e amendment. Ideas which are prominent in the industrial communication field such as frequency hopping, dedicated and shared timeslots and multichannel communication have been implemented in 802.15.4e. In this line, proposed MAC behaviors such as the Deterministic and Synchronous Multi-channel Extension (DSME) and Time Synchronous Channel Hopping (TSCH), are gaining a lot of attention. Nevertheless, to efficiently address the network demands in terms of latency, resources, and reliability, it is mandatory to carry out a thorough network planning. To achieve this, modeling the fundamental performance limits of such networks is of paramount importance to understand their behavior under the worst-case conditions and to make the appropriate design choices. Network Calculus is an established tool which can accurately compute the worst case bounds of a network. In this paper we provide an insight towards DSME and TSCH by modeling, using Network Calculus formalism, the delay bounds of these MAC behaviors. As a continuation of this work the end-to-end delay bounds will be derived for the rest of the MAC behaviors of IEEE 802.15.4e. Scheduling algorithms will be developed, analyzed and validated as a future work.

With the increasing complexity of embedded systems, it becomes unrealistic to formally verify that all the system requirements will be respected under any possible execution scenario. Moreover, the worst-case analyses that are usually performed before the system deployment are also based on a set of assumptions (e.g., minimum activation period, worst-case execution time, maximum release jitter) that may not always be respected at run-time. For those reasons, run-time monitoring and run-time verification become an interesting alternative to the traditional offline verification. Run-time verification is based on the instrumentation of the target applications. Monitors are then added to the system to verify at run-time that the system requirements are respected during the execution. If a misbehaviour is detected, an alarm can be raised so as to trigger appropriate counter-measures (e.g., execution mode change, reset or deactivation of some of the functionalities). In this work, we present four different implementations of a run-time monitoring framework suited to real-time and safety critical systems. Two implementations are written in Ada and follow the Ravenscar profile, which make them particularly suited to the development of high integrity systems. The first version is available as a standalone library for Ada programs while the second has been integrated in the GNAT run-time environment and instruments the ORK+ micro-kernel. Information on the task scheduling events, directly originating from the kernel, can thus be used by the monitors to check if the system follows all its requirements. The third implementation is a standalone library written in C++ that can be used in any POSIX compliant run-time environment. It is therefore compatible with the vast majority of operating systems used in embedded systems. The last implementation is a loadable kernel module for Linux. It has for main advantage to be able to enforce complete space partitioning between the monitors and the monitored applications. It is therefore impossible for memory faults to propagate and corrupt the state of the monitors.

Summary form only given. The existing gap between the processor and main memory operating speeds necessitates the use of intermediate cache memories to accelerate the average case access time to instructions and data that must be executed or treated on the processor. However, the introduction of cache memories in modern computing platforms is the cause of big variations in the execution time of each instruction depending on whether the instruction and the data it treats are already loaded in the cache or not. During the worst-case response time (WCRT) analysis, the existing works assume that each job released by the preempting tasks will ask for their worst-case memory demand. This is however pessimistic since there is a high chance that a big portion of the instructions and data associated with the preempting task τj, are still available in the cache when τjreleases its next jobs. We call this content persistent cache blocks (PCBs). In this work, we propose a method to accurately bound the memory overhead incurred by a low priority task due to high priority tasks executing during its response time. For this purpose, we first identify the existence of persistent and nonpersistent cache blocks (i.e., PCBs and nPCBs) associated with each task. We then show with an example that due to the existence of PCBs, the memory demand of a task can significantly vary over time. Therefore, accounting for PCBs in the memory demand of the preempting task allows to reduce the pessimism on the total memory demand considered by the WCRT analysis. Finally, we propose a refined WCRT analysis for fixed priority preemptive systems considering (i) the effect of PCBs on the memory demand of the preempting task, and (ii) accounting for the number of PCBs that can be evicted by the preempted tasks between two successive job releases of the preempting tasks.

