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This paper aims to present a framework to address the impact of people’s behaviour in the dissemination of information through mobile social networks.

This approach follows the epidemical compartmental models and uses a grid to model the nodes’ (people) behaviour in the dissemination process. The nodes’ status is determined by binary rules that update and define the flow of information between neighbour nodes. An improved stacked-layer grid model is used to implement modulations in the application of the rules and neighbourhoods to model the impact of people’s attitude, which may improve or jeopardize the efficiency of the process.

This proposal shows how grid architecture is a valuable tool to model different causes of malfunction of data dissemination. Combining different grids with different neighbourhoods and different local rules provides a wide range of possibilities to depict the impact of human awareness and decision on the dissemination of data.

This works develops a new approach for the analysis of dissemination of information which add new features to traditional methods for modelling local interactions and describing the dynamics of the communication patterns in the population.

Security is one of the major challenges in the design and implementation of protocols for mobile ad hoc networks (MANETs). In such systems, the cooperation between nodes is one of the important principles being followed in the current research works to formulate various security protocols. Many existing works assume that mobile nodes will follow prescribed protocols without deviation. However, this is not always the case, because these networks are subjected to a variety of malicious attacks. Since there are various models of attack, trust routing scheme can guarantee security and trust of the network. The purpose of this paper is to propose a novel trusted routing model for mitigating attacks in MANETs.

The proposed model incorporates the concept of trust into the MANETs and applies grey relational analysis theory combined with fuzzy sets to calculate a node’s trust level based on observations from neighbour nodes’ trust level, these trust levels are then used in the routing decision-making process.

In order to prove the applicability of the proposed solution, extensive experiments were conducted to evaluate the efficiency of the proposed model, aiming at improving the network interaction quality, malicious node mitigation and enhancements of the system’s security.

The proposed solution in this paper is a new approach combining the fundamental basics of fuzzy sets with the grey theory, where establishment of trust relationships among participating nodes is critical in order to enable collaborative optimisation of system metrics. Experimental results indicate that the proposed method is useful for reducing the effects of malicious nodes and for the enhancements of system’s security.

A mobile ad hoc network (MANET) enables providers and customers to communicate without a fixed infrastructure. Databases are extended on MANETs to have easy data access and update. As the energy and mobility limitations of both servers and clients affect the availability of data in MANETs, these data are replicated. The purpose of this paper is to provide a literature review of data replication issues and classify the available strategies based on the issues they addressed.

The selected articles are reviewed based on the defined criteria. Also, the differences, the advantages and disadvantages of these techniques are described. The methods in the literature can be categorized into three groups, including cluster-based, location-based and group-based mechanisms.

High flexibility and data consistency are the features of cluster-based mechanisms. The location-based mechanisms are also appropriate for replica allocation, and they mostly have low network traffic and delay. Also, the group-based mechanism has high data accessibility compared to other mechanisms. Data accessibility and time have got more attention to data replication techniques. Scalability as an important parameter must be considered more in the future. The reduction of storage cost in MANETs is the main goal of data replication. Researchers have to consider the cost parameter when another parameter will be influenced.

Data replication in MANETs has been covered in different available sources such as Web pages, technical reports, academic publications and editorial notes. The articles published in national journals and conferences are ignored in this study. This study includes articles from academic main international journals to get the best capability.

The paper reviews the past and the state-of-the-art mechanisms in data replication in MANET. Exclusively, data replication’s main goal, existing challenges, research terminologies and mechanisms in MANET are summarized using the answers to the research questions. This method will help researchers in the future to develop more effective data replication method in MANET.

Any kind of production flow is obtained not from individual production organizations but from a more or less widespread Production Network of interconnected production modules located in different places and times. All of these modules are, consciously or not, necessarily connected, interacting and cooperating in a coordinated way to combine and arrange, step by step, the factors, materials, components, manpower, machines and equipment to obtain flows of products’ final goods, in particular’ and to sell these where there is a demand for them. The purpose of this paper is to determine, in logical and formal terms, the minimum conditions that bring about the formation of production networks and to discover the laws that explain their dynamics over time.

At the global level, the continuous and accelerated economic progress of mankind is witnessed. There is an increase in the quantity and quality of satisfied and yet to be satisfied needs, of attained and yet to be attained aspirations. The increase in productivity and in quality has become unstoppable and appears to guide the other variables in the system. It is natural to ask who produces and governs these phenomena. It does indeed seem there is a Ghost in the “Production” machine whose invisible hand produces growing levels of productivity and quality, increases the quality and quantity of satisfied needs and aspirations and reduces the burden of work, thus producing increasingly higher levels of progress in the entire economic system. This conceptual framework gives a simple answer: there is nothing metaphysical about this evolution towards unstoppable and irreversible progress, and it is produced by the spontaneous genesis and activity of selfish nodes and governed by the rules and laws of the production networks.

The author has identified ten “rules of selfish behavior” on the part of the nodes, whose application necessarily and inevitably produces three evolutionary dynamic processes “which refer to the network as an entity” which the author has called the “rules of the production networks” to emphasize their cogency: continual expansion, elasticity-resiliency and continual improvement in performance. The cognitive and creative processes that characterize the nodes do not allow us to predict the actual evolution of production networks; nevertheless, if it is assumed that nodes “consciously or not” follow the 10 “rules of selfish behavior”, then several typical trends, or behavioural schema, can be deduced which the author has called as the “laws of networks”, to highlight their apparent inevitability and cogency.

