Scholarly article on topic 'ABAMA: An Agent-based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks'

ABAMA: An Agent-based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks Academic research paper on "Materials engineering"

CC BY-NC-ND
0
0
Share paper
Academic journal
Procedia Computer Science
OECD Field of science
Keywords
{Communication / "Wireless Sensor Networks" / "Multi-agent Systems" / "Natural Ecosystem."}

Abstract of research paper on Materials engineering, author of scientific article — Nafaâ Jabeur, Nabil Sahli, Sherali Zeadally

Abstract Wireless Sensor Networks (WSNs) comprise of hundreds of spatially distributed sensors which may collaborate, compete, and self-organize in order to solve complex tasks which are beyond their individual capabilities. The efficiency of these actions is commonly restricted by the limited energy, the environmental context, and the processing capabilities of the sensors. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and strong communication mechanisms used by living organisms. While mapping these organisms onto sensors and ecosystems onto WSNs, we identify the similarities of both parties in terms of structure, active entities, topology, goals, communications, and functions and highlight shortcomings that would prevent WSNs from matching the behavior of ecosystems. We then propose an agent-based architecture that migrates the complex processing loads outside the physical sensor network while incorporating missing characteristics such as autonomy, intelligence, and context awareness to the WSN. In contrast to existing works, we use software agents to bridge the gap between WSNs and natural ecosystems and achieve an optimal mapping between both systems. Our ultimate goal is to enhance the capabilities of WSNs to take advantage of ecology-inspired algorithms.

Academic research paper on topic "ABAMA: An Agent-based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks"

CrossMark

Available online at www.sciencedirect.com

ScienceDirect

Procedia Computer Science 34 (2014) 257 - 265

The 9th International Conference on Future Networks and Communications

(FNC-2014)

ABAMA: An Agent-Based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks

Nafaâ Jabeura, Nabil Sahlia, Sherali Zeadallyb

aComputer Science Department, German University of Technology in Oman - Gutech, P.O Box 1816, PC 130, Sultanate of Oman bCollege of Communication and Information, University of Kentucky, Lecxington, KY, 40506, USA

Abstract

Wireless Sensor Networks (WSNs) comprise of hundreds of spatially distributed sensors which may collaborate, compete, and self-organize in order to solve complex tasks which are beyond their individual capabilities. The efficiency of these actions is commonly restricted by the limited energy, the environmental context, and the processing capabilities of the sensors. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and strong communication mechanisms used by living organisms. While mapping these organisms onto sensors and ecosystems onto WSNs, we identify the similarities of both parties in terms of structure, active entities, topology, goals, communications, and functions and highlight shortcomings that would prevent WSNs from matching the behavior of ecosystems. We then propose an agent-based architecture that migrates the complex processing loads outside the physical sensor network while incorporating missing characteristics such as autonomy, intelligence, and context awareness to the WSN. In contrast to existing works, we use software agents to bridge the gap between WSNs and natural ecosystems and achieve an optimal mapping between both systems. Our ultimate goal is to enhance the capabilities of WSNs to take advantage of ecology-inspired algorithms.

© 2014 ElsevierB.V.Thisisanopen access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of Conference Program Chairs

Keywords: Communication; Wireless Sensor Networks; Multi-agent Systems; Natural Ecosystem.

1. Introduction

According to Zambonelli and Virolli1, the rapid expansion of pervasive computing systems, in particular Wireless Sensor Networks (WSNs), depends on how researchers will deal with recent emerging requirements such as the ability to integrate spatial concepts, promote adaptability, support diversity and evolution, and allow low-cost, long-term evolutions when designing these systems. These requirements cannot be fulfilled by simply adopting and

1877-0509 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of Conference Program Chairs doi: 10.1016/j.procs.2014.07.020

adjusting traditional paradigms such as Service-Oriented Architectures (SOA)2 where services are triggered and coordinated according to pre-defined interaction patterns making self-adaptability and self-management hard to be integrated in a system1. Researchers are thus exploring the adoption of nature-inspired approaches, where each service behaves as an autonomous individual in an ecosystem. An ecosystem can be defined as the dynamic compound formed by material circulation and energy flow, with reciprocity, interdependency, and self-organization function, in the biological population and interactive natural environment3. In such systems - whether physical, chemical, biological, or social - the interactions between system components are not determined by pre-defined centralized patterns but rather by a small set of natural laws1 from which complex patterns of interactions dynamically emerge via self-organization.

