Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

Bergmann, Gábor; Molnár, Miklós; Gönczy, László; Cousin, Bernard

doi:10.1109/icns.2010.33

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Finally, non-real time data packets that are sensed at a local node go to pr3, the lowest priority queue. The possible reasons for choosing maximum three queues are to process (i) real-time pr1 tasks with the highest priority to achieve the overall goal of WSNs, (ii) non real-time pr 2 tasks to achieve the minimum average task waiting time and also to balance the end-to-end delay by giving higher priority to remote data packets, (iii) non-real-time pr 3 tasks with lower priority to achieve fairness by preempting pr 2 tasks if pr 3 tasks wait a number of consecutive timeslots. In the proposed scheme, queue sizes differ based on the application requirements.…”

Section: Figure 3 Proposed Dynamic Multilevel Priority Packet Schedumentioning

confidence: 99%

Fast Response Multilevel Scheduling Scheme for Wireless Sensor Networks

Nikam¹,

Tiwari²

2014

IOSRJCE

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Section: Figure 3 Proposed Dynamic Multilevel Priority Packet Schedumentioning

confidence: 99%

Fast Response Multilevel Scheduling Scheme for Wireless Sensor Networks

Nikam¹,

Tiwari²

2014

IOSRJCE

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“…Though extensive research for scheduling the sleep/wake times of sensor nodes [5,13,15,16,33,71,72,58,89,94,92,97,109,108,112,116,128,120] have been conducted only a few studies exist in the literature on the packet scheduling of sensor nodes [27,88,102,119] that schedule the processing of data events available at a sensor node and also reduces energy consumptions. Indeed, most existing Wireless Sensor Network (WSN) applications use First Come First Serve (FCFS) [96] schedulers that process data packets in the order of their arrival time and, thus, require a lot of time to be delivered to a relevant base station (BS).…”

Section: Motivationmentioning

confidence: 99%

An integrated framework for wireless sensor network management

Karim

Mahmoud

Khan

2012

Wirel. Commun. Mob. Comput.

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Wireless Sensor Networks (WSNs) have significant potential in many application domains, and are poised for growth in many markets ranging from agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large scale commercial adoption of sensor networks are the lack of available network management and control tools for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at exact location, and scheduling data packets so that data are sent based on their priority and fairness. Moreover, due to the limited communication range of sensors, a large geographical area cannot be covered, which limits sensors application domain. Thus, we investigate a scalable and flexible WSN architecture that relies on multi-modal nodes equipped with IEEE 802.15.4 and IEEE 802.11 in order to use a Wi-Fi overlay as a seamless gateway to the Internet through WiMAX networks. We focus on network management approaches such as sensors localization, data scheduling, routing, and data aggregation for the WSN plane of this large scale multimodal network architecture and find that most existing approaches are not scalable, energy efficient, and fault tolerant. Thus, we introduce an efficient approach for each of localization, data scheduling, routing, and data aggregation; and compare the performance of proposed approaches with existing ones in terms of network energy consumptions, localization error, end-to-end data transmission delay and packet delivery ratio. Simulation results, theoretical and statistical analysis show that each of these approaches outperforms the existing approaches. To the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Hence, we efficiently integrate all network management components so that it can be used as a single network management solution for a large scale WSN, perform experimentations to evaluate the performance of the proposed framework, and validate the results through statistical analysis. Experimental results show that our proposed framework outperforms existing approaches in terms of localization energy consumptions, localization accuracy, network energy consumptions and end-to-end data transmission delay.

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“…This security mechanism is efficiently presenting through the routing protocols. It means that routing protocol should provide the security while gaining goal of efficient energy consumption [2].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of Dynamic Multilevel Priority (DMP) packet scheduling method for wireless sensor networks (WSNs)

Bansode¹,

Sambare

2015

2015 International Conference on Pervasive Computing (ICPC)

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There are many real time environment in which the use of wireless sensor networks (WSNs) is growing, especially the applications like military, health monitoring, security monitoring etc. WSN is nothing but collection of small, tiny sensor nodes which is having resource constraints like battery life. Therefore many research comes on WSNs are targeted on improving the energy efficiency of WSNs and extend the lifetime. In this paper, we are focusing to work on packet scheduling scheme which plays vital role to improve the energy efficiency and QoS performances. Currently there are many packet scheduling schemes used by researchers in WSNs applications such as First Come First Serve (FCFS), preemptive priority scheduling, nonpreemptive priority scheduling methods. However this method suffered from limitations like higher routing overhead, more end to end delay and hence more energy consumption. In this paper, we are aiming to investigate new algorithm which overcomes the limitations of this existing method and achieves the better QoS and less energy consumption results. The investigated algorithm is called Dynamic Multilevel Priority (DMP) scheduling method. As the name indicates, this method works dynamically and as per the requirement of packet scheduling. There are three queues used by this algorithm for priority scheduling and applications like real time, non-real time. In first queue, real time packets processed with highest priority. In second queue, non-real time data packets processed with highest priority than third queue. In third queue, non real time data packets those are sensed at local are processed. The practical evaluation of this method is done using NS2.

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Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

Cited by 12 publications

References 18 publications

Fast Response Multilevel Scheduling Scheme for Wireless Sensor Networks

Fast Response Multilevel Scheduling Scheme for Wireless Sensor Networks

An integrated framework for wireless sensor network management

Performance evaluation of Dynamic Multilevel Priority (DMP) packet scheduling method for wireless sensor networks (WSNs)

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