Mobile Agent Itinerary Planning Approaches in Wireless Sensor Networks- State of the Art and Current Challenges

Author(s):  
Tariq Alsboui ◽  
Mustafa Alrifaee ◽  
Rami Etaywi ◽  
Mohammad Abdul Jawad
2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668484 ◽  
Author(s):  
Huthiafa Q Qadori ◽  
Zuriati A Zulkarnain ◽  
Zurina Mohd Hanapi ◽  
Shamala Subramaniam

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 71464-71473 ◽  
Author(s):  
Huthiafa Q. Qadori ◽  
Zuriati Ahmad Zukarnain ◽  
Mohamed A. Alrshah ◽  
Zurina Mohd Hanapi ◽  
Shamala Subramaniam

2021 ◽  
Author(s):  
Mohamed Younis Mohamed Alzarroug ◽  
Wilson Jeberson

Wireless sensor networks (WSNs) consist of large number of sensor nodes densely deployed in monitoring area with sensing, wireless communications and computing capabilities. In recent times, wireless sensor networks have used the concept of mobile agent for reducing energy consumption and for effective data collection. The fundamental functionality of WSN is to collect and return data from the sensor nodes. Data aggregation’s main goal is to gather and aggregate data in an efficient manner. In data gathering, finding the optimal itinerary planning for the mobile agent is an important step. However, a single mobile agent itinerary planning approach suffers from two drawbacks, task delay and large size of the mobile agent as the scale of the network is expanded. To overcome these drawbacks, this research work proposes: (i) an efficient data aggregation scheme in wireless sensor network that uses multiple mobile agents for aggregating data and transferring it to the sink based on itinerary planning and (ii) an attack detection using TS fuzzy model on multi-mobile agent-based data aggregation scheme is shortly named as MDTSF model.


Author(s):  
LINGARAJ. K ◽  
ARADHANA. D

It has been proven recently that using Mobile Agent (MA) in wireless sensor networks (WSNs) can drastically help to obtain the flexibility of application-aware deployment. Normally, in any MA based sensor network, it is an important research issue to find out an optimal itinerary for the MA in order to achieve efficient and effective data collection from multiple sensory data source nodes. In this paper, we firstly investigate a number of conventional single MA itinerary planning based schemes, and then indicate some shortcomings of these schemes, since only one MA is used by them. Having these investigations and analysis, novel Multi-agent Itinerary Planning (MIP) algorithms to address the shortcomings of large latency and global unbalancing of using single MA and its effectiveness is proved by conducting the extensive experiments in professional environment.


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