Metaphors inspired by natural ecosystems, including digital4, knowledge5, and business ecosystems6, have provided an important source of relevant knowledge, models, and algorithms thereby allowing efficient solutions in many fields. They are well suited for the development of new computing systems, particularly when these systems are complex, large-scale, decentralized, open and heterogeneous. This is the case with WSNs which commonly consist of spatially distributed nodes, operating unattended with severe restrictions on their computation capabilities, memory space, communication bandwidth, and battery lifetime. These nodes should self-organize while collaborating and/or competing for the limited resources in similar ways the living organisms do. We thus believe that an ecosystem metaphor would be beneficial for solving the current WSN problems. To fully exploit this metaphor, we propose a better mapping between WSNs and natural ecosystems. We adopt the multiagent system paradigm13 which already has a well-defined set of formalisms, algorithms, and methodologies to bridge the gap between the WSNs and natural ecosystems. In the remainder of this paper, Section II describes the related works. Section III presents an initial mapping between sensors and living organisms. It also highlights the shortcomings of WSNs within this mapping. Section IV describes our proposed agent-based architecture which aims to address the WSN shortcomings and improves the mapping between WSNs and natural ecosystems. Section V briefly depicts the opportunities that this new architecture offers as well as the main challenges that WSN community has to deal with in the future.

2. Related Works

Thanks to their capabilities of remote and distributed sensing and their real-time data analysis, WSNs have been deployed to monitor many different ecosystems of different sizes. However, very few researchers have identified the WSN itself as an ecosystem. Jones and colleagues7 considered, indeed, sensors as organisms in an ecosystem and distributed throughout a geographic region. The proposed representation assumes that every sensor has exactly 8 neighbors and can only transmit to them. Barolli et al.8 implemented a simulation system for WSN using an approach inspired by Digital Eco-Systems. These systems use evolutionary computing to implement properties such as self-organization and scalability inspired by natural ecosystems. In spite of its good performance, the simulation did not highlight any similarities between WSNs and the natural ecosystem.

To the best of our knowledge, the only research work which has used the natural ecosystem as metaphor to model WSNs is presented by Antoniou and Pitsillides8. The authors proposed a bio-inspired congestion control approach for streaming applications in WSNs and considered a WSN to be analogous to an ecosystem. In particular, sensors are compared to species which live and interact together to meet their needs for survival and coexistence. In WSNs, traffic flows are seen as species that compete with each other for resources through a multi-hop path leading to the sink. The network is divided into small groups of sensors, called sub-ecosystems. Each sub-ecosystem involves all nodes that send traffic to a particular one-hop-away node (parent node). We argue that the proposed mapping between natural species and WSNs is partial because it does not capture all the characteristics and behaviors of both systems in addition to being designed for congestion control problems only.

Furthermore, many agent-based approaches have been proposed to solve various problems in sensor networks. Malik, E. Shakshuki9 proposed an approach where mobile agents are used to perform some of the required processing load instead of simply transferring the data to the sink. In this approach, each agent has to carry a code to a source node and bring back aggregated data to the sink, which reduces the communication cost. Garcia et al.10 proposed to reduce the WSN energy consumption by using data aggregation algorithms whereby agents act as dynamic clustering points in the network. In addition to saving energy, agents can allow a more efficient use of

sensor nodes' memories in addition to supporting code distribution among sensors11. In terms of conceptualization, sensor nodes have been modeled as software agents to achieve various objectives, such as data sampling12, improving task assignment13, and making data routing more efficient14.

