Multiple Criteria Clustering of Mobile Agents in WSN

The main goal of wireless sensor networks (WSN) is to gather data from their environment. This gathering should take into consideration the life of the battery of each sensor node to maintain the continuity of the network. The technology of multi-agent system (MAS) can be adopted in large scale WSN for reliable wireless communication with high energy efficiency. In this paper, we propose a scheme for data collection based MAS for sensor networks based on clustering with the use of a mobile agent to collect data according to a routing scheme. The main idea is to assign nodes to the data processing function rather than providing data to the base station (Sink). For this, an agent is introduced into each node of the network to process data locally and to judge their importance to remove any irrelevant redundant data. Then, the nodes are clustered together, each consisting of a ClusterHead.The latter will then determine a scheme for the nodes belonging to the same group, using the Local...

Journal of Computers, 2006

Recently, mobile agents have been proposed for efficient data dissemination in sensor networks. In the traditional client/server-based computing architecture, data at multiple sources are transferred to a destination; whereas in the mobile-agent based computing paradigm, a taskspecific executable code traverses the relevant sources to gather data. Mobile agents can be used to greatly reduce the communication cost, especially over low bandwidth links, by moving the processing function to the data rather than bringing the data to a central processor. This paper proposes to use the mobile agent paradigm for reducing and aggregating data in a planar sensor network architecture. The proposed architecture is called mobile agent based wireless sensor network (MAWSN). Extensive simulation shows that MAWSN exhibits better performance than client/server communications in terms of energy consumption and the packet delivery ratio. However, MAWSN has a longer end-to-end latency than client/server communications in certain conditions. Index Terms-mobile agent, energy efficient, aggregate, data dissemination, wireless sensor networks received the BS, MS and Ph.

Vol. 19 No. 1 JANUARY 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021

The inherent problems of client/server model of networking has been reasonably minimized by the emergence of mobile agent (MA) computing paradigm. Reliability is a major priority in any energy constrained networks. This paper adopts clustering algorithm and mobile agent technology to propose a reliable and energy-efficient data transmission to improve the life span of wireless sensor networks. The primary advantage of cluster-based architecture is that it requires less transmission power due to small communication ranges within the cluster. The subsequent simulation of the proposed algorithm in further studies will be compared with other techniques and Leach Energy Adaptive Clustering Hierarchy (LEACH) protocol and its stability over the existing LEACH protocol and other techniques will be validated in terms of lifetime and energy consumption.

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2014

The design of wireless sensor networks (WSNs) is a very challenging issue. In this article, we consider a review of wireless sensor networks designed to collect and store data using mobile agents. Mobile Agent(MA) based applications research activities have increased and are used in a wide range concerned areas like Energy Conservation ,Network Management, Electronic commerce, Wireless Multimedia Sensors, Grid Service and Grid Computing, Distributed Data Mining, Security, E-learning, Location Tracking, Semantic Web Service etc. This review focus on the roles of the MAs in WSNs.

In this article, we propose an intelligent architecture based on a multi-agent system that allows adapting the number of data-mule tours according to the state of the monitored environment. In our case robots’ tours are planned at fixed frequencies, but when the data collected by the mobile nodes shows unusual behavior, our architecture can decide to dedicate a mobile node specially to the risky area. Moreover, the gateway decides to activate the appropriate robot to achieve the goal, within the energetic constraints of the platform and making sure that the exchange of information between the robot and the gateway remains maintained up to achievement of the objective.

This paper proposes CLUSMA: a middleware framework for communication between sensor nodes in Wireless sensor network. Our proposed technique is based on clustering technique (CLUS) and it uses Mobile Agent (MA).This paper gives a sketch of the middleware and the actual implementation of the framework are in progress. The main objective of this middleware is to utilize the networks functions and resources in an effective way. Because the proposed framework is based on MA it will work in homogenous as well as heterogeneous environment. Previous such frameworks used symmetric secret key for sensor nodes authentication. The proposed framework will provide security in heterogeneous environment by using mobile agents in a cluster which will communicate with agents in other cluster.

Computer Engineering and Applications Journal, 2014

In the last few years, there are many applications for Wireless Sensor Networks (WSNs). One of the main drawbacks of these networks is the limited battery power of sensor nodes. There are many cases to reduce energy consumption in WSNs. One of them is clustering. Sensor nodes partitioned into the clusters so that one is chosen as Cluster Head (CH). Clustering and selection of the proper node as CH is very significant in reducing energy consumption and increasing network lifetime. In this paper, we have surveyed a multi agent clustering algorithms and compared on various parameters like cluster size, cluster count, clusters equality, parameters used in CHs selection, algorithm complexity, types of algorithm used in clustering, nodes location awareness, inter-cluster and intra-cluster topologies, nodes homogeneity and MAC layer communications.

The Information processing based on Data mining in WSN is at its starting stage, when compared to traditional machine learning in WSN. In order to solve a particular problem in WSN the researchers now a day were mainly focused on applying machine learning techniques. The Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences will result to a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary in order to overcome these differences. The improvement or optimizing of the performance of the entire network in terms of energy conservation and network lifetime will be one of the major objectives in wireless sensor network. This paper will survey the Data Mining in WSN applications from two perspectives, namely the network associated issue and application associated issue. In the network associated issue, different machine learning algorithms applied in WSNs were used in order to improve network performance will be discussed. In application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.

Mobile Agent is the computer program which can automatically move among different network hosts and perform the user's operations. A Mobile Agent characteristic is being automatic, cooperative, mobile, secure and intelligent. These are agents that can physically travel across a network, and perform tasks on machines that provide agent hosting capability. It allows processes to migrate from computer to computer, for processes to split into multiple instances that execute on different machines, and to return to their point of origin. Unlike remote procedure calls, where a process invokes procedures of a remote host, process migration allows executable code to travel and interact with databases, file systems, information services and other agents. Significant research and development into mobile agency has been conducted in recent years, and there are many mobile agent architectures available today. However, mobile agency has failed to become a sweeping force of change, and now faces competition in the form of message passing and remote procedure call (RPC) technologies. The strategies it flavors could be an alternate choice for routing mechanism in various networks especially wireless sensors networks which could have a specific function only to be invoked and then migrate while intelligently compressing and processing data that is collected for the sink to receive. This certainly would reduce network traffic and congestion in wireless sensor networks.

20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment (HPCS'06), 2006

This paper presents collaborative agents system architecture approach for data dissemination in a wireless sensor network (WSN). This system architecture consists of four layers of agents with different types of functionalities, including the interface layer, regional layer, query layer and data collection layer. At the interface layer, the interface agents interact with the users to fulfill their interests. At the regional layer, the regional agents and the cluster agents communicate with each other using TCP/IP protocol and facilitate collaboration between the agents of the other layers. The regional agent generates the optimized query plan to the cluster agent. At the query layer, the query agents perform data dissemination and efficient in-network processing with the other agents in the same layer; it also captures the required data through the data collection layer that has direct access to sensor nodes. This paper provides the agents' architecture, design and implementations that enable them to communicate and work together to disseminate and gather data in WSNs.