1. SPIE Smart Structures/NDE
Wireless Integration, Design, Modeling, and Analysis of Nanosensors, Networks, and Systems: A Systems Engineering Approach   Presented by Seshadri Mohan Chair and Professor, Systems Engineering Dept., ualr
SPIE Smart Structures/NDE
San diego, march 10, 2009

2. Authors Seshadri Mohan Hussain Al-Rizzo Radu Babiceanu Guoliang Huang
Kenji Yoshigoe
Remzi Seker
Vijay Varadan
Taha Elwi
Rabindra Ghimire
Daniel Rucker
Fei Song
Subramanian Vimalathithan
ACKNOWLEDGEMENT: This research was supported in part by the National Science Foundation Grant: EPS

3. Systems Engineering Framework

4. Systems Engineering Approach
Wireless nanosensor networks.
Combine sensing, communication, computation, and user interfaces in large complex systems.
Must be designed and implemented considering their intended functionality, operational requirements, and expected lifetime.
SE provides the design and implementation framework to build large complex systems.
Integrates multiple engineering disciplines into a structured development process, and includes:
Need identification,
Conceptual design,
Detailed design,
Development, and
Implementation.

5. Systems Engineering Approach
Architecture characteristics translate into system effectiveness
System Performance
Functionality: assures system objective accomplishment.
Scalability: accommodates system expansion.
System Robustness
Availability: maintains system operation.
Responsiveness: assures system agility.
System Efficiency
Affordability: provides a competitive system life-cycle cost.
Modularity: reduces system development cost and effort.
Identified Architecture
Characteristics
Required System
Characteristics
System Design
and Operation
Quantitative
and/or
qualitative
performance
measures
to be defined
for each
architecture
characteristic
identified
Functionality
Scalability
System
Performance
System
Effectiveness
Availability
Responsiveness
System
Robustness
Modularity
Affordability
System
Efficiency
- To ensure that the systems engineering process leads to a successful design, the overall system architecture through its identified layers need to address the characteristics of good system architectures.
Functionality: the architecture should be efficient in terms of timely response, transaction throughput, power management, data integrity and confidentiality.
Responsiveness: the architecture should be capable of integrating new applications, accommodating changes in system components, and respond in the needed time to changes.
Availability: the architecture should be sufficiently reliable and have fault-tolerance capabilities.
Modularity: the architecture should allow replacement of a portion of it without affecting the system.
Scalability: the architecture should be able to change the number of sensor nodes easily.
Affordability: the architecture should provide the required functionality and operational maintenance at an affordable life-cycle cost.
- To obtain a measure of the overall system architecture effectiveness, the six characteristics identified above are translated into measures of system performance and efficiency.
System performance: besides functionality which is a must in the overall system performance, scalability has an important role to maintain the system performing at required standards and be capable to address the changes in the environment to be sensed, so the presence of these two characteristics assures a high system performance.
System robustness: availability of the system during all periods of intended use and responsiveness in integrating changes in the system and timely fashion action translate into needed system robustness.
System efficiency: besides affordability which is the most important characteristic for the efficiency of a system, modularity can help in reducing overall development and operation cost and effort, thus making the overall system more efficient.
- The second step in this research is to define quantitative and qualitative performance measures for these characteristics such that they can be easily included in the design process and monitored during system operation – work in progress.

6. Systems Engineering Framework
System context architecture.
Operational mode viewed from the user level.
1st use case: Systems analyst and associated user interfaces utilize the services offered by the system.
2nd use case: systems engineering testbed used to verify and validate the technical performance measures identified in the design process.

7. Systems Engineering Framework
System level architecture.
Designed to be independent of the type of application.
Physical components that collect, transmit and process the field data information.
Six identified requirements
- Functionality
- Responsiveness
- Availability
- Modularity
- Scalability
- Affordability

8. Systems Engineering Framework
Detailed architecture.
Further decomposition to lower levels, both for the system components and their associated design and operational requirements.
System effectiveness.
Architecture characteristics translated in measures of system performance and efficiency.
Quantitative and qualitative performance measures.
Included in design process and monitored during system operation.

9. System Simulation
System modeling for verification and validation processes.
- Test system configurations and interactions that are difficult to model using the hardware testbed
- Test the quality and reliability of sensor network algorithms.
Events to be considered:
- Sensor node failures and lifetimes.
- Message delays and losses.
Use current design and experimental data obtained during the sensor development process, together with data available from COTS products for physical and lifetime characteristics not yet tested in our laboratories.

