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Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Yasir Saleem, Noel Crespi, Mubashir Husain Rehmani, Rebecca Copeland

arXiv:1704.08977v2cs.NI

TL;DR

Traditional grids face uni-directional information flow, energy wastage, rising demand, and reliability and security concerns while requiring coordination of vast device populations. The paper surveys IoT-aided Smart Grids across architectures, applications, prototypes, communications, challenges, and future research directions, concluding that substantial issues remain for complete realization.

  • Problem

    Traditional power grids require improved coordination and communication for changing demands, energy efficiency, reliability, security, and large-scale monitoring and control.

  • Method

    The paper conducts a comprehensive survey of IoT-aided Smart Grid systems, covering applications, architectures, prototypes, communications, data fusion, security, and related challenges.

  • Results

    The survey synthesizes existing IoT-aided Smart Grid work and identifies interoperability, data handling, security, communication, congestion, and experimentation gaps.

  • Takeaways & Limitations

    IoT-aided Smart Grids integrate IoT devices and communications with grid operations, but further work is needed for their better and complete realization.

Abstract

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Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

I. INTRODUCTION

The paper motivates IoT-aided Smart Grids as a response to changing electricity demands, energy wastage, and the need to monitor and control vast numbers of grid devices. It surveys the field comprehensively across technologies, applications, architectures, prototypes, and future directions.

  • Motivation: Smart Grids address traditional-grid limitations including uni-directional communication, energy wastage, growing demand, reliability, and security.They provide bi-directional communication between service providers and consumers and support demand-aware grid decisions.
  • Motivation: Hundreds of millions or billions of monitoring, analysis, and control devices create connectivity, automation, tracking, and distributed-communication requirements.These devices are deployed across power plants, transmission infrastructure, distribution centers, and consumer premises.
  • Motivation: IoT supplies sensors, actuators, and smart meters plus connectivity, automation, and tracking across generation, storage, transmission, distribution, and consumption.IoT objects sense, analyze, control, and decide through distributed, autonomous, ubiquitous, two-way digital communications.
  • Comparison with Existing Survey Articles: Existing surveys generally focus on narrower areas such as smart metering, security, communication layers, residential systems, or specific IoT and SG topics.The paper contrasts these focused efforts with a survey spanning the broader IoT-aided SG domain.
  • Novelty and Contributions: The survey claims comprehensive coverage of integration, applications, architectures, prototypes, taxonomies, communication technologies, open issues, and future research directions.Its stated contributions also include big data and cloud discussions, author insights, and a layered presentation of challenges and research directions.
  • Novelty and Contributions: The contributions include taxonomies of applications, architecture and prototype discussions, big-data and cloud analysis, communication-technology overviews, and layered future directions.The paper also presents author insights under major topics.

C. Article Organization

The paper is organized as a comprehensive survey of IoT and Smart Grid foundations, architectures, applications, prototypes, communications, and related research directions. It introduces the SG's layered structure and the role of IoT-enabled devices and networks.

  • C. Article Organization: The paper surveys IoT-aided SG technology, applications, architectures, prototypes, and experimentation across the grid.Its organization includes an IoT and SG overview, current applications, architectures, prototypes, and later technical topics.
  • A. Internet of Things: IoT connects smart devices through Internet communication to support monitoring, tracking, management, and location identification.The definition emphasizes things-oriented, Internet-oriented, and semantic-oriented concepts.
  • B. Smart Grid: SG architecture combines four power subsystems—generation, transmission, distribution, and utilization—with WAN, NAN, and HAN information networks.Power flows through the subsystems, while information flows through the networks.
  • C. Article Organization: IoT-enabled EVs support bidirectional energy exchange, allowing vehicles to consume electricity from and deliver electricity back to the grid.EVs can also exchange energy locally with other EVs through bidirectional chargers and an aggregator.

C. Importance of SG in Smart Cities

Smart Grid modernization is presented as central to smart-city infrastructure because energy supports other urban sectors and requires coordinated sensing, communication, and control. IoT supplies the connectivity and automation needed across SG layers and subsystems.

