Qi Xiaogang

Mobile Agent Routing Algorithm in Dynamic Sensor Network

An ant colony optimization-based dynamic energy efficient mobile agent routing algorithm (ADEEMA) is presented in this paper. In this algorithm, mobile agent (MA) has the character of an ant, and a novel probabilistic model is constructed to make MA can find an energy efficient route from processing node (PN) to target nodes (TN). The route considers both the energy consumption and the node residual energy, and a new concept: route optimal degree (ROD) is presented to evaluate the performance of the chosen route. In order to adapt to the topology changes in dynamic sensor network, a new local pheromone re-initialization rule is presented, the rule reinitializes the pheromone in the local area where the sensor network topology changes, so most information of the old optimization route can be reserved and meanwhile the ant can search new route in local area, thus the optimization route can be modified fast. The simulation shows that a route with minimum energy consumption and maximum node residual energy can be obtained by our method. In addition, a new optimization route can be found fast when topology changes by our algorithm.

Evaluation and Optimization of Complex Network Resilience against Attacks

With the current communication network failures intensifying, they have characteristics of complexity and adaptability. This is no longer possible that networks can completely resist all kinds of network attacks. Targeted attacks and random failures may cause link or node removal, which in turn can cause significant disruption to the availability of network services. Designing a network topology to provide acceptable levels of service in the face of these challenges can prolong the network lifetime. In this paper, an iteration algorithm called average efficiency improved (AE-improved) algorithm is presented to improve the given robustness functions. The algorithm improves the topology resilience of three complex networks by adding a set of links to maximize the average efficiency of a network. Then, non-improved and improved graphs are evaluated by applying random failure and centrality-based attacks to examine their resilience. The results show that compared with other optimization algorithms, the AE-improved algorithm we proposed yields the best network resilience against such attacks among the studied robustness metrics.

Probe Selection Algorithm for Faulty Links Localization in All-Optical Networks

We study the probe selection problem in all-optical networks for achieving unambiguous faulty links localization with two monitors in failure detection phase and one monitor in failure localization phase. The existing random walk algorithm can find feasible solutions to localize the faulty links unambiguously, but it consumed large number of probes and wavelengths in large-size networks. In this paper, we proposed a new heuristic probe selection algorithm based on adaptive probing to solve these two problems. We develop algorithms for probe selection to perform failure detection and failure localization. We show that a network must be (k+1)-edge connectivity for localizing k faulty links with one monitoring node. Compared with random walk, probe selection algorithm greatly shortens the number of probes and consumed wavelengths per link.

Topology Control Based on Steiner Tree in Cognitive Radio Networks

In Cognitive radio networks (CRN), secondary users(SUs) can access the unlicensed channel dymanically, but must vacate the spectrum on the appearance of primary users(PUs). Therefore multiple SUs will be affected, which will cause the network partition if there is no other backup channels. We combine power control and channel assignment to construct a bi-channel connected and conflict-free topology with the minimum number of required channels. Based on CGCA algorithm, we propose the improved CGCA algorithm. Particularly, we construct an underlying topology robust against interruption of PUs activities, then we assign channels to each SU according to graph coloring to achieve conflict-free transmission. Note that, the local spanning tree may be not connected after deleting SU nodes. We present the improved MPH algorithm to make it connected, whose philosophy is to give priority to the link on these nodes who have the large path weights, and its been proved that the improved MPH algorithm can achieve connecting the network while reducing the cost. Furthermore, considering that the centralized topology control algorithm will lose efficacy under the circumstance that the original topology will be divided into two parts after deleting the SU nodes by which they are connected with each other. Specifically for this situation we utilize an algorithm of adding SU nodes at the middle of the shortest link between all these edges to make the divided parts connected. Simulation has verified the correctness and effectiveness of the ICGCA algorithm.

A Novel Framework to Maximum Lifetime for Wireless Sensor Network

In this paper, a novel framework is presented to prolong the lifetime of wireless sensor networks to the maximum. This framework consists of two parts. One is a novel topology management mechanism called electric fan topology mechanism (EFTM) and the other is an efficient routing protocol called maximum lifetime routing (MLR). EFTM provides a scheduler strategy to save much energy by turning off some transceivers periodically. MLR is based on the work of EFTM, which selects nodes with high-energy reserves as router. Though we turn off some transceivers periodically, we have developed receiver-based packet routing policy and last-mile algorithm to accommodate rapid change of topology and to guarantee the robust of networks. Simulation results show that MLR based on the work of EFTM extends the lifetime of networks to the maximum. MLR is suitable for large scale non-real time wireless sensor applications. When all trajectories are unavailable, nodes can still send packets to sink efficiently. The network using MLR can adapt to the rapid change of network topology very well.

Experimental Study on Connectivity for Wireless Sensor Networks

Many works have been devoted to connectivity of wireless sensor networks. This is an important feature for wireless sensor networks (WSNs) to provide the nodes with the capability of communicating with one or several sinks.To operate successfully a sensor network must also provide satisfactory connectivity so that nodes can communicate for data fusion and reporting to base stations. Connectivity affects the robustness and achievable throughput of communication in a sensor network. We provide an experimental analysis of the fundamental relationship between network connectivity and the density of nodes, as well as the relationship between network connectivity and the nodes’ transmitting ranges. Extensive experimental results prove that our method is feasible and leads to a more accurate thresholds.

A novel evolutionary algorithm for multi-constrained path selection

For the problem of multi-constrained path selection, a novel evolutionary algorithm named MCP_EA is proposed. Firstly, a novel coding technology named PNNC (Preceding Natural Number Coding, PNNC) is designed, and no circle exists on the path coded by PNNC. Secondly, a novel crossover operator called DCC operator (Dispersing Connection Crossover operator, DCC operator) is designed to guarantee the validity of the crossed paths and the diversity of the population. Thirdly, a novel mutation operator named selective mutation operator is proposed. Finally, the theoretical analysis proves that the algorithm converges to the satisfactory solution with probability 1.0. Extensive simulations show that the novel evolutionary algorithm outperforms the H_MCOP in performance for the problem, and is a promising algorithm for the problem with high performance.