The video discusses the concept of fog computing, an evolution of cloud computing that emphasizes processing data closer to its collection point to reduce latency and improve real-time processing. Fog computing acts as an extension of cloud services, handling local data processing and reducing the need for constant cloud communication. With IoT and smart devices generating massive data, traditional cloud systems face challenges in bandwidth and response times. Fog computing helps distribute the data processing load, enabling faster responses, better resource utilization, and improved system efficiency, all while maintaining cloud computing's benefits like scalability and minimal infrastructure requirements.
Highlights
Fog computing is about processing data nearer to the source to reduce latency. 🚀
The traditional cloud model offloads local processing entirely, which may lead to higher response times. 🛠️
Fog acts as a middle layer, reducing the data sent to the cloud and handling some processing itself. 🏭
Huge data from IoT devices can congest cloud systems, fog computing eases this burden. 🏗️
Security with fog computing requires careful management due to data being processed at multiple points. 🔐
Key Takeaways
Fog computing pushes data processing closer to the data source for quicker responses. ⚡
Cloud computing is still vital but fog computing enhances it by handling certain tasks locally. 🌫️
IoT and smart devices generate massive data volumes, necessitating efficient data management. 📊
Fog computing minimizes latency and improves real-time application performance. ⏱️
Security concerns are elevated with fog computing due to decentralized data processing. 🔒
Overview
Fog computing is a revolutionary extension of cloud computing, designed to manage the overwhelming data generated by IoT and smart devices. By processing data closer to its source, fog computing reduces latency and improves the performance of real-time applications. This system distributes computing tasks between local devices and the cloud, ensuring faster response times and easing the burden on central cloud servers.
The introduction of fog computing addresses the challenges faced by traditional cloud systems, such as data congestion and slow response times due to centralized processing. By acting as an intermediary between edge devices and the cloud, fog computing handles some of the data processing on-site, which not only saves bandwidth but also supports mobile and geographically distributed applications effectively.
While fog computing maintains the benefits of cloud services like scalability and pay-as-you-go models, it introduces heightened security concerns due to its decentralized nature. Each intermediate device involved in data processing becomes a potential security risk. However, the agility and reduced latency offered by fog computing make it an essential component in handling modern data processing demands efficiently.
Chapters
00:00 - 00:30: Introduction to Cloud Computing In this chapter, the focus is on introducing the concept of cloud computing. The discussion revolves around how cloud computing involves offloading tasks and resources to a cloud-based infrastructure. This allows for more efficient resource management and scalability.
00:30 - 01:00: Cloud Computing Offloading Cloud computing involves offloading computing processes and data to the cloud, where it's managed by a third party. This allows the customer or user to focus on business processes rather than the technical aspects. Despite various technical complexities in the backend, the primary aim is to simplify and offload workload from the user's end.
01:00 - 02:00: Backbone Network and Data Transmission The chapter discusses the importance of a strong backbone network to ensure efficient data transmission. It highlights the need for a robust network that is always operational and capable of handling large volumes of data, especially as technological development advances.
02:00 - 04:00: Internet of Things and Sensor Data The chapter discusses the influx of data generated by digitally enabled devices and the challenges associated with transmitting this data from consumers to cloud service providers. The process involves sending large volumes of data to the cloud, processing it, and sometimes returning the results to the consumers or transmitting them elsewhere. The chapter highlights significant concerns related to this data transfer, particularly with the rise of the Internet of Things (IoT), which is leading to more data generation and associated challenges.
04:00 - 05:00: Edge Devices and Local Processing This chapter explores edge devices and local processing, highlighting the prevalence of varied sensors generating massive multimedia data. It discusses the need for efficient data transmission and the reliance on a robust cloud computing infrastructure. The cloud offers immense, seemingly infinite computing power far exceeding the capabilities of local devices.
05:00 - 06:00: Introduction to Fog Computing The chapter introduces the concept of Fog Computing, highlighting the increasing volume of data being generated and transmitted over the network. It discusses the evolution of mobile and other electronic devices that are becoming more powerful in computation and more resourceful in their capabilities.
06:00 - 12:00: Benefits of Fog Computing Fog computing minimizes latency by processing data closer to the source/sensor, optimizing resource usage, and reducing reliance on cloud transmission.
12:00 - 17:00: Fog Computing vs Cloud Computing In this chapter, the focus is on comparing Fog Computing and Cloud Computing. It begins with an example scenario in which a lab is equipped with ten temperature sensors. These sensors are meant to operate within a temperature range of eighteen to twenty-two degrees centigrade. The discussion highlights the process where all sensor data is transmitted to a server, potentially located in the cloud, to monitor and calculate if the temperature levels are maintained within the designated operating limits.
17:00 - 25:00: Enabling Technologies for Fog Computing The chapter discusses the role of enabling technologies in fog computing, using an example of a lab environment. It describes a scenario with multiple labs, each being monitored by sensors to manage and regulate temperature. These sensors collect data and communicate with a local decision system (ds), determining if the temperature is within an acceptable range. This reflects how fog computing utilizes local nodes (like sensors and ds) to process and act on data near the source, reducing the need for centralized cloud processing.
25:00 - 30:00: Advantages and Limitations of Fog Computing This chapter discusses the concept of fog computing, which involves processing data at the edge of the network rather than in a centralized data center. It highlights the advantages and limitations of this approach.
30:00 - 35:00: Security Concerns in Fog Computing The chapter discusses the concept of operating ranges and binary transmission using zero-one or yes-no signals. It explores the idea of sensing and transmitting data, and introduces the notion that some computing processes can occur at 'things' or local devices. The chapter suggests that with the increasing intelligence of intermediate devices, there is an opportunity to transfer some computing tasks from a centralized cloud to these more localized environments.
35:00 - 37:00: Conclusion The conclusion chapter discusses the transition from cloud computing to fog computing, emphasizing the movement of data processing from centralized cloud data centers, which can be either privately or publicly hosted. It highlights the challenges that current cloud systems face and introduces fog computing as a solution to those issues. Fog computing brings processing closer to the edge, allowing for more efficient handling of data.
Fog Computing-I Transcription
00:00 - 00:30 hello ah so we will be continuing our discussion
on cloud computing ah so what we have seen that ah in case of ah cloud computing ah what
we are trying to do we are trying to ah offload
00:30 - 01:00 our computing and computing processes and
data to the cloud right so that ah it is ah maintained by a third party and the on the
other end the customer or the consumer or the user more concentrate on the business
processes process so that is the basic ah objective of ah or
model of the things right there are there are a lot of technical ah um technicalities
at the backend but nevertheless we are offloading
01:00 - 01:30 the thing so what for that what we need a
ah very strong backbone right or a strong backbone network which should be ah always
up and ah and able to transfer data on a large volume as we see as as ah as along with the
development and ah being most of the things
01:30 - 02:00 are digitally enabled we are what we are getting
a huge volume of data in other sense a huge volume of data maybe
need to be transmitted from this customer end or consumer end to this cloud service
provider being executed the results in some cases are transmitted back or transmitted
in other places right the major issue is this huge volume or transfer of data and what we
have ah what we see in recent development with number of activities specially internet
of things ah coming up and ah more only ah
02:00 - 02:30 and huge ah variety of sensors in place
so we have lot of multimedia data which need to be transmitted right and that is one part
of the story that we require a huge backbone and type of things as for as the cloud is
concerned what we consider its a its a huge computing power much higher than what we what
the devices can do and it it is some sort of infinite computing power is there
on the other hand ah what we see that a huge
02:30 - 03:00 volume of data are being generated and being
transmitted over the network again what we see that the devices starting from as we discussed
about mobile devices ah smart mobile devices or other type of devices even intermediate
ah network devices what they are becoming is more more powerful in terms of computation
and more resourceful things ok
03:00 - 03:30 or in other sense we are not all the times
exploiting the resources available in the ah things sic ah say consider a particular
sense sensor node or a local sync node of a sensor which are collecting the data and
transmitting to the ah in the upward path maybe to the cloud so this ah this could have
been done some processing at the end like
03:30 - 04:00 i can say that suppose in this particular
room or a particular lab i have ah say ten temperature sensors right
so what what a what what is my basically business model this temperature should be varying may
between ah say um eighteen to twenty two degree centigrade that is the that is the ah operating
range of this temperature now what we are doing this all these ten or
sensors are sending the data to the up up in the server maybe in a cloud that is calculating
whether the within the limit and type of things
04:00 - 04:30 and i can have the ah say ten such labs so
there are hundred such data are going on and if the temperature is varying somewhere it
is sending error now if you consider this particular a a particular
a enclosed lab or single things otherwise i have what i have could taken i taken a local
ds and whether my temperature is ok in the lab by the by the sync node of the sensors
