Power IoT Research Articles

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Design and performance investigation of tunnel-FET based energy efficient approximate and accurate adders targeted towards low power IoT nodes

Abstract The proliferation of IoT enabled SoCs in state-of-the-art consumer electronics has necessitated the use of beyond CMOS devices for the energy efficient computations and sensing design space. In the low supply voltage (VDD) regime, Tunnel FETs (TFETs) have shown serious potential at VDD less than 0.5 V. However, the inherent structure, characteristic interband tunneling and enhance Miller capacitance in TFETs do not allow the conventional CMOS logic design styles to be universally used for TFET based logic circuits. In this paper, we propose three energy efficient and novel TFET based accurate adder circuits namely, Static Energy Recovery Full adder (SERF), Complementary and Level Restoring Carry Logic Full adder (CLRCL) and Balanced Restoring Carry Logic full adder (BRCL) based on constituent TFET based functional units which do not use the CMOS design style. Further, novel approximate adders have been developed for energy efficient implementation in IoT nodes. The BRCL adder consumes approximately 60% less area and 85.36% and 89.6% less energy in comparison to SERF and CLRCL respectively. The approximate adders in the best and worst case facilitate approximately 92% and 75% savings in area and consumes 71% less power than the BRCL adder. When compared to the standard UMC 45 nm bulk MOSFET based designs, the TFET based accurate adders operate at 25% less energy while the approximate adders consume 40% less. All the circuit simulations have been performed using Spectre Simulator of Cadence and device characterization using 2D-TCAD tool.

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

Device-free crowd counting using multi-link WiFi CSI for occupant-driven energy management of HVAC systems

ABSTRACT Accurate estimation of the number of occupants in a building’s thermal zone is a crucial input for the dynamic estimation of the cooling load of a heating, ventilation, and air conditioning equipment. This data, in conjunction with an intelligent control algorithm, can be judiciously employed for enhancing occupant comfort and achieving substantial energy savings through demand-driven operation. In this work, we propose a WiFi channel state information (CSI) based passive people counting model that utilizes a wavelet-based signal denoising scheme and an artificial neural network (ANN) model for automatic feature extraction. The application of the proposed model on a public dataset shows more than 99% accuracy in detecting up to eight people in three rooms of varying sizes. The proposed model was also validated on CSI samples collected from a laboratory environment with five occupants, using ESP32, a low cost and low power IoT device. The results show that the proposed model can perform people counting with an accuracy of up to 98%, using single- and multi-link measurements.

Optimization model of combined peak shaving of virtual power grid and thermal power based on power IoT

Optimization model of combined peak shaving of virtual power grid and thermal power based on power IoT

A Multi-Hop End-Edge Cooperative Computing Scheme for Power IoT

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm’s performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system’s energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes.

Coexistence of Energy-Minimizing URLLC and eMBB in Power IoT via NOMA-Based Collaborative MEC Heterogeneous Network

Coexistence of Energy-Minimizing URLLC and eMBB in Power IoT via NOMA-Based Collaborative MEC Heterogeneous Network

Residual Time Based Energy Harvesting for Framed Slotted ALOHA Based Wireless Powered IoT Networks

Residual Time Based Energy Harvesting for Framed Slotted ALOHA Based Wireless Powered IoT Networks

Construction and Analysis of Intelligent Analysis and Disposal Model for Internet Security Events in Power System

The reliability of nodes is rugged to determine, and the global accuracy is low in identifying secure access nodes of ubiquitous power network gateways. Therefore, this project intends to establish a universal power grid gateway security access node identification model. Sensor technology collects secure access nodes of IoT gateways and evaluates their reliability. It is integrated with the security level of the network to form a virtual security domain. Then, the access node is searched and controlled twice. The access node identification of the universal power IoT gateway is realized according to the reliability calculation. Simulation results show that under the same parameter conditions, when the node size increases and the number of malicious nodes increases, the proposed method can obtain higher accuracy of secure access to nodes. This proves the advantages of the proposed method.

A novel blockchain-enabled zero-trust-based authentication scheme in power IoT environments

A novel blockchain-enabled zero-trust-based authentication scheme in power IoT environments

Investigating Routing Protocol Attacks on Low Power and Lossy IoT Networks

Investigating Routing Protocol Attacks on Low Power and Lossy IoT Networks

Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system

Every country's foundation is its agricultural industry, which drives about half of global economic growth. Accurate farming is crucial for crop evaluation and pest control. Old-fashioned pest detection is unstable and gives inaccurate forecasts. These surveillance tactics are intrusive, expensive, and vulnerable to assumptions. Pest sounds can be caught with minimum effort by utilizing IoT networks. Deep learning algorithms, species distribution range assessments, and nature monitoring can automatically identify and categorize pest sounds. The IoT-driven computerized components in this research's unique pest identification system used machine learning on insect sound recordings. The Butterworth filter, Blackman and Flattop window, Ultraspherical Filter, Rife-Vincent Window, Cosine-Tapered Window, FFT, DFT, STFT, PNCC, RASTA-PLPCC, LSFCC, sound detectors, and PID sensors were employed. The intended study used the HFDLNet for training, testing, and validation, examining 7,200 pest sounds from 72 species to determine their unique characteristics and statistical attributes. The proposed model has 99.87 % accuracy, 99.96 % sensitivity, 99.88 % specificity, 99.96 % recall, 99.93 % F1 score, and 99.98 % precision. This study outperforms Inception-ResNet-v2, FRCNN ResNet-50, Fatser-PestNet, MD-YOLO, YOLOv5m, MAM-IncNet, and Xception. The suggested solution uses IoT networks and pest sound analysis to establish a pest preventive and management strategy and a solar-powered generator to power IoT devices across vast agricultural fields.

Task Offloading and Approximate Computing in Solar Powered IoT Networks

Task Offloading and Approximate Computing in Solar Powered IoT Networks

UAV-assisted wireless charging and data processing of power IoT devices

UAV-assisted wireless charging and data processing of power IoT devices

Edge Computing in Power IoT Security and Intelligent Monitoring

Abstract In the context of the rapid development of power IoT, the application of edge computing technology has become the key to improving the level of grid intelligence and enhancing the data processing capability. This paper initially designs the edge computing system for electric power IoT based on the edge computing model. Key-edge computing technologies are combined to process and analyze power IoT data in real-time. Simulation experiments have formed and verified an intelligent security monitoring system for electric power IoT using the LightGBM algorithm. The training convergence speed and effectiveness of this paper’s scheme are better than Stroj’s scheme, and this paper’s scheme can increase the security of power IoT data through key generation and filter de-duplication. This paper’s nodes have an average synchronization time of 9.25 ms. The 128MB data node has an upload time of 57143ms. The data sharing time is about 292~7489 ms faster than the comparison scheme, and in the data search phase, the time overhead of this paper’s scheme is less than the comparison scheme. In summary, this paper’s constructed security monitoring system can offer robust technical support for the advancement of intelligent, efficient, and omnipotent power in the Internet of Things.

Preventive Audits for Data Applications Before Data Sharing in the Power IoT

Preventive Audits for Data Applications Before Data Sharing in the Power IoT

Designed-by-purpose power sources: a cardboard primary battery for smart packaging

Battery life-cycle reconceptualization under a design-by-purpose approach to power IoT devices and then be recycled with paper and cardboard waste stream, addressing future technological needs of society from an environmentally conscious perspective.

A Lightweight Authentication Scheme for Power IoT Based on PUF and Chebyshev Chaotic Map

A Lightweight Authentication Scheme for Power IoT Based on PUF and Chebyshev Chaotic Map