  • C. Importance of SG in Smart Cities: Energy is identified as the most important smart-city sector for sustainable urban life and integration of stakeholders and sensitive infrastructure.The paper links smart cities closely to modernization of the traditional power grid.
  • C. Importance of SG in Smart Cities: IoT supports SG construction by enabling components to sense, communicate, and automate planning, maintenance, operations, and service-disruption detection.Smart meters can automatically signal disruptions when affected components stop sending collected data.
  • C. Importance of SG in Smart Cities: IoT applies across generation, transmission, distribution, and utilization for monitoring, control, prediction, storage, connection, and distributed energy management.The surveyed functions include renewable, pumped-storage, wind, biomass, and photovoltaic power systems.
  • C. Importance of SG in Smart Cities: HAN manages on-demand consumer power through smart appliances, EVs, renewable sources, and smart meters within homes, plants, and commercial buildings.HANs may use star or mesh topologies and support device commissioning and control.
  • C. Importance of SG in Smart Cities: NAN aggregates information from multiple HANs and connects smart meters and distribution devices to data collectors and WANs.NAN communication must cover approximately a thousand meters and provide interference-free channels between meters and aggregation points.
  • C. Importance of SG in Smart Cities: WAN provides the SG communication backbone linking gateways, NANs, grid devices, substations, generation systems, renewable sources, and control centers.It comprises interconnected core and backhaul networks.

E. Standardization Activities for IoT-aided SG systems

IoT-aided SG systems require joint standardization because heterogeneous technologies must interoperate across devices, meters, protocols, interfaces, workflows, and messages. The paper reviews existing standards and related applications while identifying interoperability as an unresolved issue.

  • 3) Standardization of IoT-aided SG Systems:: IoT standardization includes oneM2M, OMA LWM2M, and IETF protocols such as RPL and CoAP for connecting constrained devices to the Internet.M2M communication is described as an enabling technology for IoT.
  • 3) Standardization of IoT-aided SG Systems:: SG standardization involves IEEE, ANSI, NIST, and IETF, alongside European Expert Groups and mandates addressing SG coordination and EV infrastructure.CENELEC and CEN established the eM-CG to coordinate eMobility standardization.
  • 3) Standardization of IoT-aided SG Systems:: IoT-aided SG standardization should prioritize interoperability among heterogeneous devices, meters, protocols, interfaces, workflows, and messages.The objective is seamless bridging of different technologies and standards rather than defining one communication technology.
  • 3) Advanced Metering Infrastructure (AMI):: IoT enables AMI and remote meter reading to collect real-time electricity-consumption data for monitoring, statistics, and power-management functions.The systems can use wireless sensor networks, PLC, OPLC, and public or private communication networks.
  • 3) Advanced Metering Infrastructure (AMI):: The survey covers existing and proposed IoT-aided SG applications across WAN, NAN, and HAN and across generation, transmission, distribution, and utilization.Applications include smart homes, appliances, EV charging, metering, renewable generation, and demand management.

4) Integration of Distributed Energy Resources (DERs):

The surveyed IoT-aided SG applications address renewable integration, demand-side management, smart distribution, patrol, and transmission-tower protection. These applications combine sensing, communication, monitoring, and control across grid assets and consumers.

  • 4) Integration of Distributed Energy Resources (DERs):: Renewable generators such as solar, photovoltaic, and wind systems are being integrated into the power grid to provide electricity without carbon emissions.Their importance is associated with climate change and environmental pressures.
  • 4) Integration of Distributed Energy Resources (DERs):: Demand-side energy management changes consumer consumption profiles according to time-varying electricity prices to reduce bills, grid costs, losses, and peak loads.IoT devices transmit appliance requirements, after which SG control units schedule consumption using user preferences.
  • 4) Integration of Distributed Energy Resources (DERs):: Smart distribution grids use automated IoT technology and connect directly to users through reliable, flexible structures and open communication architectures.Their stated requirements include operational control and software components.
  • 4) Integration of Distributed Energy Resources (DERs):: IoT smart patrol addresses manual inspection limitations by enabling remote monitoring of unattended generation, transmission, and distribution equipment.The paper presents patrol as an IoT application responding to climate, human, environmental, and accessibility constraints.
  • 4) Integration of Distributed Energy Resources (DERs):: Transmission-tower protection combines vibration sensors, anti-theft bolts, leaning sensors, cameras, and a sink node to generate early warnings and forward alarms and images.A monitoring center receives real-time threat information and staff respond to reported risks.
  • 4) Integration of Distributed Energy Resources (DERs):: Manual tower patrol can occur every 1–10 weeks, while installed cameras and infrared alarms are reported to have unsatisfactory accuracy and stability.The paper identifies difficult access, manpower constraints, and divided responsibilities as practical limitations.