3. Natural Ecosystem Metaphor for Wireless Sensor Networks

In this section we depict the characteristics of ecosystems and WSNs. We demonstrate that WSNs have many shortcomings which have to be addressed before taking advantage of the natural ecosystem metaphor.

3.1. Characteristics of Ecosystems and Wireless Sensor Networks

An ecosystem is a very complex entity with many interactive components. It can be seen as "the joint functioning and interaction of populations and their environment in a functional unit of variable size"15. Ecosystems are dynamic and may be defined using a wide range of scales of observation. They include large quantities of matter, energy, and information flowing within and between components, in a way that is not yet completely understood16. These flows depend on the ecosystem structure and could be controlled by different parties including top predators' feeding behavior (top-down control), primary producers (bottom-up control), some numerically abundant species (wasp-waist control), or a combination of some or all of these16.

The functioning of an ecosystem stems from the organization of its species' populations which have their own dynamics in terms of abundance, survival, growth, production, reproductive and other strategies. The ecosystems' structure, species composition, and functioning may change sometimes in uncontrolled and unpredictable ways16. Changes may consequently create uncertainty as to the future states and behavior of the system leading to potential risks for the ecosystem itself and its environment16.

Wireless sensor networks are collections of spatially distributed nodes that commonly cooperate in order to achieve goals which are beyond their individual capabilities. These nodes may operate unattended in remote harsh areas wherein human interventions are often impossible. Due to a variety of causes including lack of support, spatiotemporal events, animals, and energy depletion of sensors, the topology of the network dynamically changes. Some sensor nodes may lose several of their neighbors and find themselves at the boundaries of physical, logical, malicious, and semantic holes17 whereas others may be overloaded with data traffic due to the absence of alternative communication pathways.

To optimize the use of the limited resources and lengthen the lifetime of WSNs, several approaches18,19 have been proposed in recent years. Some of these approaches have provided the network with the capability to self-organize by creating clusters that may shrink or grow as sensors wakeup, sleep, and/or move. The changes on every cluster are commonly controlled by a cluster head which is a sensor node generally selected for its extended capabilities, its residual energy, and/or its degree of connectivity.

3.2. Sensors as Living Organisms

In order to fully exploit the ecosystem metaphor, it is important to compare the low-level entities in natural ecosystems and WSNs, namely living organisms and sensors. On the one hand, living organisms have 7 main characteristics21: (1) Nutrition (provides the resources required to fulfill all the other functions of the organism); (2) Excretion (set of chemical reactions to remove toxic materials, waste products, and substances in excess of requirements from the organism); (3) Respiration (releases the energy from the nutrients); (4) Sensitivity (ability to detect or sense changes in the environment and to respond); (5) Reproduction (process that generates new organisms of the same species); (6) Growth (concerns the increase in size and number of the living organisms); and (7) Movement (action by which an organism changes its position). On the other hand, sensors are commonly deployed in closed or open spaces. They are capable of sensing some parameters of interest within their environments, processing and storing data, and communicating with neighboring peers within their communication ranges. In this communication, sensors can support each other (e.g., to heal voids or track intruders), compete (e.g., obtain the necessary resources for their own tasks), or show an antagonistic behavior (e.g., spy nearby peers or jam their communications). A sensor may also move (if equipped with appropriate actuators) to join or leave a subgroup of

sensors. This is the case for example when a sensor may relocate to prevent any potential physical damage due to new environmental conditions such as fire or heavy rain. During such activity, the sensor may use its limited onboard memory to store new data and experience. It may also demonstrate a certain level of cognition by learning from its previous experiences7.

Given the characteristics discussed above of both sensors and living organisms, we argue that the capabilities of sensors do not fully equate to those of living organisms. There is indeed a need to extend these capabilities with more flexibility, autonomy, intelligence, and context awareness as shown in Table 1.

