A price-based adaptive task allocation for Wireless Sensor Network

Edalat, Neda; Xiao, Wendong; Tham, Chen-Khong; Keikha, Ehsan; Ong, Lee-Ling Sharon

doi:10.1109/mobhoc.2009.5337039

Cited by 55 publications

(26 citation statements)

References 5 publications

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“…Data recognized as time frame application are given high need than non-real time data. There exist wide choice of study and investigation on planning the sleep wake times of gadget nodes are performed [1]- [17], however exclusively a little assortment of concentrates live inside of the writing on the bundle arranging of gadget hubs that calendar the managing out of data parcels gave at a gadget hub and also lessens vitality consumptions [18]- [21]. Yet, most commonly utilized errand planning equation as a part of remote gadget systems is first return first Served (FCFS) equipment formula inside of which the movement of data packets happens bolstered point in time and in this manner it takes a ton of amount of your opportunity to be conveyed to an appropriate base station (BS).…”

Section: Literature Reviewmentioning

confidence: 99%

Packet Scheduling for Wireless Sensor Network

Reddy¹

2014

IJWD

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Section: Literature Reviewmentioning

confidence: 99%

Packet Scheduling for Wireless Sensor Network

Reddy¹

2014

IJWD

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“…For efficient resource allocation, hierarchical or layered system architectures can be studied in many papers. For example, in [25], the researchers designed a layered distributed system and extended the existing disjoint coalition formation protocol to solve the multi-sensor task allocation problem which aims to automatically decide the best sensors to specified task; in [26], a market-based 4-layer architecture was presented for adaptive task allocation which formulises the pricing mechanism to achieve a fair energy balance among sensor nodes while minimizing delay; [27] designed a two-tiered on-demand resource allocation strategy which is specially designed for VM-based computing environments.…”

Section: Resource Allocation For Information Managementmentioning

confidence: 99%

Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

Guo

Silva

et al. 2013

International Journal of Distributed Sensor Networks

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In large-scale machine-to-machine sensor networks, the applications such as urban air pollution monitoring require information management over widely distributed sensors under restricted power, processing, storage, and communication resources. The continual increases in size, data generating rates, and connectivity of sensor networks present significant scale and complexity challenges. Traditional schemes of information management are no longer applicable in such a scenario. Hence, an elastic resource allocation strategy is introduced which is a novel management technique based on elastic computing. With the discussion of the challenges of implementing real-time and high-performance information management in an elastic manner, an air pollution monitoring system, called EIMAP, was designed with a four-layer hierarchical structure. The core technique of EIMAP is the elastic resource provision scheduler, which models the constraint satisfaction problem by minimizing the use of resources for collecting information for a defined quality threshold. Simulation results show that the EIMAP system has high performance in resource provision and scalability. The experiment of pollution cloud dispersion tracking presents a case study of the system implementation.

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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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A price-based adaptive task allocation for Wireless Sensor Network

Cited by 55 publications

References 5 publications

Packet Scheduling for Wireless Sensor Network

Packet Scheduling for Wireless Sensor Network

Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

An integrated framework for wireless sensor network management

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