Earticle

This article is based on the existing dynamic channel access strategy, proposes a cognitive MAC layer channel access strategy. First of all, from the physical layer of cognitive radio spectrum detection technology to detect the idle spectrum, provide cognitive users can access channel; Second, in the network, their cognitive users maintain a real-time update list of channel state, this strategy according to channel state list to find two cognitive users need communication overlapping channels are available, and calculate the overlapping each sum of the available channel utilization rate of the primary user; Finally choose the utilization ratio of the sum of the minimum channel for data transmission, improve the data channel available transfer time. The simulation results show that the strategy under the condition of without affecting the main user communication can make the cognitive users to make full use of the authorized spectrum, improve the network throughput.

A Wireless Sensor Network (WSN) is a network of distributed sensors that can collect information from a physical environment. Low-cost sensors are the backbone of a WSN. Maximizing the lifetime of sensor networks is the main challenge of a Wireless Sensor Network (WSN), such as the sensor node of the network needs to be prepared with limited battery power. This paper focuses on finding the optimal sink position. Relay nodes have been introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and, thus, their batteries are quickly depleted. A Biogeography-Based Optimization (BBO) algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. The relay nodes communicate with the sink instead of the sensor nodes. Tests show that this approach can save at least 40% of the energy and prolong the network lifetime.

The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.

Wireless body area networks (WBAN) are expected to be a breakthrough technology in healthcare areas, telemedicine, and physical rehabilitation. The nature of the propagation of electromagnetic signals in complex shaped human body in the case of WBAN is expected to be very much different from that found in other environments, e.g. home, offices, streets, etc. So in order to design a high quality WBAN it is very important to examine and improve the propagation characteristics of channel models of WBAN. The aim of this paper is to provide an overview of different channel models of WBANs. The effect of channel behavior due to dynamics has been surveyed and challenges in channel modeling have been extracted.