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The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.

Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.

With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.

This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.

In the network strategy view, relative competitive advantage stems not merely from opportunity structures embedded in networks but also from the distribution of ability and motivation among firms. Thus, there is a need to “bring the firm back in” to the network strategy narrative. We demonstrate that a mixed-methods design, blending large-sample data with micro-data on specific firms and their networks, can increase our understanding of the interplay of network structure and actor mechanisms, thus bridging the chasm between theory and practice in network strategy. We believe this is a critical step toward the “strategic design of networks.”

The paper attempts to analyze the network structure of value activity in manufacturing clusters, propose the model of value creation of cluster's value activity network, and explore the inner mechanism and optimization strategies of value creation in manufacturing clusters from the perspective of cluster's value activity network.

This paper applies a genetic algorithm to optimally search in the target space, and repeatedly exerts genetic operation (select, cross, variation) on the population to explore the optimal configuration strategy between value creation activity and resource utilization. It also analyzes the relation between object function of value creation and relative parameters.

The total value created by value activity network was impacted by the degree of effective configuration between all kinds of resources and value activities; the total value created by value activity network is positively related to activity units' elasticity coefficient of value creation of human resource, material resources and relations resource, and is negatively correlated to cost coefficient of human resource, material resources and relations resource; when the cooperative relations between activity units create positive relationship profit, the total value created by value activity network increases with the increase of cooperative relations between activity units.

Enterprises in clusters should reasonably configure and incorporate the resource among value activities through adding, deleting or reconfiguring activities, which makes the value activities network create maximum value; enterprises can transform the type of activity units to increase elasticity coefficient of value creation of human resources, such as transforming production activities into the high value‐added activities; enterprises can optimally incorporate the technical, material resources and human resources among activities to increase value creation elastic coefficient of material resources; enterprises can decrease cost coefficient by maintaining the stability of long‐term cooperation with the suppliers and strengthening the cultivation of talents; enterprises can increase profits from relation resource or reduce cost coefficient of relationship by updating activities, building trust mechanism and communication mechanisms and establishing long‐term cooperation relationship to improve value creation activities.

This paper proposes the model of value creation from the perspective of cluster's value activity network, and applies a genetic algorithm to explore the optimal configuration strategies between value creation activity and resource utilization.

Routing protocols in wireless sensor networks (WSN) are a crucial challenge for which the goal is maximizing the system lifetime. Since the sensor nodes are with limited capabilities, these routing protocols should be simple, scalable, energy‐efficient, and robust to deal with a very large number of nodes, and also self‐configurable to node failures and changes of the network topology dynamically. The purpose of this paper is to present a new algorithm for cluster forming in WSN based on the node energy required to transmit to the base station.

Rotation selection of cluster‐head considering the remoteness of the nodes to the sink, and the network node residual energy.

The simulation results show that this algorithm allows network stability extension compared to the most known clustering algorithm.

Giving a probability to become cluster‐head based on the remoteness of the node to the sink.

The purpose of this paper is to find out the different types of business networks formed by firms with the stakeholders present in a cluster, i.e. how firms in a cluster interact with the cluster stakeholders?

To answer the research question, this study uses an exploratory research design, which is carried out in two stages, Stage 1 involves use of primary data, which was collected through semi-structured personal face-to-face interview mode and Stage 2 involves survey research method where data was collected through a survey questionnaire. Data for interviews and questionnaires were collected from managers and owners of firms operating in the cluster at their offices.

The study has identified four types of business networks between a firm and its buyers, only one type of business network with the suppliers and educational institutes, finally two types of business networks with government agencies and local associations. However, with respect to network with other stakeholders such as research institutes and competitors, the study shows that the interaction between a firm and these stakeholders is not strong i.e. the linkages between them remain largely unfilled.

The study has been limited to only one cluster thus it might not be appropriate to generalise the findings. Further research in this area needs to be done by taking other clusters to generalise the findings.

The study has tried to answer the research gap of lack of literature on types of business networks formed by firms with the stakeholders present in an industrial cluster, and thus, contributed to the existing literature of business networks. The identified business networks provide a much deeper understanding of how firms connect with its buyers, its suppliers, government agencies and educational institutes operating in an auto-component cluster.