10. Systems Engineering Framework

11. A Multiscale Continuum Approach for Modeling Mechanical Behaviour of Nanowires
Nanowires are attractive components for future multifunctional nanosensors because they have some unique physical properties that are not observed in the corresponding bulk material.
For long-term reliability of various devices at nanoscale, researchers should deeply understand the mechanical properties of nanowires.
Many different approaches have been developed to try to understand the size-dependent elastic behavior of the nanowires. Based on molecular simulation, researchers at Sandia National Lab have found that the Young's modulus of the shape memory nanowires are size dependent and surface relaxation should also been considered. The research is mainly focus on static cases.
However, it is well known the capability of the approach is limited by its need of prohibitive computing time and an astronomical amount of data generated in the calculations. It is essential to develop new tools that offer the similar simplicity of continuum mechanics and the ability to account for the nano-characteristics of the material.
In the study, a multiscale semi-continuum approach is proposed to study the mechanical behavior of the nanowire. Specific attention will be paid on the dynamic behavior of the nanowire considering the coupled the size and surface relaxation effects. In the approach, the discrete atomic characteristics in the width/thickness direction of the nanowire are retained. However, the continuum description can be applied to achieve simplicity since their remaining length dimension is much larger. By adopting this semi-continuum approach, the computation time efficiency will be significantly improved. In the meantime, the surface relaxation effects are considered by using the proposed imperfection of the coordination numbers of a surface atom.
For simplification and comparison, a simple cubic lattice model is used to represent a square cross-sectional nanowire with the uniform width and thickness h and infinite length.
(MD, Sandia National Laboratory)
(Semi-continuum model)
time consuming
static simulation
time efficiency
dynamic simulation

12. Size-dependent elastic behavior and surface relaxation of nanowires.
A Multiscale Continuum Approach for Modeling Mechanical Behaviour of Nanowires
Young’s Modulus of Nanowires
Wave Dispersion of Nanowires
This figure shows the Young’s modulus E with respect to number of atomic layers (2N+1) for different relaxation coefficients . The parameters used in the calculation are , , and , respectively. It is evident that the Young’s Modulus E is all size dependent for each given relaxation coefficient . The Young’s modulus E varies with the decrease of the atomic layer number as the atomic layer numbers are less than 40. It varies significantly when the atomic layer numbers are less than 15. On the other hand, the Young’s modulus approaches a constant value when the atomic layer numbers tends to infinity. The surface relaxation has also a remarkable effect on the Young’s modulus of nanowires especially when the numbers of atomic layers are small. For example, the Young’s modulus drops with the decrease of the atomic layer numbers when the relaxation =1.00, 1.15, 1.30, and 1.45, while it increases when the relaxation =0.05, 0.70, It is also found that the smaller the relaxation coefficient is, the greater the value of Young’s modulus E is. For instance, when the atomic layer number is 5, the value of Young’s modulus E increases by 52.7% of the macroscopic value for =0.55, while it decreases 43.9% by of the macroscopic value for =1.45. It should be mentioned that the current prediction is coincident with the experimental testing about the nanowires.
This figure shows the nanowire’s size effects upon the longitudinal wave dispersion relations. In the figure, the atom layer number is selected as N=5, 15, 51 and 101, respectively. The parameters used in the calculation are , , , and h=3a. It can be found that the nondimensional phase velocity is size-dependent even when the wave frequency is very low. With the increase of the nondimensional wave number ka, the nondimensional phase velocity is more dependent on the atomic layer numbers of the nanowires for the given relaxation coefficient . For example, for ka=0.5, the nondimensional phase velocity for the nanowire with 5 atomic layers is around five times bigger than that of the nanowire with 101 atomic layers.
Along the direction, we will develop a multiscale continuum approach to accurately understand and describe the dynamic properties of nano devices with complex microstructures in designing and predicting performance of nano-devices.
It consists of the following major parts:
(1) developing an atomistic-based multiscale microstructure continuum theory to describe the dynamic behavior of nano devices and to capture microstructure effects; this theory is computationally more efficient than molecular simulation and does not have any unknown material constants compared with other existing high-order continuum theories;
(2) applying the proposed theory to other existing simplified non-local continuum theories, then, conducting finite element formulation on the simplified model for the nano devices with complex micro/nano structures;
Size-dependent elastic behavior and surface relaxation of nanowires.

13. Systems Engineering Framework

19. Systems Engineering Framework

20. Reliable and secure wireless sensor networks
Problem: Limited WSN security due to the constrained resource
Goal: Achieve confidentiality without costly encryption
Internet
Remote station
Sink
Cluster head
Super node
Source node/cluster head
Super node set leader
Method: Multi-Destination Multi-Segment (MDMS) Data Transfer
Cluster head (CH) sends segment (partial data) transmission to each super node
Super node encrypts and forwards the segment to super node set leader
Super node set leader constructs original message

21. Reliable and secure wireless sensor networks
Evaluation
Given a uniform distribution of messages &
an adversary consisting of # of attackers
a = # of attackers
n = # of paths
p = Probability (Attacker captures a segment)
P = Probability (Adversary captures a message)
Single-path
P = 1-(1-p)a
MDMS Data Transfer
P = [1-(1-p)a ]n

22. Reliable and secure wireless sensor networks
- Segment capture probability = 1% n P
# of paths
Capture probability
Increases in # of paths significantly decreases the probability of the entire message captured by an adversary.
Improved communication reliability due to the small average hop distance between a CH and a super node.

