Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Fast Detection Method in Cooperative Cognitive Radio Networks

Cognitive Radio (CR) technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted K out of N fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy

Backpropagation-Based Cooperative Localization of Primary User for Avoiding Hidden-Node Problem in Cognitive Networks

Cognitive radio (CR) is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided