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"In recent times, thedemand for the real time audio and video applications in wireless networks isvery high due to widespread use of latest wireless communication technologies.Many of these applications require different Quality of Service (QoS) in termsof delay and throughput in the resource constrained wireless networks. In orderto handle the resources effectively and to increase the QoS, proper packetscheduling algorithms need to be developed. Low-latency Queuing (LLQ) is a packet scheduling algorithm whichcombines Strict Priority Queuing(SPQ) to Class-Based Weighted Fair Queuing (CB-WFQ). LLQ places delay sensitive applications suchas voice and video in the SPQ and treat them preferentially over other trafficby 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 applicationsseparately along with the dedicated SPQ for voice applications. The performanceof the proposed algorithm is compared with other existing algorithms throughsimulations using the OPNET modeler. Simulation and Statistical results showthat the proposed algorithm has given 1.5 times performance improvement interms of throughput and delay than the existing algorithms for the real timeaudio and video applications."

"The low-cost WirelessSensor Network(WSN) consists of small battery powered devices called sensors, with limitedenergy capacity. Once deployed, accessibility to any sensor node for maintenance and battery replacement is not feasibledue 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-baseddeployment algorithm is proposed for energy optimization of the WSNand ultimately to improve the network lifetime. In the cluster initializationphase, a singlecluster is formed with a single cluster head at the center of the sensing terrain. Thesecond phase is for optimum cluster formation surroundingthe inner cluster, based on swarming bees and a piping technique. Each cluster member distributesits data to its corresponding cluster head and the cluster head communicateswith the base station, which reduces the communication distance of each node. The simulationresults show that, whencompared with other clustering algorithms, the proposed algorithm can significantly reducethe number of clusters by 38% and improve the network lifetime by a factor of1/4."