Clustering Overhead Research Articles

Cluster stability-driven optimization for enhanced routing in heterogeneous vehicular networks

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Most of the existing clustering protocols are optimized for highways only and some protocols are optimized for intersections only. The clusters that are created by the protocols which are optimized for highways breakdown at the intersections where clustering is needed more. On the other hand, the clusters that are created by the protocols optimized for intersections create extra clusters or break unnecessary on straight-roads which reduces cluster stability. Moreover, the lack of intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, and movement at the intersection, also contributes to cluster stability problems. A dynamic clustering protocol that efficiently combines all the mobility parameters can resolve these issues in VANETs by providing stability in both intersections and highways. To achieve high stability in VANET clustering, a Stable Dynamic feedback-based Predictive Clustering (SDPC) protocol is proposed for VANET to ensure cluster stability in both highway and intersection scenarios. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future position of the vehicles, relative distance between vehicles, movement of vehicles at the intersection, degree of vehicles, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering protocols in various road topologies, in terms of the average cluster head change rate, the average duration of cluster head, the average duration of cluster member, and the average clustering overhead. The simulation results show that SDPC outperforms the existing protocols, achieving higher clustering stability in terms of cluster head change rate (50%), cluster head duration (15%), cluster member duration (6%), and clustering overhead (35%).

Revisiting Unsupervised Local Descriptor Learning

Constructing accurate training tuples is crucial for unsupervised local descriptor learning, yet challenging due to the absence of patch labels. The state-of-the-art approach constructs tuples with heuristic rules, which struggle to precisely depict real-world patch transformations, in spite of enabling fast model convergence. A possible solution to alleviate the problem is the clustering-based approach, which can capture realistic patch variations and learn more accurate class decision boundaries, but suffers from slow model convergence. This paper presents HybridDesc, an unsupervised approach that learns powerful local descriptor models with fast convergence speed by combining the rule-based and clustering-based approaches to construct training tuples. In addition, HybridDesc also contributes two concrete enhancing mechanisms: (1) a Differentiable Hyperparameter Search (DHS) strategy to find the optimal hyperparameter setting of the rule-based approach so as to provide accurate prior for the clustering-based approach, (2) an On-Demand Clustering (ODC) method to reduce the clustering overhead of the clustering-based approach without eroding its advantage. Extensive experimental results show that HybridDesc can efficiently learn local descriptors that surpass existing unsupervised local descriptors and even rival competitive supervised ones.

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE– a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs

Prior study suggests that VANET has two types of communications: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. V2V is very important and ensures cooperative communications between vehicles and safety measures. It is also defined as Inter-Vehicle Communication (IVC).The communication is based on clustering the nodes to transmit the data from vehicle to vehicle. The overhead and stability are considered as main challenges that need to be addressed during vehicle intersections. In this paper, a novel self-adaptable Angular based k-medoid Clustering Scheme (SAACS) is proposed to form flexible clusters. The clusters are formed by estimating the road length and transmission ranges to minimize the network delay. And the Cluster Head (CH) is elected from a novel performance metric, ‘cosine-based node uncoupling frequency,’ that finds the best nodes irrespective of their current network statistics. The parametric analysis varies according to the number of vehicular nodes with the transmission range. The experimental results have proven that the proposed technique serves better in comparison to existing approaches such as Cluster Head Lifetime (CHL), Cluster Member Lifetime (CML), Cluster Number (CL), Cluster Overhead (CO), Packet Loss Ratio (PLR) and Average Packet Delay (APD). CHL is enhanced 40% as compare to Real-Time Vehicular Communication (RTVC), Efficient Cluster Head Selection (ECHS) whereas CML is 50% better than RTVC and ECHS. Packet loss ratio and overhead is 45% better in our proposed algorithm than RTVC and ECHS. It is observed from the results that the incorporation of cosine-based node uncoupling frequency has minimized the incongruity between vehicular nodes placed in dense and sparse zones of highways.

