Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
- Large distance between the cloud and edge devices causes propagation and transmission delays.
- Large computational load on a single cloud server causes processing and queuing delays.
- Increased number of smart devices has hindered meeting the bandwidth requirements.
- Enormous number of smart devices will bring scalability, speed, and computational issues.
- Wireless medium between cloud and smart devices brings resource management issues.
- Heterogeneity property of smart devices in terms of accessing technology will bring difficulty in handling at the cloud.
- Mobility of IoT devices bring service availability issues, cloud server may not be able to provide services due to network congestion and failure.
- Security is a very critical thread, as the cloud is exposed to the whole world over the public internet.
- Computing offloading every-time at cloud causes a loss in energy and battery lifetime.
- Although data storage at cloud brings benefits to application developers, they should be careful of integrity and authentication demands of IIoT applications.
- Cloud computing is a centralized and complex architecture for real-time applications of IIoT.
2. Evolution and Enablers of Industrial Internet of Things
2.1. Industry 4.0-Evolution
2.2. Industry 4.0-Concept
2.3. Industry 4.0-Merging CPS and IoT
2.4. Industry 4.0-Key Enabling Technologies
2.5. Industry 4.0-Building Blocks
2.5.1. Simulation, Autonomous Robots
2.5.2. Big Data and Analytics, Horizontal and Vertical System Integration
2.5.3. Additive Manufacturing
2.5.4. Augmented Reality
2.5.6. Cloud Computing
- Confidential data and personal information of an industry should not be shared with outsiders.
- Security and privacy are in high demand by an industry from the cloud service provider.
- Data location on the basis of geographic follows rules and regulations. It also helps in securing the information.
- High load demands high-speed internet connectivity. This processing causes delays in communication.
- Memory and storage capacity may get exhausted because of many applications simultaneously accessing a single cloud server.
- Context awareness is required for speedy processes.
- Different standards cause problems in exchanging data, information, services, and applications among different clouds at different locations.
- Recovery and back-up update are required for industrial processing and decision making, cloud computing will cause delay.
2.5.7. Fog Computing
- Data storage on network edge nodes eliminates the transmission delay by removing the need for accessing data from far-away clouds.
- Fog computing supports to process and analyze the data on faster speed for IIoT applications.
- Data storage on edge nodes will reduce the processing and computing delay.
- Cache enabled nodes will prevent transmission of irrelevant information over the network.
- Can give support to all IoT applications e.g., smart grids, smart cities, D2D, Vehicular Ad-hoc networks (VANETS) using edge networking concept.
- Provides filtered and required interaction between end devices and cloud service providers.
2.5.8. Edge Computing
- Encourages real-time connectivity.
- Overall network traffic reduces, as some computation is done on the edge of the network.
- Enhances security by encryption of data near to the network core.
- Optimize the resource usage.
3. Industrial Internet of Things Applications and Requirements
3.1.1. Smart City Applications
3.1.2. Smart Factory Applications
3.1.3. Smart Product Applications
3.2. IIoT Application Design Parameters
- Energy & Long Battery Life: Overall network energy should be preserved for better and efficient outcomes. Smart devices should have enough battery storage so that they can use for long time.
- Latency: Some IIoT applications are time-sensitive, a bound should be there to limit all types of delays including processing, propagation, transmission, and computation.
- Throughput: Amount of data for processing is different for different applications. It should satisfy the application requirement.
- Network Topology: How the number of servers (cloud, fog, e-node) and smart devices are placed in a network for better QoS requirements.
- Reliability: Solutions by IIoT applications demand reliable real-time connectivity.
- Security, Safety & Privacy: These are very demanding and major requirements for all IIoT applications. For example, inside a smart factory there should be privacy and security such that no one can access the private information. For healthcare applications, patient’s information should be safe and not easily accessible and changeable. 3A’s; Authentication, Access, and Authorization are steps involved in the strictly secure system. The demand of end to end communication in IIoT applications requires privacy of data as well. Sensors and actuators should be safe from intruders as well as environmental hazards.
- Low Cost: Smart devices used for IIoT applications should be low cost so that doesn’t affect the CAPEX/OPEX. Deployment involved in industry 4.0 should not be so much that will cause loss in marketplace.
- Long Coverage: A device should be capable enough to cover the desired range.
- Standardization: So far, there is no such network standardization and is an open challenge for researchers.
- Integration: IIoT applications are composed of heterogeneous devices and hybrid networks, there are a lot of issues in integration.
- Communication/Enabling Technology: Communication technology for supporting IIoT application should provide assured performance services.
