Multi-Objective Location - Allocation Planning of

Heterogeneous Networks Infrastructure applied to

surveillance problem

Ons Abdelkhalek, Saoussen Krichen and Adel Guitouni Institut Superieur de Gestion de Tunis Faculty of Law, Economics and Management, Gustavson School of Business, University of Tunis, University of Jendouba University of Victoria, LARODEC lab LARODEC lab Defense Research and Development 0

OutlineI. Problem StatementII. Literature ReviewIII. A Multi-Objectives Location - Allocation Planning of

Heterogeneous Networks InfrastructureIV. A Genetic Algorithm for a Multi-Objective Nodes Placement

Problem in Heterogeneous Network Infrastructure for Surveillance Applications

• Problem Formulation• Solution Approach: Genetic algorithm

V. Simulator Environment : INFORMLabVI. Conclusion & Future Work

Motivations

• Planning growth and extension of existing networks to avoid the network partitioning and “dead area”

• Optimizing nodes placement for both one-hop and multi-hop mode

• Creating a new robust architecture integrating and taking advantage of various networking techniques

• Multi-platforms communication device

• Addressing unexpected events by designing contingencies strategies to maximize the reliability of the network

Problem statement Given:

– A set of connected nodes that constitute an initial static networking infrastructure

– Anticipated demand distribution– Coverage gaps (dead spots) – A set of candidate sites and test points– Set of communication devices characterized by : cost, power, capacity, range

and connection bandwidth – Geographical constraints (mountains, buildings, lake, distances...)– …

Where to position which communication device in order– To optimize coverage, costs and bandwidth – To minimize the number of additional nodes – Under constraints such as capacity, frequency, connection and other physical,

environmental and technical constraints (e.g., geographical constraints)

Problem illustration Given

◦ a distribution of forecasted demand

◦ an initial network infrastructure

◦ a temporal localized demand surge

How to optimize additional networking infrastructure?

How to extend static network with Dynamic or MANETs to address opportunistic demand surge?

R: relay G:GatewayC:Control Cj: Capacity constraints

CapacityLead time…

cjcj

cmcm

c1c1

Literature review: Integration architecture in wireless networks• One-hop to one-hop integrating architecture in wireless networks

– Antennas Placement problem and Transmitters Placement problem– Heterogeneous transmitters Placement problem– Global planning problem

• One-hop to multi-hop integrating architecture in wireless networks– Multi-hop Cellular Network (MCN)– Adaptive Multi-hop Cellular Architecture (AMC)– Ad hoc GSM (A-GSM) architecture– Integrated Cellular and Ad Hoc Relaying system (iCAR)– Hybrid Wireless Network (HWN) Architecture

• Muti-hop to multi-hop– Coverage problems in wireless ad-hoc sensor networks

Literature review

Different type of wireless structures (e.g., independent (or ad hoc) and infrastructure networks : hierarchical, two tiers..) [A.Bahri and S.Chamberland, 2005]

Heterogeneous resources (e.g., relays, antennas, gateways, controllers. APs, …)

Examples of particular networks include sensor network, cellular network, ad hoc network, surveillance networks…

Frequencies, channels and capacities might be considered in the network planning

Multiple constraints: Physical limitation (e.g., capacity boundary, energy..) , geographic distribution, interferences, capacity constraints…

How to plan a new infrastructure?

How to plan a new infrastructure?

Evaluation of networks design is a multicriteria decision problem:

Many conflicting objectives: maximizing coverage, capacity, while minimizing costs and interference for example

Minimizing the number of connecting nodes

Forecasting the demand is another challenge We suppose though that the demand is known as a

starting point over the time horizon.

Initial Thoughts to Address the Planning Problem

Multi-objective optimization problem min costs, max the quality of services, min the energy

consumption, max the coverage, min interferences… How to estimate the demand?

The demand depends on the time Define the function of evaluation f(s) (dynamic nodes) Determine the user models

Maximizing the quality of signalling using metrics (Quality of Coverage, the request blocking, dropping rate..)

Unknown demand based on uncertainty We can suppose an initial predefined infrastructure

Which resources will remain and which should be changed or removed?

Costs of additional infrastructures if needed

• Extending an existing infrastructure:• Problem under uncertainty Management of the

contingency plan• Positioning of relays, antennas, nodes… • Dynamic and multifunction extra-nodes• Capacity, interference, connections, protocol constraints

• Extension depends on demand surge, coverage extension needs, time...

