Intelligent Land-Use Management and Sustainable Development:From Interacting Wireless Sensors Networks

to Spatial Emergence for Decision Making

Cyrille BertelleLITIS, University of Le Havre,

Le Havre, Francecyrille.bertelle@univ-lehavre.fr

Mohaned Al-ObaidyGulf College,Muscat, Oman

mohaned@gulfcollegeoman.com

Aladdin AyeshDe Montfort University,

Leicester, UKAayesh@dmu.ac.uk

Rawan GhnematGerman-Jordanian University,

Amman, Jordanrawan.ghnemat@gju.edu.jo

Abstract

This paper addresses the modelling of land-use develop-ment described by users-services systems. On one hand,the increasing economical, technological, social and en-vironmental changes in our world make the managementof urban services to be a great challenge. On the otherhand, new technologies provide efficient tools in order toobtain accurate information from various sensors or cap-tors leading to targeted service delivery based on local-ized factors. Within this paper, we propose a descriptivemodel to deal with land-use development, based on a three-level system in which the environment is an active levelinteracting with users and services. The three levels arerepresented in a form of a network of networks consistingof active nodes in dynamic system context. This descrip-tive model helps in isolating the decision making in a dis-tributed autonomous fashion. Bio-inspired Swarm Intelli-gence techniques are utilized to realise this objective. Theintegration between environment sensing, services informa-tion handling and planning is a key and difficult part withinthis research.

1. Introduction

1.1. Problem setting

Land-use development, as we can observe in urban dy-namics, needs to be considered from the organizational

point of view in order to manage it in sustainable way[4, 11]. How organizations evolve and how they are ableto adapt to their components. In our study, we will extractfrom the land-use development, these organizational sys-tems and focus on their emergence and their evolution assummarized in the figure 1.

Figure 1. users-services description over ge-ographical systems

In the model and conceptual architecture proposed inthis study, we have to consider the environment of users-services as an active element, which evolves dynamicallyunder the influence of the users and the services. In manymodel in land-use management, the environment itself is

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DOI 10.1109/EASe.2010.17

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a 2d or 3d space defined in continuous way but keepingthe same topology all along the simulation. In this paper,we propose an intermediate representation of the space us-ing an interaction network, linking finite number of nodes.Moreover, the final topology of this network is hierarchi-cal in order to be able to extract some relevant dynamicalinformation emerging from the space. Each cluster of thissystem could have specific meaning, like the place of a spe-cific phenomenon. The integrated view of the users-servicesdescription integrating this discrete perception of the envi-ronment is summarized in the figure 2.

Figure 2. environment-users-services de-scription over a wireless sensor network asthe topological perception of the environ-ment

1.2. Related works

The use of Wireless Sensor Networks in the context ofsustainable development and developing countries has beenstudied in previous works [7]. Water quality is for exam-ple, a topic of interest for such technologies: system tomonitor nitrate propagation through soils and ground wa-ter has been installed in California. Moving sensors us-ing infrastructure-based robotics have already been imple-mented in order to collect data in relevant position of dy-namical landscape [16].

Another use of sensor networks is search and rescue [1]operations. In such systems, the collective work of swarmrobots can provide sensing coverage and deliver responseaction. Search and rescue systems impose particular re-quirements [19] that set at the heart of the justification in

using swarming robots and swarm intelligence based con-trol. These requirements centre on the need for robustnessof the system against the loss of components. As robotssearch their way through damaged buildings or in fightingfire, they are likely to be damaged, lost in the rubble, orbecome inoperative for number of reasons.

Finally, sensors are increasingly introduced for intelli-gent homes use [12, 14, 20, 21, 22]. With the expansion inthe availability of high-speed networks the idea of bringinghome devices online is an active area. TV boxes and gameconsoles are already networked devices and it is only mat-ter of time before other daily devices become actively avail-able online. In this milieu of connected worlds, our researchconcatenate on-going research to provide the basic technol-ogy for connecting services and sensors within a context ofpractical application, for our proposed architecture, in theform of urban services planning.

2. Three-Level Model based on System of Net-works

2.1. Multiscale decision making

The environment-users-services description proposedhere allow a better understanding of multi-level decisionprocesses which have to interact and to be managed in orderto deal with a sustainable development [8].

At the user scale, individual have their own goals in or-der to optimize the benefit of services users. Technologi-cal devices, like location based services [17], are nowadaysavailable to help users in these objectives.

At the services scales, the decision-making is expectedto optimize the economical benefit of the services. How toadapt them accordingly with their users specificity, to attractthem?

At the environment scale, the decision-making is ex-pected to preserve the global natural resources or to pre-serve some social rules in order to contribute to a stabledevelopment of the whole.

2.2. Three-level model

The proposed model is fundamentally distributed. It in-duces a high level of complexity on each of the three levelsencompassed in the model description.

The users are not homogeneous but have individualgoals. Moreover they have many characteristics (age, gen-der, social level, etc.), which influence their research ofsome services. We have to face to a multi-objectives andmulti-criteria phenomenon.

The services are not homogeneous too and can proposespecific products to fit with specific users. Moreover, ourpropose is to model the adaptive properties of these services

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Figure 3. Three-level model for environment-users-services system

able to adapt with their users and also with the environmentevolution if this one could have consequence over the pro-posed services.

The environment is represented in an intermediate wayby an interaction network of sensors, which have to describeit for the users-services systems. The interaction networksare aimed to perceive environment evolution and to detectthe significant evolution by collaborative processes of self-organization leading to emergent diagnostic. A completefeedback loop is introduced at this level. Both the users-services will feedback on the environment (exploiting somenatural resources for example) but the Sensor Network willalso detect these changes and conform its topology accord-ingly. As it is the support of the environment representationfor the users-services system, its own evolution will feed-back on this user-services system (see figure 3).

1. inside the services network level,

2. between services level and users level,

3. between services level and environment via sensor net-work level.

3. Modelling the Detection of Organizations

3.1. Self-organization of the WSN

The proposed model is based on an active environment,which is able to react dynamically to changes in user-

services delivery systems. The work proposed within thispaper aims to describe this dynamic environment by utiliz-ing Wireless Sensor Networks (WSN) as a platform for col-lecting and fusing data from the environment. Sensor net-works allow operators to observe and respond to phenom-ena in the physical and cyber representation of the infras-tructure and services provided. There are, however, manyissues associated with WSN. Some of these issues are re-lated to the information collected such as coverage areas,accuracy of data collected, modelling and visualisation ofdata fused, whilst others are related to the infrastructure anddeployment of the network, such as distribution of sensors,energy consumption, type of sensors, and communicationprotocols.

In previous work [2], an algorithm to optimize largescale mobile sensor network has been developed. The op-timization model is based on energy factor. It aims to re-duce the energy consumption by reducing the distance ofcommunication required. To achieve this the optimizationtechnique aims to find an organization of sensors that re-duce the energy consumption. Furthermore, the networkshould be able re-adjust and self-organize to maintain theoptimized clustering as much as possible. This is possi-ble through splitting the processing into two phases basedon the requirements of processing and real time response ateach stage.

The initial clustering is based on hierarchical conceptwhere cluster heads, illustrated by red points on figure 3,are used to create localised networks. Each cluster head or

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Figure 4. An Optimized Cluster in Mobile Sen-sor Network with a Swarming Behaviour

sink looks after a set of local sensors providing a gatewayto the rest of the network. This clustering is done off lineusing Genetic Algorithms (GA) as described in [3]. Figure4 shows a simulated cluster moving in a swarming manner.

To maintain the optimized clustering hierarchy, an exten-sion of Particle Swarm Optimization (PSO) algorithm [10]is used to move the clusters by treating each cluster as aswarm. However, there are many issues this technique doesnot explicitly address in applying mobile sensors to urbanapplications such as what happens if members of the clus-ter are lost [1]. It implicitly relies on inherited features ofswarm intelligence such as emergent self-organization. Inaddition, this model provides flexibility in scaling the net-work and customizing the sensing to cover widest possiblearea.

The sensor network produced is compatible with the hi-erarchal approach to user-services proposed in this paper.Thus we can design and deploy a sensor network that worksat different levels of services meeting different requirementsof environment observation. As long as standard protocol(s)of communication is maintained using heterogeneous col-lection of sensors has little impact on the network organi-zation but it would need to be addressed at sensor fusion

level.

3.2. Self-organization of the users/servicessystems

A social insect collective behavioural systems [6] are uti-lized in enabling the descriptive model of user-services.

In the natural processes within an ant colony, a queenant needs to build its nest whilst ant swarms move aroundit to protect the nest. The queen emits a pheromone, whichattracts the working ants during their building of the nest.The ant has to deposit the material carried only if thepheromone quantity perceived belongs to a specific range.A pheromone template [5] is used to emulate this processwithin the system.

Figure 5. Multi-center multi-coloredpheromone template represented by radialexponential functions and the associated antnest building simulation in Repast

For multi-criteria phenomena, different pheromones areused as described in figure 5, which presents two centers,i.e. two pheromones system emission. On the top of thefigure, we represent the mathematical curves thanks to thesetwo pheromones emission.

We can define the cj-colored template function of the Pi

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Figure 6. Simulation computation, at successive steps: iterations 0, 152, 250, 370, 601, 1601.

center by the following radial exponential function

Φij(x, y) = αij exp(−βij(d((x, y), (xPi, yPi

))− rij)2)

where αij is the template amplitude, βij is the templateslope, (xPi

, yPi) are the Pi center coordinates.

On the low part of the figure, we represent in yellow thequeens that attract the ants (red and green points) aroundthem thanks to the pheromone emission. The ants are bring-ing material (blue points) around the queen implementing aclustering algorithm.

A material involved in a spatial multi-criteria multi-center simulation has to include a characteristic color ta-ble which corresponds to the only colors that the materialis able to perceive and upon which it will be able to react.Each ant of the simulation, after picking up isolated mate-rial and carrying it, has to implement a decision processwhich gives, as output, the selected pheromone templatecolor cj . The will then move by searching in its neighbour-ing position, the one with the highest value of the cj colored

pheromone following a ranking computation described in[9].

Simulation experiments are made using Repast [18] onOpenMap for a specific following configuration, defined bythe initial positions of all the components of the system: (i)the centers and queens, (ii) the materials and (iii) the ants.For this experiment, we put 8 queens on each center. Eachqueen is associated to a colored pheromone. Figure 6 showsthe result of one simulation where ants progressively aggre-gate the material around the center, following pheromonetrails and clustering algorithm. We can observe the forma-tion of material affectation to each center in order to respectthe attraction process, according to the material characteris-tics.

4. Conclusion and Perspectives

The model presented here includes various aspects offeedback and adaptive processes. The WSN management

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and users-services dynamics are typically self-organizationmodels integrating self-adaptation in dynamical way. Theservices is able to adapt from the users attracted by themand according to some specific rules based on the character-istic of the users, the template function amplitude of somecolored pheromone can be modified, generating the emer-gence of a new distribution (the ant system react dynami-cally). Moreover, the services networks can be analysed inglobal way to detect some feedback to apply to the environ-ment. In that case, information are transmitted to the WSNsink which have to send specific instruction to the clustercenters of the WSN. Concrete applications to illustrate thismodel architecture are now in progress. Water managementis for example a relevant application domain where suchcomplex feed-back processes and adaptation mechanism isneeded.

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