Energy-Efficient Channel Management Scheme for Cognitive Radio Sensor Networks

Han, Jeong Ae; Jeon, Wha Sook; Jeong, Dong Geun

doi:10.1109/tvt.2011.2128355

Cited by 87 publications

(17 citation statements)

References 10 publications

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“…Similarly, the work of [43,44,45,46] targets the energy efficiency of CR users, but they do not consider the PU activity. Also, all of the aforementioned schemes [37,38,39,40,41,42,43,44,45,46] ignores the availability of the channels due to their lack in consideration of PU activity. To illustrate the performance gain of our proposed scheme, we compare the greedy and random schemes with the proposed RF source association and channel-sharing mechanisms [34,47].…”

Section: Related Workmentioning

confidence: 99%

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

Aslam

Hasan

Jang

et al. 2016

Sensors

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This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.

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Section: Related Workmentioning

confidence: 99%

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

Aslam

Hasan

Jang

et al. 2016

Sensors

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“…In passive techniques, the radio of the sensor is turning off whenever no transmission is required. The idea is to switch between different operation modes, such as sleep, standby and wake up, based on channel state [13] or based on a predefined time schedule [14]. The active energy aware methods try to decrease energy consumption of the sensor nodes by developing energy saving protocol and algorithms in different layers of protocol stack.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Media Access Algorithm at the Presence of Channel State Information Error in Wireless Sensor Networks

Ghiasian

2015

IEEE Sensors J.

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To support the rapid emergence of applications with large throughput and low delay requirement in wireless sensor network (WSN), design of throughput optimal energy aware algorithms has attracted researchers from a wide area of expertise. Maximum Weight Scheduling (MWS) is a wellknown throughput optimal link scheduling algorithm. In addition to packet queue lengths, the algorithm needs to be aware of channel state information (CSI). However, perfect knowledge of channel state conditions is a very strong assumption in practical technologies.In this paper, the impact of CSI error on the performance of MWS algorithm is studied. We propose a throughput optimal link scheduling algorithm which is robust to CSI error. To consider CSI error, we propose an idea to use the state of battery at each node in order to estimate the amount of energy consumption per successful packet transmission. This information is used to revise MWS to the proposed algorithm termed as Error Robust Energy Aware Scheduling (EREAS). We show that EREAS outperforms MWS in terms of average number of failed transmissions and average delay in environments with high error probability. The theoretical results are confirmed by simulations.

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“…However, this scheme does not incorporate the traffic pattern for the applications running on different IoT devices. Similarly, the schemes presented in [39,40,41,42] target the energy efficiency, but they all lack in considering the PU activity and QoS of SUs. Likewise, the authors in [43,44] concentrated on congestion avoidance along with energy balancing, but do not consider individual QoS for optimal operation.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, the authors in [43,44] concentrated on congestion avoidance along with energy balancing, but do not consider individual QoS for optimal operation. Although the aforementioned schemes [33,34,35,36,37,38,39,40,41,42,43,44] are optimizing their objective functions, they are not suitable and applicable for the data dissemination of the IoT devices in a smart building environment. Hence, we propose a novel channel assignment scheme that considers the QoS requirements of different IoT devices, the availability of a channel due to the PU activity and the traffic patterns of different applications running on different IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

Aslam

Hasan

Shahid

et al. 2016

Sensors

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The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

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Energy-Efficient Channel Management Scheme for Cognitive Radio Sensor Networks

Cited by 87 publications

References 10 publications

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

Energy Aware Media Access Algorithm at the Presence of Channel State Information Error in Wireless Sensor Networks

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

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