In this work, we highlight the pessimism of independently calculating cache-related preemption delays (CRPDs) and cache persistence reload overheads (CPROs). We propose a first solution to reduce that pessimism by integrating the calculation of CRPDs and CPROs. However, the proposed result is limited to the useful memory blocks (UCB)-union and CPRO-union approaches. Two methods that are known to be simple but pessimistic.

Timely delivery of critical traffic is a major challenge in industrial applications. The Wireless Dominance (WiDOM) medium access control protocol offers a very large number of priority levels to suit time sensitive application requirements. In particular, assuming that its overhead is properly modeled, WiDOM enables an accurate evaluation of the network response time in the wireless domain, through the power of the schedulability analysis, based on non-preemptive and static-priority scheduling. Recent research proposed a new version of WiDOM (dubbed Slotted WiDOM), which offers a lower overhead as compared to the original version. In this paper, we propose a new schedulability analysis for Slotted WiDOM and extend it to handle message streams with release jitter. In order to provide a more accurate timing analysis, the effect of transmission faults must be taken into account. Therefore, in our novel analysis we consider the case where messages are transmitted in a realistic wireless channel, affected by noise and interference. Evaluation is performed on a real test-bed and the results from experiments provide a firm validation of our findings.

IEEE 802.15.6 facilitates communication in the vicinity of or even inside a human body to serve heterogeneous medical, consumer electronics, and entertainment applications. This standard operates in beacon and non-beacon communication modes, and each mode employs different protocols, including CSMA/CA, for resource allocation on the channel. The CSMA/CA protocol presented in IEEE 802.15.6 allows quick and prioritized access to the channel by differentiating contention window bounds of nodes with different priorities. This paper provides a simple and accurate analytical model to estimate the throughput, energy consumption, and delay of this protocol for different priority classes, under the assumption of a finite number of nodes in saturated and lossy channel conditions. The accuracy of the proposed model is validated by simulations. The results obtained in this paper can be used to design standard priority parameters for medical and non-medical applications.

Schedulability analyses, while valuable in theoretical research, cannot be used in practice to reason about the timing behaviour of a real-time system without including the overheads induced by the implementation of the scheduling algorithm. In this paper, we provide an overhead-aware schedulability analysis based on demand bound functions for two hard real-time semi-partitioned scheduling algorithms, EDF-WM and C=D. This analysis is based on a novel implementation that uses a global clock to reduce the overheads incurred due to the release jitter of migrating subtasks. The analysis is used to guide the respective off-line task assignment and splitting procedures. Finally, results of an evaluation are provided highlighting how the different algorithms perform with and without a consideration of overheads.

Heterogeneous multicore platforms have become an attractive choice to deploy mixed criticality systems demanding diverse computational requirements. One of the major challenges is to efficiently harness the computational power of these multicore platforms while deploying mixed criticality applications. The problem is acerbated with an additional demand of energy efficiency. It is particularly relevant for the battery powered embedded systems. We propose a partitioning algorithm for unrelated heterogeneous multicore platforms to map mixed criticality applications that ensures the timeliness property and reduces the energy consumption.

With the reduction in size and cost of sensor nodes, dense sensor networks are becoming more popular in a wide-range of applications. Many such applications with dense deployments are geared towards finding various patterns or features such as peaks, boundaries and shapes in the spread of sensed physical quantities over an area. However, collecting all the data from individual sensor nodes can be impractical both in terms of timing requirements and the overall resource consumption. Hence, it is imperative to devise distributed information processing techniques that can help in identifying such features with a high accuracy and within certain time constraints. In this paper, we exploit the prioritized channel-access mechanism of dominance-based Medium Access Control (MAC) protocols to efficiently obtain exterma of the sensed quantities. We show how by the use of simple transforms that sensor nodes employ on local data it is also possible to efficiently extract certain features such as local extrema and boundaries of events. Using these transformations, we show through extensive evaluations that our proposed technique is fast and efficient at retrieving only sensor data point with the most constructive information, independent of the number of sensor nodes in the network.