2) Online Monitoring of Power Transmission Lines:

IoT-aided monitoring supports early detection of threats and faults affecting transmission infrastructure. The survey places these applications within broader Smart Grid architectures and identifies renewable-energy monitoring as an underdeveloped area.

  • IoT enables online monitoring of transmission lines for disaster prevention and mitigation, addressing security, reliability, and stability challenges.Traditional monitoring was performed manually, while IoT uses sensing for transmission-line conditions.
  • Current IoT-aided SG applications include equipment surveillance, dynamic home-consumption scheduling, meter monitoring, EV charging, demand management, and fault detection.The survey reports that some applications are deployed, while many remain to be developed using instantaneous knowledge and massive data processing.
  • Little literature addresses IoT-aided SG applications in power generation, transmission, and utilization, including renewable-energy forecasting and equipment-efficiency monitoring.The survey identifies weather prediction and monitoring solar panels and wind turbines as examples of needed work.
  • Transmission-tower protection combines a sink node with vibration sensors, anti-theft bolts, leaning sensors, and video cameras to generate early warnings.The system targets burglary, natural disasters, barbaric construction, and growing trees.

2) SGAM Interoperability Layers:

IoT-aided Smart Grid architectures organize sensing, communication, and applications through layered designs. The surveyed models range from three-layer IoT abstractions to four-layer SG-specific mappings.

  • SGAM Interoperability Layers: The SGAM function layer represents functions and services independently of physical implementations and system actors.Its functions are mainly extracted from use-case functionality.
  • SGAM Interoperability Layers: The information layer defines exchanged data models and information objects, while the communication layer supplies protocols for interoperable exchange.Together, these layers provide common semantics and communication mechanisms between components.
  • SGAM Interoperability Layers: The component layer describes the physical distribution of applications, actors, equipment, devices, communication infrastructure, and servers.These components communicate through information objects and communication protocols.
  • Three-layered IoT Architecture: The widely used three-layer IoT architecture contains perception, network, and application layers.The perception layer is divided into perception-control and communication-extension sub-layers.
  • Three-layered IoT Architecture: The perception layer collects SG data through devices such as RFID tags, cameras, WSN, GPS, and M2M devices.Its control sub-layer processes information and performs monitoring and control, while its extension sub-layer connects devices to the network layer.
  • Four-layered Architecture: A four-layer SG-oriented model comprises terminal, field-network, remote-communication, and master-station-system layers.The terminal and field-network layers correspond to IoT perception, remote communication to networking, and the master station to applications.

D. Cloud-based Architecture

Cloud-based and related IoT architectures connect energy devices, household monitoring, and user control through location awareness, gateways, servers, and web services. These designs emphasize distributed and dynamic energy management.

  • Cloud-based Architecture: The cloud-based framework changes static, centralized energy control into dynamic, distributed control using smartphones and cloud computing.It supports user-adjustable settings and responses to events based on individual schedules.
  • Cloud-based Architecture: The framework contains multi-source energy-saving policies, mobile monitoring and control, location-based automatic control, and cloud storage and computation.Its policy plane accounts for differing energy requirements across organizational spaces and household members.
  • Cloud-based Architecture: Smartphone location data can trigger appliance control across homes and offices, shifting deserted premises into energy-saving mode and activating destination preferences.The example starts office heating while the user leaves home and moves toward the office.
  • Cloud-based Architecture: Cloud computing stores and retrieves building energy data while performing computation-intensive modeling, analysis, and communication services.The cloud also provides security, reliability, and configurability for network communication with users.
  • Web-enabled Architecture: Web-enabled SG architecture places web services over IoT devices, with digital meters and gateways collecting household consumption data from renewable and non-renewable sources.Gateways periodically update the server, which exposes the device data through web services.
  • Last-meter Architecture: Last-meter IoT architecture combines sensor and actuator networks, an IoT server, and user interfaces for consumer-facing SG interaction.The sensor and actuator networks include nodes and IP gateways and support heterogeneous wired or wireless networks.

2) IoT Server:

The IoT server coordinates bidirectional communication, data management, configuration, secure access, and user interaction in last-meter Smart Grid systems. The survey also reports limited architectural coverage and few prototypes.