Enhanced Load Balanced Clustering Technique for VANET Using Location Aware Genetic Algorithm

The vehicular Adhoc Network has unique charac-teristics of frequent topology changes, traffic rule-based node movement, and speculative travel pattern. It leads to stochastic unstable nature in forming clusters. The re-liable routing process and load balancing are essential to improve the network lifetime. Cluster formation is used to split the network topology into small structures. The reduced size network leads to accumulating the topology information quickly. Due to the absence of centralised management, there is a pitfall in network topology man-agement and optimal resource allocation, resulting in ineffective routing. Hence, it is necessary to develop an effective clustering algorithm for VANET. In this paper, the Genetic Algorithm (GA) and Dynamic Programming (DP) are used in designing load-balanced clusters. The proposed Angular Zone Augmented Elitism-Based Im-migrants GA (AZEIGA) used elitism-based immigrants GA to deal with the population and DP to store the out-come of old environments. AZEIGA ensures clustering of load-balanced nodes, which prolongs the network lifetime. Experimental results show that AZEIGA works appreciably well in homogeneous resource class VANET. The simulation proves that AZEIGA gave better perfor-mance in packet delivery, network lifetime, average de-lay, routing, and clustering overhead.

Multi-Criterion Partial Clustering Algorithm for Wireless Sensor Networks

Cluster architectures are an effective approach for organizing sensor networks to balance the load and prolong network life. To cluster wireless sensor networks, this paper proposes an energy-efficient distributed algorithm. This algorithm uses two techniques (partial clustering and multi-criterion cluster formation) for efficient use of the sensor nodes’ energy. When a header expends a certain amount of power, it only notifies the nodes in its cluster that new clustering is required in the next round. Therefore, in contrast to previous studies that performed complete clustering, clustering in the present work is performed partially, which considerably reduces the clustering overhead. In addition, a multi-criterion score is calculated for each node. In this algorithm, a node with the highest remaining energy and score is a more suitable candidate to be selected as the head of the cluster. In addition, a regular node becomes the member of the cluster with the highest score in its vicinity. The experiments reveal the superiority of the proposed algorithm over other simulated algorithms in terms of energy savings and network lifetime.

Estimating the overhead and coupling of scientific computing clusters

Since simulation became the third pillar of scientific research, several forms of computers have become available to drive computer aided simulations, and nowadays, clusters are the most popular type of computers supporting these tasks. For instance, cluster settings, such as the so-called supercomputers, cluster of workstations (COW), cluster of desktops (COD), and cluster of virtual machines (COV) have been considered in literature to embrace a variety of scientific applications. However, those scientific applications categorized as high-performance computing (HPC) are conceptually restricted to be addressed only by supercomputers. In this aspect, we introduce the notions of cluster overhead and cluster coupling to assess the capacity of non-HPC systems to handle HPC applications. We also compare the cluster overhead with an existing measure of overhead in computing systems, the total parallel overhead, to explain the correctness of our methodology. The evaluation of capacity considers the seven dwarfs of scientific computing, which are well-known, scientific computing building blocks considered in the development of HPC applications. The evaluation of these building blocks provides insights regarding the strengths and weaknesses of non-HPC systems to deal with future HPC applications developed with one or a combination of these algorithmic building blocks.

An efficient hybrid query recommendation using shingling and hashing techniques

Search engines recommend queries to improve the satisfaction level of users by shortening their search task. A proper solution for query recommendation is to analyze the users’ behaviors and mimic the query transition patterns adopted by different users who are succeeded in finding their needed information. In this paper, we propose a novel three-layer query recommendation method which is benefited from a query community graph in the first layer. This graph is generated through clustering similar queries which tend to convey the same meaning. To reduce the overhead of clustering while preserving its performance, we utilize locality-sensitive hashing of k-shingles to represent queries in a space with smaller and fixed dimensions. The second layer is enriched by a query-flow graph which models the transitional patterns made by users inside sessions. The hybrid graph, created by consolidating the query community and query-flow graphs, takes into account the lexical similarity as well as the reformulation diversity to suggest queries. The results of our experiments on data logs of two real search engines show that the proposed method outperforms some well-known algorithms by at least 14% with respect to precision and P@10 parameters.

A Power-Efficient CNN Accelerator With Similar Feature Skipping for Face Recognition in Mobile Devices

A low power face recognition (FR) convolutional neural network (CNN) processor is proposed with high power efficiency to achieve always-on FR in mobile devices. Three key features enable a power-efficient FR CNN. First, tile-based clustering (THC) is proposed for reducing the computation overhead of hierarchical clustering. It generates an average of 37.2% duplicated input features in the entire network. Second, a low latency tile-based hierarchical clustering core is proposed. It supports an approximated clustering method that removes distance updates and increases pipeline utilization by up to 98.6%. Finally, a similar feature skipping binary convolution core with separated accumulation cores is proposed to increase power efficiency through skipping redundant MAC operation between duplicated input features and weights. It can skip 38.9% of CNN operations and reduce 35.8% of the total computation considering both CNN and THC. The proposed features enable the proposed processor to operate FR CNN with 17.3 TOPS/W power efficiency, which is $1.3\times $ higher compared to the previous state-of-the-art FR CNN processor. Implemented in 65nm CMOS technology, the 6mm2 FR CNN processor shows 0.5mW power consumption at 1 frames-per-second always-on face recognition.

Enhancing the LTE-based intelligent transportation system's performance

Intelligent transportation system is considered as one of the main features of the new generation wireless systems, where both of high speed data transmission and processing play a crucial role. This work presents two propositions in order to attain the performance improvement for both of data transmission and processing speed. Thus, the presented work consists of deriving a clustering algorithm based on a weighting algorithm for the head assignments processes, and emphasizing the parallel-processing technique based on variety wavelet baby functions, respectively. Accordingly, both of the data transmission speed and power will be examined.In order to verify the findings, a simulation has been done and compared with the following clustering algorithms; namely DMAC, PC, DBC, and Lower-ID DCA. This comparison is based on the following factors; namely efficiency factors involved in this investigation; namely complementary cumulative distributions, bit rates, energy efficiency, the cluster head life time and the ordinary nodes reattaching-head average times. The depicted results for the cluster head duration at 20 km/s show a remarkable system stability based on both the clustering overhead, and the cluster head duration. The attained improvements reach the 53% and 88.4% over the DBC work and the Lowest-ID DCA work, respectively.

Load Balanced Energy Efficient Cluster-chain Based Hybrid Protocol for Wireless Sensor Networks

In this paper a novel geographical multilayer protocol named Cluster-chain Based Hybrid (CCBH) Protocol is proposed for proper load balancing across the network that enhance the network lifespan and eliminate the energy holes problem. The CCBH protocol divides the network into the multilayer square structure around the sink. Each layer is divided into to the zones in such a way that the zones near to the sink are smaller in size and size of zones increases as the separation from the sink increases. In inner two layers, each zone has a cluster head (CH) and to reduce the load of CH a leader node (LN) is assigned in every zone. LN collects and aggregates the data received from neighboring nodes and sends it to the associated CH. Outer layer zones are larger in size. To reduce the clustering overhead chain strategy is introduced in outer layer zones that ensure lesser energy consumption as compared to clustering. Multi hop communiqué is used, where data is transferred from upper zone’s CH to immediate lower zone’s CH until it reaches to the sink. Simulated tests demonstrate that proposed CCBH protocol shows evident improvement in terms of the network lifetime as compare to LBCN, LEACH, TCAC, and DSBCA protocols.

Comparative study of clustering algorithms for MANETs

Clustering algorithm in Mobile Ad Hoc Networks is considered as the suitable technique of routing. Endurance of clusterhead in a cluster for data sending improves the working in terms of established cluster composition, concentrated routing overhead and some degree of power utilization. On the other hand, due to the movement of clusterhead, common changes in clusterhead increases clustering overhead in terms of re-election of clusterhead within the current cluster. The constancy of the cluster extremely dependents on constancy of the clusterhead and hence extraordinary care should be taken during clusterhead selection and maintenance phase so that an efficient stable clusterhead is elected for longer time. In this manuscript we have carried out performance analysis of Novel Weight Based Clustering algorithm (WBC), An Optimized Stable Clustering Algorithm (OSCA) and PBWCA (Priority Based Weighted Clustering Algorithm) clustering algorithms first and foremost paying attention on reducing frequent clusterhead changing procedure. These algorithms help in reducing the frequent clusterhead changes, communication overhead and increase of broadcast in the network. The algorithm works on node degree and battery power of mobile nodes to calculate node priority. PB-WCA also focuses on reducing the cluster reformation, clusterhead re-selection and clusterhead changes.

A Reputation based Weighted Clustering Protocol in VANET: A Multi-objective Firefly Approach

Vehicular Ad hoc NETworks (VANETs) possess a dominant role in the development of Intelligent Transport Systems (ITS). VANETs, due to the rapid mobility of vehicles are a highly dynamic network. In order to make the network topology suitable for effective communication, clustering algorithms are widely used. Clustering algorithms enable VANET to efficiently handle the changing topology for Medium Access Control (MAC), routing and several other applications. In this work we put forward a Reputation based Weighted Clustering protocol (RWCP) for VANETs. The RWCP is framed by taking the direction of vehicles, position, velocity, number of nearby vehicles, lane ID, and the reputation of each node into consideration for stabilizing the VANET topology. On the other hand, dealing with diverse control parameters of RWCP makes optimizing a challenging task. The work employs a multi-objective problem which takes the RWCP’s parameters as the input and aims at providing enhanced cluster lifetime, Improved packet delivery ratio and reduced cluster overhead. Multi Objective Firefly Algorithm (MOFA), an evolutionary approach is used for optimizing the RWCP’s parameters. Simulations were done using the TETCOS NetSim simulator and MOEA framework for optimization. The results are evaluated with similar evolutionary optimization techniques. Experiments were conducted with realistic maps from OpenStreet Maps and its results were compared with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Comprehensive learning Particle Swarm Optimization (CL-PSO). The investigation proposes that, the proposed methodology performs well concerning the Mean Cluster Lifetime, Packet Delivery Ratio and Control Packer Overhead.

Optimized clustering in vehicular ad hoc networks based on honey bee and genetic algorithm for internet of things

In vehicular ad hoc network (VANET), the size of routing table can be reduced with the help of clustering architecture. The frequent changes in topology are the noteworthy characteristics of a VANET as its nature is dynamic. To manage the topology dynamics in VANET with less overhead, the concept of clustering can be used. Henceforth, an effective procedure that adjusts quickly to the topology changes should be designed. Firstly, the clustering problem (CP) in VANET is formulated into a dynamic optimization problem in this paper. Secondly, an optimization algorithm named Vehicular Genetic Bee Clustering (VGBC) based on honey bee algorithm and properties of genetic algorithm solves the CP in VANETs is suggested. In VGBC, individuals (bees) represent a realistic clustering structure and its fitness is measured on the basis of load balancing and stability. A technique that merges the properties of genetic algorithm and honey bee algorithm is proposed. It helps the population to handle the topology changes and harvest high quality solutions. The simulation results piloted for justification demonstrate that the VGBC form steady and balanced clusters. The simulation results are matched with state of the art clustering schemes in VANET. The VGBC outperform existing schemes in terms of cluster count, cluster duration, re-affiliation rate, computational overhead, load balancing, VANET lifetime and clustering overhead.

Energy and Network Balanced Distributed Clustering in Wireless Sensor Network

Balancing energy consumption of sensor nodes to extend the network lifetime is a major concern for the energy constrained wireless sensor network. Improper load balance and disproportionate energy consumption of sensor nodes during network activities such as transmitting and receiving of data cause energy hole problem and shorten the lifetime of the network. Many research works cited that clustering mechanism and the use of mobile sink can be effective to mitigate the energy hole problem and can improve the network lifetime. But using a mobile sink causes extra delay which is a major concern if the network is delay bound. In this work, we give deep insight into the problem of disproportionate energy consumption and aim to improve the network load balance and increase the network lifetime by applying efficient distributed clustering method with the help of a mobile sink. The proposed scheme named Energy Balanced Distributed Clustering Protocol (EBDCP) guarantees to transmit the sensed data to the base station within the tour deadline with the aid of a mobile sink. For this purpose, an efficient sojourn point determination algorithm has also been proposed. The simulation results prove that the proposed scheme performs significantly better than the existing works in terms of the energy distribution in the network, clustering overhead, residual energy of the network, number of alive nodes and network lifetime.

A priority-based weighted clustering algorithm for mobile ad hoc network

One major problem in clustering is the setback involved in cluster formation, cluster maintenance and in the selection of a suitable cluster head. In this paper we have proposed a priority-based weighted clustering algorithm for MANETs which works on three approaches to reduce cluster head changes and clustering overhead. The first phase helps to dynamically determine the cluster head based on their initial priority of neighbour nodes within cluster. The second and third phase helps in cluster maintenance and selecting the new cluster head automatically without any delay when remaining battery power of old cluster head reaches up to the minimum threshold value or in the absence of cluster head. From the simulation result, it is clear that the proposed algorithm reduces the frequent cluster head changes, communication overheads and also improves the efficiency of transmission in the network. Priority of each mobile node is calculated based on node degree and battery power.

A priority-based weighted clustering algorithm for mobile ad hoc network

One major problem in clustering is the setback involved in cluster formation, cluster maintenance and in the selection of a suitable cluster head. In this paper we have proposed a priority-based weighted clustering algorithm for MANETs which works on three approaches to reduce cluster head changes and clustering overhead. The first phase helps to dynamically determine the cluster head based on their initial priority of neighbour nodes within cluster. The second and third phase helps in cluster maintenance and selecting the new cluster head automatically without any delay when remaining battery power of old cluster head reaches up to the minimum threshold value or in the absence of cluster head. From the simulation result, it is clear that the proposed algorithm reduces the frequent cluster head changes, communication overheads and also improves the efficiency of transmission in the network. Priority of each mobile node is calculated based on node degree and battery power.

A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm

In mobile adhoc networks for internet of things, the size of routing table can be reduced with the help of clustering structure. The dynamic nature of MANETs and its complexity make it a type of network with high topology changes. To reduce the topology maintenance overhead, the cluster based structure may be used. Hence, it is highly desirable to design an algorithm that adopts quickly to topology dynamics and form balanced and stable clusters. In this article, the formulation of clustering problem is carried out initially. Later, an algorithm on the basis of honey bee algorithm, genetic algorithm and tabu search (GBTC) for internet of things is proposed. In this algorithm, the individual (bee) represents a possbile clustering structure and its fitness is evaluated on the basis of its stability and load balancing. A method is presented by merging the properties of honey bee and genetic algorithms to help the population to cope with the topology dynamics and produce top quality solutions that are closely related to each other. The simulation results conducted for validation show that the proposed work forms balance and stable clusters. The simulation results are compared with algorithms that do not consider the dynamic optimization requirements. The GTBC outperform existing algorithms in terms of network lifetime and clustering overhead etc.

Possibility of cost reduction by mutant clustering according to the clustering scope

SummaryMutation testing offers developers a good way to improve the quality of a test set. However, the high cost of executing a large number of mutants remains an issue. This paper examines the possibility of reducing the cost of statement‐level mutant clustering by comparing the number of mutant executions with those of expression‐level and block‐level mutant clustering. The goal is to investigate to what extent the clustering scope should be extended. The experimental results using nine real‐world programs show that statement‐level clustering can reduce the mutant executions that are required by expression‐level clustering by 10.51% on average. Block‐level clustering exhibits an unexpected result; the number of mutant executions with block‐level clustering is only 1.06% times less than that with statement‐level clustering. That is, statement‐level clustering is more cost‐effective than block‐level clustering when considering their clustering overheads. A compound expression plays a major role in providing a cost‐reduction effect in statement‐level clustering. With a compound expression, the number of candidate mutants to be clustered in a statement scope increases, and state change can be comprehensively examined, thereby increasing the possibility of cost reduction. © 2018 John Wiley & Sons, Ltd.