# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 1 Introduction  
The rapid development of tiny electronic sensors has accelerated the widespread use of wireless sensor networks (WSNs) [1; 2; 3]. WSNs are usually composed of numerous sensors deployed randomly to detect a target area for some physical features such as temperature, humidity, and gas concentration, and it has been widely applied in smart home [4; 5], smart health [6], environment detecting [7], and certain intelligent systems technologies [8; 9; 10; 11]. These sensors are battery-powered and are self-organized in order to construct wireless sensor networks. Traditional WSNs usually adopt static sinks, and that may cause hot spot problems, which makes nodes close to the sink experience premature death due to the heavy burden of data forwarding. The hot spot problems can be mitigated using special clustering and well-designed topology controls, but they cannot solve the issue fundamentally.  
Recent research on sink mobility technology provides a series of novel ideas to address the problem of hot spots and has achieved great results [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29].  
One of the problems demanding prompt solution for sink mobility is the trajectory scheduling of the mobile agent. An efficient traveling path of the mobile agent can reduce the energy consumption and mitigate network latency as well. However, the path scheduling problem has been proven to be a non-deterministic polynomial hard problem (NP-hard) [12; 13]. Therefore, it is difficult to acquire the optimal solution to the problem, and many researchers regard it as traveling salesman problem (TSP) [14]. Some intelligent heuristic methods such as ant colony optimization (ACO) [30], particle swarm optimization (PSO) [31], and glowworm swarm optimization (GSO) [32] are introduced for mobile agent path planning and they achieve some results. Many algorithms aim to search for a shorter path for the mobile agent and this inevitably increases the energy consumption during transmission. Therefore, it is hard to make a trade-off between different properties, and the routing protocol designing should be combined with specific applications. Another great achievement is the technology of MU-MIMO, which permits multiple receivers and senders to communicate at the same time by equipping multiple antennas [33; 34; 35; 36]. A mobile agent with MU-MIMO can gather data more efficiently when it accesses more than one target.  
The main contribution of this work can be concluded as follows. Firstly, we partition the target area into subdomains using virtual grids, and the cluster heads (CHs) are elected according to their weights. Secondly, the ACO algorithm is executed by the sink node to calculate an optimal sequence of CHs for traversing. Thirdly, the PSO algorithm is executed to select anchor nodes for mobile agents based on the traverse sequence obtained by the ACO algorithm. Fourthly, the communication range of the CHs are adjusted, and some anchor nodes can be merged to balance inter-cluster energy and to economize the sojourn time of the mobile agent. Finally, numerous simulations prove that the presented algorithm HM-ACPOSO plans a more efficient traveling path for the mobile sink as compared to some similar works. Section 2 introduces some of the latest work related to the enhancement of the performance of the network utilizing ACO or PSO and sink mobility technology. Section 3 describes the model of the network and energy and makes some basic assumptions. The traditional ACO and PSO algorithms are explained in Section 4. Section 5 details our presented HM-ACPOSO, and Section 6 demonstrates the simulation parameters of this work. Section 7 shows the simulation results and includes some analyses. Section 8 presents some discussions and proposes some future work. Section 9 concludes the paper.