  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 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 approach
solves problems associated with SIP in terms of task duration, energy
consumption, and reliability. However, the majority of existing MIP approaches
focus only on reducing energy consumption and task duration, while the
Event-to-sink throughput has not been considered. In this paper, a Clone
Mobile-agent Itinerary Planning approach (CMIP) is proposed to reduce task
duration while improving the Event-to-sink throughput in real-time
applications, especially when the MA is assigned to visit a large number of
source nodes. Simulation results show that the CMIP approach outperforms both
Central Location-based MIP (CL-MIP) and Greatest Information in Greatest
Memory-based MIP (GIGM-MIP) in terms of reducing task duration by about 56% and
16%, respectively. Furthermore, CMIP improves the Event-to-sink throughput by
about 93% and 22% as compared to both CL-MIP and GIGM-MIP approaches,
respectively.