Support for multiple concurrent applications is an important enabler for promoting the use of sensor networks as an infrastructure technology, where multiple users can deploy their applications independently. In such a scenario, different applications on a node may transmit packets at distinct periods, causing the node to change from sleep to active state more often, which negatively impacts the energy consumption of the whole network. In this paper, we propose to batch the transmissions together by defining a harmonizing period to align the transmissions from multiple applications at periodic boundaries. This harmonizing period is then leveraged to design a protocol that coordinates the transmissions across nodes and provides real-time guarantees in a multi-hop network. This protocol, which we call Network- Harmonized Scheduling (NHS), takes advantage of the periodicity introduced to assign offsets to nodes at different hop-levels such that collisions are always avoided, and deterministic behavior is enforced. NHS is a light-weight and distributed protocol that does not require any global state-keeping mechanism. We implemented NHS on the Contiki operating system and show how it can achieve a duty-cycle comparable to an ideal TDMA approach.

While Cluster-Tree network topologies look promising for WSN applications with timeliness and energy-efficiency requirements, we are yet to witness its adoption in commercial and academic solutions. One of the arguments that hinder the use of these topologies concerns the lack of flexibility in adapting to changes in the network, such as in traffic flows. This paper presents a solution to provide these networks with the ability to self-adapt to different bandwidth and latency requirements, imposed by traffic flows, by changing the cluster's duty-cycle and scheduling. Importantly, our approach enables a network to change its cluster scheduling without requiring long inaccessibility times or the re-association of the nodes. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs without significant changes to the protocol. Finally, we analyze and demonstrate the validity of our methodology through a comprehensive simulation and experimental validation using commercially available technology on a Structural Health Monitoring application scenario.

We present Carousel-EDF, a new hierarchical scheduling algorithm for a system of identical processors, and its overhead-aware schedulability analysis based on demand bound functions. Carousel-EDF is an offshoot of NPS-F and preserves its utilization bounds, which are the highest among algorithms not based on a single dispatching queue and that have few preemptions. Furthermore, with respect to NPS-F, Carousel-EDF reduces by up to 50% the number of context switches and of preemptions caused by the high-level scheduler itself. The schedulability analysis we present in this paper is grounded on a prototype implementation of Carousel-EDF that uses a new implementation technique for the release of periodic tasks. This technique reduces the pessimism of the schedulability analysis presented and can be applied, with similar benefits, to other scheduling algorithms such as NPS-F.

The four papers in this special section present examples of recent advances in the state-of-the-art of cyber-physical systems and cooperating objects.

The IEEE 802.15.4 standard provides appealing features to simultaneously support real-time and non realtime traffic, but it is only capable of supporting real-time communications from at most seven devices. Additionally, it cannot guarantee delay bounds lower than the superframe duration. Motivated by this problem, in this paper we propose an Explicit Guaranteed time slot Sharing and Allocation scheme (EGSA) for beacon-enabled IEEE 802.15.4 networks. This scheme is capable of providing tighter delay bounds for real-time communications by splitting the Contention Free access Period (CFP) into smaller mini time slots and by means of a new guaranteed bandwidth allocation scheme for a set of devices with periodic messages. At the same the novel bandwidth allocation scheme can maximize the duration of the CFP for non real-time communications. Performance analysis results show that the EGSA scheme works efficiently and outperforms competitor schemes both in terms of guaranteed delay and bandwidth utilization.

Several projects in the recent past have aimed at promoting Wireless Sensor Networks as an infrastructure technology, where several independent users can submit applications that execute concurrently across the network. Concurrent multiple applications cause significant energy-usage overhead on sensor nodes, that cannot be eliminated by traditional schemes optimized for single-application scenarios. In this paper, we outline two main optimization techniques for reducing power consumption across applications. First, we describe a compiler based approach that identifies redundant sensing requests across applications and eliminates those. Second, we cluster the radio transmissions together by concatenating packets from independent applications based on Rate-Harmonized Scheduling.

