6. Preprocessing

As smart things collect huge amount of sensor data, compute and storage resources are required to analyze, store, and process this data. The most common compute and storage resources are cloud based because the cloud offers massive data handling, scalability, and flexibility. But this will not be sufficient to meet the requirements of many IoT applications because of the following reasons.

  1. Mobility: most of the smart devices are mobile. Their changing location makes it difficult to communicate with the cloud data center because of changing network conditions across different locations.
  2. Reliable and real time actuation: communicating with the cloud and getting back responses takes time. Latency sensitive applications, which need real time responses, may not be feasible with this model. Also, the communication may be lossy due to wireless links, which can lead to unreliable data.
  3. Scalability: more devices means more requests to the cloud, thereby increasing the latency.
  4. Power constraints: communication consumes a lot of power, and IoT devices are battery powered. They thus cannot afford to communicate all the time.

To solve the problem of mobility, researchers have proposed mobile cloud computing (MCC). But there are still problems associated with latency and power. MCC also suffers from mobility problems such as frequently changing network conditions due to which problems such as signal fading and service degradation arise.

As a solution to these problems, we can bring some compute and storage resources to the edge of the network instead of relying on the cloud for everything. This concept is known as fog computing (also see Section 2.2). The fog can be viewed as a cloud, which is close to the ground. Data can be stored, processed, filtered, and analyzed on the edge of the network before sending it to the cloud through expensive communication media. The fog and cloud paradigms go together. Both of them are required for the optimal performance of IoT applications. A smart gateway can be employed between underlying networks and the cloud to realize fog computing as shown in Figure 7.


Figure 7 

Smart gateway for preprocessing.


The features of fog computing are as follows:

  1. Low latency: less time is required to access computing and storage resources on fog nodes (smart gateways).
  2. Location awareness: as the fog is located on the edge of the network, it is aware of the location of the applications and their context. This is beneficial as context awareness is an important feature of IoT applications.
  3. Distributed nodes: fog nodes are distributed unlike centralized cloud nodes. Multiple fog nodes need to be deployed in distributed geographical areas in order to provide services to mobile devices in those areas. For example, in vehicular networks, deploying fog nodes at highways can provide low latency data/video streaming to vehicles.
  4. Mobility: the fog supports mobility as smart devices can directly communicate with smart gateways present in their proximity.
  5. Real time response: fog nodes can give an immediate response unlike the cloud, which has a much greater latency.
  6. Interaction with the cloud: fog nodes can further interact with the cloud and communicate only that data, which is required to be sent to the cloud.

The tasks performed by a smart gateway are collecting sensor data, preprocessing and filtering collected data, providing compute, storage, and networking services to IoT devices, communicating with the cloud and sending only necessary data, monitoring power consumption of IoT devices, monitoring activities and services of IoT devices, and ensuring security and privacy of data. Some applications of fog computing are as follows:

  1. Smart vehicular networks: smart traffic lights are deployed as smart gateways to locally detect pedestrians and vehicles through sensors, calculate their distance and speed, and finally infer traffic conditions. This is used to warn oncoming vehicles. These sensors also interact with neighboring smart traffic lights to perform traffic management tasks. For example, if sensors detect an approaching ambulance, they can change the traffic lights to let the ambulance pass first and also inform other lights to do so. The data collected by these smart traffic lights are locally analyzed in real time to serve real time needs of traffic management. Further, data from multiple gateways is combined and sent to the cloud for further global analysis of traffic in the city.
  2. Smart grid: the smart electrical grid facilitates load balancing of energy on the basis of usage and availability. This is done in order to switch automatically to alternative sources of energy such as solar and wind power. This balancing can be done at the edge of the network using smart meters or microgrids connected by smart gateways. These gateways can analyze and process data. They can then project future energy demand, calculate the availability and price of power, and supply power from both conventional and alternative sources to consumers.