Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

Raee, Vahid Maleki; Naboulsi, Diala; Glitho, Roch

doi:10.1109/iscc.2018.8538632

Cited by 14 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on various virtualized WSN architectures, most of the related work focuses on resource optimization, which is to abstract physical sensors based on application requirements, and improve resource utilization by executing multiple application-centered tasks in sensors. For example, literature [ 15 ] considered both the sensor energy consumption and resource utilization, which allowed the same physical sensor to perform multiple tasks in the WSN virtualization environment. Literature [ 16 ] modeled and analyzed the joint problem of multi-task control admission and physical resource allocation in virtual WSN.…”

Section: Related Workmentioning

confidence: 99%

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Wang

Liu

Yang

et al. 2020

Sensors

View full text Add to dashboard Cite

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

show abstract

Section: Related Workmentioning

confidence: 99%

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Wang

Liu

Yang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, evolutionary game theory was applied to allocate data sensing load among different sensor nodes under the data rate requirement constraint of a certain application [ 14 ]. In [ 15 ], the application sensing task assignment problem was studied to maximize the overall energy consumption, where sensor nodes’ available energy and virtualization overhead were taken into account. In [ 16 ], the authors focused on energy efficiency maximization and then proposed a novel cyber-physical-social smart system.…”

Section: Related Workmentioning

confidence: 99%

“…The authors therein employed wireless network virtualization to enhance the diversity and the flexibility of the service operation and the system management, and proposed a robust energy-efficient resource allocation scheme to outage probability requirements of controllers and actuators. Works [ 12 , 13 , 14 , 15 , 16 ] have provided insightful results on performance optimization in virtualized WSN. However, latency analysis is absent in those works.…”

Section: Related Workmentioning

confidence: 99%

“…The authors therein proposed two different schemes to optimize the system time and energy harvesting rate for the two scenarios, respectively. Similar to works [ 12 , 13 , 14 , 15 , 16 ], works [ 19 , 20 , 21 , 22 ] also aimed to optimize the energy efficiency or network throughput for a WSN. How to guarantee the application latency was still unknown.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

Wang

Zhong

et al. 2020

Sensors

View full text Add to dashboard Cite

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

show abstract

“…Recent success of emerging AWSN technology has encouraged researchers to develop new task allocation algorithm in this field. Generally, temperature and humidity sensors are small and have limited abilities of computing [10]. Careful task allocation scheme can be an effective optimization mean for achieving the desired high-energy consumption goals, comprising computing capability constraints.…”

Section: Introductionmentioning

confidence: 99%

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Zhou

2020

Journal of Sensors

View full text Add to dashboard Cite

The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

show abstract

Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

Cited by 14 publications

References 13 publications

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Contact Info

Product

Resources

About