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Consensus algorithms are applied in the context of distributed computer systems to improve their fault tolerance. The explosive development of distributed ledger technology following the proposal of ‘Bitcoin’ led to a sharp increase in research activity in this area. Specifically, public and permissionless networks require robust leader selection strategies resistant to Sybil attacks in which malicious attackers present bogus identities to induce byzantine faults. Our goal is to analyse the entire breadth of works in this area systematically, thereby uncovering trends and research directions regarding Sybil attack resistance in today’s blockchain systems to benefit the designs of the future. Through a systematic literature review, we condense an immense set of research records (N = 21,799) to a relevant subset (N = 483). We categorise these mechanisms by their Sybil attack resistance characteristics, leader selection methodology, and incentive scheme. Mechanisms with strong Sybil attack resistance commonly adopt the principles underlying ‘Proof-of-Work’ or ‘Proof-of-Stake’ while mechanisms with limited resistance often use reputation systems or physical world linking. We find that only a few fundamental paradigms exist that can resist Sybil attacks in a permissionless setting but discover numerous innovative mechanisms that can deliver weaker protection in system scenarios with smaller attack surfaces.

Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA’s process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.

In recent years, a number of new, values-based, or ideological models focusing on leader behavior have been proposed. These models include authentic, servant, character-based, ethical, spiritual, and aesthetic leadership. In the present effort, we argue that these models, despite some differences in key dimensions, are tied together by a focus on moral behavior. The available evidence indicates that these models have only modest predictive power with respect to organizational performance criteria. More centrally, we argue that tests of these models are characterized by significant methodological problems with respect to both measurement and control. Moreover, these models suffer from some serious substantive concerns, including the explicit confounding of leadership and morality, discounting of system impacts, inappropriate assumptions about follower needs, and inappropriate scientific inferences. These models also fail to provide viable new approaches for leader development. We conclude that caution must be exercised when these models are employed as a basis for understanding leadership.

Promoting high-performing employees to leadership positions is a pervasive practice and has high face validity. However, little is known about the actual link between employee and subsequent leader performance as prior results are inconsistent. Given the prevalence of this performance-based promotion strategy, we conducted a study to address this inconsistency. To account for prior diverging results, we (a) competitively tested predictions from different theoretical perspectives (i.e., the performance requirements perspective, the follower-centric perspective, and the Theory of Expert Leadership), (b) considered possible changes in the predictive validity of this strategy over time, and (c) included job complexity as potential moderator of the link between employee and subsequent leader performance. In a high stakes context (i.e., the first German soccer league), we tested the predictive validity of employee performance for leader performance. Our results suggest a low validity of performance-based promotion, as we could not find evidence for a link between employee performance and leader performance—neither initially following the promotion nor over time, which is most in line with the performance requirements perspective. We, thus, caution against the (sole) application of performance-based promotion principles.

Local mutual exclusion (LME) extends traditional mutual exclusion by preventing two neighboring nodes from accessing the Critical Section (CS) simultaneously while allowing concurrent access for non-neighboring nodes. In Flying Ad-Hoc Networks (FANETs), shared resources are hosted on Unmanned Aerial Vehicles (UAVs), and user nodes within a UAV’s transmission range can request access to the resources. However, resource allocation and token management in FANETs remain unexplored. This paper addresses the LME problem for FANETs through Q-learning-based Local Mutual Exclusion (Q-LME). Q-LME employs a token-based LME algorithm with a Q-learning-based leader selection mechanism (QLS). The leader is responsible for coordinating access to shared resources and managing tokens in the distributed system. The proposed QLS mechanism dynamically calculates Q-parameter values by considering environmental factors. Rewards are derived from the number of hops, direction of node movement, link quality, and distance to the resource. The learning rate is calculated based on the number of role changes of a node, while the discount factor reflects the node’s speed. The Q-LME algorithm ensures safety, prevents starvation, and enhances system performance by reducing the leader selection frequency, the average waiting time for CS access, the average number of messages per CS and the average hop count. Additionally, Q-LME improves efficiency and fault tolerance in dynamic FANET environments.

In this paper, a novel multi-objective artificial sheep algorithm (MOASA) is proposed. The basic search idea of MOASA inherits from the BASA, which is inspired by the social behavior of sheep herd, while some modifications are made to extend the algorithm to multi-objective problems. The Pareto-based theory is adopted in the MOASA along with external archive and leader selection mechanism to bring about multi-objective optimization. Furthermore, a novel neighborhood search method is proposed and applied to the external archive to enhance the performance of the algorithm. The proposed MOASA is then tested on 17 multi-objective benchmark problems to verify its efficiency and effectiveness by comparing with six powerful multi-objective optimization algorithms (MOAs). Experimental results show that the MOASA is generally superior to its competitors in solving those benchmark problems in terms of convergence and Pareto front distribution.

Blockchain networks are consistently challenged with security and accessibility issues, and technological developments call for the need for their security and integrity more than ever. Security vulnerabilities such as distributed denial-of-service (DDoS) attacks and Eclipse attacks influence public and private blockchains imposing significant losses to the network. Considering the key distribution and management service as a major part of the blockchain architecture, the present study proposes a combinatorial design-based token management system (CD-TMS) while offering optimum accessibility for the blockchain network. Our combinatorial design-based, clustered tokenization system enables the blockchain to prevent DDoS attacks. We also offer a leader selection mechanism relying on a probabilistic tokenization system that reduces communication overhead compared to voting-based systems. In this regard, CD-TMS integrates Balanced incomplete block designs and transversal designs (TD), as well as Eschenauer and Gligor (EG) designs, to distribute tokens on a public blockchain framework (i.e., Bitcoin), though it focuses mainly on DDoS and Eclipse attacks. The performance function, evaluated in terms of security, resiliency, communication overhead, connectivity, reliability, availability, and scalability, has shown the proposed architecture's superiority over conventional methods.

Laplacian controllability and observability of a consensus network is a widely considered topic in the area of multi-agent systems, complex networks, and large-scale systems. In this paper, this problem is addressed when the communication among nodes is described through a starlike tree topology. After a brief description of the mathematical setting of the problem adopted in a wide number of multi-agent systems engineering applications, some novel results are drawn based on node positions within the network only. The resulting methods are graphical and thus effective and exempt from numerical errors, and the final algorithm is provided to perform the analysis by machine computation. Several examples are provided to show the effectiveness of the algorithm proposed.

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios.

In 6G Cognitive Radio Internet of Things (CR-IoT) networks, multi-band spectrum sensing cooperatively provides access to extensive spectrum resources. The suggested learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme addresses intelligent spectrum access challenges. A cooperative strategy is introduced into a dueling deep Q network to facilitate multi-user reinforcement learning. This study selects the most suitable IoT secondary users (SU) to sense channels using the proposed learning-based M2CSS scheme. With the restriction that each IoT SU can serve as a front-runner for a single network and that there will only be one leader for individual frequency, the proposed work expresses an optimization difficulty in choosing leaders through k-means and SOM, who can efficiently interact with other SUs. Next, choose matching cooperative SUs for each frequency and express additional optimization problems. Following this phase, a subset of cooperative secondary users (SUs) senses frequencies and employs accurate knowledge to determine the channels' availability in a distributed manner. The simulation findings demonstrate significant improvements in detection performance, preventing the misuse of specific devices, providing reliable sensing data over extensive IoT connections, and achieving energy efficiency—all essential for IoT implementations. These advantages make the proposed M2CSS system suitable for the massive machine-type communications anticipated in 6G IoT scenarios.