23. Concurrent Multilayer Restoration Scheme for IP over WDM Networks
Backbone networks may transmit large volume of data collected by sensors
will likely consist of IP routers as well as optical cross connects and will deploy Generalized Multiprotocol Label Switching (GMPLS) as the control plane protocol.
Failitate recovery of optical fibers from failures
Objective: Investigate the problem of autonomous recovery in GMPLS-based optical networks.
Results
Formulated a recovery procedure that recovers concurrently in both the optical and IP planes.
Proposed extensions to OSPF-TE protocol employed with GMPLS-based WDM networks.
The proposed protocol provides a speedup of 40% over sequential recovery scheme proposed in the literature

24. Concurrent Multilayer Restoration Scheme for IP over WDM Networks
The OSPF-TE extension requires that the link state information propagated by the protocol must also carry available unused lambda label switched paths (λLSPs).
The concurrent multilayer recovery scheme proposed here uses this information to switch traffic in both optical and IP layer concurrently.
An OPNET-based simulation study shows that the concurrent two-layer recovery scheme performs as much as forty-four percent faster than the sequential two-layer recovery scheme.
Fig. 1. Concurrent Two layer restoration vs
Optical layer restoration
Fig. 2. Concurrent Two layer restoration vs Sequential Two layer Restoration

25. Systems Engineering Framework

26. Stand-off-Server (SoS) - Type 1 Architecture
In the previous sections we have seen how data is transferred reliable and secure manner to the sinks. Now this work show how data is securely transferred from the sinks(remote sites) to the central server. We consider 2 types of architecture. Type 1 architecture emphasize on strict confidentiality (For example for military use) and Type 2 architecture ensures security with high availability (ex. For banking sector).
Here we introduced Stand-off-server which is in between the remote servers and central server with defense in depth mechanism. The first layer would be BBMC (Break before make connection) => At any point of time only one server is connected to SoS either the remote server or the central server. The Second layer is employing virtual machines => by this we ensure that the remote servers are not allowed to have access beyond the virtual machine. Third layer is using multiple operating system for consecutive connections => Successive virtual machine will have different operating system to prevent OS guessing. By this the adversary cannot predict the OS and employ OS specific attacks. In the Fourth layer we used different storage locations for different remote servers. By this we avoid coordinated attack and we prevent the remote site to access other storage areas. Apart from this SoS runs policy verifier to look for any policy violations and if there is any violation is found then the data is discarded and not transferred to the central server. Only upon successful policy verification the data is transferred to CS.
The Flow : Transfer data from Remote site to SoS
SoS runs policy verifier
Upon successful verification data connection to CS is established
Data transferred to CS
S.Vimalathithan, R.Seker et.al, Secure Data Aggregation in Heterogeneous and Disparate Networks Using Stand off Server Architecture to appear in SPIE Defense Security + Sensing Conference 2009

27. SoS - Type 2 Architecture
Here the Central server is connected to SoS through a virtual machine and not disconnected unlike Type 1 architecture. The remote servers are connected to SoS through multiple instances of simultaneous connections. Other security mechanisms are same as that of Type 1 architecture.

28. Attack Statistics for Type 1 Architecture
Here we found that there were about 161 attacks detected by the central server firewall for time duration of 10 minutes when the remote server is connected directly to central server. Out of these 161 attacks, 112 of them were probing attacks such as icmp ping attacks. 36 attacks are based on various applications installed on the remote system. And 13 of them were OS specific attacks. Here we focused only on OS-Specific attacks. SoS is not running any other applications.
The attacks were reduced when we introduced a SoS with single operating system. And the attacks were further reduced when we introduced virtual machines with different operating systems.

29. Attack Statistics for Type 2 Architecture

30. Conclusions Proposed a systems engineering framework
Developed and analyzed a model for system integration
Developed and simulated a multiscale continuum model for nanowires
Developed and analyzed mechanisms secure and reliable data transmission over wireless sensor networks

31. Conclusions
Studied autonomous recovery in optical backbone networks and proposed concurrent schemes that achieve 40% speed up in recovery in comparison to sequential schemes
Proposed a stand-off server concept to increase security, provide data partitioning and transparency to multiple networks.