Consider the problem of scheduling a set of sporadic tasks on a multiprocessor system to meet deadlines using a task splitting scheduling algorithm. Task-splitting (also called semi partitioning) scheduling algorithms assign most tasks to just one processor but a few tasks are assigned to two or more processors, and they are dispatched in a way that ensures that a task never executes on two or more processors simultaneously. A certain type of task-splitting algorithms, called slot-based task-splitting, is of particular interest because of its ability to schedule tasks at high processor utilizations. We present a new schedulability analysis for slot-based task-splitting scheduling algorithms that takes the overhead into account and also a new task assignment algorithm.

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users being concerned about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano-CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate-Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

Modeling the fundamental performance limits of Wireless Sensor Networks (WSNs) is of paramount importance to understand their behavior under the worst-case conditions and to make the appropriate design choices. This is particular relevant for time-sensitive WSN applications, where the timing behavior of the network protocols (message transmission must respect deadlines) impacts on the correct operation of these applications. In that direction this article contributes with a methodology based on Network Calculus, which enables quick and efficient worst-case dimensioning of static or even dynamically changing cluster-tree WSNs where the data sink can either be static or mobile. We propose closed-form recurrent expressions for computing the worst-case end-to-end delays, buffering and bandwidth requirements across any source-destination path in a cluster-tree WSN. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study using commercially available technology, namely TelosB motes running TinyOS.

We focus on large-scale and dense deeply embedded systems where, due to the large amount of information generated by all nodes, even simple aggregate computations such as the minimum value (MIN) of the sensor readings become notoriously expensive to obtain. Recent research has exploited a dominance-based medium access control(MAC) protocol, the CAN bus, for computing aggregated quantities in wired systems. For example, MIN can be computed efficiently and an interpolation function which approximates sensor data in an area can be obtained efficiently as well. Dominance-based MAC protocols have recently been proposed for wireless channels and these protocols can be expected to be used for achieving highly scalable aggregate computations in wireless systems. But no experimental demonstration is currently available in the research literature. In this paper, we demonstrate that highly scalable aggregate computations in wireless networks are possible. We do so by (i) building a new wireless hardware platform with appropriate characteristics for making dominance-based MAC protocols efficient, (ii) implementing dominance-based MAC protocols on this platform, (iii) implementing distributed algorithms for aggregate computations (MIN, MAX, Interpolation) using the new implementation of the dominance-based MAC protocol and (iv) performing experiments to prove that such highly scalable aggregate computations in wireless networks are possible.

Wireless sensor networks (WSNs) emerge as underlying infrastructures for new classes of large-scale networked embedded systems. However, WSNs system designers must fulfill the quality-of-service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness, and system lifetime. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs-the ldquohidden node problem.rdquo In wireless contention-based medium access control (MAC) protocols, when two nodes that are not visible to each other transmit to a third node that is visible to the former, there will be a collision-called hidden-node or blind collision. This problem greatly impacts network throughput, energy-efficiency and message transfer delays, and the problem dramatically increases with the number of nodes. This paper proposes H-NAMe, a very simple yet extremely efficient hidden-node avoidance mechanism for WSNs. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes that scales to multiple clusters via a cluster grouping strategy that guarantees no interference between overlapping clusters. Importantly, H-NAMe is instantiated in IEEE 802.15.4/ZigBee, which currently are the most widespread communication technologies for WSNs, with only minor add-ons and ensuring backward compatibility with their protocols standards. H-NAMe was implemented and exhaustively tested using an experimental test-bed based on ldquooff-the-shelfrdquo technology, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. H-NAMe effectiveness was also demonstrated in a target tracking application with mobile robots over a WSN deployment.

Modelling the fundamental performance limits of wireless sensor networks (WSNs) is of paramount importance to understand the behaviour of WSN under worst case conditions and to make the appropriate design choices. In that direction, this paper contributes with a methodology for modelling cluster tree WSNs with a mobile sink. We propose closed form recurrent expressions for computing the worst case end to end delays, buffering and bandwidth requirements across any source-destination path in the cluster tree assuming error free channel. We show how to apply our theoretical results to the specific case of IEEE 802.15.4/ZigBee WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study, therefore validating the theoretical results through experimentation.

The availability of small inexpensive sensor elements enables the employment of large wired or wireless sensor networks for feeding control systems. Unfortunately, the need to transmit a large number of sensor measurements over a network negatively affects the timing parameters of the control loop. This paper presents a solution to this problem by representing sensor measurements with an approximate representation-an interpolation of sensor measurements as a function of space coordinates. A priority-based medium access control (MAC) protocol is used to select the sensor messages with high information content. Thus, the information from a large number of sensor measurements is conveyed within a few messages. This approach greatly reduces the time for obtaining a snapshot of the environment state and therefore supports the real-time requirements of feedback control loops.

Distributed real-time systems, such as factory automation systems, require that computer nodes communicate with a known and low bound on the communication delay. This can be achieved with traditional time division multiple access (TDMA). But improved flexibility and simpler upgrades are possible through the use of TDMA with slot-skipping (TDMA/SS), meaning that a slot is skipped whenever it is not used and consequently the slot after the skipped slot starts earlier. We propose a schedulability analysis for TDMA/SS. We assume knowledge of all message streams in the system, and that each node schedules messages in its output queue according to deadline monotonic. Firstly, we present a non-exact (but fast) analysis and then, at the cost of computation time, we also present an algorithm that computes exact queuing times.

Consider a network where all nodes share a single broadcast domain such as a wired broadcast network. Nodes take sensor readings but individual sensor readings are not the most important pieces of data in the system. Instead, we are interested in aggregated quantities of the sensor readings such as minimum and maximum values, the number of nodes and the median among a set of sensor readings on different nodes. In this paper we show that a prioritized medium access control (MAC) protocol may advantageously be exploited to efficiently compute aggregated quantities of sensor readings. In this context, we propose a distributed algorithm that has a very low time and message-complexity for computing certain aggregated quantities. Importantly, we show that if every sensor node knows its geographical location, then sensor data can be interpolated with our novel distributed algorithm, and the message-complexity of the algorithm is independent of the number of nodes. Such an interpolation of sensor data can be used to compute any desired function; for example the temperature gradient in a room (e.g., industrial plant) densely populated with sensor nodes, or the gas concentration gradient within a pipeline or traffic tunnel.

Consider the problem of deciding whether a set of n sporadic message streams meet deadlines on a Controller Area Network (CAN) bus for a specified priority assignment. It is assumed that message streams have implicit deadlines and no release jitter. An algorithm to solve this problem is well known but unfortunately it time complexity is non-polynomial. We present an algorithm with polynomial time-complexity for computing an upper bound on the response times. Clearly, if the upper bound on the response time does not exceed the deadline then all deadlines are met. The pessimism of our approach is proven: if the upper bound of the response time exceeds the deadline then the response time exceeds the deadline as well for a CAN network with half the speed.

Consider the problem of scheduling a set of sporadically arriving tasks on a uniform multiprocessor with the goal of meeting deadlines. A processor p has the speed Sp. Tasks can be preempted but they cannot migrate between processors. We propose an algorithm which can schedule all task sets that any other possible algorithm can schedule assuming that our algorithm is given processors that are three times faster.

The IEEE 802.15.4 protocol has the ability to support time-sensitive Wireless Sensor Network (WSN) applications due to the Guaranteed Time Slot (GTS) Medium Access Control mechanism. Recently, several analytical and simulation models of the IEEE 802.15.4 protocol have been proposed. Nevertheless, currently available simulation models for this protocol are both inaccurate and incomplete, and in particular they do not support the GTS mechanism. In this paper, we propose an accurate OPNET simulation model, with focus on the implementation of the GTS mechanism. The motivation that has driven this work is the validation of the Network Calculus based analytical model of the GTS mechanism that has been previously proposed and to compare the performance evaluation of the protocol as given by the two alternative approaches. Therefore, in this paper we contribute an accurate OPNET model for the IEEE 802.15.4 protocol. Additionally, and probably more importantly, based on the simulation model we propose a novel methodology to tune the protocol parameters such that a better performance of the protocol can be guaranteed, both concerning maximizing the throughput of the allocated GTS as well as concerning minimizing frame delay. Keywords - IEEE 802.15.4; GTS; OPNET Modeler; simulation model; analytical model

