A novel strategy for ensuring reliable and effective communication between sensor nodes and the industrial control systems is the QoS (Quality of Service) Localized Routing scheme for Wireless Industrial sensor Networ...
A novel strategy for ensuring reliable and effective communication between sensor nodes and the industrial control systems is the QoS (Quality of Service) Localized Routing scheme for Wireless Industrial sensor Networks. This plan is built on the idea of localized routing, a routing technique based on the network's local topology. This system enables effective network performance monitoring and may be used to give QoS assurances to apps that need them. In order to regulate the network, centralized scheduling methods rely on a centralized controller. This controller is responsible for making decisions about the scheduling of resources, such as frequency allocation and transmission power. These algorithms offer a number of advantages, such as scalability, reliability, and security. However, they can be difficult to implement in large networks and may require significant amounts of communication and computation resources. distributed scheduling algorithms, on the other hand, do not rely on a centralized controller. Instead, they use distributed algorithms to make scheduling decisions. These algorithms are typically more efficient and require less communication and computation resources. They are also better suited to large networks, since they do not require a centralized controller
Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces i...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.
Batteryless sensor nodes, powered solely by energy harvesting, are a promising alternative to battery-powered sensor nodes. However, energy harvesting rates being very low, unreliable, and time-varying, nodes cannot s...
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ISBN:
(数字)9798331535513
ISBN:
(纸本)9798331535520
Batteryless sensor nodes, powered solely by energy harvesting, are a promising alternative to battery-powered sensor nodes. However, energy harvesting rates being very low, unreliable, and time-varying, nodes cannot sustain a continuous operation, making them intermittently powered. As a result, these nodes incur unpredictable wakeup times due to continuously varying off-times. To perform tasks like distributed sensing, time synchronization, and communication for intermittently powered nodes, timely execution and robust coordination is vital. To achieve robust coordination, nodes must guarantee to be ON at a coordinated target time regardless of harvesting variations. To ensure robust coordination of on-times, we propose PAIL, a novel hardware-software approach where the hardware component enforces a constant off-time, with small variations. The software component dynamically corrects for those variations. We fabricated a PAIL sensor node and validated its ability to discover neighboring nodes. Using extensive simulations calibrated from experimental measurements, we observe that PAIL maintains nearly 99.3% coordination at steady state. In the context of pairwise communication, we show that leveraging PAIL coordination, nodes improve packet tail latency by 99%.
Wireless Battery Management systems (WBMS) are significant steps in monitoring and controlling energy storage, which varies from electric vehicle applications to renewable energy applications and smart grid systems. T...
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ISBN:
(数字)9798331533663
ISBN:
(纸本)9798331533670
Wireless Battery Management systems (WBMS) are significant steps in monitoring and controlling energy storage, which varies from electric vehicle applications to renewable energy applications and smart grid systems. They operate on wireless communication technologies such as Zigbee, Bluetooth, and Wi-Fi, eliminating the need for complex wiring increasing the potential weight reduction of the system, and minimizing potential reliability issues. This paper elaborates on designing and implementing a microcontroller-based WBMS that monitors key parameters such as voltage, current, and temperature in real time. All these parameters are transmitted wirelessly to centralized management units or mobile devices for analysis. State estimation algorithms further enhance the reliability of key metrics such as SOC, SOH, and fault diagnosis. WBMS catches real-time information from all cells in the battery using sensors and wirelessly transmits it to a central monitoring unit. This paper aims to analyze recent advancements in EV BMS technology and identify potential directions for future research in this area. This paper explores the challenges and opportunities associated with implementing BMS in electric vehicles and presents an in-depth review of the existing literature.
Nowadays, networks are increasingly reliant on software frameworks and virtualization. To obtain relevant pre-dictions of the behavior of their protocols, network emulation tools play a crucial role. However, as netwo...
Nowadays, networks are increasingly reliant on software frameworks and virtualization. To obtain relevant pre-dictions of the behavior of their protocols, network emulation tools play a crucial role. However, as networks grow in size and complexity, the emulation task demands more physical resources such as CPU and memory. Consequently, a single physical machine is no longer sufficient to handle large-scale network emulation. Instead, distributed network emulation tools must be employed for resource-intensive network emulation tasks. In resource-intensive distributed network emulation, the physical resource allocation is NP-hard to perform appropriately. In this paper, we propose SCBG (spectral clustering based greedy node placement algorithm) as a means to enhance the bandwidth utilization between physical nodes in distributed emulation. SCBG aims to address the communication bottleneck in distributed network simulation, surpassing the algorithm proposed by Distrinet, while also improving the algorithm's execution speed. Additionally, SCBG incorporates optimization for time dilation when physical bandwidth resources are severely insufficient. This optimization leads to a reduction in the additional time required for network simulation after applying time dilation. Experimental results demonstrate that SCBG surpasses the algorithm in Distrinet across a diverse range of intricate large-scale networks.
With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the dominant solutions for short-range wireless communication in IoT. BLE mitigates interference via Adaptive Fre...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the dominant solutions for short-range wireless communication in IoT. BLE mitigates interference via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum. However, the ever-growing number of BLE devices and WiFi traffic in the already crowded 2.4 GHz band lead to situations where the quality of BLE connections dynamically changes with nearby wireless traffic, location, and time of day. These dynamic environments demand new approaches for channel management in AFH, by both dynamically excluding frequencies suffering from localized interference and adaptively re-including channels, thus providing sufficient channel diversity to survive the rise of new *** introduce eAFH, a new channel-management approach in BLE with a strong focus on efficient channel re-inclusion. eAFH introduces informed exploration as a driver for inclusion: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-inclusion into the hopping sequence. As a result, eAFH adapts in dynamic scenarios where interference varies over time. We show that eAFH achieves 98-99.5% link-layer reliability in the presence of dynamic WiFi interference with 1% control overhead and 40% higher channel diversity than state-of-the-art approaches.
The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on t...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on the other hand, will become critically unwell and require medical assistance. People over the age of 65, as well as those with underlying medical diseases such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer, are at a higher risk of developing serious illness. Being thoroughly informed on the disease and how it spreads is the best strategy to avoid and slow down transmission. Stay at least 1 metre apart from other people to avoid infection. In this research work, we focus on how non-contact sensing technology and deep learning technique are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. The proposed system captures images from the patient using non-contact sensing technologies and feeds the data into deep learning convolutional neural network architectures such as VGG16, VGG19, ResNet101, NASNet, DenseNet121, MobileNet, Xception, EfficientNet, and InceptionV3. In comparison to other architectures, the VGG16 architecture delivers superior accuracy.
Due to advancements in technology, electrical appliances are now inter-connected. The goal of Internet -of-things (IoT) is to access every appliance or device through the Internet. This is done in order to operate the...
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ISBN:
(纸本)9781665449359
Due to advancements in technology, electrical appliances are now inter-connected. The goal of Internet -of-things (IoT) is to access every appliance or device through the Internet. This is done in order to operate these gadgets from remote locations. The goal is to improve our day-to-day life. However, this technology raises serious privacy and security issues. As IoT devices are resource-constrained, it is impractical to secure them using traditional approaches. Hence, a light-weight Intrusion detection system (IDS) is required. In this work, we implement a machine learning based Network Intrusion Detection (NID) system in a multi-node fog environment using a Raspberry Pi cluster on a local area network. The proposed Pi-IDS system has been evaluated on ADFA-LD datasets. These datasets comprise of new generation system calls for various attacks on different applications. The proposed fog architecture offers significant advantages in terms of latency, energy consumption and cost over traditional cloud or dedicated personal computer systems. The experiments show that we are able to achieve a Recall of 89%in ADFA-LD with the XGBoost model. The proposed system was able to predict intrusion with an inference time 130 ms in comparison to Cloud with 735 ms, with an estimated running cost of 201 INR/month in comparison to the Cloud cost of 2051 INR/month.
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decompo...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main channel). Based on real-time appliance power prediction using sensory technology, energy disaggregation has great potential to increase electricity efficiency and reduce energy expenditure. With the introduction of transformer models, NILM has achieved significant improvements in predicting device power readings. Nevertheless, transformers are less efficient due to O(l 2 ) complexity w.r.t. sequence length l. Moreover, transformers can fail to capture local signal patterns in sequence-to-point settings due to the lack of inductive bias in local context. In this work, we propose an efficient localness transformer for non-intrusive load monitoring (ELTransformer). Specifically, we leverage normalization functions and switch the order of matrix multiplication to approximate self-attention and reduce computational complexity. Additionally, we introduce localness modeling with sparse local attention heads and relative position encodings to enhance the model capacity in extracting short-term local patterns. To the best of our knowledge, ELTransformer is the first NILM model that addresses computational complexity and localness modeling in NILM. With extensive experiments and quantitative analyses, we demonstrate the efficiency and effectiveness of the the proposed ELTransformer with considerable improvements compared to state-of-the-art baselines.
Serverless computing is gaining traction as an at-tractive model for the deployment of a multitude of work-loads in the cloud. Designing and building effective resource management solutions for any computing environme...
Serverless computing is gaining traction as an at-tractive model for the deployment of a multitude of work-loads in the cloud. Designing and building effective resource management solutions for any computing environment requires extensive long term testing, experimentation and analysis of the achieved performance metrics. Utilizing real test beds and serverless platforms for such experimentation work is often times not possible due to resource, time and cost constraints. Thus, employing simulators to model these environments is key to overcoming the challenge of examining the viability of such novel ideas for resource management. Existing simulation software developed for serverless environments lack generalizibility in terms of their architecture as well as the various aspects of re-source management, where most are purely focused on modeling function performance under a specific platform architecture. In contrast, we have developed a serverless simulation model with induced flexibility in its architecture as well as the key resource management aspects of function scheduling and scaling. Further, we incorporate techniques for easily deriving monitoring metrics required for evaluating any implemented solutions by users. Our work is presented as CloudSimSC, a modular extension to CloudSim which is a simulator tool extensively used for modeling cloud environments by the research community. We discuss the implemented features in our simulation tool using multiple use cases.
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