Mobile agent-based data gathering approaches for static multiple mobile agent itinerary in wireless sensor networks

In order to mitigate the problem of data congestion, increased latency, and high energy consumption in Wireless Sensor Networks (WSNs), Mobile Agent (MA) has been proven to be a viable alternative to the traditional client-server data gathering model. MA has the ability to migrate among the network nodes based on an assigned itinerary which can be formed via Single Itinerary Planning (SIP) or Multiple Itinerary Planning (MIP). MIP based data gathering solves the problems associated with SIP in terms of task duration, energy consumption, and reliability. However, the determination of the optimal number of distributed MAs and their itinerary in MIP remains a major challenge to minimize the energy consumption and task duration, as well as improve the successful MA’s round-trip and event-to-sink throughput. In this regard, three problems and their corresponding proposed solutions in this thesis are given below: Firstly, the existing MIP approaches assume that each MA starts and end its itinerary at the sink node. Furthermore, each MA has to carry its processing code (data aggregation code) for the data collection process. These assumptions would result in an increase in the number of MA’s migration hops which leads to increase the energy consumption. Accordingly, a Spawn Mobile agent Itinerary Planning (SMIP) approach is proposed to address these issues. In SMIP, the Main MA (MMA) is able to spawn a new MA (SMA) which carries back the aggregated data of the MMA to the sink. This spawning mechanism has reduced the number of MA’s migration hops, thereby reducing the energy consumption since the MMA does not need to get back to the sink to unload data and resume another MA journey. SMIP achieved promising results by 12.16% and 9.77% energy consumption decrease as compared to the well-known CL-MIP approach and the existing GIGM-MIP approach, respectively. Secondly, most of the proposed itineraries of the MA employ a single distance based parameter to determine the next MA’s migration hop which results in an unsuccessful MA’s round-trip, especially when the remaining energy of the selected node is very low. Also, certain nodes will be chosen more than once due to their nearest distance, thereby, these nodes lose their energy quickly and could lead to a substantial imbalance in the energy dissipation of the nodes. To deal with this, a Fuzzy-based Mobile Agent Migration approach (FuMAM) is proposed. In FuMAM, the next MA’s migration hop is determined by considering three parameters: distance, remaining energy, and the number of neighbors. Such an approach has improved the success of MA’s round-trip by 67.01% and 56.56% compared with CL-MIP and GIGM-MIP, respectively, through selecting the node based on the three parameters. Lastly, attaining energy efficiency has been the main focus of previous MIP approaches which make them best suited for environmental monitoring applications as sensor nodes would be unattended to for a long period of time. These schemes perform poorly in real-time applications, which requires a higher event-to sink throughput to effectively deliver real-time data for timely decisions to be made within the network. As a solution, a Clone Mobile-agent Itinerary Planning approach (CMIP) is proposed. In CMIP, the MA cloning concept is used to decrease the task duration while maximizing data collection which has a direct impact on event-to sink throughput. CMIP approach reduces the task duration of CL-MIP and GIGM-MIP by 56.12% and 15.96%, respectively. Similarly, as compared to both CL-MIP and GIGM-MIP approaches, CMIP also improves the event-to-sink throughput by 93.05% and 21.9% respectively. The performance of the above proposed approaches have been tested with the simulation benchmark using MATLAB.

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