  • IoT Server: The IoT server comprises a message dispatcher, data-management and database storage, configurator and database, and secure-access manager with a user database.These components collectively support communication, storage, configuration, and access control.
  • IoT Server: The message dispatcher decrypts uplink packets for storage and interpretation and encrypts downlink configurator messages for gateway communication.It manages connections from IP nodes joining the system.
  • IoT Server: The data-management unit handles network-specific message formats and stores sensor measurements and other payloads in the SG database.Database decoupling lets nodes sleep most of the time, benefiting heterogeneous and battery-powered sensor networks.
  • IoT Server: The configurator manages node and network settings, while the secure-access manager restricts stored information and configuration to authorized users and applications.Access permissions are maintained through the user database.
  • IoT Server: User interfaces provide visualization, remote configuration, and web-service APIs for household data, device control, and authorized third-party access.Administrative users can view device status and configure devices dynamically.
  • Summary and Insights: Existing architectures focus mainly on generic layered models or HANs, while NAN and WAN architectures and other HAN areas remain absent.The survey recommends architectures tailored to HAN, NAN, and WAN characteristics and their eventual integration.
  • Summary and Insights: IoT-aided SG prototypes are few, although prototypes are used to test functions and verify operation before commercial implementation.A reported prototype enabled one user to control appliances in an apartment and office through smart devices, servers, meters, and controllers.

B. Integration of Renewable and Non-renewable energy Sources at Home

The surveyed prototypes demonstrate IoT-enabled household energy-source switching and monitoring, alongside real-time medium-voltage grid state estimation. They combine sensing, communication, control, and web-based access across different smart-grid settings.

  • Home energy-source integration: The home prototype connects electric meters and IoT embedded devices to renewable and non-renewable sources, enabling controlled source switching.The devices record current and voltage readings and control source changers connected to the SG supplies.
  • Home energy-source integration: An ARM Cortex-M3-based IoT device uses Ethernet, LCD, RS232, CMSIS, and LwIP to support prototype processing and TCP/IP connectivity.The LPC1768 processor provides the main platform, while CMSIS supports task optimization and LwIP provides TCP/IP functionality.
  • Home energy-source integration: Consumers can access web services to monitor average household power consumption and plan power-source scheduling.The interface supports registration, authentication, and browser-based access to household energy information.
  • Home energy-source integration: The in-home prototype uses ZigBee smart plugs and an IoT server to measure appliance loads and switch appliances on or off.A ZigBee/IP gateway connects the home network to the IoT server, while each smart plug acts as both a measurement and actuator node.
  • Medium-voltage monitoring: The medium-voltage prototype estimates the state of a 20kV active distribution system using PMUs and a centralized Phasor Data Concentrator.The PDC time-aligns and aggregates synchrophasor measurements before forwarding subsets to state-estimation or visualization applications.
  • Medium-voltage monitoring: The medium-voltage monitoring prototype appears compatible with real-time protection and control functions for active distribution networks.Its communication and measurement design targets low-latency state estimation and high frame rates.

E. Summary and Insights

The prototype literature covers household monitoring, energy-source switching, and medium-voltage state estimation, but the published prototypes are few and generally simple. The survey also identifies missing open-source experimentation infrastructure and data-management and security requirements.

  • Prototype insights: Published IoT-aided SG prototypes are few and tend to be very simple.Examples include a single user controlling appliances across a home and office building, plus web-connected household prototypes.
  • Prototype insights: Existing prototypes support appliance control, energy-consumption tracking, renewable and non-renewable source switching, and 20kV grid-state estimation.The surveyed examples span household web services and PMU-based monitoring of an active distribution system.
  • Research gaps: The prototype literature lacks easily available open-source testbeds and simulation tools for experimentation and performance evaluation.The survey identifies this infrastructure gap as a limitation of the current prototype landscape.
  • Big-data management: IoT-aided SG systems must manage large volumes of frequently collected data from loads, meters, faults, outages, network status, and forecasts.Utilities therefore require hardware and software capable of storing, managing, and processing this data efficiently.
  • Big-data management: SCADA collects distributed IoT data for real-time monitoring, control, power-flow management, consumption efficiency, and supply reliability.The survey presents SCADA as the main decision-making element in SG systems.
  • Big-data management: Cloud computing offers shared service-based resources, whereas fog computing processes data at the network edge with lower latency and greater distribution.Fog computing is presented as an alternative when cloud sharing creates security concerns for SCADA data.
  • Big-data management: MapReduce suits static or non-real-time workloads, while stream processing supports real-time applications such as online monitoring, self-healing, and fraud detection.The classification distinguishes cloud and fog platforms from MapReduce and stream-processing techniques.

D. Summary and Insights

IoT-aided SGs require big-data platforms and communication technologies matched to application timing, scale, and network requirements. The survey organizes these options while highlighting cloud-security, technology-selection, and coverage limitations.

