The document proposes using a distributed and cooperative communication approach for controlling future robots. A distributed approach would provide robustness over a centralized system by avoiding single point of failures. However, the environment is unpredictable, so cooperative mechanisms are needed like storing commands that can be used when needed. The document also suggests combining centralized and distributed systems by allowing the centralized system to change dynamically. This would decrease the probability of failure enormously. It then discusses using concepts from network science like scale-free networks to help define fast routing protocols for vehicle communication networks where nodes change positions quickly.

1. Impromptu Idea in Respect
of Robotics
Communication
By Harshit Srivastava

2. A Historic Idea
• In star wars “The phantom of menace” we learned
one thing,
• The bad robots had centralized failure, and whole
army was useless when main controlling computer
was destroyed.

3. Solution
• So, for controlling of future robot a distributed
communication approach should be followed.
• The distributed approach requires a complex
management system, but gives robustness.
• But, the environment is unpredictable and can be
unplanned so, a cooperative mechanism is needed.
• One feature of store command and use it when
needed( Can be used for high latency)
• Prioritization.
• The next key thing is “Prediction.”

4. Note:
• Still, I believe the centralized and distributed
communication if can be combined where the
centralized system can be changed dynamically.
• Then, probability of failure will decrease
enormously.

5. Lets say!
RSU== Virtual Cloud RSU== Virtual Cloud
RSU== Virtual Cloud RSU== Virtual Cloud
Broadcast
*Safety message are priority, so they should be broadcasted

6. Lets say!(A more predictable)
RSU== Virtual Cloud RSU== Virtual Cloud
RSU== Virtual Cloud RSU== Virtual Cloud
Broadcast
*Safety message are priority, so they should be broadcasted

7. • So, in previous slide an approach towards creating a
temporary centralized network through distributed
system.
• Now, the problems are,
• How to determine super node(centralized node)
• If previous super node has failed how to create new or
find new super node.

8. Now we again look into the model
RSU== Virtual Cloud RSU== Virtual Cloud
RSU== Virtual Cloud RSU== Virtual Cloud
Broadcast
*Safety message are priority, so they should be broadcasted
Greedy Node
Greedy Node
Greedy Node
Greedy Node

9. • So, we can go through network science and say that if
the greedy node is present in a network the graph will
grow as scale free network.
• This scale free network has a can be said small-world
network. This network where the average shortest path
length between its nodes is very small compared to the
total number of nodes in the network.
• Average shortest path length which grows
proportionally to the logarithm of the number of nodes
of the network.
𝐿(𝑁) ∝ log(𝑁)

10. Prediction Approach
• So, for prediction approach two methods I think we can look into
• Through Jaccard Coefficient where the set of similarities is measured to
predict node.
• Another, method is through Hidden Markov Model(Markov process and
Markov chain).
• Since, for HMM state at discrete moment of timein basically
unknown, only physical event is observed, where the observation is
a probabilistic function of hidden state and can be characterized as,
• N, number of states in the model. The individual states can be denoted as
𝑠 = (𝑠1, 𝑠2 , 𝑠3 … . . , 𝑠 𝑁 )
• M, the number of distinct observation symbols. The individual symbols can
be denoted as O= {𝑜1, 𝑜2 , 𝑜3 … , 𝑜 𝑀 }
• The state transition probability matrix 𝐴 𝑁𝑋𝑀 = {𝑎𝑖𝑗}(1 ≤ 𝑗 ≤ 𝑁)
• The observation symbol probability matrix
𝐵 𝑁𝑋𝑀 = 𝑏𝑗𝑘 𝑤ℎ𝑒𝑟𝑒 𝑏𝑗𝑘 = 𝑃 𝑂 𝑘 𝑎𝑡 𝑡 𝑆𝑡 = 𝑠𝑗 , 𝑡 = 1,2. .
• The initial state distribution vector 𝜋 = {𝜋𝑖} where:
𝜋𝑖 = 𝑃 𝑆1 𝑠𝑖 , 1 ≤ 𝑖 ≤ 𝑁
• Where 𝑆𝑡 denotes the state space at 𝑡 time.

11. References
• Shahzad A.Malik,Madad Ali Shah, Shahid A.Khan,M.Jahanzeb, Umar Farooq and Adnan,
Performance Evaluation of IEEE 802.11p MAC Protocol for VANETs; Khan. Australian
Journal of Basic and Applied Sciences, 4(8): 4089-4098, 2010.
• Mir, Zeeshan Hameed, and Fethi Filali. "LTE and IEEE 802.11 p for vehicular networking: a
performance evaluation." EURASIP Journal on Wireless Communications and
Networking 2014.1 (2014): 1-15.
• Robert Gallager, “Stochastic Process and its application”
• Zhigang Wang, Lichuan LIU, “Protocols and applications of Ad-hoc robot wireless
communication networks: Overview”, International Journal of Intelligent Control and
Systems.