• Forecasted versus non forecasted• Dynamic decision problem Adaptation strategies

Develop contingencies strategies to include MANETs to extend coverage

and services

Validate the proposed approach on an empirical case

Consider use cases Surveillance and mobile platforms Cellular phone networks Military applications (Piracy) Vehicular wireless ad hoc networks

Equip cars with wireless transceivers

Location-Allocation Planning of Heterogeneous Networks

Infrastructure

• One-hop to Multi-Hop and Multi-Hop to Multi-Hop networks connections

• The majority of research efforts focus on possible communication scenarios, technical architecture and routing protocols in an heterogeneous environment

• None of the previous works considered multi-objective mathematical model to optimize the infrastructure of an existing network using heterogeneous nodes

• The problem is how, what and where to place nodes (heterogeneous nodes)

Problem illustration

Agents

Candidate sites

Communication device

Test points

Cost

Capacity

Power

Type

Bandwidth

Data demand Signal

threshold

Spatial coordinate

s

Spatial coordinate

s

1 *

1

*

1

1

have

cover

*

1

Existing

infrastructure

*

1

*

*

Find the optimal number, position, communication types and connections in a special area of coverage.

Range Cost

Wide-Area Surveillance Problem

Source: APL Technical Digest July-Sept. 2000, Vol. 21, No.3

A combined operation of many platforms, sensors and communication network systemsOptimize the infrastructure in order to allow platforms to communicate between each other

Notation

Problem Formulation (1/4)o Maximizing the coverage of the integrated networks

Where

And

• Signal strength between test points and receivers (Ting et al 2009)

o Minimizing costs

o Maximizing bandwidth

o Constraints: Agent connection constraints:

Problem Formulation (2/4)

Problem Formulation (3/4) Link connection constraints

Assignment constraints

Each node can have more than one communication device

Each node is assigned to one candidate site

Each candidate site is assigned to at most one node

Each receiver can be assigned to at most one node

Each node should be assigned to at least one other node

At least one node should be connected to the existing networks Z

Problem Formulation (4/4) Nodes capacity constraints

Where

Agents connection capacity constraints

Binary constraints

Investigation of Solution Approaches

• Multiple issues: – flexibility in the number of placed nodes, – heterogeneity of nodes, – optimization of multi-objective functions, and – satisfactions of multiple network constraints.

• Solution approach:– Meta-heuristics– Evolutionary algorithm:

• Each node into the encoded chromosome presents a substring that consists of the position of candidate site where it is located, the communication devices that he is using and the number of other nodes that he is assigned to.

Multi-Objective Genetic Algorithm

Initial population: Randomly generating the number of substring in a chromosome where positions are picked

from the set of candidate site, type CD from the input matrix and number of other nodes that are connected to are generated randomly

Fitness evaluation We adopted the NSGA II method for MO problem. Rank and crowding distance depend on a

comparison of the objectives. Selection: We used roulette wheel selection where the probability that a chromosome will be selected is

proportional to its fitness. Crossover: We adopted the one-point crossover operation, where the chromosome is divided into two

parts at a random point between substring. Then the two parts are exchanged with each other Mutation: We adopted the bit-flip mutation where Pm = (1/substring_length), thus each bip has a

probability of Pm to be flipped

Representation of chromosome and substring

15 011001….001 {mode1,mode3}…..{mode 5} {mode1}

(X1)(CD1)(Y1) (X2)(CD2)(Y2) …... (Xn)(CDn)(Yn)

{list of common modes}, if both agents are charring the same network)and the distance between node i and the existing node is < max(range of the two agents) so we connect the two agents .

chromosome

substring

0 1 N-1

Candidate site indexCommunication device is assigned to node i or not

Multi-Objective Genetic Algorithm

Crossover representation

Implementation Environment : INFORMLAb

InformLab Run

Implementation Approach in IL

GML points are extracted from Canada's topographical maps of south-eastVancouver Island, the Gulf Islands andpart of the Lower Mainland.

Inform Lab simulator

Iterations

Muti-agent systemAnalyzing and modeling the

problem

Resolution approach

A set of non dominated solutions

Implementation run

IL simulator

Iterations

Mathematical formulation

Meta-heuristique

Illustrative Example The MOGA parameters: The problem parameters:

Example’s Results:

Experiments configuration:

Experimental Results:

Random generated instances

Empirical results • The CPU time is proportional to the problem size and, in average, is about to 2 seconds.

As the number of test points is greater in the region of interest, the execution of our optimizer remains longer.

• The cost is proportional to the size of the land. We can notice that the more we have test points to cover, the more expensive is the cost of our placement due to communication devices' cost.

• In all the problem instances, almost all test points were covered and their demands were satisfied by the new placed nodes. It shows that our MOGA almost converges to the optimal solution.

• The number of potentially efficient solution is not really high compared to the generations instances. It can be justified by the small number of candidate sites considered in this problem instances or the communication devices. Other problems should be considered for additional empirical validation.

Conclusion & Future works

• INFORMLab vignettes will use our solution in order to optimize nodes placement

• Compare different node placement solutions in real-time simulation environment

• Future works:– Better integration within INFORMLab– Investigate a combination between meta-heuristics and exact

methods (Integration of CPlex)– Test our approach in other environment like cellular wireless

networks architecture – Use dynamic node placement with stochastic demand

distribution

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