SLIDE 1 On the Analysis of Expected Distance between Sensor Nodes and the Base Station in Randomly Deployed WSNs
Cüneyt Sevgi1 & Syed Amjad Ali2
1Işık University, Istanbul & 2Bilkent University, Ankara, Turkey
SLIDE 2 Agenda
– Why determining the expected distance is important in randomly deployed WSNs?
- Related work
- Network Model
- Our Approach
– E[dtoBS] Derivation
- Validation
- Conclusion
- Future Work
SLIDE 3 Why is Expected Distance important?
- In a deterministic scenario,
– the average distance between each node and its neighbors – Similarly, the average distance between each node and the BS
are known in advance.
- In random deployment scenarios,
– these distances,
are NOT known in advance.
SLIDE 4 Why is Expected Distance important?
- In the random deployment scenarios, these
distances, which indeed affect
– the energy consumption – the lifetime of an application – etc.
1
The Energy-hole problem
SLIDE 5
The energy-hole problem
SLIDE 6
The energy-hole problem
SLIDE 7
The energy-hole problem
SLIDE 8
The energy-hole problem
SLIDE 9 Why is Expected Distance important?
- To find out the modes of communication
adopted by the network.
– the multi-hop communication – the direct communication (a.k.a., single-hop)
2
SLIDE 10 Why is Expected Distance important?
- More importantly, E[dtoBS] value also has an
important role particularly for the clustered RDWSNs.
3
SLIDE 11 Why is Expected Distance important?
– sensor nodes are basically grouped into clusters based on
- the proximity of the neighboring nodes,
- the average distance to the BS, and energy levels, etc.
to overcome some of the inherent challenges of WSNs.
– What is the optimum # of clusters (kopt)?
SLIDE 12 What is the optimum # of clusters?
- A notable work* in proposes a number of
closed-form expressions to identify kopt.
– provide a complete theoretical framework for characterization of kopt with respect to a set of parameters of the system scenario listed as follows:
- the number of nodes to be deployed (N)
- the area of sensing field (A)
- E[dtoBS].
*Amini, N., Vahdatpour, A., Xu, W., Gerla, M., Sarrafzadeh, M.: Cluster size
- ptimization in sensor networks with decentralized cluster-based protocols. Computer
Communications 35(2), 207–220 (2012)
SLIDE 13
E[dn
toBS] n=1, n=2, and n=4
@ Center @ Perimeter Outside the Field (on the axis)
SLIDE 14
E[dn
toBS] n=1, n=2, and n=4
E[dtoBS] E[d2toBS] E[d4toBS] E[dtoBS] E[d2toBS] E[d4toBS] E[dtoBS] E[d2toBS] E[d4toBS]
SLIDE 15 Related Work
- Low-Energy Adaptive Clustering Hierarchy (LEACH)
– the pioneer work, influential* & well-known* – integrates the concept of energy-efficient cluster-based routing & medium access to prolong the system lifetime.
*Tyagi, S., Kumar, N.: A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications 36(2), 623–645 (2013)
SLIDE 16 Related Work
- Low-Energy Adaptive Clustering Hierarchy (LEACH)
– cluster head election by devising a mechanism that the cluster head role is rotated randomly among all the nodes in the network.
- by consuming the energy in a balanced fashion
- it prolongs the lifetime of the WSN applications
– an approximate expression to determine the optimum number of clusters (kopt). – There are many variants of LEACH and many of non- LEACH protocols are benchmarked with LEACH.
SLIDE 17 Related Work
Categorization of LEACH Related Routing Protocols for WSNs
SLIDE 18 Related Work
Categorization of Cluster Head election for clusterbased routing protocols.
SLIDE 19 Related Work
Categorization of multihop data transmission for clusterbased routing protocols.
SLIDE 20 Related Work
Categorization of heterogeneous networks
SLIDE 21 Related Work
Categorization of chain based routing protocols
SLIDE 22 Related Work
– which clustering technique is employed or – similarly which communication mode (i.e., multi-hop
- r single-hop) is exploited or
– whether heterogeneity is used
- a WSN application can only take the advantage
- f clustering if and only if the application is
grouped with the optimum number of clusters.
SLIDE 23
Network Model
Before Clustering After Clustering The nodes are randomly and uniformly deployed
SLIDE 24
Network Model
Before Clustering After Clustering The nodes are randomly and uniformly deployed What is the optimum # of clusters?
What is the Expected distances? Which energy scheme will be used (n=1, 2, 4)?
SLIDE 25
E[dtoBS] Derivation
In the Cartesian Coordinates
SLIDE 26
E[dtoBS] Derivation
In the Polar Coordinates
SLIDE 27
E[dtoBS] = E[dtoBS−tri] +E[dtoBS−trap]
SLIDE 28
E[dtoBS] = E[dtoBS−tri] +E[dtoBS−trap]
SLIDE 29
E[dtoBS] = E[dtoBS−tri] +E[dtoBS−trap]
SLIDE 30
E[dtoBS] = E[dtoBS−tri] +E[dtoBS−trap]
SLIDE 31 Validation
- We have validated our analytical results with
simulations.
- We have double checked the boundary values with
the previous works.
SLIDE 32
What if k=0?
SLIDE 33 Conclusion
- We formulated E[dtoBS] when
– sensor nodes are deployed randomly & uniformly over a square-shaped sensing field – the BS is located outside the field.
- The formulation of E[dtoBS] is important
– the calculation of the kopt – the decision whether multi-hop or direct communication – can be also exploited in any domain when there is a need for a probabilistic approach
SLIDE 34 Future Work
- One of the limitations of E[dtoBS] derivation in
this paper is that the BS is assumed to be located
- n the axis of (outside) the sensing field.
- Our future work will explore E[dtoBS] when the
BS is located at any arbitrary point outside the sensing field.
- Any given random probability distribution.
– Not only uniform distribution
SLIDE 35
Future Work
Outside the Field (on the axis) Arbitrary point Outside the Field
SLIDE 36 Questions & Suggestions
- Thanks for attending
- For further questions
– csevgi@isikun.edu.tr
