| Recent advances in the fields of micro electro mechanical system (MEMS) and wireless communication technologies have directed research towards the development of a new generation network system - the sensor network [1][2][3][4]. It is composed of small sensor nodes integrating the capabilities of sensing, computing, communication, and even mobility.A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it. The position of sensor nodes need not be engineered or predetermined. This allows random deployment in inaccessible terrains or disaster relief operations. On the other hand, this also means that sensor network protocols and algorithms must possess self-organizing capabilities. The above described features ensure a wide range of applications for sensor networks. Some of the application areas are health, military, and home. In military, for example, the rapid deployment, self-organization, and fault tolerance characteristics of sensor networks make them a very promising sensing technique for military command, control, communications, computing, intelligence, surveillance, reconnaissance, and targeting systems. In health, sensor nodes can also be deployed to monitor patients and assist disabled patients. Some other commercial applications include managing inventory, monitoring product quality, and monitoring disaster areas.These sensors are typically lightweight with limited processing power, battery capacity, and communication bandwidth. The communication tasks consume the limited power available at such sensor nodes and, therefore, in order to ensure their sustained operations, the power consumption must be kept to a minimum.Most distributed sensor networks use a common network computing model: the client/server model. However, the client/server model is not appropriate for data integration in DSNs. First, the data integration at the server requires data transfer from local sensor nodes. When the size of data file is large and the number of sensor node is big, the network traffic can be extremely heavy, resulting in poor performance of the system. Second, the client/server based DSN cannot respond to the load changing in real time. When more sensors are deployed, it cannot perform load balancing without changing the structure of the network. To meet these new challenges, the concept of mobile agent-based distributed sensor networks (MADSNs) has been proposed by Qi et al. [13] MADSN offers the following important benefits: Network bandwidth requirement is reduced; Better network scalability; Extended Extensibility; Stability. Wsq et al. [13] proposed the mobile agent routing problem (MARP) in some practical MADSNs deployed for target detection and tracking. We formulate the mobile agent routing problem (MARP) in an MADSN as a combinatorial optimization problem involving the cost of communication and the path loss due to wireless propagation. The overall routing objective is to minimizing the power needed for communication and the path losses. We show MARP to be NP-complete by using a reduction from a variation of the multi-traveling salesman problem (MTSP). We then propose an approximate solution based on an improved Ant System (AS).Ant System algorithm, originally introduced by M. Dorigo in [28][29][30][31]in the 1990s ,is a new cooperative search algorithm inspired by the behavior of real ants. Great progress has been made in theoretic and applied research of the Ant system. It gave encouraging results, yet its performance was not competitive with state of the art algorithms for the TSP. We proposed an improved version of AS, which significantly speeds up the convergence rate of the searching for solution. Simulation results for TSPs with different node sizes and node distributions show that our algorithm has superior performances compared to MMAS. |
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