- Device Maintenance: Heterogeneous device in an industry 4.0 environment, require constant device management as devices are connected with each other and the Internet. Software Defined Networking (SDN) is used for such failure and changing maintenance issues of devices.
- Monitoring Network: Wireless, environmental and mobility nature may cause a change in network topology which requires the system to be monitored and managed frequently.
- Configuration & Management of System: Self-configurable, self-control, reconfiguration functionality in addition of new devices in network.
- Traffic congestion & Overload: Smart devices will be increased with time in any IIoT application. System should be able to adjust according to the traffic burden and data requirement.
- Mobility: IIoT applications, such as transportation, inside industry and healthcare devices, have the property of mobility from one place to another.
- Scalability: Scalability brings many issues, some are: How many numbers of smart devices are enough to support an industrial application environment? or how many devices are served by a server easily? how to optimally design a system under energy/spectrum issues?
- Heterogeneity & Interoperability: Heterogeneous smart IIoT devices have to communicate and collect information among themselves and the Internet. This integration is an issue to solve. Standardization is required for interoperability of IIoT devices.
- Performance: There is always a performance trade-off among these QoS requirements. There should be an optimized, supportive, and efficient trade-off among the factors affecting performance. Performance maintenance solutions are required for future automation.
3.3. IIoT Applications in Relation to Cloud, Fog and Edge Computing
4.2. Resource Allocation
4.3. Load Balancing
5. Challenges with Solutions
- Power Consumption/Energy Efficiency: Several smart devices supporting an IIoT application will consume a massive amount of energy on a different scale according to their requirements. Ensuring network QoS with minimum energy consumption of smart IoT devices, fog nodes and cloud in an optimized way is an open challenge for every upcoming future IIoT application.
- Throughput/Rate/Capacity: Throughput or network bandwidth, data rate and storage capacity depends on how much data is used and where data is stored in a fog network. This data placement on fog nodes or edge devices or cloud server has effects on cost, delays, bandwidth, and network coverage. The optimal placement of data on cloud server or fog cloudlet is one of the critical technical challenges for fog-IoT architecture.
- Spectrum Use/Resource Allocation: Geographically separated fog, cloud nodes and their interconnection makes the backbone of any network that relies on offloading services. Most of the cloud computing interconnection mechanisms are not enough for fog networking due to their limitations including relying on a centralized cloud which cannot fulfill the latency and location awareness requirements of distributed devices, etc. Fog computing must encompass features, such as multi-tenancy, scalability, heterogeneity and quick resource provisioning. An architecture including fog and cloud computing must meet all these requirements for which resource allocation/use is the most critical challenge for better network performance. It has effects on all other QoS parameters.
- Latency: IIoT applications requirement is real-time connectivity. All applications are time-sensitive and require real-time streaming rather than batch processing. Fog computing gives a better result for such decentralized solutions. It gives low latency with reliable connectivity and mobility. Optimized placement of data centers, resource allocation, network architecture, energy consumption of nodes, and storage capacity of nodes have impact on latency. Latency for a network is the sum of transmission, processing, propagation and queuing delays. To achieve the low-latency requirement, there is need mitigate all types of delays.
- Cache Enabled Edge Devices: Caching content locally, reduces the access delay time and increases the energy and spectral usage efficiency. Since the Internet has multiple bottlenecks while accessing data from across continents and oceans, caching does not have to be dependent on any of these bottlenecks and instead makes the same data available locally. Furthermore, caching incredibly reduces the load on backhaul links since they do not have to be used anymore for accessing data. Since all the users have to access the data from the same centralized location (internet/cloud server), a certain degree of fairness is needed to avoid inefficiency in accessing data. Since all the backhauls have certain capacity constraints, there is a need for an efficient load balancing mechanism to overcome this issue.
5.1. Power Consumption/Energy Efficiency
5.3. Spectrum Use/Resource Allocation
5.5. Cache Enabled Edge Devices
6. Open Research IIoT Application Domains, Fog Computing as an Enabler
6.1. Micro-Grids (MGs)
6.2. Smart-Grids (SGs)
6.4. Device to Device (D2D) Communication
6.5. Vehicular Ad-hoc Networks (VANETs)
6.6. Big-Data Analytics
6.7. Software Defined Networking (SDN)
Conflicts of Interest
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|Ref. No||IIoT Application Domain||R.A||L||E||T/R/C||Cc||P||H||B||S||T.L||Architecture|
|||Big Data Analytics||✓||✓||Routing||HetNets|
|||Smart IoT devices||✓||✓||✓||Downlink||Cloud Computing|
|||Smart IoT devices||✓||✓||Downlink||RANs|
|||Big Data Analytics||✓||✓||Downlink||NOMA+RANs|
|||Smart IoT devices||✓||✓||✓||Downlink||Fog-IoT|
|||Virtualized Passive Optical Networks (VPON)/5G||✓||✓||Downlink+Uplink||RANs + Cloud Computing|
|||Small Cell Networks (SCNs)/5G||✓||✓||Uplink||RANs|
|||Heterogeneous IoT applications||✓||✓||Downlink||HetNets|
|||Smart monitoring systems||✓||Downlink||WSN+CPS|
|||Heterogeneous IoT applications||✓||✓||Downlink||Cloud Computing|
|||Time-sensitive IoT applications||✓||✓||Uplink||RANs|
|||Heterogeneous IoT applications||✓||✓||✓||Downlink||Cloud Computing|
|||Smart city||✓||✓||Routing||CPS+Cloud Computing|
|||Secure and time saving multimedia||✓||✓||Routing||ICN|
|||Secure IoT applications||✓||Downlink||D2D|
|||5G mobile network+V2G services||✓||Routing||V2G|
|||Mobility+Smart city||✓||✓||RANs+Cloud Comptinig|
|||Heterogeneous IoT applications||✓||✓||Routing||SDN+Blockchain|
|||Big-Data Analytics+ security||✓||Cloud Computing|
|||Smart home||a case study||Cloud Computing|
|||Smart city video applications||✓||✓||Routing||Cloud Computing|
|Ref. No||IIoT Application Domain||Case Study: Key Focus|
|||smart city||Smart city solutions have been deployed in cities, such as Barcelona and Venice, to make further advancements in e-governance|
|||smart traffic control and health monitoring||To increase flexibility in a fog computing in the context of Complex event processing (CEP), a case study is presented. The methodology, called “mechanism transitions”, is used to study how and where a query should be processed and how this decision affects the performance.|
|||smart city||Fog Computing Architecture Network (FOCAN) is presented to give low-latency and energy-efficient solution for smart city applications. It manages different application’s requirements by categorizing the traffic type and its flow.|
|||city, factory, building, home||Using an open-source platform Distributed Node-RED (DNR), authors have presented how applications can be decomposed and deployed. They build prototype for scalability and dynamic nature solutions using the network simulator Omnet++.|
|||smart pipeline monitoring||A sequential machine learning algorithm on every layer of fog-cloud architecture, sensors and Markov model are used to monitor, control and detection of hazardous events of a pipeline system. A working prototype was constructed to observe 12 distinct events. This prototype could be used for future city-wide pipeline safety measurements|
|||smart transportation||The extended policy management to support secure travel to user’s is presented by the authors. Four different route guiding scenarios are explained; namely depending on traffic condition, emergency connected vehicles (ECV), connected vehicle (CV) and probable collision detection.|
|||smart transportation||Smart transportation framework is proposed for Vehicle to Vehicle (V2V) communication by the authors, on basis of the current traffic situation (road and vehicle’s condition, capacity).|
|||big data||Case study named as “Streamcloud” is presented to provide real-time energy-efficient solution.|
|||healthcare||Personalized missing data resilient decision-making approach is validated on a real human subject trial on maternity health. Data missing in critical applications is a very crucial challenge, that needs to be solved.|
|||healthcare||Table 2 in the mentioned paper gives some projects for healthcare monitoring supported by fog computing, cloud computing, and IoT.|
|||healthcare||A demo test-bed is developed on edge-IoT architecture for e-healthcare applications. Proposed EH-IoT gives better results towards bandwidth and latency requirements. The article also presents the benefits leveraging from IoT and edge computing from an industrial perspective.|
|||cardiac diseases||A case study using Electrocardiogram (ECG) feature is discussed in the article to monitor health in real time.|
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Basir, R.; Qaisar, S.; Ali, M.; Aldwairi, M.; Ashraf, M.I.; Mahmood, A.; Gidlund, M. Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges. Sensors 2019, 19, 4807. https://doi.org/10.3390/s19214807
Basir R, Qaisar S, Ali M, Aldwairi M, Ashraf MI, Mahmood A, Gidlund M. Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges. Sensors. 2019; 19(21):4807. https://doi.org/10.3390/s19214807Chicago/Turabian Style
Basir, Rabeea, Saad Qaisar, Mudassar Ali, Monther Aldwairi, Muhammad Ikram Ashraf, Aamir Mahmood, and Mikael Gidlund. 2019. "Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges" Sensors 19, no. 21: 4807. https://doi.org/10.3390/s19214807