With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of ...this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.
Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of ...nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.
With the continuous renewal and rise of various flight equipment, this article designs a method of water data collection, which is realized through the coordination of an unmanned surface vehicle ...(USV) carrying unmanned aerial vehicles (UAVs, commonly called drones). In reality, multiple UAVs take-off and landing sites, energy consumption, and other complex issues closely related to USV recovery, UAVs must be considered. If mixed integer linear programming (MILP) is used directly, it is difficult to get satisfactory results in an acceptable time range. Therefore, a heuristic algorithm is proposed to solve the problem of data collection by these devices, which can not only quickly solve the large-scale collaborative optimization problem, but also rationally utilize the total energy consumption of the equipment. The effectiveness of the proposed algorithm is verified and analyzed by experimenting a different number of monitoring nodes, different number of UAVs and different task time. It can not only shorten the cycle of data acquisition task, but also reduce the waiting time of the UAV and USV.
Internet of things (IoT) monitoring systems have been extensively applied in smart homes, underwater monitoring, volcano monitoring, and health monitoring. In IoT applications, a wireless sensor ...network (WSN) is deployed to collect data. Hierarchical routing protocols that effectively maintain the energy consumed by sensor nodes (SNs) are usually employed in WSNs. Cluster heads (CHs) are important in this type of protocol. An effective fault tolerance mechanism for CHs in this system can guarantee reliable data acquisition. In this paper, a fault tolerance mechanism that combines CH static backup and dynamic timing monitoring (SBDTM) is proposed, a CH reliability model based on the Markov model is developed, and the minimum number of CHs necessary to satisfy the given reliability requirement is obtained. The data structures and fault-tolerant operations are described, and the energy consumption and the latency of the recovery of the SBDTM mechanism are quantitatively analysed. Simulations were carried out to compare the total network energy consumption, number of dead nodes, throughput, and packet loss rate of the proposed model with those of other methods presented in the literature. The simulation results show that the proposed SBDTM fault tolerance mechanism is superior to current models. This study presents important theoretical and application-based knowledge that can guarantee reliable data acquisition for IoT-based monitoring systems.
With the development of society and information technology, people’s dependence on the Internet has gradually increased, including online shopping, downloading files, reading books, and online ...banking. However, how to ensure the safety and legitimacy of these network user behaviors has become the focus of attention. As we all know, cybersecurity and system resilience originate from symmetry. Due to the diversity and unpredictability of cyber-attacks, absolute cybersecurity is difficult to achieve; system resilience indicates that protecting system security should shift from resisting attacks to ensuring system continuity. The trust evaluation of network users is a research hotspot in improving network system security. Aiming at the defects of incomplete evaluation processes and inaccurate evaluation results in current online user behavior trust evaluation methods, this paper combines the basic principles of online user trust evaluation and proposes a trust evaluation model that combines fuzzy Petri nets with user behavior analysis. First, for “unfamiliar” users, we used fuzzy Petri nets to calculate the user’s recommended trust value as the system’s indirect trust value; next, we used the user’s behavior record as evidence to conduct direct trust evaluation on the user to obtain the system’s direct trust in the user’s value; finally, the two calculation results were combined to obtain the user’s comprehensive trust value. In terms of experimental verification, the experimental data came from a self-developed e-book management system. Through theoretical analysis and simulation results, it was shown that the model met the optimization conditions of subjective and objective relative balance, the evaluation process was more complete, and the trust evaluation values of network users could be obtained more accurately. This evaluation method provides solid theory and research ideas for user credibility judgment of key network basic application platforms such as online shopping malls, online transactions, and online banking.
In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user ...information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user’s historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user’s behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user’s abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.
With the popularization of Internet of things (IoT), the interaction between human and IoT has become a daily life. In this interaction, the objects of IoT usually require access to personal data, ...which are often sensitive. We propose a lightweight privacy-preserving model based on the trust evaluation that it can effectively protect privacy based on simple threshold detection. The key issue we address in this work is how to construct trust model so that non trusted objects were prevented from accessing private data. This work can be considered as a lightweight approach to access control for privacy-preservation. The main algorithm in the proposed model is a kind of dynamic self-adjusting trust evaluation mechanism that uses a combination of interaction information occurs between the human and the Internet of things, between the human and the human. According to the given threshold, the trust model can determine the data level of object access in the IoT. We have implemented a prototype of the proposed scheme, thereby demonstrating the feasibility of the proposed scheme on resource-constrained devices.
Reputation evaluation plays an important role in determining the credibility of online entities. Especially in e-commerce systems, consumers usually give priority to this indicator when choosing ...vendors. Reputation bootstrapping, which can determine the default reputation value of new entities, is still a challenging problem. Most current bootstrapping methods fail to consider the complexity, the ambiguity of trust, or the reputation and internal correlation characteristics of influential factors in the prediction process. Therefore, in this paper, a novel reputation bootstrapping model that combines the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method with neural network prediction is established. First, we adopt a fuzzy multi-criteria decision-making model, named fuzzy DEMATEL method to discuss the intrinsic causal relationships between influential factors and identify the critical success factors (CSFs) for reputation estimation. Then, we adopt back propagation (BP) neural network to generalize the correlations between the CSFs and the initial reputation value. Finally, a case study is constructed to verify the proposed model. The experimental results indicate that the proposed model has better accuracy and efficiency compared with other reputation bootstrapping methods.
Due to the dynamics and uncertainty of the current network environment, access control is one of the most important factors in guaranteeing network information security. How to construct a scientific ...and accurate access control model is a current research focus. In actual access control mechanisms, users with high trust values bring better benefits, but the losses will also be greater once cheating access is adopted. A general access control game model that can reflect both trust and risk is established in this paper. First, we construct an access control game model with user behavior trust between the user and the service provider, in which the benefits and losses are quantified by using adaptive regulatory factors and the user’s trust level, which enhances the rationality of the policy making. Meanwhile, we present two kinds of solutions for the prisoner’s dilemma in the traditional access control game model without user behavior trust. Then, due to the vulnerability of trust, the user’s trust value is updated according to the interaction situation in the previous stage, which ensures that the updating of the user’s trust value can satisfy the “slow rising-fast falling” principle. Theoretical analysis and the simulation experiment both show that this model has a better performance than a traditional game model and can guarantee scientific decision-making in the access control mechanism.
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