which are collecting this data of this particular room and it takes a call that whether higher
or upper and then sense that say its some
04:30 - 05:00 statistical data or what we say some aggregated
data to the sensor it may be the average or it may be average with other standard deviation
etcetera to the things in other sense it is not in ah in other than
sensing this ah transmitting this ten ah sensors data i am sending one average data or ah and
which has my purpose even you can say that the if the if my sync node is intelligent
enough it can take a call that whether the
05:00 - 05:30 temperature is within this ah operating range
yes ah or outside thing some zero one or yes no type of things and then transmit this
in other sense this is taking a some part of computing at the things so with the intermediate
devices becoming more intelligent whether there is a possibility of pushing the computing
from logically centralized cloud to somewhere
05:30 - 06:00 more down the line right towards the edge
of the things right thats exactly what we are trying to discuss today is what we say
this sort of computing is fog or from cloud to fog right so cloud is the whole thing and
fog computing so as as we see the challenges or the the
data what the cloud computing todays is doing the processing of huge data in the datacenters
datacenters may be privately hosted or publicly
06:00 - 06:30 available by paying rent that is it can be
a public cloud or a private cloud all necessary information has to be uploaded or transmitted
to the cloud for processing and extracting knowledge of it right so ah the whole data
as we are discussing need to be ah transmitted to the ah ah cloud
now ah also we have seen the typical characteristics of cloud for wa for which we ah we are ah
the todays world is inclined towards is that
06:30 - 07:00 dynamic scalability i can scale up or scale
down based on my need so another is that no ah infrastructure management or practically
ah minimal infrastructure management at the user end so if i i i offload everything that
computing etcetera on the cloud so i require very less infrastructure management ah at
my user end and secondly ah and finally what we have a metered service right pay as you
go model
07:00 - 07:30 so these three things that dynamic scalability
ah minimal management or all my ah infrastructure management pushing it to the cloud and metered
service pay as you go model these are primary ah features of the cloud which makes is popular
there are several other things which are which are there but never the less these are the
three things which are the driving force so whatever we do we do not want to lose out
of the things if we compromise on those type
07:30 - 08:00 of ah features then the very ah motivation
to going towards cloud may be challenged now there are issues with cloud only computing
so what we say that only the cloud is computing register sitting duck or maybe the issues
especially in todays applications which variety of sensors variety of real time operations
ah and lot of redundant data right there are lot of data which ah are redundant like if
i am sending temperature things it may not
08:00 - 08:30 mean may be meaningless to sense all the sensors
data which are which are more or less same information unless there is a different in
some sensor data i may not want to send the data all are reporting between around twenty
degree centigrade it does not require a cloud to take a call it could have been done as
a much lower level so that or in other sense i have a huge amount of digital data to be
transmitted so communication takes place takes a long
time due to hum human smart for interaction
08:30 - 09:00 and type of things if the state still datacenters
are centralized right datacenters and in some woodson so all the data from different region
can cause congestion in the core right so being transmitted ah things especially in
case of exigencies where a lot of volumes of data suddenly pushed into the thing right
in case of say some disaster or ah some ah huge amount of in flux due to some event this
is a lot of volume of data suddenly in flux
09:00 - 09:30 so there is a huge volume of data to be ah
transmitted and there can be congestion and such a task requires very low response time
to prevent further crashes so if i have this sort of things which has
a some sort of accident some accident prevention mechanisms can into ah should be activated
so where we require a very low response time so immediately need to be act acted so ah
waiting for that cloud to take a call revert
09:30 - 10:00 back and all those things may take lot of
time so that is another problem so ah so the emergence of a concept called
fog computing so on the cloud we are to the we are talking about fog that is little bit
bringing tao down to the ground or in other sense ah we are pushing this computing thing
from the from the centralized datacenter or
10:00 - 10:30 the cloud datacenters to this edges right
or intermediate or the ah edges of the network aj edge of the network
so fog computing also known as fogging and edge computing though some people have little
other views of that ah edge computing but nevertheless it is a fogging or edge computing
it is a model in which data process applications are concentrated in devices at the network
edge rather than existing almost entirely
10:30 - 11:00 on the cloud so now not only the cloud at
the centralized things the data application and processes are distributed between the
edge right which which somes effect of some way of distributing this whole processing
whether things what it helps us it helps us in reducing the data load in the communication
i can have a local decision and which is not needed for the global type of things they
say smart traffic light ah management system
11:00 - 11:30 the traffic light management system in kolkata
is nothing to do with the traffic light management this system in delhi apparently right for