Scalability is a fundamental problem in mobile ad hoc networks (MANETs), where network topology includes large number of nodes and demands a large number of packets in network that characterized by dynamic topologies, existence of bandwidth constrained, variable capacity links, energy constraint and nodes are highly prone to security threats. The key purpose of this paper is to overview the efficiency of the proposed clustering scheme for large-scale MANETs and its performance evaluation and especially in the case of a large number of nodes in the network.

Designing clustering schemes for MANETs, which are efficient and scalable in the case of large number of mobile nodes, has received a great attention in the last few years. It is widely used to improve resources management, hierarchical routing protocol design, quality of service, network performance parameters such as routing delay, bandwidth consumption, throughput and security. MANETs are characterized by limited wireless bandwidth, nodes mobility that results in a high frequency of failure regarding wireless links, energy constraint and nodes are highly prone to security threats. Due to all these features, the design of a scalable and efficient clustering scheme is quite complex. Many clustering schemes have been proposed to divide nodes into clusters, focusing on different metrics and purposes.

To the best of the author's knowledge, the different proposed clustering schemes are not scalable when the network size increases to a very large number. The paper presents the clustering scheme in detail and its performance evaluation by simulating MANETs composed of a large number of mobile nodes. The authors compare the performance of the scheme with a number of existing clustering schemes such as lowest-ID, highest degree, and weighted clustering algorithm, based on a number of performance metrics. Simulation results show that the scheme performs better than other clustering schemes, based on the performance metrics considered, for large-scale MANETs.

This paper addresses the problem of scalability in MANETs when there are high numbers of node in the network. The paper analyses the performance of the proposed clustering scheme for large-scale MANETs. The obtained results show that the different proposed clustering schemes do not allow the scalability when the network size is very large. The scheme supports scalability efficiently when the number of nodes increases in the network (more than 2,000 nodes).

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

In particular, this paper aims to systematically analyze a few prominent wireless sensor network (WSN) clustering routing protocols and compare these different approaches according to the taxonomy and several significant metrics.

In this paper, the authors have summarized recent research results on data routing in sensor networks and classified the approaches into four main categories, namely, data-centric, hierarchical, location-based and quality of service (QoS)-aware, and the authors have discussed the effect of node placement strategies on the operation and performance of WSNs.

Performance-controlled planned networks, where placement and routing must be intertwined and everything from delays to throughput to energy requirements is well-defined and relevant, is an interesting subject of current and future research. Real-time, deadline guarantees and their relationship with routing, mac-layer, duty-cycles and other protocol stack issues are interesting issues that would benefit from further research.

This paper aims to analyze the relationship between different kinds of networking and the performance of firms in industry clusters. In particular, it studies the importance of local embeddedness and external openness for product success in two wine clusters in Chile and Italy.

The analysis is based on original firm‐level data. A case‐study methodology is combined with econometric analysis.

The empirical analysis shows that local embeddedness positively influences the development of successful products, but with decreasing returns. More importantly, however, the author finds that external openness is more significant than local embeddedness for explaining firm success.

The paper has implications for managers not working in current “hot spots” who are keen to transform their environments into thriving economies. The author recommends that managers look beyond the local context and establish extra‐cluster linkages with relevant knowledge sources, which may vary from sector to sector. Managers should tap into local knowledge but avoid local over‐embeddedness.

The paper contributes to the understanding of how and whether networks influence the performance of cluster firms.

This research paper examines how hub firm transformation and restructuring of network partnerships shape the development of industrial clusters in China.

Questionnaire data were collected from 210 managers (response rate 70.9 percent) from the manufacturing industrial clusters in Eastern China.

The results inform that a cluster’s hub firm transformation influences the evolution of the cluster. Though the hub firm may possess transformation capabilities, the cluster is likely to be weakened if network partnerships and resource synergy are not formed amongst the cluster members.

This paper, in examining the individual- and firm-level attributes of orchestration capability and their interactions, sheds light on the firm level and inter-firm level relationships between resources and innovation in an industrial cluster.

To facilitate learning and the upgrading of firms within an industry cluster and promote a cluster’s innovation network, policymakers can initiate preferential policy measures to cultivate support to strategically transform a cluster’s hub firm, thus fostering cluster network growth.

The paper studies the evolution of clusters by investigating the hub firm transformation and member firm interaction. Focusing on the inter-firm network interactions lends a richer understanding of the nuances of the evolution of industrial clusters in Asia.