More than any other structure, Production Networks display Holland’s features and Arthur’s properties as their modules, viewed as autonomous entities with cognitive functions, represent a collectivity of agents that interact and exchange information with their environment to maintain over time their internal processes through adaptation, self-preservation, evolution and cognition, making individual and collective decisions as part of a network of micro behaviours.

This new conception of production through production networks, which takes into account the “rules” and “laws” regulating their behaviour, also sheds new light on the development of networks and their natural tendency to become globalized.

Although the concept of a network is becoming more popular in economic and business studies, it is yet to see an interpretation of production as deriving exclusively from the actions of increasingly larger networks. This paper presents an integrated view of production that does not discard the notion that production is carried out by organizations and companies but introduces the broader concept of the integration among organizations, which must be interpreted as nodes of a broader network that produces the flows of all the components needed to obtain the flow of a specific product. This represents an innovative view that will help us in understanding the difficulties policymakers encounter in governing production and controlling the basic variables that characterize it, specifically productivity, quality, quantity, prices and value. This perspective also allows to derive rules and laws for the behaviour of production networks that appear to be cogent and unvarying over time.

The purpose of this paper is to ensure power efficiency in wireless sensor networks (WSNs) through a new framework-oriented middleware, based on a biologically inspired mechanism that uses an evolutionary multi-objective optimization algorithm. The authors call this middleware framework multi-objective optimization for wireless sensor networks (MONet).

In MONet, the middleware level of each network node autonomously adjusts its routing parameters according to dynamic network conditions and seeks optimal trade-offs among performance objectives for a balance of its global performance. MONet controls the cooperation between agents (network nodes) while varying transmission paths to reduce and distribute power consumption equitably on all the sensor nodes of network. MONet-runtime uses a modified TinyDDS middleware platform.

Simulation results confirm that MONet allows power efficiency to WSN nodes while adapting their sleep periods and self-heal false-positive sensor data.

The framework implementation is lightweight and efficient enough to run on resource-limited nodes such as sensor nodes.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

The purpose of this paper is to propose the use of priority‐based incentives for collaborative hiding of confidential information in dynamic environments, such as self‐organized networks, peer‐to‐peer systems, pervasive and grid computing applications.

The paper documents the necessity of ISSON (Incentives for Secret‐sharing in Self‐Organised Networks); it provides functional and technical details on the proposed architecture; and, it assesses its feasibility in mobile ad‐hoc networks through real experiments. The paper elaborates on the availability of the hidden information through an analytical framework.

Through the real experiments, ISSON was found to be efficient in terms of communication and processing costs. Additionally, it avoids collusions for unauthorized revealing of the hidden information, and ensures the unlinkability and availability of the secret when it is divided and stored to peers.

The proposed, incentive‐based, privacy enforcement architecture is novel and applies to distributed, dynamic, and self‐configured computing environments.

This paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET).

The new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust.

Based on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes.

The advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.

Ad hoc mobile networks are commonplace in every aspect of our everyday life. They become essential in many industries and have uses in logistics, science and the military. However, because they operate mostly in open spaces, they are exposed to a variety of dangers. The purpose of this study is to introduce a novel method for detecting the MAC layer misbehavior.

The proposed novel approach is based on exponential smoothing for throughput prediction to address this MAC layer misbehavior. The real and expected throughput are processed using an exponential smoothing algorithm to identify this attack, and if these metrics exhibit a trending pattern, an alarm is then sent.

The effect of the IEEE 802.11 MAC layer misbehavior on throughput was examined using the NS-2 network simulator, as well as the approval of our novel strategy. The authors have found that a smoothing factor value that is near to 0 provides a very accurate throughput forecast that takes into consideration the recent history of the updated values of the real value. As for the smoothing factor values that are near to 1, they are used to identify MAC layer misbehavior.

According to the authors’ modest knowledge, this new scheme has not been proposed in the state of the art for the detection of greedy behavior in mobile ad hoc networks.

Mobile ad hoc networks rely on cooperation to perform essential network mechanisms such as routing. Therefore, network performance depends to a great extent on giving participating nodes an incentive for cooperation. The level of trust among nodes is the most frequently used parameter for promoting cooperation in distributed systems. There are different models for representing trust, each of which is suited to a particular context and leads to different procedures for computing and propagating trust. The goal of this study is to analyze the most representative approaches for mobile ad hoc networks. It aims to obtain a qualitative comparison of the modeling approaches, according to the three basic components of a trust model: information gathering, information scoring and ranking, and action execution.

The paper identifies the different tasks required by a trust system and compares the way they are implemented when the system model itself is based on information theory, social networks, cluster concept, graph theory and game theory. It also provides a common nomenclature for the models. The study concentrates exclusively on the trust models themselves, without taking into account other aspects of the original articles that are beyond the scope of this analysis.

The study identifies the main components that a trust model must provide, and compares the way they are implemented. It finds that the lack of unity in the different proposed approaches makes it difficult to conduct an objective comparison. Finally, it also notices that most of the models do not properly manage node reintegration.

The best of our knowledge, the study is the first that uses information scoring and ranking as classification key. According to this key, approaches can be classified as based on information theory, clusters and social network theory, and cooperative and non‐cooperative game theory. It also provides a common nomenclature for all of them. Finally, the main contribution of the paper is to provide an analysis of the most representative approaches and present a novel qualitative comparison.