Adaptive Cluster Head Selection Research Articles

Efficient Collaborative Learning Over Unreliable D2D Network: Adaptive Cluster Head Selection and Resource Allocation

Efficient Collaborative Learning Over Unreliable D2D Network: Adaptive Cluster Head Selection and Resource Allocation

Modified User Grouping and Hybrid Precoding for Information Decoding and Energy Harvesting in Hardware Impaired Point-To-Point MM-Wave Massive MIMO NOMA

The fifth generation (5G) and beyond 5G race covers both spectral-efficient and energy-efficient mechanisms such as simultaneous wireless information decoding and energy harvesting (SWIDEH). Structured as full or sub-connection of hybrid-precoded mm-wave massive multiple-input multiple-output non-orthogonal multiple access (NOMA) systems. However, the more practical hardware-impaired (HI) functionality yet to be considered for point-to-point (p-to-p) MIMO is conceived from an existing system. A hardware impairment-based power splitting is proposed for energy harvesting. The energy of the HI signal is harnessed through the joint optimization of power allocation (PA) for information decoding (ID) and the power splitting (PS) factor for energy harvesting (EH) using zero-forcing (ZF) precoding and minimum mean square error (MMSE) downlink (DL) detection. Based on the simulation results, an adaptive cluster head selection (CHS) criterion is proposed for analog precoding to mitigate intracluster interferences at a high signal-to-noise ratio (SINR). A successive interference cancellation (SIC) is carried out to enable dynamic channel overhead estimation and user signal detection. An investigation is done by simulating the system performance at varied numbers of users, the number of radio-frequency (RF) chains, and channel characteristics for modelling the user group threshold coupled with channel size reduction via an antenna selection scheme. Observing the execution time and some graphical plots shows that the spectral and energy efficiency can be improved by a random (R) or maximum norm average (MNA) channel vector formation from each user’s channel matrix combined with the best adaptive correlation threshold during CHS and a regularized ZF (RZF) precoder.

Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.

Adaptive clustering routing protocol for underwater sensor networks

In underwater sensor networks, the limited energy of sensor nodes and the difficulty of replacing the power supply means that one faulty node will severely affect the lifetime of the entire network. The design of energy-saving and efficient routing protocols is the key to prolonging the network lifetime. This paper describes an adaptive clustering routing protocol for underwater sensor networks based on multi-agent reinforcement learning. The proposed protocol models the network as a multi-agent system, and allows the nodes to select the global optimal route collaboratively through reinforcement learning. To reduce the probability of hotspots generation, an adaptive cluster head selection algorithm that does not incur any additional communication overhead and does not require consensus from surrounding nodes is proposed, which enables nodes to autonomously decide whether they can act as cluster heads using routing and environment information. Additionally, a biased reward function is designed to feedback the effect of the adaptive cluster head selection algorithm on the routing performance and to encourage the nodes to select the cluster heads as relays. Simulation results show that the proposed adaptive clustering routing protocol achieves higher routing efficiency, lower energy consumption, and longer network lifetime than existing approaches.

Energy-efficient cluster head selection algorithm for IoT using modified glow-worm swarm optimization

Internet of Things (IoT) is a new age technology that connects virtually every IP-enabled things in the world. Because of the constrained nature of the resources, energy-efficient clustering plays a very important role in successful implementation of these networks and also in increasing its lifetime. The proposed methodology provides an adaptive cluster head selection algorithm based on glow-worm swarm optimization algorithm. Most of the existing approaches groups nodes into clusters containing a fixed number of nodes. This is not applicable in certain cases, such as, when many of the nodes are dead. In the proposed approach, the number of nodes in each cluster is not fixed, and it automatically changes according to the number of alive nodes in the network, which increases the lifetime of the network. The proposed approach also ensures the minimum overlapping of the clusters by selecting geographically distributed cluster heads which increases the energy efficiency of the network by minimising the communication overhead. Repeated selection of cluster head also helps in load balancing in the network. We have implemented the proposed algorithm in OMNET++ simulator and demonstrated how it behaves with different densities of sensor deployment and communication ranges. The proposed algorithm is found to work notably well as compared to state-of-the-art IoT clustering protocol even when more than 20% nodes run out of energy. As time progresses, the improvement is even more apt.

Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks

Due to the development in the field of Wireless Sensor Networks (WSNs), its major application, Wireless Body Area Network (WBAN) has presently become a major area of interest for the developers and researchers. Efficient sensor nodes data collection is the key feature of any effective wireless body area network. Prioritizing nodes and cluster head selection schemes plays an important role in WBAN. Human body exhibits postural mobility which affects distances and connections between different sensor nodes. In this context, we propose maximum consensus based cluster head selection scheme, which allows cluster head selection by using Link State. Nodal priority through transmission power is also introduced to make WBAN more effective. This scheme results in reduced mean power consumption and also reduces network delay. A comparison with IEEE 802.15.6 based CSMA/CA protocol with different locations of cluster head is presented in this paper. These results show that our proposed scheme outperforms Random Cluster head selection, Fixed Cluster head at head, Foot and Belly positions in terms of mean power consumption, network delay, network throughput and bandwidth efficiency.

Improving adaptive cluster head selection of teen protocol using fuzzy logic for WMSN

In this paper, we proposed the clustering algorithm using fuzzy inference system for improving adaptability the cluster head selection of TEEN for the wireless multimedia sensor network. The stochastic selection method cannot guarantee available of cluster head. Furthermore, as the formation of clusters is not optimized, the network lifetime is impeded. To improve this problem, we propose the algorithm that gathers attributes of sensor node to evaluate probability to be cluster head.

An Energy-Efficient Cluster-Head Selection Algorithm for Wireless Sensor Networks

Wireless sensor netwo rk has a wide range of applications in vario us fields, extending rapidly its area due to the development of technologies both in sensor and IT. However, technology of efficient battery energy utilization cannot catch up with the rapidly developing technology in releva nt fields. Therefore, energy efficiency becomes the critical factor in wireless sensor networks that requires more attention. Clustering technology is emerging as a promising method to solve efficient energy utilization problem and various cluster-head selection techniques are now under research. This paper suggests an energy efficient cluster-head selection algorithm considering the optimal number of cluster-heads and transmission energy. Our proposed scheme selects cluster-head by computing communi cation cost in probability by adopting distance between base station and n odes to assure an o ptimal number of clusters for a given wireless sensor network. With rapidly increasing amount of information produced by the Internet users along with the constant information technology development, clustering methods are gaining more importance. Clustering technology classifies a given data into significant subgroups, minimizes energy consumption, and maximizes the life span of a network by using limited resources efficiently in various fields such as data analysis, visualization, compression, and data processing. The important goal of clustering technology research is t o extend the l ife span o f a network by distributing energy consumption fairly among all nodes. Among many clustering approaches to extend the life span of a network there are two typical methods, now under research, a cluster-head selection and optimization of the number of cluster-heads or clusters. The typical head selection methods include LEACH-C (Low-Energy Adaptive Clustering Hierarchy-Centralized) (1), EACHS (Energy Adaptive Cluster-head Selection) (2), HEED (Hybrid Energy Efficient Distributed Clustering) (3). But, LEACH- C requires a big initial cost for hardware and EACHS bears a trans mission cost to acknowledge a quantity of energy, while HEED has no guarantee on the number of cluster-heads. This research suggests the energy efficient cluster-head selection technique (EECHS), an efficient header selection algorithm that takes into account the optimal number of cluster-heads and the transmission energy. E ECHS adopts distance in formation between a n ode and base station (BS) as communication cost to ensure the optimal number of clusters. A proposed cluster-head selection method operates by computing communication cost as internal information probability to save overall communication or transmission energy. The rest of this paper consists of following chapters. Chapter 2 d escribes related cluster header selection techniques and examines HEED, the basis of selection method for EECHS, in more detail. Chapter 3 explains the cluster-head selection technique of EECHS, Chapter 4 identifies performance of EECHS whose technique suggested through analysis on the test result using a simulator, and Chapter 5 deduces a conclusion.

DACH: An Efficient and Reliable Way to Integrate WSN with IPv6

IPv6 has many advantages such as the massive amount of addresses, high security, and high robustness, which are beneficial for wireless sensor networks (WSNs). However, it is almost impossible to use IPv6 directly in WSN due to its huge energy consumption. This paper proposes a double adaptively clustering hierarchy (DACH) algorithm which enables using IPv6 in WSN in an efficient and reliable way. Firstly, we present a clustering method to adaptively divide the whole sensor network into clusters according to its energy consumption in the last round. Then we propose an adaptive cluster head selection algorithm which employs a strategy to choose the most suitable cluster heads; meantime, this selection algorithm is integrated into DACH. Finally, the complete framework is built between headers and their slave nodes based on IEEE 802.15.4, and IPv6 is used to connect the headers and the base stations. Experimental and simulation results demonstrate that the DACH algorithm has lower time and energy consumption. Moreover, it is more reliable and applicable than many other IP-based WSN algorithms.