Cluster Maintenance Research Articles

Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm.

Cell shape-independent FtsZ dynamics in synthetically remodeled bacterial cells

FtsZ is the main regulator of bacterial cell division. It has been implicated in acting as a scaffolding protein for other division proteins, a force generator during constriction, and more recently, as an active regulator of septal cell wall production. FtsZ assembles into a heterogeneous structure coined the Z-ring due to its resemblance to a ring confined by the midcell geometry. Here, to establish a framework for examining geometrical influences on proper Z-ring assembly and dynamics, we sculpted Escherichia coli cells into unnatural shapes using division- and cell wall-specific inhibitors in a micro-fabrication scheme. This approach allowed us to examine FtsZ behavior in engineered Z-squares and Z-hearts. We use stimulated emission depletion (STED) nanoscopy to show that FtsZ clusters in sculpted cells maintain the same dimensions as their wild-type counterparts. Based on our results, we propose that the underlying membrane geometry is not a deciding factor for FtsZ cluster maintenance and dynamics in vivo.

Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm

In vehicular ad hoc networks, the vehicle speed can exceed 120 kmph. Therefore, any node can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To overcome these problems, many routing protocols based clustering technique have been proposed. Indeed, the poor assignment of vehicles to clusters is the most important shortcomings where an inaccurate affiliation may reduce the algorithm’s effectiveness and disrupt all results and analyzes. Therefore, in this paper, we used a hybrid genetic algorithm to improve the cluster maintenance phase in our Weighted K-medoid Clustering Algorithm (WKCA) proposed recently. The proposed model incorporated the tabu search within genetic algorithm to allow the scan of all search space and to reach the best solution without falling into the local optima. This model improves the assignment of nodes to clusters, which in turn achieves efficient vehicle communication and ensures more stability in clustered architecture. Based on relevant metrics, the results obtained in a simulation game show that the enhanced model (E-WKCA) achieves more stability and robustness when compared to the original algorithm (WKCA) and other approaches designed for the same objective.

Balanced Rac1 activity controls formation and maintenance of neuromuscular acetylcholine receptor clusters.

Rac1, an important Rho GTPase that regulates the actin cytoskeleton, has long been suggested to participate in acetylcholine receptor (AChR) clustering at the postsynaptic neuromuscular junction. However, how Rac1 is regulated and how it influences AChR clusters have remained unexplored. This study shows that breaking the balance of Rac1 regulation, by either increasing or decreasing its activity, led to impaired formation and maintenance of AChR clusters. By manipulating Rac1 activity at different stages of AChR clustering in cultured myotubes, we show that Rac1 activation was required for the initial formation of AChR clusters, but its persistent activation led to AChR destabilization, and uncontrolled hyperactivation of Rac1 even caused excessive myotube fusion. Both AChR dispersal and myotube fusion induced by Rac1 were dependent on its downstream effector Pak1. Two Rac1 GAPs and six Rac1 GEFs were screened and found to be important for normal AChR clustering. This study reveals that, although general Rac1 activity remains at low levels during terminal differentiation of myotubes and AChR cluster maintenance, tightly regulated Rac1 activity controls normal AChR clustering.

Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITSs). The current researches are intensely focusing on the problems of routing protocol reliability and scalability across the urban VANETs. Vehicle clustering is testified to be a promising approach to improve routing reliability and scalability by grouping vehicles together to serve as the foundation for ITS applications. However, some prominent characteristics, like high mobility and uneven spatial distribution of vehicles, may affect the clustering performance. Therefore, how to establish and maintain stable clusters has become a challenging problem in VANETs. This paper proposes a link reliability-based clustering algorithm (LRCA) to provide efficient and reliable data transmission in VANETs. Before clustering, a novel link lifetime-based (LLT-based) neighbor sampling strategy is put forward to filter out the redundant unstable neighbors. The proposed clustering scheme mainly composes of three parts: cluster head selection, cluster formation, and cluster maintenance. Furthermore, we propose a routing protocol of LRCA to serve the infotainment applications in VANET. To make routing decisions appropriate, we nominate special nodes at intersections to evaluate the network condition by assigning weights to the road segments. Routes with the lowest weights are then selected as the optimal data forwarding paths. We evaluate clustering stability and routing performance of the proposed approach by comparing with some existing schemes. The extensive simulation results show that our approach outperforms in both cluster stability and data transmission.