  • Big-data management: IoT-aided SG data has high volume, variety, and velocity, motivating big-data management platforms and processing techniques.The surveyed classification separates cloud and fog computing from MapReduce and stream processing.
  • Big-data management: Cloud computing provides shared on-demand resources, while fog computing processes locally at the network edge to reduce latency and avoid cloud transfer.Cloud storage also introduces security risks because storage is shared among users.
  • Communication technologies: Selecting SG communication technologies is difficult because many IoT and non-IoT options exist without adequate selection guidelines.The survey compares technologies to help users choose according to their needs.
  • Communication technologies: SG communication uses one information flow between meters, IoT devices, sensors, and appliances, and another between meters and utility control centers.The first can use powerline or local wireless technologies, while the second can use cellular networks or the Internet.
  • Wireless technologies: 5G offers high bandwidth, low latency, and support for many devices, making it suitable mainly for NAN monitoring and control.The survey reports data rates up to 20 Gbps and latency of 1ms, while noting setup, coverage, security, and privacy concerns.
  • Wireless technologies: Z-Wave and 6LoWPAN target low-power home-area communication, while LoRaWAN supports wider-area IoT connectivity across SG NANs and WANs.6LoWPAN is robust and low-power but has short range and low transmission rate, limiting its suitability for NAN and WAN.

B. Non-IoT Technologies

Non-IoT communication technologies provide alternatives for SG connectivity across home, neighborhood, wide-area, and utility networks. Their trade-offs involve coverage, bandwidth, infrastructure cost, reliability, interference, and latency.

  • Cellular communications: Cellular networks leverage existing infrastructure, offering broad coverage, lower deployment costs, and rapid installation for SG applications.The survey identifies AMI, HAN, outage management, and demand response as relevant applications.
  • Cellular communications: Shared cellular capacity can cause congestion during emergencies, and unfavorable weather may prevent guaranteed service for mission-critical SG communications.Utilities may need private communication infrastructure when continuous availability is required.
  • Wireless mesh: Wireless mesh networks provide low-cost, self-healing, self-organizing, self-configuring, and scalable connectivity through multi-hop routing.Smart meters can act as relay nodes, increasing network coverage and capacity in urban and suburban areas.
  • Wireless mesh: Wireless mesh deployment is difficult in rural areas because insufficient meter density limits coverage, while multi-hop routing can add overhead and bandwidth impacts.Capacity limits, fading, interference, and routing loops are additional challenges.
  • WiMAX: WiMAX provides long-range, high-rate connectivity but requires costly RF hardware and involves a performance-distance trade-off.The survey also notes obstacle penetration problems at high frequencies and spectrum-leasing constraints.
  • Mobile broadband wireless access: MBWA offers mobility, bandwidth, and low latency for NAN and WAN applications, but its lack of existing infrastructure increases deployment cost.Applications include EV broadband communication, SCADA, and wireless backhaul for SG monitoring.
  • Digital microwave technology: Digital microwave technology supports point-to-point SG links with up to 155 Mbps and coverage up to 60 km, but fading and encryption add constraints.The technology is prone to multipath and precipitation fading, and encrypted messages incur additional latency.

2) Wired Technologies:

Wired technologies support IoT-aided Smart Grid communication through existing infrastructure, broadband links, and optical backbones. Their suitability depends on deployment setting, capacity, cost, reliability, and device requirements.

  • a) Powerline Communication (PLC): PLC reuses existing powerlines to connect smart meters with data concentrators, reducing infrastructure installation costs.Its powerline medium is noisy, and signal quality depends on connected devices, wiring distance, and network topology.
  • b) Digital Subscriber Lines (DSL): DSL uses telephone network wires and offers widely available, high-bandwidth, low-cost communication for smart metering and data transmission.Its performance and deployment are constrained by cable maintenance, rural installation costs, subscriber distance, unreliability, and downtime.
  • c) Optical Communications: Optical communications provide long-distance, high-rate, interference-resistant connectivity and can form transmission and distribution backbones.Optical fiber deployment and terminal units are expensive, difficult to upgrade, and generally unsuitable for narrow-band, memory-constrained metering devices.

C. Summary and Insights

The survey organizes open issues and future directions for IoT-aided Smart Grids across communication, device, data, and operational concerns. Key challenges include harsh environments, constrained devices, congestion, and efficient data handling.

  • Adverse Environmental Conditions and Constrained Devices: IoT-aided Smart Grid devices must maintain reliability, availability, compatibility, and coverage under severe environmental conditions.The requirements particularly apply to monitoring power transmission lines and hybrid communication technologies.
  • Adverse Environmental Conditions and Constrained Devices: Constrained devices have limited memory and processing power, while long lifecycles of up to 10 years make backward compatibility important.Ruggedization may support adverse conditions but does not remove computational limitations.
  • Adverse Environmental Conditions and Constrained Devices: Battery-powered sensors, cameras, and backbone nodes create serious energy-acquisition challenges for online transmission-line monitoring.These devices may be installed on transmission towers and lines.
  • Adverse Environmental Conditions and Constrained Devices: Outdoor and electromagnetic environments require IoT devices to incorporate dustproof, waterproof, anti-electromagnetic, anti-vibration, and temperature-resistant technologies.These protections are intended to prolong device and chip lifetimes under severe conditions.
  • Data Fusion: Data fusion can filter and aggregate useful information before transmission, improving collection efficiency while saving energy and bandwidth.The approach addresses constrained processing, storage, battery, and bandwidth resources.
  • Congestion: Congestion can delay or lose messages from NAN or HAN gateways, degrading control-center decisions and user requirements.HAN gateway memory overload can also cause message drops when multiple messages arrive.

D. Application Layer

The application layer must address interoperability, heterogeneous communication systems, data growth, standardization, machine learning, cloud analytics, and security. Resource constraints make security design especially difficult for large deployments.

  • Interoperability: Interoperability is challenging because IoT-aided Smart Grids combine heterogeneous devices, gateways, resources, communication stacks, and protocols.Smart Grid subsystems also span generation, transmission, distribution, consumption, storage, renewables, and smart homes.
  • Integration and Architecture: IoT-aided Smart Grid architectures must allow multiple communication technologies and standards to coexist across interconnected grid elements.Each element requires communication independent of the physical medium, manufacturer, and device type.
  • Big Data and Cloud: Integrating IoT with Smart Grids increases processing and storage demands for consumption, load, metering, and fault data, potentially creating network bottlenecks.Higher bandwidth and data rates improve transport capacity but can shift bottlenecks elsewhere.
  • Standardization: Standardization supports interoperability, compatibility, reliability, and security, but no standardization activity specifically targets IoT-aided Smart Grid systems.OneM2M is viewed by the energy industry as unsuitable for constrained devices, while LWM2M is gaining popularity for its simplicity.
  • Machine Learning: Machine learning, including deep learning, has been applied to electrical load prediction using large-scale information from IoT and smart meters.Load prediction must account for variable factors such as weather and time.
  • Security: Security solutions must accommodate resource-constrained devices that cannot run complex algorithms, making classical PKI and public-key cryptography more difficult to apply.The constraint is especially relevant for devices deployed in large numbers.

2) Privacy:

Privacy and security in IoT-aided Smart Grids involve protecting household behavior data, establishing trust across organizations, preserving data integrity, and preventing attacks on grid operations. The survey also identifies broad gaps in applications, architectures, prototypes, and standardization.

  • 2) Privacy: Smart meters and appliances can reveal household routines, occupancy, and travel, so utilities must prevent unauthorized access to private user data.Such information could support marketing or be misused for burglaries.
  • Trust: Large-scale systems face trust-establishment challenges when devices are managed by different entities, such as consumers and meter operators.Trust is easier when devices share the same owner or manager.
  • Authentication and Authorization: Authentication and authorization ensure that only approved users or devices can access resources or perform tasks such as smart-meter configuration.The energy provider also authenticates each smart meter for billing.
  • Data Integrity: Data integrity protects meter exchanges from unauthorized modification and ensures consumers are charged according to exact energy consumption.Attackers could alter household consumption data or manipulate peak-hour pricing exchanges.
  • Security: Cyberattacks can disrupt asset management, maintenance, billing, smooth operations, and energy-supply balancing across IoT-managed grid infrastructure.The need for protection increases as more IoT-aided Smart Grid applications are developed.
  • Scalability: IoT-aided Smart Grid systems face scalability challenges because many devices and smart objects are deployed across large geographic areas.The deployment may span several cities within a country.
  • Conclusion: The survey identifies missing comprehensive coverage and continuing needs in standardization, applications, architectures, prototypes, interoperability, analytics, data handling, security, communications, and congestion.Existing architectures often omit important networks and systems, while published prototypes are few and simple and open-source testbeds are unavailable.
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