day to day traffic management right ah so i could have done it locally or even i can
say that a region of a particular city may may have only aggregated data which need to
be transmitted at the higher level for traffic management right so that basic ah intermediate
management could be done locally
11:30 - 12:00 so those things could be done in a in a concept
of what we say fogging or ah fog computing the term fog computing was ah first introduced
ah by cisco as a new model to ease wireless data transfer to distributed devices in the
internet of things network paradigm so as iot is becoming omnipresent or iot is becoming
ah a everywhere it is there internet of things
12:00 - 12:30 so its huge volume of data devices which mass
computing capability or resources ah much higher resources can do a bit of a job which
could have been solved at a at a lower level so ciscos vision if we look at that ah fog
computing is to enable application on billions of connected devices to run directly on the
network edge since cisco is primarily a network driven organization so it has a huge number
of devices across the world and those devices
12:30 - 13:00 are somewhat ah managed etcetera managed by
ah a a a some sort of a homogeneity is therefore because upon the one make and there are resourceful
devices which could have done some sort of ah computing ah things and i can even run
applications on the devices and doing so on and so forth right
so user can develop manage run software application of cisco framework of network devices including
harden hardened routers switches etcetera
13:00 - 13:30 cisco brings say open source ah linux and
network operating system together in a single network devices so it it helped to do ah not
only computing but it was if you want to do computing you need to give some sort of a
platform to run the applications for the computing things right so those things are they are
in the devices and this this ah this is possible because of there are ah resources available
at different layer of the network towards
13:30 - 14:00 the edge
so if we look at a view so this cloud are at the top it is still there and it should
be there there are intermediate devices which we are now helping only a so far was only
transmitting the data now can they do ah some sort of a computing what we say fogs fog computing
and there are end user devices ah which are
14:00 - 14:30 spread over different locations starting form
say ah smart vehicles or ah which can communicate devices servers ah smart cameras and ah anything
which can do write any any any any device which can ah capture detailed data compute
and transmit right so bringing intelligence down from cloud closer
to the end user of the edge of the network
14:30 - 15:00 that is one of the thing cellular base station
network routers wifi gateways will be capable of running these applications right so there
are because whenever i communication we have ah cellular networks wifi router into place
and if those are having surplus resources and they are able to do that so that a my
my application can run say i want to run a application for monitoring the environment
of different labs starting from temperature
15:00 - 15:30 to ah humidity may be some sort of a what
sort of ah air pollution or air content etcetera so this sort of things can be done ah end
devices like sensors are able to perform basic data processing right so ah the sensors can
do a basic data processing processing close to the devices lowers the response time enabling
real time applications right so ah whenever we process close to the devices so the response
time reduces that is ah obvious and i can
15:30 - 16:00 do lot of real time processing of the things
right so i can do a real time processing of a of a say of of any applications like i do
a application based on that ah what we say dynamic ah signalling mechanism of a traffic
light based on the traffic on the road so the cameras which are on the road capturing
that how many ah what is the traffic ah flow
16:00 - 16:30 based on that ah i ah the traffic signalling
may change if that is the that is the need of this traffic management so that is local
right local to a particular portion local to a region local to a city right so that
ah definition of locality may vary from application to application but what we require that your
ah devices like that traffic light device etcetera should be able to run this application
which can take a call right so those are things
16:30 - 17:00 nevertheless this is this is about the fog
so if we look at fog computing enable some transactions and resources at the edge of
the cloud rather than establishing channels for the cloud storage and you utilization
so rather than just transmitting it do some sort of a transaction processing or application
running on the things fog computing reserves reduces the need of bandwidth by not sending
every bit of information to the cloud channels over the cloud channel instead aggregating
at a certain axis ah point
17:00 - 17:30 so it aggregates and send the aggregate data
this kind of distributed strategy may help in lowering cost and improve efficiency so
this sort of it is a distributed ah strategy and this type of distributed phenomena may
help us in lowering the overall cost not only in terms of monetary if the cost of transmission
in terms of time etcetera and i can we can do efficiency right i can i can run several
applications which can be real time and type
17:30 - 18:00 of things
so ah this motivation is obvious already ah whatever we have discussed the motivation
the fog computing a paradigm that extends ah cloud and its services to the edge of the
network ah fog provides data compute storage application services to the end user if you
see it says some sort of a small form of a ah instance of the cloud for that local type
of things right so its its a doing some sort
18:00 - 18:30 of a computing or giving some sort of a cloud