087 IL-27 signaling is relevant for IL-15 production and T cell cluster maintenance in allergic contact hypersensitivity

Allergic contact dermatitis (ACD) results from a T-cell response to hapten/allergens applied to the skin. Following sensitization, specific T-cells are first generated and activated in the lymph node, and some Ag-specific T cells remain in the skin. Re-exposure to the relevant hapten/allergen initiates the efferent phase and clinical expression of ACD, characterized by local T cell activation and recruitment of specific T cells to the sites of allergen challenge. IL-15 is essential for allergic contact hypersensitivity pathogenesis and plays a pivotal role in both T-cell activation, survival and effector functions and, yet, paradoxically, it also provides anti-apoptotic signals in keratinocytes. IL-15 regulation in the skin is not well known. We made the unexpected observation, that in contrast to non-lesional skin of ACD patients, in the skin of 72 hrs patch-tested individuals (effector phase of ACD), IL-15 and anti-apoptotic BCL2 localized to dermal T cell DC clusters and to a much lesser extent to the epidermis. Interestingly, increased BCL2 was expressed in T cells within the clusters and was associated with and directly adjacent to IL-27-producing CD14+ cells in positive patch-tested induced ACD. Monocytic cells, including human DC-like THP1, and murine BMDC up-regulated IL15 mRNA upon IL-27 stimulation. IL-27 was also found to directly stimulate IL15 mRNA in human epidermal keratinocytes in a STAT-1 dependent manner as evidenced by rapid p-STAT1 nuclear translocation and abrogation of this response by silencing STAT1 (p<0.05). We next tested the functional in vivo relevance of IL-27 signaling in a mouse model of hapten/allergen-induced dermal T cell clusters. Administration of IL-27 neutralizing antibody (i.d.) in these mice decreased the number of T cell clusters and resulted in downregulation of BCL2 in T cells supporting a regulatory role of IL-27 on T cell survival possibly via IL-15. Overall, these findings implicate the IL-27 pathway as a potential therapeutic agent in regulating cutaneous T cell immunity.

NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis.

Environmental nutrient levels impact cancer cell metabolism, resulting in context-dependent gene essentiality. Here, using loss-of-function screening based on RNA interference, we show that environmental oxygen levels are a major driver of differential essentiality between in vitro model systems and in vivo tumours. Above the 3-8% oxygen concentration typical of most tissues, we find that cancer cells depend on high levels of the iron-sulfur cluster biosynthetic enzyme NFS1. Mammary or subcutaneous tumours grow despite suppression of NFS1, whereas metastatic or primary lung tumours do not. Consistent with a role in surviving the high oxygen environment of incipient lung tumours, NFS1 lies in a region of genomic amplification present in lung adenocarcinoma and is most highly expressed in well-differentiated adenocarcinomas. NFS1 activity is particularly important for maintaining the iron-sulfur co-factors present in multiple cell-essential proteins upon exposure to oxygen compared to other forms of oxidative damage. Furthermore, insufficient iron-sulfur cluster maintenance robustly activates the iron-starvation response and, in combination with inhibition of glutathione biosynthesis, triggers ferroptosis, a non-apoptotic form of cell death. Suppression of NFS1 cooperates with inhibition of cysteine transport to trigger ferroptosis in vitro and slow tumour growth. Therefore, lung adenocarcinomas select for expression of a pathway that confers resistance to high oxygen tension and protects cells from undergoing ferroptosis in response to oxidative damage.

Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol

Wireless Sensor Networks (WSN) became a key technology for a ubiquitous living and remains an active research due to the wide range of applications. The design of energy efficient WSN is still a greater research challenge. Clustering techniques have been widely used to reduce the energy consumption and prolong the network lifetime. This paper introduces an algorithm named Fuzzy logic based Unequal clustering, and Ant Colony Optimization (ACO) based Routing, Hybrid protocol for WSN to eliminate hot spot problem and extend the network lifetime. This protocol comprises of Cluster Head (CH) selection, inter-cluster routing and cluster maintenance. Fuzzy logic selects CHs efficiently and divides the network into unequal clusters based on residual energy, distance to Base Station (BS), distance to its neighbors, node degree and node centrality. It uses ACO based routing technique for efficient and reliable inter-cluster routing from CHs to BS. Moreover, this protocol transmits data in a hybrid manner, i.e. both proactive and reactive manner. A threshold concept is employed to transmit/intimate sudden changes in the environment in addition to periodic data transmission. For proper load balancing, a new routing strategy is also employed where threshold based data transmission takes place in shortest path and the periodic data transmission takes place in unused paths. Cross-layer cluster maintenance phase is also used for uniform load distribution. The proposed method is intensively experimented and compared with existing protocols namely LEACH, TEEN, DEEC and EAUCF. The simulation results show that the proposed method attains maximum lifetime, eliminates hot spot problem and balances the energy consumption among all nodes efficiently.

Cluster based routing in cognitive radio adhoc networks: Reconnoitering SINR and ETT impact on clustering

A novel clustering approach for Cognitive Radio Ad Hoc Networks (CRAHNs) is proposed in this paper. The Signal to Interference and Noise Ratio (SINR) produced by Primary Users (PUs) on collocated Cognitive Users (CUs) along with Expected Transmission Time (ETT) among CUs is taken into account for cluster formation. The primary concern of the proposed work is to find a suitable method of cluster formation for efficient routing in CRAHN. The suggested algorithm is simulated in ns-3. Through the observed results, values of some base parameters for efficient clustering are established. Efficiency of the proposed approach is also compared with three existing cluster based protocols. Results reveal that the proposed algorithm performs better. One cluster maintenance algorithm is also proposed in the paper. Future scope of the work is outlined.

A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

$\mathsf {pSCAN}$ : Fast and Exact Structural Graph Clustering

We study the problem of structural graph clustering, a fundamental problem in managing and analyzing graph data. Given an undirected unweighted graph, structural graph clustering is to assign vertices to clusters, and to identify the sets of hub vertices and outlier vertices as well, such that vertices in the same cluster are densely connected to each other while vertices in different clusters are loosely connected. In this paper, we develop a new two-step paradigm for scalable structural graph clustering based on our three observations. Then, we present a $\mathsf {pSCAN}$ approach, within the paradigm, aiming to reduce the number of structural similarity computations, and propose optimization techniques to speed up checking whether two vertices are structure-similar. $\mathsf {pSCAN}$ outputs exactly the same clusters as the existing approaches $\mathsf {SCAN}$ and $\mathsf {SCAN\text{++}}$ , and we prove that $\mathsf {pSCAN}$ is worst-case optimal. Moreover, we propose efficient techniques for updating the clusters when the input graph dynamically changes, and we also extend our techniques to other similarity measures, e.g., Jaccard similarity. Performance studies on large real and synthetic graphs demonstrate the efficiency of our new approach and our dynamic cluster maintenance techniques. Noticeably, for the twitter graph with 1 billion edges, our approach takes 25 minutes while the state-of-the-art approach cannot finish even after 24 hours.

Energy-efficient multi-mode clusters maintenance (M2CM) for hierarchical wireless sensor networks

How to energy-efficiently maintain the topology of wireless sensor networks (WSNs) is still a difficult problem because of their numerous nodes, highly dynamic nature, varied application scenarios and limited resources. An energy-efficient multi-mode clusters maintenance (M2CM) method is proposed based on localized and event-driven mechanism in this work, which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs. M2CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field., the triggers of M2CM include such events as nodes' residual energy being under the threshold, the load imbalance of cluster head, joining in or exiting from any cluster for new node or disable one, etc. Based on neighboring relationship of the damaged clusters, one can start a single cluster (inner-cluster) maintenance or clusters (inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs. The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network, effectively narrow down the influenced area of clusters maintenance, and increase transmitted data and prolong lifetime of network compared to the traditional schemes.

