Head Nodes Research Articles

Head Node Selection Algorithm in Cloud Computing Data Center

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.

A Novel Energy-Efficient Clustering Algorithm for More Sustainable Wireless Sensor Networks Enabled Smart Cities Applications

Wireless Sensor Networks (WSNs) is a major sensing technology that has revolutionized the way information is collected, processed, and used in many smart cities’ applications that rely on sensing technologies for event detection and monitoring. Despite the multiple benefits that such technology offers, the quick depletion of sensors’ battery power represents a major concern, mainly due to the extensive computational tasks and communication operations performed by individual sensors. Indeed, the cost of replacing batteries can be prohibitively expensive, especially when sensors are deployed in areas where access is difficult, in urbanized cities. To extend sensors’ lifetime, this paper proposes a new variant of LEACH protocol named LEACH enhanced with probabilistic cluster head selection (LEACH-PRO). LEACH-PRO introduces several measures to extend WSNs nodes’ lifetime such as cluster head node selection using a probabilistic function based on maximum residual energy and minimum distance to the sink. The obtained simulation results have proven the supremacy of LEACH-PRO over LEACH and direct transmission protocol in terms of the achieved network lifetime and the generated traffic overhead. Most importantly, LEACH-PRO will significantly extend the sensors’ lifetime, which would make this type of deployment more viable in smart city scenarios.

A Novel Static Cluster-Based Hierarchical Protocol for Wireless Sensor Networks

Wireless sensor networks have recently gained a lot of attention from the scientific community due to their very wide spectrum of applications. In such networks, the sensor nodes have limited resources. These constraints impose many challenges to the design of related protocols. Especially, routing protocols should be energy-efficient for the prolonged network lifetime. The LEACH protocol is the most popular energy-efficient hierarchical clustering protocol for WSNs that was proposed for reducing power consumption. However, LEACH suffers from several drawbacks such as the non uniform distribution of Cluster Head nodes, the possibility of choosing a low energy node as Cluster Head, etc. In this paper, an attempt is made to overcome this shortcoming by introducing a new hierarchical clustering protocol, called SCHP (Static Cluster-based Hierarchical Protocol). The SCHP protocol is based on a static cluster creation and an optimal cluster head selection. Simulation results show that the proposal guarantees better performance than the LEACH Protocol that is considered as the baseline in the literature. We used many metrics, as packet loss rate, end-to-end delay, and energy consumption to evaluate the efficiency of our proposal. We show also that the SCHP protocol can improve the network lifetime.

A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

Novel Authentication and Secure Trust based RPL Routing in Mobile sink supported Internet of Things

ABSTRACT In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such as rank attack, password spoofing and more. To this end, most of the works have focused their research on securing the RPL-based IoT network. However, still there exist downsides such as high energy consumption, lack of effective authentication and high packet losses. Motivated by these preceding defects, this paper proposes the Novel Authentication and Secure Trust-based RPL Routing in Mobile sink-supported Internet of Things (SecRPL-MS). At first, SecRPL-MS performs a registration process where all IoT nodes in the network register themselves in the security entity. In this work, the frequent death of IoT nodes is alleviated through deploying mobile sink in the network. If any grid member (GM) node wants to transmit their data to the grid head (GH) node, then it must undergo authentication process. Secure routing is adopted in RPL by utilising the sail fish optimisation algorithm. Each GM node encrypts its sensed data using the prince algorithm before transmitting it to the GH node. The moving points are selected for the mobile sink using the Quantum Inspired Neural Network (QINN) algorithm. This proposed SecRPL-MS performance is evaluated using the Network Simulator 3 (NS3) in terms of the Packet Delivery Ratio (%), Delay (ms), Energy Consumption (mJ), Key Generation Time (ms) and Malicious Node Detection Accuracy (%). The proposed SecRPL-Ms outperforms 23% of malicious node detection accuracy when compared to existing systems, which represent the proposed SecRPL-MS system providing high security by mitigating the following attacks such as rank attack, Sybil attack, blackhole attack and man in the middle attack.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative parameter identification method for estimating the common model factors in WSN. The former method optimises the decentralised topology such that all the leaf nodes in a community connect to the head node directly. A circle topology is built to enable the remote leaf nodes to link to the head node through two adjoining relay nodes to reduce the whole communication distance and power consumption. Based on the optimised topology, an iterative identification method is proposed to minimise the information capacity by transmitting the processed results instead of raw data to reduce the data amount for calculation and storage. Then, we prove the consensus and convergence of the proposed identification method. Finally, two simulations verify the effectiveness of the proposed method and the comparative results present the data reduction for the on-board calculation, communication, and storage in the practical use of WSN.