12. Thank you

13. • Let’ say we have any one of finite or countable
infinite states(𝑠1, 𝑠2, 𝑠3 … 𝑠 𝑛) and 𝜏 denote set of
states and is a state space of system for time=1,2,..T
• 𝑆𝑡 denotes the state space at 𝑡 time. Then first
order Markov chain prob. of the system 𝑆𝑡 at time 𝑡
and depends only on the previous state of 𝑡 − 1 as,
• Type equation here.

14. Drones Drones
Controlling Nodes
Controlling Nodes

15. Introduction through VANETS
• A vehicular Ad-Hoc Network (VANET) is a technology that
uses moving vehicles as nodes in a network to create a
mobile network.
• VANET turns every participating vehicle into a wireless
router or node, and in turn create a network with a wide
range.
Goal: Provide Road Safety
Challenges in V2V:
1.Relative speed
2.Anonymity
3.Changes in position
Case Study: As More vehicles and range increase in VANET
lead to reduce access prob. by 35%.[1]

16. Ideas that were floated
Future
Research
Scope
Vehicula
r Cloud
Mac
Layer
Protocol
Mobility
Model
Image
Processing
Fault
ToleranceVehicular
Cloud
Mac
Layer
Protocol
Mobility
Model
Routing
Algorithm
Design

17. So what we can choose?
• We can easily analyse that information hopping should be fast, so if
node is lost then next super node should be found for information
hopping, and that node should depend on prediction.
• So, what we can do, for prediction in network, Jaccard Coefficient for
predicting next super node.(jaccard algo can also be sued to refine data
and predicting the best node)
𝑠𝑐𝑜𝑟𝑒 𝑥, 𝑦 =
𝑠𝑒𝑡 𝑥 ∩ 𝑠𝑒𝑡 𝑦
𝑠𝑒𝑡 𝑥 ∪ 𝑠𝑒𝑡 𝑦
Or , 𝑠𝑐𝑜𝑟𝑒 𝑥, 𝑦 = 𝑙=1
∞
𝛽 𝑙
|𝑝𝑎𝑡ℎ𝑠 𝑥,𝑦
𝑙
|
• In which similarity of nodes are found.
• If we look this network in new aspect, we should try to model it as a
scale free network.
• Now, if there is a network and nodes, lets take a look in respect of
network science.
• In this the connectivity can easily be defined according to scale free
property.

18. • So, there should be a feature that can store
protocol commands and can restore when needed.

19. Is scale free relevant?
• Since we know, A small-world network is a network
where the average shortest path length between its
nodes is very small compared to the total number
of nodes in the network.
• Average shortest path length which grows
proportionally to the logarithm of the number of
nodes of the network.
𝐿(𝑁) ∝ log(𝑁)
Scale free has a special property, in which structure
of network remains same regardless of scale of
observation.

20. A(v,a) B(v’,a) C(v’’,a)
Now after t time B changes acceleration

21. • Since last slide shows the network connection of
different vehicles.
• To comprehend that, we have data grabber
nodes(cloud), super node.
• The problem here lies, if super node is dropped or
changed, which one to choose to hop information.
• Many, algorithms can be tried like short path and link
state, but all requires destination node and they
determine first path before sending the data.
• So, what we can choose?

22. • We know scale free have fault tolerant behaviour,
like if failure occurs in random and vast majority of
nodes have small degree, then most probably hub
will be safe.
• Another property of clustering coefficient which
decreases as node degree increases

23. What can this give?
• Can help us to define fast routing protocols in
which node changes quickly.
• This allows a vehicle to determine whether it is the
vehicle with the higher number of neighbours in
the network.
• Then this node vehicle can be utilized for
information hoping.

24. Future Delivery System.
• Last year Amazon has tested their drones to deliver
parcels.
• But, still, drone delivery system is at preliminary stage,
as still I believe proper ground is not prepared for there
control.
• For, me the main problem is like how to control these
drones when to deliver good for larger distance.
• So, I think this vehicular communication can indirectly
solve this in respect of virtual cloud control, in
delivering command with low latency.

25. One Future communication
• Brain to machine communication
• Brain machine interface can be said as neural
interface with computer.
• Brain machine communication can be used as
command , control and communicate with the
integrated systems

26. Lte for Vehicular Communication

27. Issues
• Coverage and mobility: This will rely only on nodes
organised by network infrastructure.
• Centralized architecture: It can not natively support
V2V, but require passing through infrastructure
nodes that should intercept and distribute.
• Channel and transport modes have problem.