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Load balancing is an effective enhancement to the proposed routing protocol, and the basic idea is to share traffic load among cluster members to reduce the dropping probability due to queue overflow at some nodes. This paper aims to propose a novel hierarchical approach called distributed energy efficient adaptive clustering protocol (DEACP) with data gathering, load-balancing and self-adaptation for wireless sensor network (WSN). The authors have proposed DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well and the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

A WSN consists of large number of wireless capable sensor devices working collaboratively to achieve a common objective. One or more sinks [or base stations (BS)] which collect data from all sensor devices. These sinks are the interface through which the WSN interacts with the outside world. Challenges in WSN arise in implementation of several services, and there are so many controllable and uncontrollable parameters (Chirihane, 2015) by which the implementation of WSN is affected, e.g. energy conservation. Clustering is an efficient way to reduce energy consumption and extend the life time of the network, by performing data aggregation and fusion to reduce the number of transmitted messages to the BS (Chirihane, 2015). Nodes of the network are organized into the clusters to process and forwarding the information, while lower energy nodes can be used to sense the target, and DEACP makes no assumptions on the size and the density of the network. The number of levels depends on the cluster range and the minimum energy path to the head. The proposed protocol reduces the number of dead nodes and the energy consumption, to extend the network lifetime. The rest of the paper is organized as follows: An overview of related work is given in Section 2. In Section 3, the authors propose an energy efficient level-based clustering routing protocol (DEACP). Simulations and results of experiments are discussed in Section 4. In Section 5, the authors conclude the work presented in this paper and the scope of further extension of this work.

The authors have proposed the DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well, the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.

In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.

So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.

Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster.

Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm.

Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm.

This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

The purpose of this paper is to propose an energy‐proportional routing (EPR) algorithm, which effectively extends the lifetimes of sensor networks.

The algorithm makes no specific assumption on network topology and hence is suitable for improving sensor networks with clustering. To optimally utilize energy, light‐load units – nodes or clusters that conserve energy are ideal candidates as intermediate units for forwarding data from others. To balance the load, first, the proposed algorithm predicts energy consumption of each node in each round. Then the algorithm controls the energy consumption of each unit as close as possible to the threshold representing the energy utilization mean value among clusters. Finally the algorithm checks satisfaction of the energy constraints in terms of distances and predicted data amounts. The proposed algorithm performs routing by determining whether a cluster head or a node should either undertake forwarding tasks or transmit data to intermediate hops. In this way, energy dissipation is evenly distributed to all units and the lifetime of the whole wireless sensor network is ultimately extended.

The algorithm applies hierarchically to different levels of network topology. In addition to experiments, the mathematical proofs of lifetime extension by the proposed routing algorithm are given in accordance with three widely accepted criteria – total energy dissipation, the number of live nodes in each round and the throughput (data amount per round).

A new routing algorithm is proposed.

The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found.

In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.

Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks.

The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.

This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.

The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.

For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.

The purpose of this paper is to study various combination forms of the three basic sharing elements (i.e. orders sharing, manufacturers capacity sharing and suppliers capacity sharing) in the cluster supply chain (CSC), formulate a distributed model to protect enterprises’ decision privacy and seek to develop an effective method for solving the distributed complex model.

A distributed assembly cluster supply chain configuration (ACSCC) model is formulated. An improved augmented Lagrangian coordination (ALC) is proposed and used to solve the ACSCC model. A series of experiments are conducted to validate the improved ALC and the model.

Two major findings are obtained. First, the market order’s quantity change and the sales price of the product have a great impact on both the optimal results of the ACSCC and the cooperative strategy, especially, when the market order increases sharply, enterprises have to adopt multiple cooperative strategies to complete the order; meanwhile, the lower sales price of the product helps independent suppliers to get more orders. Second, the efficiency and computational accuracy of the improved ALC method are validated as compared to the centralized ALC and Lingo11.

This paper formulated the single-period ACSCC model under certain assumptions, yet a multi-period ACSCC model is to be developed, a more comprehensive investigation of the relationships among combination forms is to be extended further and a rigid proof of the improved ALC is necessary.

Enterprises in the industrial cluster should adopt different cooperative strategies in terms of the market order’s quantity change and the sales price of the product.

The proposed various combination forms of sharing elements and the formulated ACSCC model provide guidance to managers in the industrial cluster to choose the proper policy.

This research studies various combination forms of the three basic sharing elements in the CSC. A distributed ACSCC model has been established considering simultaneously multiple sharing elements. An improved ALC is presented and applied to the ACSCC problem.

The purpose of this paper is to provide a novel training-responding controlling approach for human–robot interaction. The approach is inspired by the processes of muscle memory and conditioned reflex. The approach is significant for dealing with the problems of robot’s redundant movements and operator’s fatigue in human–robot interaction system.

This paper presented a directional double clustering algorithm (DDCA) to achieve the training process. The DDCA ensured that the initial clustering centers uniformly distributed in every desired cluster. A minimal resource allocation network was used to construct a memory responding algorithm (MRA). When the human–robot interaction system needed to carry out a task for more than one time, the desired movements of the robot were given by the MRA without repeated training. Experimentally demonstrated results showed the proposed training-responding controlling approach could successfully accomplish human–robot interaction tasks.

The training-responding controlling approach improved the robustness and reliability of the human–robot interaction system, which presented a novel controlling method for the operator.

This approach has significant commercial applications, as a means of controlling for human–robot interaction could serve to point to the desired target and arrive at the appointed positions in industrial and household environment.

This work presented a novel training-responding human-robot controlling method. The human-robot controlling method dealt with the problems of robot’s redundant movements and operator’s fatigue. To the authors’ knowledge, the working processes of muscle memory and conditioned reflex have not been reported to apply to human-robot controlling.

Recent wireless communication and electronics technology has enabled the development of low‐cost, low‐power, and multi‐functional sensor nodes. However, the fact that sensor nodes are severely energy‐constrained has been an issue and many energy‐efficient routing protocols have been proposed to resolve it. Cluster‐based routing protocol is one of them. To achieve longer lifetime, some cluster‐based routing protocols use information on GPS‐based location of each sensor node. However, because of high cost, not all sensor nodes can be GPS‐enabled. The purpose of this paper is to propose a simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks (WSN).

Instead of using location information of each sensor node, this approach utilizes information of remaining energy of each sensor node and changes in the number of cluster head nodes dependent on the number of sensor nodes alive. Performance results are presented and compared with some related protocols.

The simulations described in the paper show that both residual energy of each sensor node and changing cluster head nodes depending on the number of sensor nodes alive are very critical factors to obtain performance enhancement in terms of lifetime and data transmission. Especially, in some special environment, the proposal has better performance than GPS‐enabled protocol.

The paper is of value in proposing a simple dynamic clustering approach to achieve energy efficiency for WSN.