Liangrui Tang

Energy-Efficient Routing Algorithm Based on Multiple Criteria Decision Making for Wireless Sensor Networks

To solve the problem of using a single routing method and insufficient dynamic adjustment ability in existing energy-efficient routing algorithms for wireless sensor networks, a novel routing algorithm is presented in this paper, which turns the selection of next hop into a multiple criteria decision making procedure. First of all, the concept of potential energy in classical physics is introduced to create a hybrid virtual potential field, then chaos genetic algorithm is adopted to optimize the weight of each potential field, so that the data packet is forwarded to the next hop driven by the joint force generated from the hybrid virtual field and finally reaches the sink. Simulation results show that, the proposed scheme performs better on the effectiveness as well as balance of nodes energy consumption and prolongs the network lifetime compared with the existing typical energy-efficient routing algorithms.

A Hybrid Dynamic Load Balancing Algorithm in Heterogeneous Wireless Packet Networks

Based on the study of channel borrowing scheme and load transfer scheme, a hybrid dynamic load balancing algorithm for packet services is proposed. Firstly, the triangle module fusion method is used to determine which light-loaded cell is suitable for lending the two-dimensional resource unit to the hot-spot cell. And the number of two-dimensional resource units borrowed is computed according to certain conditions. Then if the load conditions of the hot-spot cell could not be converted into balanced condition, the cell with lightest load among the overlapping heterogeneous light-loaded cells is determined to receive services transferred, and services transferred are determined in accordance with the principle where the non-real time service and the service occupying more two-dimensional resource units is first considered. Simulation results show that the proposed hybrid dynamic load balancing algorithm can effectively decrease the blocking probabilities of hot-spot cells as well as the overall heterogeneous system. And, the two-dimensional resource unit utilization of overall heterogeneous system is greatly improved.

A novel load balancing algorithm based on utility functions and fuzzy logic in heterogeneous wireless networks

Aiming at working out the problem of existing load balancing algorithms that failed to consider the requirements of different types of services sufficiently in heterogeneous wireless networks, a new load balance scheduling algorithm was proposed in this paper. Firstly, three utility functions were designed to reflect three implementation-specific aspects, including the effective bandwidth, real-time calls blocking rate and the average non-real-time calls transmission time as per the needs of different types of services combined with the actual performance of each available network in heterogeneous wireless networks. Further, a fuzzy logic algorithm was adopted to fulfill the fuzzy decision of three utility functions above. Finally, we adjusted the load of each network according to the fuzzy decision value to achieve equilibrium. Simulation results showed that the proposed load balancing algorithm performs much better than reference algorithms with respect to the system blocking probability, average transmission time and network load rate equalization.

An improved algorithm based on network load prediction for 802.11 DCF

In order to improve the performance of IEEE 802.11 Distributed Coordination Function (DCF) on throughput and packet loss, a novel backoff algorithm based on network load prediction is presented in this paper. The new algorithm firstly introduced a parameter to describe the network load condition. Then the prediction processing is implemented using the parameter sequence to get a predictive value. At last, a slot selection program is proposed based on the predictive value to overcome the bad influence caused by the uneven slot selection. The new algorithm reduces the collision probability of sending data packet by nodes, which does not need to estimate the number of network nodes, and can adaptively make the slot selection to send data packet by the node itself according to the network load level. Theoretical analysis and experimental results show that the new algorithm can optimize and adjust the network conditions automatically in different scales of network, and elevate the network performance on throughput, packet loss rate, energy efficiency and other property.

Forecasting network traffic at large time scale by using dual-related method

The accuracy of network traffic prediction has received significant interest in various domains, such as capacity planning, anomaly detection, admission control, and traffic engineering. For large-time scale traffic variation, it shows both a daily pattern and an hour pattern, which means the model based on single trend has not met the needs of prediction. Therefore, by dealing with the internal relationship of large-time scale network traffic, this paper combines the regular trend and the smooth or seasonal trend of hours and days, then fit the dual-related model to predict large-time scale traffic. The result indicates that the proposed model effectively identified the correlations of data between days and hours, and is successful in forecasting approaches.