Consider the problem of scheduling a set of sporadically arriving tasks on a uniform multiprocessor with the goal of meeting deadlines. A processor p has the speed Sp. Tasks can be preempted but they cannot migrate between processors. On each processor, tasks are scheduled according to rate-monotonic. We propose an algorithm that can schedule all task sets that any other possible algorithm can schedule assuming that our algorithm is given processors that are radic2/radic2-1ap 3.41 times faster. No such guarantees are previously known for partitioned static-priority scheduling on uniform multiprocessors.

Consider a communication medium shared among a set of computer nodes; these computer nodes issue messages that are requested to be transmitted and they must finish their transmission before their respective deadlines. TDMA/SS is a protocol that solves this problem; it is a specific type of time division multiple access (TDMA) where a computer node is allowed to skip its time slot and then this time slot can be used by another computer node. We present an algorithm that computes exact queuing times for TDMA/SS in conjunction with rate-monotonic (RM) or earliest-deadline-first (EDF).

We propose a wireless medium access control (MAC) protocol that provides static-priority scheduling of messages in a guaranteed collision-free manner. Our protocol supports multiple broadcast domains, resolves the wireless hidden node problem and allows for parallel transmissions across a mesh network. Arbitration of messages is achieved without the notion of a master coordinating node, global clock synchronization or out-ofband signalling. The protocol relies on bit-dominance similar to what is used in the CAN bus except that in order to operate on a wireless physical layer, nodes are not required to receive incoming bits while transmitting. The use of bit-dominance efficiently allows for a much larger number of priorities than would be possible using existing wireless solutions. A MAC protocol with these properties enables schedulability analysis of sporadic message streams in wireless multihop networks.

Wireless networks play an increasingly important role in application areas such as factory-floor automation, process control, and automotive electronics. In this paper, we address the problem of sharing a wireless channel among a set of sporadic message streams where a message stream issues transmission requests with real-time deadlines. For this problem, we propose a collision-free wireless medium access control (MAC) protocol, which implements static-priority scheduling and supports a large number of priority levels. The MAC protocol allows multiple masters and is fully distributed; it is an adaptation to a wireless channel of the dominance protocol used in the CAN bus, a proven communication technology for various industrial applications. However, unlike that protocol, our protocol does not require a node having the ability to receive an incoming bit from the channel while transmitting to the channel. The evaluation of the protocol with real embedded computing platforms is presented to show that the proposed protocol is in fact collision-free and prioritized. We measure the response times of our implementation and find that the response-time analysis developed for the protocol indeed offers an upper bound on the response times

The IEEE 802.15.4 medium access control (MAC) protocol is an enabling technology for time sensitive wireless sensor networks thanks to its guaranteed-time slot (GTS) mechanism in the beacon-enabled mode. However, the protocol only supports explicit GTS allocation, i.e. a node allocates a number of time slots in each superframe for exclusive use. The limitation of this explicit GTS allocation is that GTS resources may quickly disappear, since a maximum of seven GTSs can be allocated in each superframe, preventing other nodes to benefit from guaranteed service. Moreover, the GTSs may be only partially used, resulting in wasted bandwidth. To overcome these limitations, this paper proposes i-GAME, an implicit GTS allocation mechanism in beacon-enabled IEEE 802.15.4 networks. The allocation is based on implicit GTS allocation requests, taking into account the traffic specifications and the delay requirements of the flows. The i-GAME approach enables the use of a GTS by multiple nodes, while all their (delay, bandwidth) requirements are still satisfied. For that purpose, we propose an admission control algorithm that enables to decide whether to accept a new GTS allocation request or not, based not only on the remaining time slots, but also on the traffic specifications of the flows, their delay requirements and the available bandwidth resources. We show that our proposal improves the bandwidth utilization compared to the explicit allocation used in the IEEE 802.15.4 protocol standard. We also present some practical considerations for the implementation of i-GAME, ensuring backward compatibility with the IEEE 801.5.4 standard with only minor add-ons

