Agent Routing Algorithm Research Articles

Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network

The sensor nodes have limited sensing, computation, communication capabilities and are mostly operated by batteries in a harsh environment with non-replenishable power sources. These restrictions make the sensor network prone to failures because most of the energy is spent on data transmission, sensing, and computing. Many applications such as habitat monitoring, military surveillance and forest fire detection expect the sensor nodes to last for a long time because they operate human unattended. Therefore, the major challenges in designing a wireless sensor network (WSN) are energy conservation, reducing data transmission delay and improving the network lifetime. In this context, data aggregation is an intelligent technique used in WSN, wherein the data from disparate sources are accumulated at intermediate nodes, thereby reducing the number of packets to be sent to the sink. Literature study shows that various routing algorithms are used to perform data aggregation based on the network topology. In order to provide an improved performance amongst the existing, a routing algorithm called cluster-chain mobile agent routing (CCMAR) is proposed in this work. It makes full use of the advantages of both low energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor information systems (PEGASIS). CCMAR divides the WSN into a few clusters and runs in two phases.The proposed system is simulated and evaluated for the performance metrics such as energy consumption, transmission delay and network lifetime. The results demonstrate that the proposed CCMAR outperforms LEACH, PEGASIS and other similar routing algorithm, energy efficient cluster-chain based protocol.

Hierarchical Supervisor and Agent Routing Algorithm in LEO/MEO Double-layered Optical Satellite Network

AbstractA novel routing algorithm (Hierarchical Supervisor and Agent Routing Algorithm, HSARA) for LEO/MEO (low earth orbit/medium earth orbit) double-layered optical satellite network is brought forward. The so-called supervisor (MEO satellite) is designed for failure recovery and network management. LEO satellites are grouped according to the virtual managed field of MEO which is different from coverage area of MEO satellite in RF satellite network. In each LEO group, one LEO satellite which has maximal persistent link with its supervisor is called the agent. A LEO group is updated when this optical inter-orbit links between agent LEO satellite and the corresponding MEO satellite supervisor cuts off. In this way, computations of topology changes and LEO group updating can be decreased. Expense of routing is integration of delay and wavelength utilization. HSARA algorithm simulations are implemented and the results are as follows: average network delay of HSARA can reduce 21 ms and 31.2 ms compared with traditional multilayered satellite routing and single-layer LEO satellite respectively; LEO/MEO double-layered optical satellite network can cover polar region which cannot be covered by single-layered LEO satellite and throughput is 1% more than that of single-layered LEO satellite averagely. Therefore, exact global coverage can be achieved with this double-layered optical satellite network.

Mobile Agent routing algorithm for WSN based on Q learning hybrid with ant colony optimization

In view of mobile Agent routing problem in Wireless Sensor Networks( WSN), a mobile Agent routing algorithm for WSN based on Q learning hybrid with ant colony optimization was proposed. A new path choosing probability model was introduced and the optimal path was efficiently maintained in the algorithm. The simulation results show that the mobile Agent routing efficiency is highly improved and delay requirements in multiple tasks are fulfilled, the reliability of the optimal path is enhanced, and network energy consumption is reduced.

A Mobile Agent Routing Algorithm in Dual-Channel Wireless Sensor Network

A mobile agent routing algorithm (MARA) is presented in this paper, and then based on the dual-channel communication model, the two-layer network combination optimization strategy is also proposed. Since this strategy deals with the collision between packets and the multicast suppression in channel competitive process well, the blocking probability of network and the error rate of packet transmission can be cut down by utilizing this strategy. Furthermore, a restore rule for the failure optimal route is presented too. By using this rule, the optimal route can restore quickly in the local of fail nodes and most of the information of original optimal route can be reserved. Compared with other existing algorithms like ant colony optimization-based dynamic energy-efficient mobile agent routing algorithm (ADEEMA) and mobile agent-based wireless sensor network (MAWSN), simulation results show that MARA performs better in improving the success rate of packet transmission and cutting down the delay of communication. The success rate of packet transmission is improved 15%. Simultaneously, this algorithm can keep the ant agents away from the nodes with less residual energy in searching for the optimal route. This way can make the energy of each nodes on the optimal route overall decline and hence improve the lifetime of network.