Table 1. Mapping living organisms' characteristics to sensors.

Characteristic Sensor conformity

Sensor limitation

Nutrition Supported: acquire information from the environment or from peers to fulfill

Excretion Supported: clean memory from obsolete data

Respiration Not supported, not necessary

Sensitivity Supported: sense/detect changes in the environment

Reproduction Supported: reproduce some capabilities with software components

Growth Supported: grow in terms of capabilities and knowledge

Movement Supported: move when actuators are available

Hardware and intelligence to get the right information

Intelligence to filter data

Context awareness Autonomy to reproduce Processing capabilities Intelligence to make the right move

3.3. Mapping Ecosystems onto Wireless Sensor Networks

Jones et al.7 and Antoniou and Pitsillides8 have argued that the WSN could be modeled based on observations of living systems which are likely to provide realistic models for sensor network design. Indeed, rather than adapting conventional techniques of centralized computer control, new techniques dependent on local cooperation among network nodes will lead to self-sustaining communities of machines with emergent behavior that autonomously operate and adapt to changes in the environment8. According to this vision, Jones and colleagues7 have perceived massively deployed motes as organisms which interact, learn, and make local decisions to achieve globally meaningful effects within their community. We also share this vision and propose, in Table 2, a mapping between WSN and ecosystems. Our mapping is basically done by emphasizing the three basic elements of an ecosystem which are21: Structure (represents a high level view of the ecosystem and refers to all of the living and non-living physical components that make up that ecosystem), composition (refers to the variety of living entities found within an ecosystem as well as their types/roles), and function (reflects the dynamic behavior of the ecosystem and refers to the natural ecological processes of the ecosystem). Furthermore, we emphasize additional features by comparing the topologies resulting from the organization of the entities found in ecosystems and WSNs. We also highlight the aims behind organism organizations as well as the communication between the different components of the system. Based on our mapping, we argue that in terms of logical view (components, organizations, and their relationships) and functional view (aims), ecosystems and WSN match quite well. However, WSN presents several shortcomings for the dynamic view (behavior). This may be explained by the limited capabilities of sensors that do not usually allow for complex, efficient behavior of WSNs.

Table 2. Mapping natural ecosystems onto Wireless Sensor Networks.

Ecosystem WSN Comments

Structure (Components making up the system)

Contains living organisms Contains sensors Good match

Contains non-living physical components Contains only sensors The space where the WSN is deployed

could represent its non-living physical component

Composition (variety of active entities within the system)

Organisms may be producers, consumers, or Sensors may be data collectors

In both systems, roles could change

predators (producers), sinks/gateways (consumers), depending on the environmental context

intruders (predators), or relays and human interventions

Topology (organization of entities that make up the system)

Structured into populations (also called communities and colonies)

Populations have dynamic structures

Populations may have different geographic scales

Commonly structured into clusters

Clusters have dynamic topologies

Clusters may have different geographic scales

Clusters could be predefined by human operators or result from the network self-organization

In both systems, topological changes are driven by internal and external factors

In both systems, inheritance relationships may exist between populations/clusters

Goals (aims of the system)

Depends on the ecosystem. Can be survival (nutrition and protection from predators) and/or growth (nutrition and reproduction)

Depends on the WSN but generally collecting, processing, and routing data while optimizing the use of the limited resources (survival)

The goals of WSNs are well known, whereas those of ecosystems are not always understood

Communication (data flow between entities composing the system)

Large quantities of matter, energy and information flow, within and between components

Flows of energy, matter, and information are in some cases controlled by one or more entities

Usually large quantity of data is exchanged between sensors

Data traffic may be controlled be one or more entities, generally cluster heads/gateways

Sensors may not be able to support high data traffic because of energy restrictions

Communications between sensors are very costly and are generally controlled to reach the predefined aims while preserving energy

Function (behavior of entities composing the system)

Living organisms may be in a dormant state

Organisms interact while exhibiting collaborative, competing, or antagonistic behaviors

Populations self-organize to adapt to environmental changes

Populations may have unpredictable and uncontrolled changes/behaviors

Ecosystem's operation results from the organization of its populations and the behavior of its organisms

Organisms have the important characteristic of evolution in terms of number, structure, and behavior

Sensors usually have to sleep

Sensors interact while exhibiting collaborative, competing, or antagonistic behaviors

Clusters can partially self-organize to react to internal and external changes

Clusters generally have predicable and controlled behaviors unless unexpected events affect sensors

WSN's operation results from the organization of its clusters and the behavior of its sensors

Sensors may be enhanced with mechanisms to learn and evolve thanks to artificial intelligence concepts (e.g., multiagent systems)

Sensors are constrained to sleep to save energy

Much more restrictions on sensors' interactions compared to organisms' interactions (due to limited communication ranges and energy)

Self-organization is usually a complex task for sensors because of their limited capabilities, lack of intelligence and autonomy

Sensors have limited context awareness

In both systems, complex functions result from simple behaviors of active entities which collectively achieve goals beyond their individual capabilities

Evolution in WSNs takes much less time than in ecosystems, but consumes a lot of energy and requires intelligence and autonomy from sensors

4. Proposed Ecosystem-Oriented Architecture for Wireless Sensor Networks

To exploit the benefits of ecosystems' features in the design of effective pervasive WSN services, two key challenges have to be addressed1. First, adequate methodologies and tools for the dynamic and decentralized control of the system should be defined. This control should support a tradeoff between top-down adaptation and a bottom-

up one. Second, the overall system has to be monitored by measuring its behaviors in order to make sure that the control is effective. By taking into consideration these requirements, we propose an Agent-Based Architecture for MApping natural ecosystems onto wireless sensor networks (ABAMA). Our architecture (Fig. 1) is indeed based on the multiagent system technology, which has proved its flexibility, autonomy, and intelligence to solve complex problems within highly dynamic, constrained, and uncertain environments20. Following Zambonelli and Virolli's vision1, ABAMA allows sensors to behave like natural organisms while keeping control on the overall network. ABAMA reflects the fact that sensors could be collaborating, competing, and even antagonistic. Several notations and acronyms on Fig. 1 will be explained in the upcoming subsections.

As a WSN may be deployed to provide several services to end users concurrently, subsets of sensors with each subset including a population of sensors can be created in response to one or more users' queries. The structure and composition of each subset (that we call here Service Sensor Network (SSN)) may be dynamic particularly because users may request the same service from different areas with different quality of service parameters. SSNs may compete with each other to acquire/secure the necessary resources for their tasks. Sensors in each SSN along with the supporting software agents form a small-scale ecosystem that we call EcoSSN (as shown in Fig. 1). We describe in the next sections some of three important tasks carried out by our proposed architecture, namely: processing users' requests, creating Service Sensor Networks (SSNs), and controlling SSNs by agents. Additional important tasks such as monitoring agents and the whole WSN as well as inter-clusters collaboration will be addressed in our future works. The different types of agents used in ABAMA to achieve these tasks are summarized in Fig. 2.

Legend: SSN: Service Sensor Network, ASSN: Agent SSN, WSN: Wireless Sensor Network 0 Static sensor Q) Mobile sensor •—«Temporarily relation...... Competition-------Antagonism

Fig. 1. Proposed ABAMA architecture.

Collaboration

4.1. Processing Users' Requests

ABAMA receives requests for services from end-users or from other WSNs through an Agent Input/Output Interface (AIOI). When a service query is received, the agent AIOI assesses the current situation/capabilities of the WSN to which it belongs. This assessment is mainly based on the data collected from the WSN itself (i.e., from the sensor nodes) as well as the agents which continuously monitor specific aspects of the network such as the network connectivity, energy levels, communication pathways, and progress in supporting current services. Any requested service may involve collecting data from several distributed areas called Areas of Interest (AoI).

