Adaptive Cluster Head Research Articles

Adaptive Reliable Cluster Head Selection Strategy for Multi-Hop Multimedia Wireless Sensor Networks

Adaptive Reliable Cluster Head Selection Strategy for Multi-Hop Multimedia Wireless Sensor Networks

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

Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

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.

A clustering algorithm based on nonuniform partition for WSNs

Abstract Wireless sensor networks (WSNs) have great application potential in partition parameter observation, such as forest fire detection. Due to the limited battery capacity of sensor nodes, how to reduce energy consumption is an important technical challenge. In this paper, we propose an energy efficient routing algorithm of adaptive double cluster head (CH) based on nonuniform partition for WSN. Firstly, according to the distance information from the base station (BS) to every sensor node, the network is divided into several uneven partitions. Secondly, CH is selected for each partition as the primary cluster head (PCH). Because of the cluster-level routing, the CHs close to the BS need to forward more data than the CHs in other areas, which consumes more energy. Therefore, an adaptive double CH method can be used to generate a secondary cluster head (SCH) in the cluster near the BS according to the parameters. Finally, the PCH is responsible for data collection, data integration, and data transmission. while the SCH is in charge of data routing. Simulation results show that the proposed algorithm can reduce the energy consumption and extend the life of the WSNs, compared with LEACH protocol and the HEED protocol.

Dynamic coefficient-based cluster head election in wireless sensor networks

Owing to the fact that the selection of cluster heads has a significant effect on the lifetime of the network, many researches have proposed various cluster head election methodologies for cluster-based WSNs. Although recent studies have focused on adaptive approaches, in which different parameters are assembled under a function, the effect of these parameters on cluster head election is not investigated in detail. In this paper, initially, a small-scale dataset is constructed by evaluating the death of the first, the half and the last node in a cluster-based WSN using three popular cluster head parameters, including the remaining energy of the nodes, the intra-cluster communication cost and the number of neighbours. In consideration of the results, a dynamically changeable coefficient based adaptive cluster head election, DCoCH, is proposed. The coefficients of the cluster head election parameters show alteration within three different periods of the lifetime of the network. DCoCH is compared with two recent adaptive based cluster head election methodologies for various WSN parameters and the results show that DCoCH outperforms equivalent approaches for different values of the location of the base station, the size of the network, the number of the nodes, the initial batteries of the nodes and the distribution of the nodes.

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.

Design and implementation of a low cost experimental testbed for wireless sensor networks

As wireless sensor networks (WSNs) become essential part of modern day infrastructure, researchers have presented loads of algorithms and models aimed at optimizing several aspects of the technology. These models are often developed and analyzed in simulated environments. The obvious need to experiment and confirm the actual performance of these algorithms and models on the field or in a more realistic environment is hampered in many cases by the high cost of advanced sensor node available in the market. This study therefore presents an inexpensive WSN test bed designed and constructed from an ESP8266EX Wi-Fi module. An experiment was conducted and results revealed that ESP8266EX based sensor nodes have a wider network coverage compared to the Arduino sensors based test bed. The proposed test bed was used to compare the performances in terms of power management of two WSN routing algorithms; normal Low Energy Adaptive Clustering Hierarchy (LEACH) and Cluster Head Assisted Routing (CHAR). Results show that the rate at which batteries depleted in LEACH and CHAR are 50% and 10% respectively; and this indicates that batteries depleted faster in LEACH than CHAR; therefore it can be stated that there is an improvement in energy conservation in CHAR over LEACH. It was also observed that CHAR produced a more even and gradual battery depletion rate leading to a longer network life of 20 rounds more than normal LEACH. http://dx.doi.org/10.4314/njt.v37i1.30

Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

How to efficiently collect sensory data for supporting energy-efficient operation of buildings has become a great challenge, especially for large-scale networks in buildings. In this paper, a spatio-temporal compression-based optimized clustering scheme is proposed for energy-efficient environmental data collection in buildings. In the scheme, first, according to the establishment of a cluster, an adaptive dynamic cluster head selection method for prolonging the lifetime of sensory nodes in buildings is developed. Meanwhile, to further reduce energy consumption, we construct the dynamic optimal quantity of the cluster heads solution method to achieve minimum energy consumption. Moreover, the spatio-temporal correlations of sensory data are explored in the data sampling and transmission processes, which can decrease the amount of data transmission and further extend the lifetime of the entire data collection network. The proposed scheme provides sensing data with high quality for environmental quality management in buildings and supports energy-efficient building operation. Finally, the simulation results show that the proposed scheme obtains obvious advantages in energy consumption compared with other related schemes, and the lifetime of the proposed scheme is also longer than others when it maintains high reconstruction precision.

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.

