Energy-efficient dynamic channel allocation algorithm in wireless body area network

Ashraf, Muhammad; Hassan, Shahzad; Rubab, Saddaf; Khan, Muhammad Attique; Tariq, Usman; Kadry, Seifedine

doi:10.1007/s10668-021-02037-0

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…Each node communicates its position to the SN and receives input from the SN during the initial setup phase. Equations ( 4) and (5) show how much energy nodes require for message transmit and receive. The communication phase begins after the setup phase, when nodes begin sensing and transmitting their observed data to the sink.…”

Section: Energy Model (Leach)mentioning

confidence: 99%

“…With recent advancements in sensor devices, microelectronics, and wireless communication systems, WSNs are gaining more and more interest. A WSN is a network of dispersed sensors with low power, low storage capacity, and limited processing capabilities that sense and relay data to a SN [1][2][3][4][5]. In the concept of a 5G communication system, WSNs can be used to monitor the health system, agriculture, oil and gas exploration, smart homes and security, military applications, environmental monitoring, and industrial machine status [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

et al. 2022

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The wireless sensor network’s (WSNs) lifetime is mainly dependent on the RE of the sensor nodes (SeN). In recent years, energy minimization in a WSN has been a prominent research topic, and numerous solutions have been proposed. This research focuses on the energy minimization of the SeNs where firstly, K-medoid clustering algorithm is applied to create clusters. Second, a weighted cluster head selection technique is used to choose a cluster head (CH) by integrating three independent weights associated with an SeN: energy, distance from the centroid, and distance from the sink node (SN). According to the energy level and distance from the SN and cluster’s centre, each node is assigned a constant weight. The simulation results are compared to existing methodologies, and the results show that the suggested network’s lifetime enhances.

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Section: Energy Model (Leach)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

et al. 2022

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“…The measurement matrix Ω 1 is a p × q -order matrix generated by the Logistic chaotic system of Equation ( 7), and the sparsity is set to δ. The auxiliary matrix Ω 2 is a matrix generated by the Tent chaotic system of Equation (10). To ensure the uniqueness of sparse recovery, the maximum value of sparsity δ is p/2.…”

Section: • Data Reconstruction Errormentioning

confidence: 99%

“…Many studies [3][4][5][6][7][8][9][10][11][12][13][14] were conducted in an effort to realize energy saving and efficiency improvement for WBAN data transmission by using various methods or technologies, such as developing new energy-saving sensors [3,4], enhancing the performance of existed data transmission schemes [5][6][7], improving channel environment [8][9][10] or compressing the amount of data to be transmitted [11][12][13][14]. Usually, most studies adopted one of the above methods to improve the quality of services (QoS) of WBAN.…”

Section: Introductionmentioning

confidence: 99%

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

Xiao

Yuan

2023

Sustainability

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Due to the limited and difficult access to sensor energy, energy conservation has always been an important issue in wireless body area network (WBAN). How to make full use of the limited energy of heterogeneous sensors in WBAN to achieve lightweight and high-reliable data transmission has also become key to the sustainable development of telemedicine services. This paper proposes a two-tier cooperation based high-reliable and lightweight forwarding (TTCF) mechanism via minimizing the amount of transmitted data and optimizing forwarding performance, so as to improve the efficiency and reliability of WBAN and reduce system energy consumption. In TTCF, an adaptive semi-tensor product compressed sensing evolution (STPCSE) model is first constructed to minimize the amount of data to be transmitted and extend the lifetime of sensors. Then, the important factors closely related to the energy consumption of human body sensors, including sampling frequency, residual energy and their importance in the network, are analyzed and redefined, and a high-reliable and lightweight forwarding model based on a multi-factor dynamic fusion is built. Finally, the performance and energy-saving effect of TTCF in a dynamic WBAN environment are compared and analyzed. Simulation results show that the system with our TTCF always performs the best in terms of data reconstruct accuracy, cumulative delivery rata, energy consumption and throughput. For example, its cumulative delivery rate is about 12% and 20.8% higher than that of UC-MPRP and CRPBA, and its residual energy and throughput are 1.22 times and 1.41 times, 1.35 times and 1.6 times of the latter two, respectively.

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“…The IoT systems can save a lot of human resources and will generate businesses. Due to the minimum energy resources of IoT systems, efficient energy and spectrum allocation are the most important factors in IoT communication [3,4]. On the other hand, CR communication is a promising technology to deal with spectrum allocation problems.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

Self Cite

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The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

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Energy-efficient dynamic channel allocation algorithm in wireless body area network

Cited by 13 publications

References 25 publications

A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

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