The IEEE 802.15.4 protocol proposes a flexible communication solution for low-rate wireless personal area networks including sensor networks. It presents the advantage to fit different requirements of potential applications by adequately setting its parameters. When enabling its beacon mode, the protocol makes possible real-time guarantees by using its guaranteed time slot (GTS) mechanism. This paper analyses the performance of the GTS allocation mechanism in IEEE 802.15.4. The analysis gives a full understanding of the behavior of the GTS mechanism with regards to delay and throughput metrics. First, we propose two accurate models of service curves for a GTS allocation as a function of the IEEE 802.15.4 parameters. We then evaluate the delay bounds guaranteed by an allocation of a GTS using network calculus formalism. Finally, based on the analytic results, we analyse the impact of the IEEE 802.15.4 parameters on the throughput and delay bound guaranteed by a GTS allocation. The results of this work pave the way for an efficient dimensioning of an IEEE 802.15.4 cluster

Consider the problem of scheduling a set of periodically arriving tasks on a multiprocessor with the goal of meeting deadlines. Processors are identical and have the same speed. Tasks can be preempted and they can migrate between processors. We propose an algorithm with a utilization bound of 66% and with few preemptions. It can trade a higher utilization bound for more preemptions and in doing so it has a utilization bound of 100%

Consider the problem of scheduling sporadic message transmission requests with deadlines. For wired channels, this has been achieved successfully using the CAN bus. For wireless channels, researchers have recently proposed a similar solution; a collision-free medium access control (MAC) protocol that implements static-priority scheduling. Unfortunately no implementation has been reported, yet. We implement and evaluate it to find that the implementation indeed is collision-free and prioritized. This allows us to develop schedulability analysis for the implementation. We measure the response times of messages in our implementation and find that our new response-time analysis indeed offers an upper bound on the response times. This enables a new class of wireless real-time systems with timeliness guarantees for sporadic messages and it opens-up a new research area: schedulability analysis for wireless networks

Time-sensitive wireless sensor network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worst-case dimensioning of cluster-tree WSNs. We provide a fine model of the worst-case cluster-tree topology characterized by its depth, the maximum number of child routers and the maximum number of child nodes for each parent router. Using Network Calculus, we derive "plug-and-play " expressions for the end-to-end delay bounds, buffering and bandwidth requirements as a function of the WSN cluster-tree characteristics and traffic specifications. The cluster-tree topology has been adopted by many cluster-based solutions for WSNs. We demonstrate how to apply our general results for dimensioning IEEE 802.15.4/Zigbee cluster-tree WSNs. We believe that this paper shows the fundamental performance limits of cluster-tree wireless sensor networks by the provision of a simple and effective methodology for the design of such WSNs

We propose a schedulability analysis for a particular class of time division multiple access (TDMA) networks, which we label as TDMA/SS. SS stands for slot skipping, reflecting the fact that a slot is skipped whenever it is not used. Hence, the next slot can start earlier in benefit of hard real-time traffic. In the proposed schedulability analysis, we assume knowledge of all message streams in the system, and that each node schedules messages in its output queue according to a rate monotonic policy (as an example). We present the analysis in two steps. Firstly, we address the case where a node is only permitted to transmit a maximum of one message per TDMA cycle. Secondly, we generalise the analysis to the case where a node is assigned a budget of messages per TDMA cycle it may transmit. A simple algorithm to assign budgets to nodes is also presented

The marriage of emerging information technologies with control technologies is a major driving force that, in the context of the factory-floor, is creating an enormous eagerness for extending the capabilities of currently available fieldbus networks to cover functionalities not considered up to a recent past. Providing wireless capabilities to such type of communication networks is a big share of that effort. The RFieldbus European project is just one example, where PROFIBUS was provided with suitable extensions for implementing hybrid wired/wireless communication systems. In RFieldbus, interoperability between wired and wireless components is achieved by the use specific intermediate networking systems operating as repeaters, thus creating a single logical ring (SLR) network. The main advantage of the SLR approach is that the effort for protocol extensions is not significant. However, a multiple logical ring (MLR) approach provides traffic and error isolation between different network segments. This concept was introduced in, where an approach for a bridge-based architecture was briefly outlined. This paper will focus on the details of the inter-Domain Protocol (IDP), which is responsible for handling transactions between different network domains (wired or wireless) running the PROFIBUS protocol.

PROFIBUS is an international standard (IEC 61158) for factory-floor communications, with some hundreds of thousands of world-wide installations. However, it does not include any wireless capabilities. In this paper we propose a hybrid wired/wireless PROFIBUS solution where most of the design options are made in order to guarantee the proper real-time behaviour of the overall network. We address the timing unpredictability problems placed by the co-existence of heterogeneous transmission media in the same network. Moreover, we propose a novel solution to provide inter-cell mobility to PROFIBUS wireless nodes.

Technological developments are pulling fieldbus networks to support a new wide class of applications, such as industrial multimedia applications. To enable its use in this kind of applications the TCP/IP suite of protocols can be integrated within a fieldbus stack, leading to a dual-stack approach that is briefly outlined in the paper. One important requirement that must be fulfilled by this approach is that the hard real-time guarantees provided to the control-related traffic ("native" fieldbus traffic) are kept. At the same time it must also provide the desired quality of service (QoS) to IP applications. The focus of the paper is on how, in such a dual-stack approach, QoS can be efficiently provided to IP applications requiring quasi-constant bandwidth.

Fieldbus communication networks aim to interconnect sensors, actuators and controllers within distributed computer-controlled systems. Therefore, they constitute the foundation upon which real-time applications are to be implemented. A specific class of fieldbus communication networks is based on a simplified version of token-passing protocols, where each station may transfer, at most, a single message per token visit (SMTV). In this paper, we establish an analogy between non-preemptive task scheduling in single processors and the scheduling of messages on SMTV token-passing networks. Moreover, we clearly show that concepts such as blocking and interference in non-preemptive task scheduling have their counterparts in the scheduling of messages on SMTV token-passing networks. Based on this task/message scheduling analogy, we provide pre-run-time schedulability conditions for supporting real-time messages with SMTV token-passing networks. We provide both utilisation-based and response time tests to perform the pre-run-time schedulability analysis of real-time messages on SMTV token-passing networks, considering RM/DM (rate monotonic/deadline monotonic) and EDF (earliest deadline first) priority assignment schemes.

In this paper we address the real-time capabilities of P-NET, which is a multi-master fieldbus standard based on a virtual token passing scheme. We show how P-NET's medium access control (MAC) protocol is able to guarantee a bounded access time to message requests. We then propose a model for implementing fixed priority-based dispatching mechanisms at each master's application level. In this way, we diminish the impact of the first-come-first-served (FCFS) policy that P-NET uses at the data link layer. The proposed model raises several issues well known within the real-time systems community. Message release jitter; pre-run-time schedulability analysis in non pre-emptive contexts; non-independence of tasks at the application level. We identify these issues in the proposed model and show how results available for priority-based task dispatching can be adapted to encompass priority-based message dispatching in P-NET networks.

The paper provides a comprehensive study on how to use Profibus networks to support real time communications, that is, ensuring the transmission of the real time messages before their deadlines. Profibus is based on a simplified Timed Token (TT) protocol, which is a well proved solution for real time communication systems. However, Profibus differences from the TT protocol prevent the application of the usual TT analysis. The main reason is that, conversely to the TT protocol, in the worst case, only one high priority message is processed per token visit. The major contribution of the paper is to prove that, despite this shortcoming, it is possible to guarantee communication real time behaviour with the Profibus protocol.