Weight Value Based Clustering for Dissemination of Emergency Message with Selective Reliable Broadcasting in VANETs

Vehicular networks being a sub class of MANET is a challenging and dynamic field of wireless communication. It is a trend in which vehicles are manufactured with onboard for performing communication with other vehicles while moving to enhance the overall driving experience and security. Such mechanism enables vehicle to vehicle (V2V) and vehicle to infrastructure communication and provides vehicles with route information and traffic information. Due to the nature of dynamic topology, frequent disconnections occur. For this various number of protocols have designed for its efficient and reliable working. Efficient data dissemination to a set of receivers is a challenging one considering the dynamic nature of VANETs. In the proposed scheme, we guarantee efficient cluster formation and maintenance through weighted cluster algorithm and emergency message is disseminated through SRB Protocol.

QOS enabled data dissemination in hierarchical VANET using machine learning approach

Vehicular ad hoc networks (VANETs) are a collection of vehicular nodes that perform as a mobile hosts form a temporary network without the aid of any centralised infrastructure, so it is a sub-class of ad hoc network. It ensures the quality of service (QoS) for different VANET applications. Although it provides the QoS services to the process, mobility and routing play an important challenge in the VANET environment. So, different researches have revealed that the hierarchical routing schemes have numerous benefits over the traditional ones. Stable cluster formation and maintenance with the guarantying QoS in intra-cluster communications has always remained as a great challenge. For overcoming this issue, this paper proposes a QoS enabled data dissemination using an improved Kruskal's algorithm to provide efficient data dissemination and QoS in hierarchical VANET. This approach constructs the minimum spanning trees using Kruskal's algorithm in every road segment, where the vehicle has been clustered using the fuzzy c-means clustering method by considering the intra-cluster QoS. Each spanning tree will have a cluster head that is responsible to collect the data from the leaf nodes and disseminates the data to other coordinator nodes and vice versa. The simulation results show that the proposed approach performs better than the existing routing approach in terms of delay, throughput and packet loss.

Multi-mode clustering model for hierarchical wireless sensor networks

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial–temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

Graph cut based clustering for cognitive radio ad hoc networks without common control channels

Clustering is an efficient tool to improve the routing and data transmission performance in large scale networks. However, in cognitive radio ad hoc networks (CRAHNs), clustering design is challenging due to the dynamic spectrum access and the blind information environment. In this paper, we propose a novel distributed clustering algorithm for CRAHNs, where neither a dedicated common control channel (CCC) nor prior topology information is required. First, a neighbor discovery protocol without relying on CCC is proposed to construct the local topology. Then, we model the network as a undirected graph and formulate the clustering process as a graph cut problem. We design a mincut based heuristic algorithm to approximate the optimal clustering solution. After this, we also present a synchronize protocol to achieve the global consistency of cluster memberships. Finally, we propose a proactive cluster maintenance mechanism to reduce the interferences caused by PU activities. We validate our work through comparisons with other clustering methods. The simulation results show that, by adjusting the cluster structure according to the changing spectrum, the proposed method reduces the interference and improves the network efficiency.

