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"In recent times, the demand for the real time audio and video applications in wireless networks is very high due to widespread use of latest wireless communication technologies. Many of these applications require different Quality of Service (QoS) in terms of delay and throughput in the resource constrained wireless networks. In order to handle the resources effectively and to increase the QoS, proper packet scheduling algorithms need to be developed. Low-latency Queuing (LLQ) is a packet scheduling algorithm which combines Strict Priority Queuing (SPQ) to Class-Based Weighted Fair Queuing (CB-WFQ). LLQ places delay sensitive applications such as voice and video in the SPQ and treat them preferentially over other traffic by allowing the application to be processed and sent first from the SPQ. In this paper, an Enhanced LLQ (ELLQ) is proposed. An additional SPQ is introduced for scheduling the video applications separately along with the dedicated SPQ for voice applications. The performance of the proposed algorithm is compared with other existing algorithms through simulations using the OPNET modeler. Simulation and Statistical results show that the proposed algorithm has given 1.5 times performance improvement in terms of throughput and delay than the existing algorithms for the real time audio and video applications."

"The low-cost Wireless Sensor Network (WSN) consists of small battery powered devices called sensors, with limited energy capacity. Once deployed, accessibility to any sensor node for maintenance and battery replacement is not feasible due to the spatial scattering of the nodes. This will lead to an unreliable, limited lifetime and a poor connectivity network. In this paper a novel bio-inspired cluster-based deployment algorithm is proposed for energy optimization of the WSN and ultimately to improve the network lifetime. In the cluster initialization phase, a single cluster is formed with a single cluster head at the center of the sensing terrain. The second phase is for optimum cluster formation surrounding the inner cluster, based on swarming bees and a piping technique. Each cluster member distributes its data to its corresponding cluster head and the cluster head communicates with the base station, which reduces the communication distance of each node. The simulation results show that, when compared with other clustering algorithms, the proposed algorithm can significantly reduce the number of clusters by 38% and improve the network lifetime by a factor of 1/4."