Modelling of wireless sensor networks (WSNs) can be addressed from various aspects, such as energy efficiency, scalability, fault tolerance and network lifetime. Most of the algorithms proposed in literature which add...
详细信息
As well as the design and optimised parameters, will be transmitted for the essential information exchange. This research study focuses on integrating WSN architecture. In actuality, many automated systems still requi...
详细信息
With the increasing demands of data computation and storage for cloud-based services, the energy demand of data centers (DCs) is rising rapidly and becoming a noticeable challenge for current power networks. The smart...
详细信息
Smart meter data with energy disaggregation can be used to identify energy usage of appliances, offering valuable insights for consumers and energy companies to better manage energy. However, these techniques also pre...
详细信息
ISBN:
(数字)9798350369441
ISBN:
(纸本)9798350369458
Smart meter data with energy disaggregation can be used to identify energy usage of appliances, offering valuable insights for consumers and energy companies to better manage energy. However, these techniques also present privacy risks, such as the potential for behavioral profiling. Local differential privacy (LDP) methods provide strong privacy guarantees with high efficiency in addressing privacy concerns. Exiting LDP methods primarily focus on securing aggregated energy data, ignoring the streaming nature of smart meter data and individual appliance data. In this paper, we propose a novel LDP approach (named LDP–Energy) to facilitate the sharing of appliance-level energy data over time while not revealing individual users’ appliances and their usage patterns. By incorporating a sliding window concept, LDP–Energy is able to efficiently handle the streaming smart meter data. Our evaluations indicate that LDP–Energy performs efficiently compared to other methods, striking a proper balance between privacy and data utility for effective analysis.
Simulation is the most commonly used method for evaluating protocols and algorithms within vehicular networks (VANETs). Typically, simulation tools employ mobility traces to reconstruct the network topology, which rel...
详细信息
ISBN:
(数字)9798350369441
ISBN:
(纸本)9798350369458
Simulation is the most commonly used method for evaluating protocols and algorithms within vehicular networks (VANETs). Typically, simulation tools employ mobility traces to reconstruct the network topology, which relies on existing interactions among mobile nodes. However, the quality of these traces, particularly their spatial and temporal resolution, plays a critical role in accurately shaping the network topology. Consequently, the validity of simulation outcomes heavily depends on the mobility model’s ability to mirror the real network topology accurately. We demonstrate that actual bus mobility traces exhibit gaps, which lead to outcomes that fail to represent reality accurately. In this study, we introduce a method to address these gaps, resulting in enhanced characteristics that contribute to more trustworthy simulation outcomes. Moreover, we present evaluation results comparing the communication metrics of both the original and adjusted traces. These findings indicate that the gaps create network topologies that deviate from the actual scenario, diminishing the credibility of the evaluation results. Although mobility data for vehicular networks is beneficial, it necessitates a process of quality enhancement.
A novel, high-density, miniature and compact chip- less RFID sensor tag is presented in this article. The proposed work comprises fourteen nested quatrefoil-shaped slot resonators for ID coding with the additional fea...
详细信息
Collaborative learning is an emerging field of machine learning. In this framework, multiple learning algorithms try to learn from a distributed database. The main idea is to improve the performance of each algorithm ...
详细信息
The proceedings contain 58 papers. The topics discussed include: POS: an operator scheduling framework for multi-model inference on edge intelligent computing;CoEdge: a cooperative edge system for distributed real-tim...
ISBN:
(纸本)9798400701184
The proceedings contain 58 papers. The topics discussed include: POS: an operator scheduling framework for multi-model inference on edge intelligent computing;CoEdge: a cooperative edge system for distributed real-time deep learning tasks;PointSplit: towards on-device 3D object detection with heterogenous low-power accelerators;addressing practical challenges in acoustic sensing to enable fast motion tracking;CMA: cross-modal association between wearable and structural vibration signal segments for indoor occupant sensing;interpersonal distance tracking with mmWave radar and IMUs;mmRipple: communicating with mmWave radars through smartphone vibration;DeepGANTT: a scalable deep learning scheduler for backscatter networks;ARSteth: enabling home self-screening with AR-assisted intelligent stethoscopes;hydra: concurrent coordination for fault-tolerant networking;MicroDeblur: image motion deblurring on microcontroller-based vision systems;and mosaic: extremely low-resolution RFID vision for visually-anonymized action recognition.
暂无评论