Design and Comparison of LEACH and Improved Centralized LEACH in Wireless Sensor Network

A WSN consists of a setup of sensor nodes/motes which perceives the environment under monitoring, and transfer this information through wireless links to the Base Station (BS) or sink. The sensor nodes can be heterogeneous or homogeneous and can be mobile or stationary. The data gathered is forwarded through single/multiple hops to the BS/sink. In this paper, propose improvements to LEACH routing protocol to reduce energy consumption and extend network life. LEACH Distance Energy (LEACH-DE) not only selects the cluster head node by considering that the remaining energy of the node is greater than the average remaining energy level of the nodes in the network, but also selects the cluster head node parameters based on the geometric distance between the candidate node and the BS. The simulation results show that the algorithm proposed in this work is superior to LEACH and LEACH-C (Centralized) in terms of energy saving and extending the lifetime of wireless sensor networks.

Clock Synchronization using Truncated Mean and Whale Optimization for Clustered Sensor Networks

Clock synchronization is an important component in many distributed applications of wireless sensor networks (WSNs). The deprived method of clock offset and skew estimation causes inaccuracy, synchronization delay, and communication overhead in the protocols. Hence, this paper exploits two techniques of variation truncated mean (VTM) and whale optimization (WO) to enhance the synchronization metrics. Sensor nodes are grouped into several non-overlapped clusters. The cluster head collects the member nodes’ local time and computes the synchronization time 𝑆𝑍𝑡 using the truncated mean method. Nodes with a high variation in the timings compared to a preset value are truncated. The head node broadcasts the 𝑆𝑍𝑡 in which the whale optimization is aiming at each node to reduce the synchronization error. The intra and inter-cluster synchronizations are accomplished through the multihop message exchange approach. The theoretical analysis is validated, and the simulation outcomes show that the performance metrics in the proposed work are better than the conventional methods by achieving minimum error value.

An Algorithm with Efficiently Collecting and Aggregating Data for Wireless Sensor Networks

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

Simulation of optimal selection algorithm for wireless sensor cluster head node Bayesian statistical network

This paper proposes a routing algorithm of cluster tree network and further combines the hierarchical structure of clustering with that of neural network, and designs a data fusion algorithm based on clustering routing protocol. Then, aiming at the difficulty in selecting the weights of neural network, a weight optimization neural network based on particle swarm optimization algorithm is proposed and applied to multi-sensor fusion. The simulation results show that the number of cluster heads of ACEC protocol is more concentrated on the expected value and has good stability. The algorithm selects cluster head nodes by non-uniform clustering and dynamic threshold, which ensures the balanced distribution of cluster head nodes in the network, reduces the network energy consumption and prolongs the service life of the network. The success rate of ancec protocol is similar to debug protocol, but with the increase of transmission time, LEACH protocol and debug protocol do not consider the link quality factor when forwarding data, so the communication link quality is uneven when selecting the next hop relay point in each round, so the data transmission success rate has a relatively obvious downward trend. The fusion result is clearly better than the poor two sensors, but inferior to the best sensor. This is due to the low SNR of sensors SNL and SN2, so their recognition effect is relatively poor, which also conforms to the rule of multi-sensor fusion. The results show that the method based on qdpso-bp network fusion is feasible.

Trusted node selection in clusters for underwater wireless acoustic sensor networks using fuzzy logic

Cluster based routing are the popular techniques in Underwater Wireless Acoustic Sensor Networks (UWASNs) owing to its advantages like better network life time, low energy consumption and scalability. However selection of trusted cluster head and cluster members is a challenging task due to the divergent features and constraints of UWASNs. In this paper we propose a trusted cluster head and member nodes selection scheme using fuzzy logic. The scheme works in the following steps. Initially the security check of the member nodes in the cluster are performed using a light weight XOR encryption algorithm in order to authenticate the nodes. Secondly, the trusted node is chosen based on trust value using fuzzy logic rules. The trust value of a node is calculated based on the fuzzy parameters such as distance, energy and relative mobility of the node. The scheme is simulated in MATLAB. It performs better when compared with the existing schemes in terms of malicious node detection and packet delivery.

An efficient transmission algorithm for power grid data suitable for autonomous multi-robot systems

Under the power environment of Internet of things used in autonomous multi-robot systems, due to the influence of various actual environmental factors, the security protection method based on security partition and physical isolation of nodes is difficult to fully meet the information security protection requirements of the power system, and the network threats such as impersonation, eavesdropping, and dexterity on communication links are still difficult to avoid. In order to improve the security of node data transmission in autonomous multi-robot power systems, reducing network energy consumption has become the bottleneck of network application and development. In this paper, we propose an Improved Effective Clustering Algorithm (IECA) and transmission algorithm of power grid data based on autonomous multi-robot systems. The distance between nodes sending data to the next-hop node is equivalent to the energy model. The remaining energy in the cluster head node sending data in the next hop is recorded, and the remaining energy is subtracted from the equivalent energy to find a maximum value. The corresponding node of the maximum value is taken as the number of nodes According to the received node. Compared with the LEACH algorithm and GAF algorithm, this algorithm can not only extend the lifetime of nodes but also has more efficient performance.

Multimodality oncologic evaluation of superficial neck and facial lymph nodes.

