SOFT COMPUTING IN WIRELESS SENSORS NETWORKS Averkin A.N. Dorodnicyn Computing Center of the Russian Academy of Sciences
[email protected]
Abstract The embedded soft computing approach in wireless sensor networks is suggested. This approach means a combination of embedded fuzzy logic and neural networks models for information processing in complex environment with uncertain, imprecise, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems. Keywords: embedded fuzzy logic, data fusion, clusterization, aggregation, fuzzy distributed knowledge base.
1
Introduction
The technology of fuzzy and neuro-fuzzy systems in WSN uses soft computing approach to increase performance of wireless sensors networks (WSN) and to make them more intelligent. WSN is one of the most promising technologies of the 21st century. For the first time «smart» sensors were implemented by Berkeley University of California together with INTEL corporation. The prototype of WSN node is a softwarehardware platform for deployment of several specialized sensors on the base on an autonomous wireless controller. WSN consists of a large number of tiny devices, which are deployed in real environment and function as a united network. To provide sensor nodes with a possibility of selforganization the specialized software was designed together with IDE for application development. The specialized software implements the possibilities of communication, routing and application support for WSN. The increasing of WSN performance means preliminary processing of raw data, data fusion, clusterization and aggregation. Intelligent WSN provides also distributive decision-making and queries processing, knowledge-based routing and power consumption. Methods of decision-making and information processing based on symbol models of classic artificial intelligence are too complex for
Belenki A.G. Lomonosov Moscow State University
[email protected]
embedded realization in WSN due to limited communication and power resources of WSN. Only few companies in the world solve this problem by embedded soft computing approaches. These approaches for WSN mean a combination of embedded fuzzy logic and neural networks models for information processing in complex environment with uncertain, imprecise, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems. These approaches were suggested by Russian scientists in 1997 [2], when hybridization of soft computing and mathematical statistics was used to process the results of heterogeneous measurements for environment monitoring applications. The first realization of these approaches was made in 2004 [1,2,4]. The main part of our embedded soft computing and soft computing approaches is Smart Node (SN) model for WSN [3]. The core of SN is Fuzzy Engine, which consists of three modules: knowledge base (a set of fuzzy production rules), fuzzification and defuzzification modules, which transform numerical measurements in linguistic form and vice-versa. The output of SN can approximate of any function of input parameters, e.g. when it is impossible or difficult to measure certain parameter, it can be computed by SN with the use of special rules from knowledge base. Similarly the special rules can be created for data fusion, clusterization, aggregation, routing and power consumption. The knowledge base of SN can be created as a result of knowledge acquisition from exert or by supervised neural network learning. Application knowledge in nodes can significantly improve the resource and energy efficiency, for example by application-specific data caching and aggregation in intermediate node SN are realized inside MeshNeticsTM platform [4]. MeshNetics™ is a family of software components, algorithms, hardware designs and solutions that enable next generation M2M applications [3]. By adding expert system to monitoring, controlling, tracking applications, it enables new generation of solutions optimized for the needs of end-users. With MeshNetics™ businesses profit from reduced pricing structure, life-cycle time and enhanced competitiveness of their new and existing products. MeshNetics™ enables expert remote monitoring and control of a wide range of processes, assets, systems, and facilities. Built-in expert system with advanced algorithms, neural networks and fuzzy logic creates a new generation of WSN’s and frees businesses from
being trapped into costly and complex wired “static” systems and enhances the possibilities by listening actively to your environment. Built-in expert system on the base of SN, that allows to support hybrid distributive expert system on the nodes of WSN, which can realize, together with data collection and communication, a large class of existing algorithms of data fusion and aggregation. MeshneticsTM SN is the node of MeshneticsTM platform, which include Smart Engine. Smart Engine is given by a number of its parameters, i.e. rules patterns, variables, terms, membership functions, triangular norms. These parameters can transmit across WSN. These transmissions can be defined by user, or by Smart Node (SN) itself. E.g., in goal tracking process these transmissions can be realized by SN in dependence of mutual smart node and goal positions. Software environment of SN is universal tool for intellectual decision support in WSN and it is strongly connected with following poweraware&networked embedded Computer Systems research areas: application-driven network architectures, emerging platforms and technology, resource constrained real-time OS’s, distributed algorithms (broadcast, anycast, multicast, convergecast) in lossy wireless networks, ad hoc multi-hop routing, , in-network aggregation and processing, coverage and density, ranging and localization, resilient aggregators, distributed feature extraction, tracking, and collaborative signal processing.
2 2.1
Smart Nodes Challenges Data Processing Challenges
The main goal of WSN activity is collecting a tremendous amount of data and transmitting them to the user. Collected data can be interpreted as distributed knowledge base. In this approach both system user and system designer have the problems of control for distributed data processing processes of uncertain, incomplete or redundant sensor measurements. The main factors, that influence on WSM effectiveness and make problems for designers are the following: • WSN nodes have very restricted computational and storage power. • Node communication range is limited. In most cases nodes can directly communicate with immediate neighbors only. • WSN consists of a large number of unreliable nodes, that produce measurement or transmission errors
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WSN must continue to operate at all times even when some of it nodes get physically destroyed at unpredictable times. • WSN must continue to operate without interruption when new nodes are added to the network in order to replace the failed ones or extend the network. • As a result, node communication may require different paths at different times depending on the state of end-to-end link between communicating parts of the network. • The decrease of battery power is different in different nodes. • Data transmission time and power losses increase with the size of WSN. Two last problems are of great importance to the user. The carrying capacity of network only slightly depends on number of nodes (it increases as log N, where N – number of nodes). The most rational output is decreasing of traffic by adding distributive hierarchical data processing inside network and by providing the user with relevant answers only. There are a number of various algorithms for this processing realization. These algorithms depends on data types and data generalizations levels. But in traditional models of distributive hierarchical data processing each algorithm is strictly connected with certain node for given network topology. Changing of network topology implies reboot of nodes and this process needs transmitting of large pieces of code. To solve the problem in SN transmitting of code units has changed by transmitting of knowledge units. These knowledge units are used for reboot local note with tuning parameters. The last problem (irregularity of power consumption) is solved by embedding power consumption rules in given sensor node. On the base of these rules the sensor node can make autonomous decision about utility of participation in data collection and data transmission for given states of the environments, neighbor nodes and decision-making node. As software tool for this purpose we have realized universal tool of fuzzy sensor shell with production knowledge model, embedded in all nodes of SN. This shell may approximate a large class of existing algorithms of data fusion and aggregation. In this approach each sensor becomes intelligent agent with knowledge about himself and its’ environment and it is able to autonomous decision-making. Sensor may control this knowledge and send it to other node. In this case WSN can be interpreted as distributed data base and knowledge base with the possibilities of mobility and adaptability. The fact allows using multi-agent technologies and distributive intelligent decision support systems.
2.2
Middleware Challenges
SN technology sits between the operating system and the application and thus belongs to middleware. Thus the main purpose of middleware for sensor networks is to support the development, maintenance, deployment, and execution of sensing-based applications. This includes mechanisms for formulating complex high level sensing tasks, communicating this task to the WSN, coordination of sensor nodes to split the task and distribute it to the individual sensor nodes, data fusion for merging the sensor readings of the individual sensor nodes into a high-level result, and reporting the result back to the task issuer. The most part of these mechanisms were successfully realized in SN. Unique property of SN embedded middleware for WSN is imposed by the design principle application knowledge in nodes. Traditional middleware is designed to accommodate a wide variety of applications without necessarily needing application knowledge. SN embedded middleware for WSN has to provide mechanisms for injecting application knowledge into the infrastructure and the WSN. For this purpose SN embedded middleware for WSN has to provide special knowledge representation language, special query language, special protocols of query forwarding, special methods of data fusion and aggregation and special methods of software update management. These new technologies have been realized on the base of fuzzy systems technology.
3 3.1
Possible Application Fields of SN Smart Node in WSN Control.
When we use knowledge (meta-rules) to control WSS we have analogy with active network paradigm. The similarity is in sending together using together with each request special block of rules (capsule for active networks) to process the request by SN (server for active networks). The difference is that SN suppose two types of traffic – knowledge traffic and data traffic and in active networks there no knowledge traffic. For knowledge traffic and for communication with other subsystems (e.g. neuro-fuzzy systems) we can use FULL-like special language for fuzzy knowledge representation. Using SN we can realize: • Routing algorithms • Optimal control of power consumption in WSN • Data traffic control • Q&S control
3.2 ing
Using SM for Fuzzy Data Base Design-
From one perspective sensor networks are similar to distributed database systems. They store environmental data on distributed nodes and respond to aperiodic and long-lived periodic queries. Data interest can be pre-registered to the sensor network so that the corresponding data is collected and transmitted only when needed. These specified interests are similar to views in traditional databases because they filter the data according to the application’s data semantics and shield the overwhelming volume of raw data from applications. Fuzzy query approach can be used to reduce this volume of raw data. The extension of TinyDB by fuzzy attributes can be interpreted as fuzzy TinyDB and fuzzy active TinyDB. This possibility can be easy realized but does not seems very useful because there are only few possible classes of requests to WSN and representation of fuzzy modifier in TinyDB are rather restricted. Besides volume of DB is too large for SN memory. So using of extended SQL for fuzzy requests processing is not effective. Thus in Fuzzy MeshneticsTM expert WSN on the base of SN we can completely substitute functions of fuzzy data base for WSN (possible TinyDB fuzzy extensions) by SSM data bases functions with special query processing language for SN. The language can be used for: • Fuzzy queries to WSN (fuzziness can be in query only and also in WSN); • For fuzzy triggers designing; • For active data base designing; • For communication between SN. Traditional Event-Condition-Action triggers (active database rules) include a Boolean predicate as a trigger condition. As far as WSN can be considered as distributive database, we can see that SN with EventCondition-Actions in KB realize embedded fuzzy triggers for this distributive database.
3.3 Using of SN for Data Aggregation and Fusion. If all raw data is sent to base stations for further processing, the volume and burst ness of the traffic may cause many collisions and contribute to significant power loss. To minimize unnecessary data transmission, intermediate nodes or nearby nodes work together to filter and aggregate data before the data arrives at the destination. Five general, goal-oriented, data fusion methods are in use today in WSN (ordered by data complexity) data association, identity fusion, effect estimation, pattern recognition and artificial intelligence. Ten discrete data fusion techniques can be identified within these five general categories: figure of merit and gating technique in the data association, Kalman
filters in the identity fusion, Bayesian decision theory and Dempster-Shafer evidentional reasoning in the effect estimation, adaptive neural networks and cluster methods in the pattern recognition, expert systems, blackboard architecture and fuzzy logic in the artificial intelligence. The SN data fusion technology focuses on the acquisition of high-level information (artificial intelligence level), i.e. information that is related to many conventional physical quantities in a nonanalytical way. In these complex cases, fuzzy production systems and fuzzy neural networks are more effective and they compute and report linguistic assessments of numerically acquired values. Two methods are proposed to realize the aggregation from basic measurements. The first one performs a combination of the relevant features by means of a rule-based description of the relations between them. With the second, the aggregation is realized through an interpolation mechanism that creates a fuzzy partition of the numeric multidimensional space of the basic features. This partition can be realized with fuzzy neural networks. But SN can also can be used on low levels of data fusion, e.g. for filtering and for pattern recognition. Data fusion algorithms in production form can be easily decomposed and they have hierarchical form by nature. So sensor nodes hierarchy can realize data fusion inside WSN. The knowledge bases for processing of data in given node is distributed inside WSN. But a naive placement of the fusion functions on the network nodes will diminish the usefulness of in-network fusion, and reduce the longevity of the network (and hence the application). Thus, managing the placement (and dynamic relocation) of the fusion functions on the network nodes with a view to saving power becomes an additional responsibility of the application programmer. Dynamic relocation may be required either because the remaining power level at the current node is going below threshold, or to save the power consumed in the network as a whole by reducing the total data transmission. Supporting the relocation of fusion functions at run-time has all the traditional challenges of process migration.
are to provide distributed accumulation, transmitting and using of these knowledge. One of approaches is to use expert system with knowledge base distributed among SNs in WSN. The real data attributes (IPA) are processed by SN knowledge base and by knowledge bases of neighbor SNs. But the main problem is the cost and the complexity of data delivery in data fusion SN, because this data fusion SN, because this position of this SN must be fixed and close to the user. So the assignment of WSN as point for data fusion must be dynamic procedure and the SN position should be optimized in regarding of the query, WSN and environment status. Together with SN’s assignment its knowledge base should be changed. When cluster head function is delivered inside cluster of nodes from one SN1 to SN2 , than knowledge base with cluster head functions of SN1 should be send to SN2. Thus fuzzy distributed knowledge base for distributed data base query processing has the following properties: • It functions as distributed expert system. • Knowledge in production form can be transmitted between nodes. • Knowledge base for inquiry answer can be send in SN together with the inquiry • Special language is used for knowledge base transmission between nodes. • Knowledge-based program of data fusion uses parallel computing algorithms and destines for the whole WSM and not only for certain SN.
References: [1]
[2]
3.4 Using of SN for Fuzzy Distributed Expert System Designing
[3]
WSNs are huge dynamic databases but for more effective using of information we need more effective organization. The most interesting approach is to use WSN as distributed computing environment for intelligent data processing methods and as storehouse of this methods and not only tools for data measuring and transmitting.. Thus methods
[4]
Suvorov V., Zubkov G., Averkin A. In formation Technologies on the Base of Wireless Sensors Networks // Proceedings of the International conference on Information problems in the 3rd millennium (Kazan, 2004). Averkin A., Belenki, Suvorov V., Zubkov G. Soft Computing in Wireless Sensors Networks, International Conference on Soft Computing and Measurements SCM’2005, St.Petersburg, 2005. Averkin A.N., Prokopchina S.V. Survey of Soft Measurement Conception, St.Peterburg: Gydrometeoisdat, 1997. http://meshnetics.com