Priority based Multi SenCar Technique in Wireless Sensor Networks

Background/Objectives: In recent years Mobile WSN (Wireless Sensor Networks) has obtained more attention because it has an ability to allow economical and effective solutions in different fields. When designing the Mobile WSN, energy saving becomes an issue. So a network clustering technique is used to divide the network into small group of clusters and every cluster group is controlled and examined by one node called CH (Cluster Head). An effectual procedure for clustering and LEACH (Low Energy Adaptive Cluster Head Election) can forcefully decrease the consumption of energy and improve the lifetime of network. The existing WSN contains more sensor nodes that vary from one to another based on performance, computing power, initial energy, etc. According to decrease congestion and undesirable power, during the transmission of data long distance, stable number of transmit nodes are placed and selected as router to keep the connectivity. Then the Transmitted node collects the aggregated data using cluster head and moves it to base station. In this system, we are going to work the tradeoff between data assembly latency and power saving in gathering of mobile data by exploring an aggregation of local data and the SenCar moving length. A framework of three layers is suggested in WSN for mobile data group, which comprises layer of cluster head, layer of mobile collector and layer of Sencar. Methods: This framework deals with Dual Data Uploading and Load Balanced Clustering which is abbreviated as LBC-DDU. In emergency situations the signal priority is not mentioned in LBC-DDU. To achieve this problem, we suggested the Multi sensor techniques of priority Based in WSN (Wireless Sensor Networks). To assess the efficiency of the suggested priority scheme a large simulation are regulated. The proposed algorithm of shuffling allows the base station to identify the head of cluster and transmit node on the basis of residual energy, node density, base station distance and nodes overlapping ratio, etc. Findings and Improvements: We correlate the SenCar mobility in multi-hop routing to complete the advantages of double data uploading, which eventually shows data collection in priority based with both short time uploading of data and short path of moving.

Topology control of tactical wireless sensor networks using energy efficient zone routing

The US Department of Defense (DoD) routinely uses wireless sensor networks (WSNs) for military tactical communications. Sensor node die-out has a significant impact on the topology of a tactical WSN. This is problematic for military applications where situational data is critical to tactical decision making. To increase the amount of time all sensor nodes remain active within the network and to control the network topology tactically, energy efficient routing mechanisms must be employed. In this paper, we aim to provide realistic insights on the practical advantages and disadvantages of using established routing techniques for tactical WSNs. We investigate the following established routing algorithms: direct routing, minimum transmission energy (MTE), Low Energy Adaptive Cluster Head routing (LEACH), and zone clustering. Based on the node die out statistics observed with these algorithms and the topological impact the node die outs have on the network, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm tactically controls the topology of the network, thereby maintaining significant service area coverage when compared to the other routing algorithms.

Energy-Balanced On Demand Clustering Algorithm Based On Leach-C

As the use of Wireless Sensor Networks (WSNs) has grown enormously, the need for energy-efficient management has also risen. With advances in ubiquitous computing environment, WSNs have been broadly studied and many energy-efficient routing protocols had been proposed. LEACH (Low Energy Adaptive Clustering Hierarchy) is a popular cluster-based protocol, which provides distributed adaptive clustering and periodic cluster head (CH) selection rotation. As extension to LEACH, LEACH-C (LEACH Centralized) was proposed, in which the energy is utilized to select CH. However, both can’t guarantee cluster head distribution, in addition to considerable periodic clustering overhead. Furthermore, network topology change is a critical characteristic that has influence on communication path and load distribution among nodes. To resolve such problems, Energy-Balance on Demand Clustering Algorithm Based on LEACH-C is proposed. The algorithm adopts centralized cluster formation and distributed CH selection methods. Minimum energy clustering is used to divide the network into clusters, while energy and total communication distance are considered as secondary criteria to select optimal CH. From simulation results the proposed algorithm outperforms LEACH-C in life time, stability period and performance efficiency.

Trust Factor based LEACH-C protocol for Wireless Sensor Networks

Trust in wireless sensor network is an essential feature to detect various kind of attacks in the network. Wireless sensor nodes are more prone to attacks as nodes are deployed in an open environment. In case of centralized low energy adaptive cluster head protocol cluster heads in each round are selected by sink node. We propose a trust factor based LEACH-C protocol, to detect malicious nodes based on trust calculations which involves observation of various parameters and evaluation of trust factors. Simulation is conducted in using NS-2, by varying the number of malicious nodes and results are analyzed for energy consumption, number of data packets sent and percentage of malicious node detection.

A Protocol For Energy Efficient Mechanism For Wireless Sensor Network With Symmetric Cluster Formation

The network scalability can be achieved by grouping sensor nodes into a clustering hierarchy. Cluster head is referred as the leader of every cluster. In order to achieve stable clustering for mobile environments many clustering schemes are used for Wireless Sensor Network. In this paper, we have proposed an extension to Low Energy Adaptive Cluster Head protocol namely a protocol for energy efficient mechanism for wireless sensor network with symmetric cluster formation which is known as Highest Energy Clustering Hierarchy (HECH) protocol. Mathematical simulation studies show the correctness and effectiveness of our protocol.