Involvement of lymph nodes in patients with head and neck cancers impacts treatment and prognosis. Head and neck lymph nodes are comprised of superficial and deep groups which are interconnected. The deep lymph nodes, predominantly centered along internal jugular veins, are very well-known to radiologists and clinicians. However, superficial lymph nodes that drain lymph from the scalp, face, and neck are much less recognized. Here, we describe the anatomic and imaging features of these superficial lymph nodes on CT, MRI, and PET in oncologic settings.

Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network

The nodes in the wireless sensor network are furnished with restricted and irreplaceable battery power. The continuous sensing, computation, and communication drain out the energy of sensors very quickly. The optimal utilization of the sensor energy has always been a key issue for all the applications in the wireless sensor network. To manage the energy issue of nodes, various approaches were proposed in the past which focused on designing the proper energy management methods. The clustering of sensors is one of the most popular techniques used to manage the energy-related concerns of networks. In this paper, a particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance. The proposed protocol uses the particle swarm optimization technique to select the cluster head and relay nodes for the network. The cluster head is selected by employing the particle swarm optimization based fitness function which considers the energy ratio (initial energy and residual energy) of nodes, distance between nodes and cluster head, and node degree to appoint the most optimal node for the cluster head job. For the data transfer to base station, the proposed scheme uses the fitness value based on residual energy of cluster head and distance to base station parameters to nominate the relay nodes for the multi-hop data transfer to the base station. The performance of the proposed protocol is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness. The proposed scheme has improved the lifetime of the network by 238%, 136%, 106%, and 71% as compared to the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO techniques used in the simulation results for the comparison purpose. The stability period of the network in proposed scheme is approximately 396%, 321%, 246%, and 126% more than the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO protocol .

Enhancement of Clustering Techniques Efficiency for WSN using LEACH Algorithm

The Low Energy Adaptive Hierarchy is perhaps the most popular hierarchical protocol (LEACH). LEACH is a hierarchical structure that implements the creation of a disseminated cluster; nodes select themselves as the head node with any probability at any point in it. In view of the fact that a wireless node may only be configured with a restricted power supply, energy consumption is considered to be the key design target. In many implementations, it is not feasible to substitute energy sources, and therefore the life cycle of the node shows a heavy dependency on the battery life. With the aim of leveraging the network life cycle by applying clustering designs, several algorithms have been published. The paper presents numerous routing protocol definitions to save resources and prolong the existence of the sensor network. WSN applications, such as communication architectures, security and management, still have several problems to be resolved. We will close the distance between technology and implementation by addressing these problems.

The value of imaging model in the differential diagnosis of benign and malignant head and neck lymph nodes.

The value of imaging model in the differential diagnosis of benign and malignant head and neck lymph nodes.

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.

A nomogram for predicting the risk of neck node metastasis in oral cavity carcinoma using acoustic radiation force impulse imaging (ARFI)

BackgroundThe study was conducted to assess the accuracy power of Acoustic radiation force impulse imaging (ARFI) and generate a nomogram using ultrasound and ARFI to predict malignant cervical lymph nodes in head and neck squamous cell carcinoma. Material and methods374 cervical lymph nodes from 67 patients were evaluated. The B-mode ultrasonography and the elastography findings were compared with the final histopathological diagnosis. Radiological variables were used to construct nomogram and clinical utility of the nomogram was cross-validated. ResultsIn univariate analysis, status of the hilum, Long Axis Diameter, Short axis diameter, colour virtual touch imaging grade (VTI) and shear wave velocity were significant in predicting metastasis in the cervical lymph nodes. In multivariable analysis, it was found that predominance of red over yellow area on colour VTI was significantly associated with lymph node metastasis. A multiple logistic regression performed to ascertain the effects of on the likelihood that patients had lymph node metastasis on histopathology was statistically significant, χ2(10) = 44.96, p < 0.001. The model was able to correctly classify 93.28% of cases and the concordance index (c-index) was estimated to be 0.8773. A nomogram was thus established to predict metastasis in cervical lymph nodes. ConclusionsARFI increases the diagnostic accuracy of conventional USG in predicting metastatic lymph nodes in HNSCC. Adding the constructed nomogram to the conventional diagnostic pathway can provide an alternative option to frozen section and FNAC.

A novel method of mobility-based clustering protocol in software defined sensor network

The distributed clustering method is widely used to extend network lifetime in traditional wireless sensor networks. However, it is difficult to achieve the idea performance of the whole network, such as transmission rate, energy consumption, and control overhead, neglecting the global stability of the network. To tackle this problem, a centralized mobility-based clustering (CMBC) protocol is proposed in the software defined sensor network. The method of CMBC mainly consists of two aspects: first, CMBC clusters the nodes with the connection time between the mobile nodes (i.e., noncluster head nodes, non-CH) and the cluster head (CH) and establishes stable topological structures between the non-CHs and the CH; second, when emergencies occur, the centralization management control center sends the configuration files to replace the CH. Compared to the distributed network of MBC, WCRA and IMP-MECA protocol, the proposed method can be applied in scenarios with high-speed mobile nodes to improve the network performance in terms of transmission successful rate, average power consumption, and average control overhead.

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

AbstractWireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach.