A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.
A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive moving average model （ARMA） to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i. e. , artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can ① effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, ② minimize the total communication consumption, and ③prolong the lifetime of networks.
The paper proposes a prediction-mode-based filtering mechanism（PMF） to solve the problems of transmission energy wasting caused by time-redundant data in wireless sensor networks（WSN）,according to the characteristic of spatio-temporal correlations on sampling series in data-collection.Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure.Distinguish from those methods in above researches,our proposed scheme mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect via self-adaptive filtering structure.The framework of PMF for energy-efficient collection is composed of prediction module for mining the change law of time domain,self-learning module for updating model,and driving module for controlling data filtering operation.Combined with the design of error driving rule and threshold distributing rule,which is the middleware in the above filtering mechanism,the quantity of transmission load in networks can be greatly inhibited on the premise of quality of service（QoS） assurance and energy consumption can be reduced consequently.Finally,the experimental results show that the performance of PMF can significantly outperform some classical data-collection algorithms on energy-saving effect and self-adaptability.