service at that time the at ah at that portion of that ah um region ah recent and we and
there is another ah side of the things because we have ah several series of developments
one is the smart grid ah other is the smart traffic lighting in cities specially cities
connected vehicles or strong regular networks
18:30 - 19:00 which is coming up and also the software defined
network so these are the different aspects which are
itself is a ah um topic ah to work at but the smart grid smart traffic lighting smart
vehicles as ah software defined network and so on and so forth they are becoming pretty
popular and in turn they generate huge volume of data right everyone is generating huge
volume of data which are being transmitted
19:00 - 19:30 at the higher ah up in the layer for doing
that so all ah this ah different aspects has motivated
or what a what it has pushed the push the processing towards doing it at the edges or
intermediate layer rather than pushing everything to the cloud so this is this ah is what we
look at the fog
19:30 - 20:00 so this is ah the same thing what we discussed
so we have one in this cloud so which has a datacenter with huge capability massive
parallel data processing big demand big data mining machine learning algorithms etcetera
so which is they are and should be their intermediate layer which is more near to this ah edge or
the devices so they are can act as a fog so
20:00 - 20:30 they are ah they can be there can be fog sides
with real time data processing data caching computation of offloading and those type of
things so these are not so powerful at that but as such they are intermediate devices
we are which are used for transmitting data so that these are this can be used at the
at the end or the at the front end or the edge or the last mind what we say what we
have the sensors we are connecting different type of data perform data pre processing and
compression mobile ah device serve as a human
20:30 - 21:00 computer interfaces like this these are the
different type of things which are transmitting out here and in turn transmitting to the things
so these some sort of communication yes if we see that both way arrow can be taken a
call at this end itself right without transmitting the whole data at the things it may be some
sort of aggregated reporting and type of things or aggregating the data and taking putting
it to the cloud for ah running some intelligent
21:00 - 21:30 algorithm and machine learning based algorithm
and type of things so we have more interactive and more responsive
end to more computing power and more storage end at the other end so ah instead of just
putting a channel to transmit everything to the cloud and compute and come back we are
doing some intermittent the provisioning of intermediate ah processing for ah to serve
the application based so this is this is ah
21:30 - 22:00 definitely a major motivation
and ah what we try to look at that has as we have seen that the typical properties of
ah cloud that ah having ah here infinite scalability theoretically or quote unquote infinite scalability
ah or ah off loading or having ah infrastructure ah no need of maintaining infrastructure at
the client end or meter services those need
22:00 - 22:30 to be ah need to be preserved or respected
right ah in case of a fog and those are ah definitely are still there as what we are
discussed so there are fog computing there are several
enablers as as ah those are true for our cloud computing also one is that virtualization
so virtual machines can be used as the edge devices right so there are there can be virtual
machines containers ah or containers services
22:30 - 23:00 ah are reduces the overhead of resource management
by using lightweight virtualization or what we say container based application or services
ah is one of the popular container is ah docker container right so it ah the idea is it docks
into that particular things and run on the thing so you dont have to that dependencies
is carries along with the thing right so its
23:00 - 23:30 a again a separate topic ah um it possible
we will discuss sometime but ah that is this docking or container services are becoming
very popular so that is another enabling technology out here
such this oriented architecture as as we have ah discussed which is a ah enabling technology
for cloud also ah is ah here also that soa is a style of software design where services
are provided to the other components by application
23:30 - 24:00 component a component through a communication
protocol over a thing so you have a service oriented architecture which three major component
of service provider service comma consumer and service registry
so so that heterogeneous loosely coupled ah parties can talk to each other right so in
soa architecture is one of the driving ah enabling technology and also what we are looking
ah seeing at is the software defined network right sdn so sdn is an approach of using open
protocols like for example open flow to apply
24:00 - 24:30 globally aware software control at the edges
of the network to access network switches routers that are typically would use closed
and proprietary from one so this is this is ah another ah technology which which as which
is becoming pretty popular or already popular in software defined ah network and which which
is a which is enabling technology for our
24:30 - 25:00 for for fog also so with this ah several enabling
technology fog is a becoming a reality and being deployed and used in several cases
so in looking at so we should not see that fog as a replacement of cloud it is it is
not a replacement of count not ah neither a ah competitor in that sense right it is
basically offloading some of these workload
25:00 - 25:30 from the cloud to this ah edge devices because
the resources are available because there are applications which are real time and needs
more ah more quick responses and overall process may be cost effective and efficient
so fog edge devices are there to help cloud datacenters to better response time for real
time applications right handshaking amount
25:30 - 26:00 fog and cloud is needed appropriate handshaking
or synchronization ah between these ah fog and cloud is ah very much needed broadly benefits
of flog computing can be that low latency and location awareness so it is aware that
which location is operating widespread geographical distribution ah especially with the sensors
etcetera so it has ah thing mobility there is another important things like nowadays
devices are we have lot of mobile devices right so ah the distance from the cloud ah
or the intermediate devices which which a
26:00 - 26:30 div ah say end device passing through the
intermediate devices will change ah based on the mobility of the things
now this require a resynchronization reestablishment of the path had it been in locally somewhere
it may is ah the computing and response time so low latency and location awareness widespread
these mobility very large number of nodes
26:30 - 27:00 with as we are discussing with sensors and
things predominant role of wireless access right huge volume of wireless accesses strong
presence of streaming and real time applications so these days we are having a huge streaming
and real time applications and which requires quick response time huge volume of data and
type of things need to be processed quickly ah and may not require all data to be transmitted
right so this huge volume of data can be locally
ah processed and aggregated data can be transmitted
27:00 - 27:30 so that the overall response time improves
in a considerable way ok strong presence of streaming and real time and heterogeneity
different sort of devices different type of the mix and ah inner and the heterogeneous
so i can have ah it some sort of a fog ah sort of ah intermediate ah um framework which
basically talked to some devices which may
27:30 - 28:00 be different from other devices like i have
a group of sensors i have a their sink node which talks to the sensor also do this aggregation
which may be different in another ah other set of ah sensors which has a different sink
note but nevertheless when they do this aggregated data that is in a more standardized format
so i can handle heterogeneous devices so advantage is already we have already ah
discussed ah so can be distinguished from cloud by proximity to the end user that is
one of the advantage or over this free service
28:00 - 28:30 cloud dense geographical distribution and
its support for mobility right so we can have ah instead of scintillate i can have a lot
of distribution it provides low latency low awareness and improves quality of service
and real time applications right so there is a there is a chance of ah better performing
the things rather we try to look at it is not isolated fog but fog plot cloud o a as
a whole can give a better ah service to these
28:30 - 29:00 consumers right in terms of ah cost in terms
of scalability in terms of ah your efficiency and type of thing specially applications where
we have high quality of services and real time services streaming videos and type of
things there are of course some ah issues ah related
to ah security so one is that ah as devices
29:00 - 29:30 are dispersed right so maintainability of
the security ah protocols at different fog devices is a serious challenge right so it
is at different location now had it been cloud you have a provider at a particular centralized
things you can put lot of security mechanism in the place but if you once you distribute
over the form then you have to maintain so many things ah on the on different edge devices
so it is not only the data processing etcetera
29:30 - 30:00 so there can be ah security issues so an man
in the middle attack type of things can happen so as devices are disparts differ a as as
this computing ah data are being there in the in different edge devices so there is
a issue of ah things of man in the middle attack can be there there are issues of privacy
issues ah as as a as same that it is ah it
30:00 - 30:30 is being ah processed at different edges and
ah whether the data leakage is there then whether you know about the things like
if i consider smart grid or connected vehicles so if you do the intermediate ports processing
whether you are basically tracking ah the vehicle or looking at the ah processing of
the consumption of a individual house or home
30:30 - 31:00 and type of utilization those can be there
like in case of a smart grid smart meter installed at the consumer home each smart meter and
smart appliance as an ip address a malicious users ah can either tamper with its own smart
meter report false reading or spoof ip addresses and so on and so forth
so whatever it comes with ah typical network security related issues may also come ah may
also be ah problem out here so may be a challenge
31:00 - 31:30 so there are definitely ah security issues
there are security issues in the cloud but this extend that to much more things as you
have ah different devices are activated so what we sees today that this fog is just
not extension of the cloud its a its a necessity based on the different application huge volume
of data and the devices intermediate devices becoming more ah resourceful right and they
are able to capable to do this type of ah
31:30 - 32:00 calculations computation and secondly in case
in order to in doing so i may not be doing all these high profile computation but i can
we can basically do ah some sort of a aggregation of the informations and sending only the aggregated
informations which lowers the ah basic ah bandwidth requirement ah intermediate bandwidth
requirement also lowers the data load at the
32:00 - 32:30 cloud end so what we see it is a ah technology
which is a need of the hour and ah especially with iots and other things coming in a big
way so with this we will stop today thank you