sensor network service infrastructure for real- …ewg-dss-2012-liverpool 10/04/2012 6 efficient and...

SENSOR NETWORK SERVICE

INFRASTRUCTURE FOR REAL-

TIME BUSINESS INTELLIGENCE

A. Musa and Y. Yusuf

Institute of Logistics and Operations Management

University of Central Lancashire, Preston

10/04/2012 EWG-DSS-2012-Liverpool

2

Our interest in RFID dates back to 2005

Initial interest was in supply chain applications of RFID

• Focus was on industrial case studies

Our interest has gradually shifted to

• Generalized sensor data sources and their integration

• Service development and deployment

• How edge sensor data is used in supply chain operations to derive

business intelligence (BI) and how the BI is transformed into

concrete actions at the edge

• How sensor data may be used to control supply chains in real-time

• Applications of system dynamics and control theory


3

Review RFID products and services from several vendors

•Three RFID standards organizations (ISO, EPCglobal and DASH7) were

surveyed

•10 vendors were surveyed

Assess the level of adoption of other sensor types beyond

RFID

•DASH-7 (ISO 18000-7)

•MEMS sensors (accelerometer, gyro and pressure)

•Light sensor

•Temperature sensor

•GPS and other location technologies

•wireless connectivity (active RFID, WiFi, Bluetooth, GSM/GPRS)


4

Review reference architectures and stacks for

sensor network deployment

• EPCglobal

• Microsoft BizTalk RFID

• SAP auto-ID

• DASH-7

Identify some knowledge gaps in deriving business

intelligence from sensor data

Focus on and seek to contribute to addressing one

of the identified gaps

10/04/2012 EWG-DSS-2012-Liverpool 5

A generic stack for device deployment


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Efficient and effective enterprise system

scalability and decomposition of business logic

between the backend and the enterprise edge

• For instance, a large retailer that uses RFID across its

network on most of its merchandize might require an

annual throughput rate of up to 60 billion items

• When replicated across retailers and supply chains, this

has the potential to put severe stress on network resources

• Performing process logic on the mobile thin client, at the

enterprise edge, reduces communication costs and

computational overheads at the backend


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… Scalability

• In order for this to be realized, there is a need for

algorithms that scale sufficiently well in terms of

bandwidth, energy and computational power

requirements with respect to client topology

• Products such as DASH-7-compliant thin clients can

communicate peer-to-peer and execute simple logic

locally

• DASH-7 products are likely to become cheaper and much

more widespread in the future; much as local processing

capacities of thin devices are likely to increase, and

their energy budgets shrink, in the medium term


8

Event-based communication can relieve bandwidth

requirements and improve operational efficiency

• Optimal models and automatic (or even semi-automatic)

systems for handling exceptions in real-time are needed

• For example, if there is a sudden general or specific

breakpoint in the supply chain, how is the chain able to re-

organize itself in real-time so as to minimize the negative

consequences of the break?

• An efficient, but effective, control system for event-based

management systems in supply chains is desired

• What other data sources beyond sensors exist or are needed

for system control


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Multidirectional decision flow

• If decisions are taken at the strategic or tactical levels of the

supply chain to address an identified break, how are these

decisions communicated and turned into concrete actions at

the operational levels of the chain or enterprise in real-time?

• If actions are not taken quickly at the operational level to

resolve identified system deficiencies or failures, then the

spirit of urgent data acquisition at the enterprise edge and its

transmission to managerial levels will be defeated

• Supply chains are best in acquiring edge data; they are good in

deriving intelligence from data; but they are poor in turning

intelligence into action at the operational level in real time

• Beyond sensor data, ontological data sources are needed for

this task (source, user, shared, and application ontologies)


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There are groups focussing on

optimization in supply chain

management in the areas of

Inventory decision and policy development

Time compression

Measures to counter Forrester effects

(demand de-amplification)

Supply chain design and integration

International supply chain management

Aspects of risk management

Our approach differs from these strands


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The rest of the presentation describes our

approach and the modelling issues we have been

considering

Our aim is to build an ‘optimal’ closed-loop MIMO

control system for supply chains with data from

the enterprise’s frontline

In the current study, data come only from sensors at

the edge

No ontological or contextual information is being used

yet

Supply chains are dominated by open-loop (feed-

forward) controls that rely on dashboard reporting

Open-loop systems may serve to improve reference

tracking performance but they’re not enough for supply

chain management


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Closed-loop controllers have the

following advantages over open-loop:

Disturbance rejection (eg, unmeasured friction)

Guaranteed performance even with model

uncertainties, when

Model structure does not match perfectly the real

process and

Model parameters are not known precisely

Unstable processes can be stabilized

Reduced sensitivity to parameter variations

Improved reference tracking performance

(especially when combined with open-loop)


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We’re using state-space representation

(SSR) because

It provides a convenient and compact way

to model and analyze systems with multiple

inputs and outputs

Unlike the frequency domain approach, the

use of SSR is not limited to systems with

linear components and zero initial

conditions

Unobservable poles are not present in the

transfer function realization of a SSR


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Stability

Stability for nonlinear systems that take

input is an input-to-state stability (ISS)

This combines Lyapunov stability and a

kind of BIBO (bounded-input bounded-

output) stability

Controllability, observability,

detectability

Supply chains are detectable and

observable, but are they actually

controllable? Calculative opportunism –

transaction cost economics


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Choice of controller

PID controllers are often general enough

Control specifications

Stability, ensuring that poles of the TF

satisfy Re[]<-1, rather than just Re[]<0

Rise time, peak overshoot, settling time,

quarter-decay

Performance assessment (we’re using

integrated tracking errors)

Robustness: controller properties

should not change much when applied

to a system slightly different from the

one used for synthesis


17

System identification and robustness

We’re using both offline and online

(adaptive*) model identification methods.

See later

Choice of nominal parameters

Robustness of SISO controls are relatively

straightforward (gain, phase margin and

amplitude margin), but MIMO controls are

quite hard to robustify

Our MIMO control will have robustness

qualities decided by us (see constraints

below)


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Constraints

The control system must perform properly

in the presence of input and state

constraints; the controller should not send

signals that can’t be followed by the supply

chain team

We are investigating the applicability of

model predictive controls (MPCs) and anti-

wind up systems to supply chain dynamics.

See modelling strategies later


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Dealing with nonlinearity

Supply chain processes, like other multi-

echelon setups, exhibit strong nonlinear

dynamics

In control theory it is sometimes possible to

linearize and apply linear techniques, but

We wish to devise from scratch the means to

the nonlinear system (feedback linearization,

backstepping, sliding mode control, trajectory

linearization* control)

These approaches are still based on Lyapunov’s

results pertaining to linear cases

They often disregard the inner dynamics of the

system*


20

Differential geometry?

This has been widely used as a tool for

extending well-known linear control

theories to the nonlinear case, as well as

demonstrating the complexities that make

non-linear cases a more challenging

problem

We aren’t currently considering this issue


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Centralized or decentralized control

Use of single or multiple controllers

Can supply chains be directed effectively by

single controllers? No!

Supply chains operate over large geographical

area and at various managerial levels

Agents in decentralized controls can

interact using communication channels and

coordinate their actions

But our current effort is focussed on single

controller scenarios

This must be followed eventually by multiple

controllers


22

Adaptive control

Using on-line identification of the process

parameters, or modification of controller gains, and

hence ensuring strong robustness

Hierarchical/networked control

Arranging devices and guidance software in a

hierarchical tree

Intelligent control

AI approaches like neural networks, Bayesian

probability, fuzzy logic, machine learning,

evolutionary computation and genetic algorithms

We’ll briefly review the control strategies we have

considered and elaborate on the ones we have adopted.


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Optimal control

The control signal optimizes a certain cost index

For example, in the case of a supply chain,

we may think of the ‘jet thrusts’ needed to

bring the supply chain to the desired

trajectory that consume the least amount of

resources

Two optimal control design methods that often

guarantee closed-loop stability are model predictive

control (MPC) and linear-quadratic-Gaussian control

(LQG)

Together with PID controllers, MPC systems are the

most widely used approaches in process control


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Robust control

Deals explicitly with uncertainty in its

approach to controller design

A modern example of a robust control

technique is H-infinity loop-shaping

(Duncan McFarlane and Keith Glover)

Robust methods aim to achieve robust

performance and/or stability in the

presence of small modelling errors


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Stochastic control

Deals with control design with

uncertainty in the model

It is assumed that there exist random

noise and disturbances in the model

and the controller, and the control

design must take into account these

random variations

At this experimental stage we’re focussing on adaptive

control only. Work on robust and stochastic controller

types will be conducted in the future


Thank you

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