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In order to deepen the understanding of self-organization, the purpose of the paper is to raise and analyze the state of the art in the area of innovation networks, particularly the characteristics of self-organizing, relying on the theory of complex systems to overcome any shortcomings.

The databases selected for the search were Web of Science and Scopus; the keywords searched in the titles of articles were innovation networks, complex systems, self-organization and self-organizing; the timeline of the search covers the period between 2000 and 2014 due to the presence of important studies in the field of networks starting in the early 2000s; only studies published in English were used; the articles selected were examined by first reading the titles, then the abstracts, and finally the texts in full.

The way the main constructs from the analytical perspective of innovation networks intersect with complex systems explains how self-organization is presented and how it can be allowed to occur within a view of expected benefits for the purposes of these networks.

The originality of the research lies in the questioning of the classical form of organizational management in innovation networks, essentially based on the concentration of hierarchical power.

The aim of this paper is to explore the possibility of retrieving information with Kohonen self‐organising maps, which are known to be effective to group objects according to their similarity or dissimilarity.

After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self‐organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries.

Self‐organising maps ordered documents to groups from which it was possible to find relevant targets.

The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self‐organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop document searching in them.

With self‐organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size.

The paper reports on an approach that can be especially used to group documents and also for information search. So far self‐organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.

This research paper proposes that the conduct of supplier relationships can be improved through a “self‐organized logistical network”.

An analysis has been conducted on logistical supply chain which ensures to define a self‐organized logistical network. In such a network, each supplier can evaluate its own performance by using a decision‐making method involving multiple criteria. Indeed, such method is therefore recommended to reach a satisfactory solution. For that, a typology of performance was proposed and a multiple criteria method was chosen. Indeed, among several methods available, the Analytic Hierarchy Process (AHP) method has been chosen.

The paper suggests quantifying an evaluation of each potential supplier who responds to a call for proposal from a customer, according to rules and criteria that are impartial and common to all. The process enables the emergence of the “best” supplier. The proposed approach allows a balance to be achieved between load and capacity at the supplier level, and produces a smoothing of the load curve among the various suppliers with the long‐term objective of establishing a fair system among the suppliers on the network. Our approach suggests a customer‐supplier (C‐S) relationship control where all entities C‐S partners, communicate and negotiate to respond as best as possible to the customers requirements. To each supplier, we associate a decision‐making centre through which he can self evaluate his performance in order to be able to take part to negotiations within a self organized logistical network.

The research focuses particularly on the study of the decision‐making centre.

This study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.

The authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.

The study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.

The study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination

Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE.

A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2.

However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture.

The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level.

This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

A logit regression was applied and the prediction performance in two out‐of‐sample periods, 2007‐2009 and 2010 was examined. On the other hand, feed‐forwards neural networks with Levenberg‐Marquardt error backpropagation algorithm were applied and then neural networks self‐organizing map (SOM) on the training outputs was estimated.

The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011‐2012, if government does not apply direct drastic measures.

Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.

Reviews the suitability of different neural network architectures for use with typical multisensor systems required by their increasing use in complex engineering applications. Outlines the learning mechanisms that are required [to generate the transformation between the data at the input and the corresponding output] involving back‐propagation networks and self‐organising map networks. Looks at the three main problem areas of classification, quantification and descriptions and uses the case study of an electronic nose as a system which encounters each of these problems. Concludes that the combination of artificial neural networking tools with mutisensors is becoming more widely accepted and defines the need for the investigation of alternative supervised and unsupervised architecture if the true potential of multisensor systems is to be realized.

The purpose of this paper is to introduce a network perspective to the study of collective consumption. The authors examine the characteristics of heterogeneous consumption collectives formed around a Finnish footwear brand. The case is both theoretically and practically relevant. It differs from previous research by featuring consumer grassroot activities, face-to-face interaction and strong pre-existing social relationships.

Qualitative case study research was conducted with different methods of data generation including interviews, participant observation and cultural materials such as newspaper articles and photos.

A new concept of collective consumption network is introduced. Five kinds of consumption collectives are identified, including place focussed, brand focussed, activity focussed, idea focussed and social relations focussed consumption collectives. The strength of ties as well as the role of the brand varies within the collectives.

Suppliers should find an appropriate network position, where they can enable and support shared value creation. Developing skills to identify and cultivate weak links as well as mobilize resources are important.

The findings illustrate the heterogeneity and complexity of collective consumption. In particular, the paper discusses the way self-organizing and emergent consumption collectives and the supplier interact and integrate resources within the network.

The symbol‐based epistemology used in artificial intelligence is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the system mutually support each other. Gordon Pask’s entailment meshes are reviewed as a basic application of this approach, and then extended to entailment nets: directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring of the conceptual network. Semantic networks and frame‐like representations can be expressed in this scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative networks with weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext users. It is argued that such bootstrapping methods can be applied to make the World Wide Web more intelligent, allowing it to self‐organize and support inferences.

This paper aims to analyses the capital structure of the Spanish chemical industry during the period between 1999 and 2013, with a twofold objective. First, to determine whether the assumptions of pecking order theory are fulfilled throughout the study's timeframe. Second, by using data covering the years before the crisis and the worst years thereof, this study shows how the crisis has affected the capital structure of the companies included in this sample.

A particular kind of unsupervised neural network, self-organizing maps, is applied. This methodology allows to cluster firms avoiding the need to establish relationships between the different variables involved in the problem beforehand.

Companies are clustered into groups with different degrees of accomplishment of the pecking order theory. The hypothesis about risk is the one that experience a greater variation in the period before and after the crisis. Moreover, companies' capital structure has been lightly disrupted by the crisis.

The originality of this paper lies in applying an unprecedented methodology to the problem of capital structure. Therefore, the capital structure problem can be approached without setting any function relationship previously.