Hierarchical Clustering Architecture Research Articles

Hybrid 802.11p‐cellular architecture for NDN‐based VANET

SummaryVehicular network communications (VANET) face multiple challenges due to their intermittent connections and the rapid changes in their topologies. In recent years, several research efforts have explored the use of content‐centric approaches to alleviate some of these challenges. One of these promising network designs is Named Data Networking (NDN), which has become a valid solution to support VANET applications. However, in the NDN architecture, the main forwarding mechanism for the interest packets is flooding. This forwarding mechanism will result in excessive collisions, which will lead to the broadcast storm problem. In this paper, we propose VC‐NDN: a hybrid and hierarchical Named Data Networking architecture for VANETs. VC‐NDN improves content retrieval efficiency through an adapted NDN‐based communication model. VC‐NDN includes a new interest forwarding scheme to reduce packet collision in the network and an efficient mechanism to support push‐based traffic. Furthermore, to reduce communication costs, VC‐NDN uses two communication technologies in parallel, namely, IEEE 802.11p and cellular communications, while keeping the usage of the cellular network at a minimum level. Finally, to reduce the impact on vehicle mobility, VC‐NDN follows a hierarchical clustering architecture. Specifically, a density‐based clustering algorithm is designed to create and maintain stable clusters with multihop communication capability. Our performance study shows that VC‐NDN outperforms the basic VNDN solutions in terms of data retrieval delay and packet delivery ratio while minimizing the usage of the cellular network.

A Clustering Approach for Jamming Environment Classification

A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter-free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and model-order selection rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated and measured data (via software-defined radio devices) to highlight the benefits of the approach.

Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks

The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of clustering and routing protocols can make a vast difference. Existing classical and evolutionary optimization technique-based protocols have high computational complexity since clustering and routing problems are solved separately. Moreover, these protocols suffer from hot-spot problem due to uneven formation of clusters. In this paper, we propose a multi-level clustering protocol (MLCP) for energy-efficient data gathering in large-scale WSNs. Additionally, a hierarchical clustering architecture is designed in MLCP to jointly solve the problems of clustering and routing. Further, for the purpose of cluster head selection, a hybrid dragonfly algorithm-based particle swarm optimization technique is proposed which combines the exploration and exploitation capabilities of dragonfly algorithm and particle swarm optimization, respectively. MLCP considers intra-cluster distance, node degree and inter-cluster distance for the formation of scalable, load-balanced and energy-efficient clusters. To demonstrate the full potential of MLCP, network simulations have been carried out in diverse network conditions. MLCP has shown up to 90% increase in the network lifetime and an improvement of 19.36% in conservation of energy in comparison with the competent protocols. The comparison of obtained results with state-of-the-art clustering protocols clearly establishes the superiority of MLCP in achieving load-balanced, scalable and energy-efficient clustering.

An Improved Watermarking Scheme for Secure Data Aggregation in WSNs

Digital watermarking technology has already been used for secure data aggregation in wireless sensor networks. However, most of the existing watermarking technologies are irreversible, and the embedded watermark will cause some damage to the data collected by sensors. In this paper, we proposed a novel distributed reversible watermarking technology based on an improved difference expansion scheme. Due to the hierarchical clustering architecture organized according to LEACH protocol, a distributed self-organized mechanism is established within clusters for the watermark generation. The whole watermark is split into several segments and the watermark segments are assigned randomly to different sensors within a group. In addition, a pair of synchronized random sequence generators is applied on both the sensors and the sink to fresh the data.

Group negotiation-based digital watermarking for wireless sensor network

Traditional digital watermarking cannot be applied directly in Wireless Sensor Network(WSN)that has special features,such as collecting data in distributed sub-clusters and processing data in-network.According to the hierarchical clustering architecture of WSN,a distributed consultative mechanism was established in clusters to generate the proper watermarking;thus,a distributed watermark algorithm was proposed.Sensor nodes of each group were embedded with watermarks with low computational complexity.The watermarks have shown the superiority of resistance to the attacks and the robustness of the lossy data compression.The results of analysis and experiments show that the watermarking proposed is easy to be checked.It can successfully identify whether data had been illegally tampered and thus effectively protect the security of WSN.

Criticality of spreading dynamics in hierarchical cluster networks without inhibition

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The nerve fibre network of the mammalian cerebral cortex possesses a modular organization extending across several levels of organization. Using a basic spreading model without inhibition, we investigated how functional activations of nodes propagate through such a hierarchically clustered network. The simulations demonstrated that persistent and scalable activations could be produced in clustered networks, but not in random networks of the same size. Moreover, the parameter range yielding critical activations was substantially larger in hierarchical cluster networks than in small-world networks of the same size. These findings indicate that a hierarchical cluster architecture may provide the structural basis for the stable and diverse functional patterns observed in cortical networks.

Hierarchical Cluster Routing in Mobile Multihop Networks

Mobile multihop networks are collections of entirely mobile nodes that have no fixed infrastructure or central controlling mechanism for routing or location management. In this paper, routing protocols based on a hierarchical clustering architecture are proposed. The basic idea is to partition the network into clusters and define routing mechanisms within and between clusters using a hierarchical architecture. The main advantage of this architecture is efficient routing and reduced communication and computational overhead. Simulation experiments indicated that the proposed hierarchical cluster-based routing architecture performs well in relatively low mobile networking environment. 1 Lecturer, Department of Computer Sciences, Faculty of Science, University of Swaziland, P/B 4, Kwaluseni, Swaziland

Multiview access protocols for large-scale replication

The article proposes a scalable protocol for replication management in large-scale replicated systems. The protocol organizes sites and data replicas into a tree-structured, hierarchical cluster architecture. The basic idea of the protocol is to accomplish the complex task of updating replicated data with a very large number of replicas by a set of related but independently committed transactions. Each transaction is responsible for updating replicas in exactly one cluster and invoking additional transactions for member clusters. Primary copies (one from each cluster) are updated by a cross-cluster transaction. Then each cluster is independently updated by a separate transaction. This decoupled update propagation process results in possible multiple views of replicated data in a cluster. Compared to other replicated data management protocols, the proposed protocol has several unique advantages. First, thanks to a smaller number of replicas each transaction needs to atomically update in a cluster, the protocol significantly reduces the transaction abort rate, which tends to soar in large transactional systems. Second, the protocol improves user-level transaction response time as top-level update transactions are allowed to commit before all replicas have been updated. Third, read-only queries have the flexibility to see database views of different degrees of consistency and data currency. This ranges from global, most up to date, and consistent views, to local, consistent, but potentially old views, to local, nearest to users but potentially inconsistent views. Fourth, the protocol maintains its scalability by allowing dynamic system reconfiguration as it grows by splitting a cluster into two or more smaller ones. Fifth, autonomy of the clusters is preserved as no specific protocol is required to update replicas within the same cluster. Clusters are, therefore, free to use any valid replication or concurrency control protocols.

Image restoration by a neural network with hierarchical cluster architecture

A neural network approach for image restoration is presented. The proposed method is based on a neural network with hierarchical cluster architecture (NNHCA), one of the recently emerged neural networks with sophisticated architectures. The method is motivated by the universally accepted concept in digital image processing that natural image formation is a local process. Therefore, the inverse problem of image restoration can be expressed by a globally coordinated local parallel processing (GCLPP) model. The GCLPP model can be readily realized by NNHCA. By utilizing the symmetric positive-definite quadratic structure of the formulation, a model-based local neuron evaluation algorithm is proposed. The algorithm significantly increases the convergence speed of restoration compared with previously proposed neural computing methods. A coordination scheme is also introduced to systematically resolve conflicting boundary conditions in the problem formulation. Visual examples are given to demonstrate that the proposed method not only produces good restoration results, but also provides a genuine parallel processing structure that ensures computationally feasible space domain image restoration.