• ETRI Journal
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• v.33 no.6
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• pp.857-863
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• 2011

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.884-891
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• 2006

A wireless sensor node is typically battery operated and energy constrained. Therefore it is critical to design efficient power management technique and scheduling technique. In this paper, we propose an OS-level power management technique for energy saving of wireless sensor node, it is called EA-SENTAS (Energy-Aware Sensor Node TAsk Scheduling). It can decrease the energy consumption of a wireless sensor node to use task scheduling technique that shut down components or use low power mode of each component when not needed. Simulation results show that EA-SENTAS saves energy up to 56 percent to compare with conventional duty cycle.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.215-217
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• 2004

In wireless sensor networks, fair allocation of bandwidth among different nodes is one of the critical problems that effects the serviceability of the entire system. Fair bandwidth allocation mechanisms, like fair queuing, usually need to maintain state, manage buffers, and perform packet scheduling on a per flow basis, and this complexity may prevent them from being cost-effectively implemented and widely deployed. It is a very important and difficult technical issue to provide packet scheduling architecture for fairness in wireless sensor networks. In this paper, we propose an packet scheduling architecture for sensor node, called FISN (Fairness Improvement Sensor Network), that significantly reduces this implementation complexity yet still achieves approximately fair bandwidth allocations. Sensor node for sensing estimate the incoming rate of each sensor device and insert a label into each transmission packet header based on this estimate. Sensor node for forwarding maintain no per flow state; they use FIFO packet scheduling augmented by a probabilistic dropping algorithm that uses the packet labels and an estimate of the aggregate traffic at the gathering node. We present the detailed design, implementation, and evaluation of FISN using simulation. We discuss the fairness improvement and practical engineering challenges of implementing FISN in an experimental sensor network test bed based on ns-2.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.10
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• pp.708-714
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• 2014

Wireless sensor networks (WSNs) generally consist of densely deployed sensor nodes that depend on batteries for energy. Having a large number of densely deployed sensor nodes causes energy waste and high redundancy in sensor data transmissions. The problems of power limitation and high redundancy in sensing coverage can be solved by appropriate scheduling of node activity among sensor nodes. In this paper, we propose a cellular automata based node scheduling algorithm for prolonging network lifetime with a balance of energy savings among nodes while achieving high coverage quality. Based on a cellular automata framework, we propose a new mathematical model for the node scheduling algorithm. The proposed algorithm uses local interaction based on environmental state signaling for making scheduling decisions. We analyze the system behavior and derive steady states of the proposed system. Simulation results show that the proposed algorithm outperforms existing protocols by providing energy balance with significant energy savings while maintaining sensing coverage quality.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1423-1435
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• 2010

Building wireless sensor networks requires a constituting sensor node to consider the following limited hardware resources: a small battery lifetime limiting available power supply for the sensor node, a low-power microprocessor with a low-performance computing capability, and scarce memory resources. Despite such limited hardware resources of the sensor node, the sensor node platform needs to activate real-time sensing, guarantee the real-time processing of sensing data, and exchange data between individual sensor nodes concurrently. Therefore, in this paper, we propose an energy-efficient real-time task scheduling technique for battery-powered wireless sensor nodes. The proposed energy-efficient task scheduling technique controls the microprocessor's operating frequency and reduces the power consumption of a task by exploiting the slack time of the task when the actual execution time of the task can be less than its worst case execution time. The outcomes from experiments showed that the proposed scheduling technique yielded efficient performance in terms of guaranteeing the completion of real-time tasks within their deadlines and aiming to provide low power consumption.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.43-50
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• 2018

In this paper, we propose a hexagon model-based efficient beacon frame scheduling approach for wireless sensor networks. The existing beacon frame scheduling approaches use a lot of slots and subslots for the beacon frame scheduling. Thus, the data from source nodes are not efficiently delivered to a sink node. Also in case a sink node needs to broadcast a beacon frame to the nodes in the network, delivering the beacon frame to the network nodes is not efficient as well. Thus, to solve the problem, we use a hexagon model to find the number of slots and subslots for the beacon frame scheduling. By using them for the beacon frame scheduling, the proposed approach performs better than other approaches in terms of the data transmission delay, the number of received data, the beacon transmission delay and the number of relaying the beacon frames.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1820-1831
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• 2011

When both types of periodic and aperiodic tasks are required to run on a sensor node platform with limited energy resources, we propose an energy-efficient hybrid task scheduling technique that guarantees the deadlines of real-time tasks and provides non-real-time tasks with good average response time. The proposed hybrid task scheduling technique achieved better performance than existing EDF-based DVS scheduling techniques available in the literature, the FIFO-based TinyOS scheduling technique, and the task-clustering based non-preemptive real-time scheduling technique.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.962-970
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• 2009

This paper presents a data gathering and scheduling scheme for wireless sensor networks. We use a data gathering tree for sending the data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, a time tree is built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possible different activation rate. Through the simulation, we found that the proposed scheme that uses time trees shows better characteristics in energy and data arrival rate when compared with other schemes such as SMAC and DMAC.

• Journal of KIISE:Information Networking
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• v.35 no.1
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• pp.21-33
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• 2008

The wireless sensor nodes near to the fixed sink node suffer from the quickly exhausted battery energy. To address this problem, the mobile sink node has been applied to distribute the energy consumption into all wireless sensor nodes. However, since the mobile sink node moves, the data collection scheduling scheme is necessary for the sink node to receive the data from all sensor nodes as fair as possible. The application fields of wireless sensor network need the real-time processing. If the uneven data collection occurs in the wireless sensor network, the real-time processing for the urgent events can not be satisfied. In this paper, a new method is proposed to support the lair data collection between all sensor nodes. The proposed method performs the scheduling algorithm based on the resident time of the sink node staying in a radius of communication range and the amount of data transferred already. In this paper, the proposed method and existing data collection scheduling schemes are evaluated in wireless sensor network with the mobile sink node. The result shows that the proposed method provides the best fairness among all data collection schemes.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.260-273
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• 2007

The wireless sensor network (WSN) nodes are required to operate for several months with the limited system resource such as memory and power. The hardware platform of WSN has 128Kbyte program memory and 8Kbytes data memory. Also, WSN node is required to operate for several months with the two AA size batteries. The MAC, Network protocol, and small application must be operated in this WSN platform. We look around the problem of memory and power for WSN requirements. Then, we propose a new computing model of system software for WSN node. It is the Atomic Object Model (AOM) with Dual Priority Scheduling. For the verification of model, we design and implement IEEE 802.15.4 MAC protocol with the proposed model.