Browsing by Subject "Energy consumption"
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Item A Decision Support System For Optimal Selection Of Roadway Alignments Using Sustainability Metrics(2014) Shariat, Shaghayegh; Saka, Anthony Amos; Transportation; Doctor of EngineeringSustainability, as a long-term concern about environmental, economic, and social issues that affect the well-being of humans and environment, and the need for its implementation in engineering has been highlighted in the last few decades. New concepts such as "Green" and "Sustainable" have penetrated into most planning and engineering fields. Transportation and specifically roadways, being one of the major sources of emission, are under intense attention of environmental legislations. Roadway alignments are usually designed based on short-term cost minimization (e.g. minimization of earthwork cost). However, the need for adopting a long-term perspective in the design process that incorporates other important factors such as overall amount of fuel consumption and accident costs in the roadway lifetime is becoming self-evident. Achieving a sustainable roadway is not possible without addressing its footprint on environment, society, and economy. In this study, a decision support system is developed for evaluation and comparison of alternative geometric alignments based on their sustainability ratio determined from fuel consumption (environmental impact), safety (social impact), and earthwork (economic impact) as follows: a) Development of a Fuel Consumption evaluation model associated with roadway geometric design parameters, which evaluates and compares alternative alignments based on their fuel efficiency. b) Development of a Sight Distance evaluation model that evaluates and compares the alternative alignments safety based on their available and required sight distance profiles. c) Development of a Terrain Disturbance evaluation model that evaluates and compares the alternative alignments earthwork and minimizes the environmental impacts on natural terrain shape. The formulation for all three models is then implemented in AutoCAD Civil 3D software package (programmed in Visual Basic language) to automatically evaluate any given alignment based on these three metrics. The developed model provides the tools for designers and planners to quantitatively compare and rate various alternatives and select the optimal alignment. This model is advantageous over existing rating systems that evaluate metrics qualitatively.Item A Novel Clustering Protocol for Network Lifetime Maximization in Underwater Wireless Sensor Networks(IEEE, 2024-03-12) Jha, Amitkumar V.; Appasani, Bhargav; Khan, Mohammad S.; Song, HoubingUnderwater wireless sensor network (UWSN) is a pervasive technology with different characteristics and requirements, where energy conservation is a stringent requirement. Improving the network lifetime can have tremendous practical utility in these networks. The energy of the nodes in the network can be conserved by devising an efficient cluster head selection mechanism. This paper presents a novel energy-efficient clustering protocol (EECP) for the UWSN. The proposed protocol segregates the network based on horizontal clustering. In every iteration, the cluster heads are selected based on the energy level of the nodes. The performance of the proposed protocol is measured in terms of energy efficiency and network lifetime. Moreover, the performance of the EECP is further improved by adding nearest neighbor criteria for selecting the cluster head. This protocol is named as energy-efficient clustering protocol with nearest neighbor (EECP-NN). The efficacy of the proposed protocols is evaluated by comparing their performance with some of the state-of-the-art cluster-based protocols in this study.Item AARPA: Combining Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoring(IEEE, 2015-09-14) Roy, Nirmalya; Pathak, Nilavra; Misra, ArchanTo promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with 87% accuracy.Item A Cross-Stack QoS Routing Approach For Underwater Acoustic Sensor Networks(IEEE, 2018-10-18) Emokpae, Lloyd E.; Liu, Zhiqiang; Edelmann, Geoffrey F.; Younis, MohamedIn this paper, we utilize a novel cross-stackc design that factors in the dynamics of the underwater channel to optimize the single-hop performance amongst multiple node pairs. This will result in a set of links that meet or exceed QoS requirements, which is further leveraged for network discovery and energy-efficient routing with minimum end-to-end packet delay. Thus, our proposed routing approach will provide means to guarantee application-specific QoS while also maximizing the network lifetime. Simulation experiments were conducted to validate the approach in a shallow water multipath environment.Item Reinforcement Learning-Based Offloading for RIS-Aided Cloud-Edge Computing in IoT Networks: Modeling, Analysis and Optimization(IEEE, 2024-03-08) Zhang, Tiantian; Xu, Dongyang; Tolba, Amr; Yu, Keping; Song, Houbing; Yu, ShuiThe rapid advancement of wireless communication and artificial intelligence (AI) has led to a plethora of emerging applications that require exceptional connectivity, minimal latency, and substantial computing resources. The widespread adoption of cloud-edge intelligence is propelling the development of future networks capable of supporting intelligent computing. Mobile edge computing (MEC) technology facilitates the movement of computing resources and storage to the network’s edge, enabling cost-effective offloading of computational tasks for related applications which needs for reduced latency and improved energy efficiency. However, the offloading efficiency is hindered by limitations of wireless transmission capacity. This paper aims to address this issue by integrating reconfigurable intelligent surfaces (RISs) into a cell-free network within an intelligent cloud-edge system. The core idea is to strategically deploy passive RISs around base stations (BSs) to reconstruct the transmission channel and improve the corresponding capacity. Subsequently, we formulate an optimal problem involving joint beamforming for RISs and BSs, which is characterized by non-convexity and complexity. To tackle this challenge, we employ an alternating optimization scheme to ensure the effectiveness of joint beamforming. In particular, deep reinforcement learning (DRL) is leveraged to reduce the computational complexity involved in optimizing task offloading. Additionally, Lyapunov optimization is utilized to model the latency queue and improve the learning efficiency of the offloading framework. We conduct comprehensive evaluations on the wireless system’s capacity, average latency, and energy consumption, considering the integration of RIS with the DRL offloading framework. Experimental results demonstrate that our proposed scheme achieves superior efficiency and robustness.Item Smart-Energy Group Anomaly Based Behavioral Abnormality Detection(IEEE, 2016-12-15) Alam, Mohammad Arif Ul; Roy, Nirmalya; Petruska, Michelle; Zemp, AndreaMonitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance's energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.