A Cognitive Self-Organising Clustering Algorithm for Urban Scenarios

Cooperative communications based on data sharing and relaying have been gaining huge interest lately, due to the increase in the number of mobile devices and the advancement in their capabilities. Research on green communications, location based services and mobile social networking have fueled research on this topic. Vehicular technology have also fostered this cooperative approach as a means to provide scalability and privacy preserving mechanisms. In these scenarios, a commonly suggested approach to benefit from cooperation is the formation of virtual groups of mobile terminals, usually referred to as clusters. Mobility-aware clustering algorithms are commonly proposed to form such clusters based on the mobility characteristics of the mobile devices. However, these solutions are limited by the unpredictable nature of mobility behavior that leads to frequent disconnections of nodes from clusters; hence reducing the time availability of cooperative relationships. In this paper, we go beyond existing research on clustering by including a cognitive perspective. We propose data mining and cooperative optimization in order to deduce mobility pattern information in conjunction with the clustering process. We propose a low complexity algorithm that can dynamically adapt to different mobility characteristics of an urban scenario, more importantly without the need for previous configuration/information. The proposed technique achieves considerable gains in terms of stability in urban scenarios. Additionally, the paper presents a comprehensive analytical evaluation of the problem and the proposed solution, and provides extended simulation results in both matlab and ns2. Results show an outstanding gain up to 150 % in cluster lifetime and 250 % in residence time of nodes within clusters and reduces the overhead for clustering maintenance in 70 %.

Distributed and Weighted Clustering based on d-Hop Dominating Set for Vehicular Networks

Clustering is one of the key technologies in vehicular networks. Constructing and maintaining stable clusters is a challenging task in high mobility environments. DWCM (Distributed and Weighted Clustering based on Mobility Metrics) is proposed in this paper based on the d-hop dominating set of the network. Each vehicle is assigned a priority that describes the cluster relationship. The cluster structure is determined according to the d-hop dominating set, where the vehicles in the d-hop dominating set act as the cluster head nodes. In addition, cluster maintenance handles the cluster structure changes caused by node mobility. The rationality of the proposed algorithm is proven. Simulation results in the NS-2 and VanetMobiSim integrated environment demonstrate the performance advantages.

Cooperative path planning with applications to target tracking and obstacle avoidance for multi-UAVs

In this paper, we propose a hybrid approach based on the Lyapunov Guidance Vector Field (LGVF) and the Improved Interfered Fluid Dynamical System (IIFDS), to solve the problems of target tracking and obstacle avoidance in three-dimensional cooperative path planning for multiple unmanned aerial vehicles (UAVs). First, LGVF method is improved for UAV cooperative target tracking in 3D environment by introducing vertical component, with two guidance layers containing steering control and speed control. Second, IIFDS method is presented for UAVs to avoid obstacles or threats in complicated environment, where the local minimum problem is well resolved. Moreover, some cooperative strategies are added into the IIFDS framework to satisfy the constraints of obstacle avoidance and cluster maintenance. Finally, the missions of tracking target and avoiding obstacles can be performed simultaneously, by replacing the original sink fluid of IIFDS with the vector field of LGVF. Besides, the reactive parameters of IIFDS can be adjusted by the rolling optimization strategy to enhance the path quality. The experimental results validate the effectiveness of the hybrid method.

An enhanced low overhead and stable clustering scheme for crossroads in VANETs

In this paper, we study the clustering problem for crossroads in Vehicular Ad hoc Networks (VANETs). Considering the load balancing of both the whole network and each cluster based on the multiple metrics, an Enhanced Low Overhead and Stable Clustering (EnLOSC) scheme is presented to ensure the stability and security of clusters and to reduce the communication overhead in this case. The proposed capability metric, designed to find the vehicles with similar direction and better channel quality, is exploited in the processes of formation and maintenance to determine which node is suitable for a cluster head. Based on this, a Cluster Head Electing in Advance Mechanism (CHEAM) is developed in order to fairly select a new head for “isolated” vehicles that may not belong to a cluster. Meanwhile, other metrics are related to the node density and cluster size, which are exploited in the Cluster Merging and Splitting Mechanisms to keep the system load balancing and to improve the communication quality. Furthermore, the proposed Discovery and Elimination Scheme (DES) is designed to tackle the malicious nodes that may hurt the cluster communication. Accordingly, an enhanced cluster maintenance strategy with multi-metrics and a secure scheme is proposed so as to reduce the number of isolated vehicles, keep appropriate loading for each cluster head, and protect the whole link over cluster communication. Numerical results and discussion indicate that the cluster stability, communication overhead, load balance, and security can be significantly enhanced by our proposed scheme.