If the service was already requested by a previous user and is currently being processed then the agent AIOI forwards the request to an Agent Controller (AC) (as shown in Fig. 1) that monitors the different agents, the whole WSN, and the progress of delivering the requested services. The agent AC assigns the request to an existing Agent Service Sensor Network (ASSN) which is in charge of monitoring the current requested service. Since the users might not necessarily request the same service from the same AoIs, the agent AC also informs the agent SSNA

about the additional sensors that will be used to provide the service from the right areas. Once the service is achieved, the agent ASSN notifies the agent AC which in turn replies back the result to the agent AIOI. If the service was not requested before, then the agent AIOI passes the request along with the AoIs to the WSN in order to create the SSN necessary to provide the requested service. The AIOI also contacts the agent AC to create a new agent SSNA that will be in charge of controlling the new SSN. The process of creating an SSN is described below.

4.2. Creating Service Sensor Networks (SSNs)

In order to create a new SSN, the base stations in the WSN broadcasts a message JoinService() within the AoIs (explicitly selected by the user or identified by the agent AIOI). If a sensor A already received the message then it simply acknowledges it. Otherwise, it also sets up its role and then broadcasts it. The sensor A could be a Backbone Sensor (BS) if it is able to collect the requested type of data (e.g., pressure sensor if the requested service is to measure the current atmospheric pressure) or a Support Sensor (SS) which serves as relay to route the data collected by the BSs. Every BS sensor that receives a JoinService() message directly from a base station or from a sequence of SS sensors only (i.e., does not include any other BS sensor) will be elected as a cluster head. Every sensor will then promote the cluster head to which it belongs. In addition to the JoinService() message, the sensor A may receive a ReplyJoinService() message or a RollBack() message. In the first case, the sensor A stores the role, the id, and the type of the sender sensor and marks it as a next hop. In the second case, the sensor A receives the paths leading to all the leaf sensors through the sender sensor. Once the timer of the sensor A expires, it sends aggregates of all the paths received from all its next hops and then sends a RollBack() message to its predecessor sensor. Every cluster head will aggregate all the paths received from its next hops. It will also nominate one of the members of its cluster as a Cluster Subordinate Sensor (CSS). The CSS sensor, which is selected based on its current energy and the number of hops it is away from the cluster head, will be delegated to carry out some processing (such as broadcasting updates within the cluster) that ultimately free the cluster head and preserve its energy.

4.3. Controlling SSNs with Agents

Unlike the living organisms that possess enough energy for their interactions, sensors' communications must be controlled in order to preserve their limited energy. This task is commonly assigned in WSNs to gateways which are specific sensor nodes with extended capabilities. Similar to the entities controlling colonies in ecosystems, gateways have limited awareness of the geographic space where the WSN is deployed. To overcome this shortcoming (as presented in Table 2), software agents are used to enhance the control of SSNs while adding flexibility to clusters to self-organize and increase their awareness of sensor communications and mobility. We thus assign the overall control of every SSN to a software agent called SSN Agent (see Fig. 1). This agent, which is hosted on a super node within the WSN or hosted on a remote server, analyzes the data collected from the cluster heads and then forwards its instructions in order to prevent or recover communication holes, advise new communication paths, or recommend new CSS sensors, especially when data traffic is increased. When necessary, the agent ASSN may create a mobile agent, called Agent Delegate (AD) and send it within a specific cluster with the mission to update current processing, collect data, or transfer some knowledge. Every agent SSNA reports to the agent AC the progress of processing. If an agent SSNA expects serious difficulties or encounters a failure in achieving the assigned service, the agent AC will then take the decision either to abort the current processing, instruct free mobile sensors to join the SSN, or borrow additional resources from neighboring SSNs. The architecture of our multiagent system and its relation with the physical WSN is depicted in Fig. 2 below. The categorization of sensors into several types will ultimately allow agents to manage and control sensors based on different levels of priorities.

Legend: AIOI: Agent Input/Output Interface, AC: Agent Controller, ASSN: Agent Service Sensor Network, ABS: Agent Backbone Sensor, ACSS: Agent Cluster Subordinate Sensor, ASS: Agent Support Sensor, AD: Agent Delegate

Fig. 2. ABAMA's agents (shaded boxes) and their relations with the WSN and its sensors (white box).

5. Opportunities and Challenges

On its own, a WSN has limited capabilities in terms of self-organization, learning, enhancing processing, and detection of malicious nodes. However, with the help of software agents, it is possible to enhance the capabilities of the physical network and ultimately match them with those of a natural ecosystem. Indeed, the agents of ABAMA can implement appropriate bio-inspired algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weeds Optimization (IWO), and Bee Colony Optimization (BCO) to optimize data traffic and carry out the right processing at the right time. Multilevel collaboration can also be implemented by allowing dedicated mobile agents to migrate on a meeting infrastructure (remote server or super node) to make collaborative decisions and share knowledge and experience learned from previous experiences at low cost. These improvement opportunities can be achieved without affecting the physical resources of sensors and the overall performance of the network. Agents can also be used to make soft copies of some capabilities of a given sensor and share or implement them on other sensors.

To take full advantage of the natural metaphor and enjoy the opportunities presented above, the WSN community has to deal with several challenges, especially the adaptation of agents' deployment to the WSN context and the application of natural ecosystem theories and models to the WSN. Indeed, although sensor agent technology has become sufficiently reliable for operational use in the field13, deploying agents on sensor nodes suggests additional research efforts that take into account the WSN constraints restricting agents to appropriately exchange data for the sake of increasing their knowledge, competencies, and context awareness without consuming a lot of the limited energy of the network. Mobile agents allow for the reduction of energy consumption; however, they cannot carry out extended expertise while moving. Collaboration and negotiation algorithms should also be tailored to use as least interaction as possible. Furthermore, because of the variety of behaviors that a sensor may exhibit (e.g., collaborative, competitive, antagonist) and the change on its capabilities and processing loads, several algorithms based on the theories and models of natural ecosystems have to be further adapted to coexist within the same WSN, and eventually on the same sensor node.

6. Conclusion

Ecosystems and WSN exhibit several similarities, particularly in terms of structure and goals. They are indeed both composed of interactive components (organisms and sensors) which could self-organize, collaborate, and compete to achieve complex functions far more than what they are capable of. We found that a big gap exists between both systems in terms of behaviors due to the limited capabilities of sensors. Indeed, while living organisms' communications cost less and generally lead to evolution, sensors' communications are more costly in terms of energy usage and may lead to a rapid depletion of the energy of sensors. We thus argued that the metaphor

of ecosystems could be applied to WSNs provided that we extend the current capabilities of sensors. To this end and while comparing sensors to living organisms, we have identified the need for sensors to be more flexible, autonomous, and intelligent. We then argued that this can be achieved by using a multiagent system approach to bridge the gap between ecosystems and sensor networks. We proposed the ABAMA architecture where software agents can ensure a better use of the limited WSN resources by implementing a multilevel control (over the entire network and over individual sensors). These agents are either situated on sensor nodes or on a virtual platform (super node) where the heavy processing tasks of the WSN are migrated to so that we can increase the context awareness and save energy.

We are currently implementing our ABAMA architecture using the java-based platform Jade. We are leveraging the capability of sensors with software agents using bio-inspired algorithms to optimize communication load. We are planning to test our solution in the context of hazard management and extend it with capabilities of inter-cluster collaboration.

References

1. Zambonelli F., Viroli M. A survey on nature-inspired metaphors for pervasive service ecosystems, J. Pervasive Computing and Communications, vol. 7, no 3, 2011, pp.186- 204

2. Huhns M.N., Singh M.P. Service-oriented computing: key concepts and principles, J. IEEE Internet Comp., 2005, vol. 9, no. 1, pp. 75-81

3. Bai Z. Thinking about the Ecosystem Characteristics Based on Systems Science, J. Central South University of Forestry & Technology, 2007,

vol. 12, no 6, pp. 174-178

4. Digital-ecosystems. http://www.digital-ecosystems.org/. 2014, Accessed 20 May 2014

5. Bray D. Knowledge Ecosystems: A Theoretical Lens for Organizations Confronting Hyperturbulent Environments, Proc. International

Federation for Information Processing, Manchester, UK, 2007

6. Moore J.F. Predators and Prey: A New Ecology of Competition, Harvard Business Review, May-June 1993, pp.75-86

7. Jones K.H., Lodding K.N., Olariu S., Wilson L., Chunsheng X. Communal Cooperation in Sensor Networks for Situation Management, Proc.

9th Int. Conf. on Information Fusion, 2006, pp.1-8

8. Antoniou P., Pitsillides A.Congestion Control in Autonomous Decentralized Networks Based on the Lotka-Volterra Competition Model, J.

Artificial Neural Networks, 2009, vol. 5769, pp 986-996

9. Malik H., Shakshuki E. Data Dissemination in Wireless Sensor Networks Using Software Agents. Proc. HPCS, IEEE Computer Society

2007:28

10. Garcia M.S., Carvalho D., Zlydareva O., Muldoon C., Masterson B.F., O'Grady M.J., Meijer W.G., O'Sullivan J.J., O'Hare G.M.P. An Agent-Based WSN for Water Quality Data Collection", UCAmI, LNCS Springer 2012, 2012, 7656, pp. 454-461

11. Hui J.W., Culler D. The dynamic behavior of a data dissemination protocol for network programming at scale, Proc. 2nd Int Conf. on Embedded Networked Sensor Systems, ACM Press, 2004 pp. 81-94

12. Kho J., Rogers A., Jennings N.R. Decentralised adaptive sampling of wireless sensor networks, Proc. 1st Int. Workshop on Agent Technology for Sensor Networks, in 6th Int. Joint Conference on Autonomous Agents and Multiagent Systems, Honolulu, Hawai'i, USA, 2007

13. Rogers A., Corkill D., Jennings N.R. Agent Technologies for Sensor Networks," J. IEEE Intelligent Systems, 2009, vol. 24, no. 2, pp. 13-17

14. Kho J., Tran-Thanh L., Rogers A., Jennings N.R. Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks, Proc. 3rd Int. Workshop on Agent Technology for Sensor Networks, Budapest, May, 2009, pp. 63-70

15. Scialabba N. Integrated coastal area management and agriculture, Forestry and fisheries. FAO Guidelines, 1998, pp. 256

16. Cury P., Shannon L., Shin Y.J. The functioning of the marine ecosystems: a fisheries perspective, Responsible fisheries in the marine ecosystem, M. Sinclair and G. Valdimarsson eds., 2003, pp. 103-123

17. Jabeur N., Sahli N., Khan I. Survey on Sensor Holes: A Cause-Effect-Solution Perspective. Proc. Procedia Computer Science, vol. 19, 2013, pp. 1074-1080

18. Liang W. Constrained resource optimization in wireless sensor networks with mobile sinks. J. of Communications, 2012, vol. 7, no. 7, pp. 494-499

19. Lee K., Lee H. Energy-Efficient Self-Organized Clustering with Splitting and Merging for Wireless Sensor Networks, J. of Distributed Sensor Networks, 2013, vol. 2013, Article ID 487846, 11 pages

20. Bandyopadhyay S., Coyle E.J. An energy efficient hierarchical clustering algorithm for wireless sensor networks", Proc. of INFOCOM 20013, IEEE Societies, 2013, vol. 3, pp. 1713-1723

21. Vold, T. and D.A. Buffett (eds.). 2008. Ecological Concepts, Principles and Applications to Conservation, BC. 36 pp. Available at: www.biodiversitybc.org Accessed May 25, 2014