A controllable chaotic immune algorithm for risk-aware routing in DiffServ networks

An integrated routing risk model is constructed, which takes into account the effects of unicast routing on DiffServ network risk consisting of the impacts of interrupted services on network users and path availability. With the objective of minimizing integrated routing risk, a novel controllable chaotic immune routing algorithm (CCIRA) is proposed. Due to the inefficiency of traditional path generation methods, a path generation method based on chaotic search and dynamic adjacency matrix is proposed, improving the generation efficiency of available solutions of routing optimization algorithms. An evolutionary strategy which combines dynamic vaccination and free mutation is used in order to ensure the population diversity and the global convergence of CCIRA. Chaotic search is introduced to population initialization, vaccination and free mutation in order to overcome the uncertainty of the optimization process and optimization results in traditional evolutionary algorithms due to the crossover and mutation strategies being based on random numbers. Simulation results prove that CCIRA is highly efficient and practical. Combining the integrated routing risk model and CCIRA, the risk control performance of our risk-aware routing algorithm is also proved to be superior by the comparison with other algorithms.

An energy efficient routing algorithm based on radar chart for wireless sensor networks

In wireless sensor networks, energy conservation has been the most important design criterion for the routing algorithms. In order to reduce overall energy consumption and prolong the network lifetime, an energy efficient routing algorithm based on radar chart (EERC) is proposed in this paper. This routing scheme adopts multiple individual metrics to determine the selection of the next hop, which contains the node residual energy, the distance from the node to the sink, and the distance between the node and its neighbors. Then a comprehensive evaluation model for alternative nodes is built based on radar chart. Finally the node with maximum evaluation value is selected to be the next hop. Simulation results show that the proposed algorithm decreases the energy dissipation and extends the lifetime of the network.

Positioning Algorithms by Information Fusion in Wireless Sensor Networks

To overcome the disadvantages of the positioning technologies by fuzzy theory in Wireless Sensor Networks (WSNs), positioning algorithms based on information fusion are presented in this article. The fuzzy theory is used to deal with the randomness and fuzziness in the WSNs. And the information fusion is introduced to improve the location accuracy. If the collinearity of the anchor nodes is larger, the misjudged reference nodes may be caused. They are removed by using clustering method. The algorithms in this paper can enhance the location accuracies compared with using the fuzzy theory and alleviate the effect of the RSSI (Received Signal Strength Indication) measure errors. Moreover, the algorithms avoid the high complexity of computation and the requirement of more anchor nodes. Simulation results indicate that the algorithms are more precise, robust as well as with good suitability.

A Heterogeneous Network Access Selection Algorithm Based on Attribute Dependence

In order to solve the problem which fails to consider the degree of attribute dependence in current network access selection schemes, a novel heterogeneous network access selection scheme based on attribute dependence is proposed in this paper. The scheme translates the network access selection problem into the problem of multi-attribute decision making based on attribute dependence and solves it using the chaotic glowworm swarm based algorithm. First, the degree of attribute dependence is measured and the access selection model is established based on the degree of attribute dependence. Then, the chaotic glowworm swarm based algorithm is used to solve the optimal weight in the model. Finally, the user accesses the network with the best performance based on the access selection model. The simulation results demonstrate the improved performance of the proposed access selection scheme compared with other schemes. The proposed scheme can reduce blocking and handoff dropping rate, as well as the number of handoff. Moreover, the proposed scheme achieves the load balance of each network.

Differentiated traffic grooming algorithm based on multi-objective optimization

To solve the static traffic grooming problems for differentiated traffic model a MO - DSG algorithm is proposed. The algorithm defines the business value to describe the requirements for QoS performance. Let traffic grooming method be equivalent to gene, and grooming scheme to chromosome, hence the original problem of traffic grooming is transformed into a typical mathematical model which can be solved by multi target optimization algorithm. To prove the superiority of the algorithm, we compared the proposed algorithm with the NSGA-II algorithm by using the classical test functions. In addition, simulation based on the actual scene of MO-DSG algorithm proves its superior performance to TBSG-c algorithm, IONT algorithm, on construction cost and risk.