Election Algorithm Research Articles

A secured and fault-tolerant algorithm for electing an efficient coordinator in distributed networks

Selecting a reliable and stable coordinator is a crucial aspect of distributed systems, often vulnerable to node failures and security breaches. In response to these challenges, we present the Reliable and Stable Coordinator Election Algorithm in Distributed Systems (RSCEA). This algorithm is designed to withstand node failures and security threats during the election process. A key highlight of our approach is the utilization of a preference-based voting mechanism that incorporates essential nodal characteristics into the election procedure. By including nodal properties, the system's robustness is greatly increased, and a long-lasting and dependable coordinator is elected. The complexity of the RSCEA space, execution time and communication complexity were thoroughly examined. The findings show that RSCEA effectively selects a coordinator with linear message complexity proportional to the number of nodal features, denoted as O(n.m) where n is number of nodes and m indicates the number of attributes. RSCEA demonstrates scalability with a storage expense linear in number of nodes, thus markedly enhancing communication efficiency. RSCEA elects a reliable and stable coordinator in distributed networks. Its distinctive design, resilience, and effectiveness make it an important asset in the progression of distributed systems.

Estimation of shifted weibull distribution parameters using optimization algorithms for optimal investment decisions making

This study examines the estimation of parameters for the Shifted Weibull Distribution (SWD) using several robust metaheuristic algorithms, with a focus on enhancing precision and reliability in investment data analysis. Utilizing investment return data from the Malaysian property sector, we evaluate the performance of five metaheuristic models: Election Algorithm (EA), Artificial Dragonfly Algorithm (ADA), Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The evaluation criteria include Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), and accuracy. Results reveal that EA consistently outperforms other models, achieving the lowest BIC value of 147.2 and an impressive accuracy rate of 94.90% at a sample size of 1,000. The Genetic Algorithm (GA) shows the lowest RMSE of 0.99, indicating strong predictive performance. Tukey's HSD test highlights significant accuracy variations among the models, with EA and GA notably outperforming ACO and DE. However, RMSE and BIC metrics do not demonstrate clear variations among the models. These findings underscore the superior performance of the EA model in the context of SWD parameter estimation, making it the preferred choice for modeling investment return data. Future research should explore additional factors influencing model performance and validate these models with diverse real-world datasets to further enhance their applicability in financial decision-making.

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware.

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

A balanced leader election algorithm based on replica distribution in Kubernetes cluster

A balanced leader election algorithm based on replica distribution in Kubernetes cluster

VCubeChain: A scalable permissioned blockchain

This work presents vCubeChain, a scalable permissioned blockchain based on the vCube virtual topology. vCube is a virtual hierarchical topology that presents several logarithmic properties. vCubeChain employs a leader election algorithm that relies on the failure detection information that vCube provides. The leader employs a vCube-based autonomic reliable broadcast algorithm to disseminate blocks, each consisting of multiple transactions. In case multiple leaders end up concurrently elected due to false suspicions, vCubeChain is proven to recover to a consistent state upon the discovery of contradictory blocks. vCubeChain is described, specified, and correctness and liveness draft proofs are presented. The blockchain was implemented on the Blocksim simulator, and a set of experiments are presented, including comparisons with Bitcoin, Ethereum and Hyperledge Fabric. Results demonstrate the scalability of the solution.

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Near-Optimal Time–Energy Tradeoffs for Deterministic Leader Election

We consider the energy complexity of the leader election problem in the single-hop radio network model, where each device v has a unique identifier ID ( v ) ∈{ 1, 2, ⋖ , N } . Energy is a scarce resource for small battery-powered devices. For such devices, most of the energy is often spent on communication, not on computation. To approximate the actual energy cost, the energy complexity of an algorithm is defined as the maximum over all devices of the number of time slots where the device transmits or listens. Much progress has been made in understanding the energy complexity of leader election in radio networks, but very little is known about the tradeoff between time and energy. Chang et al. [STOC 2017] showed that the optimal deterministic energy complexity of leader election is Θ (log log N ) if each device can simultaneously transmit and listen but still leaving the problem of determining the optimal time complexity under any given energy constraint. Time–energy tradeoff: For any k ≥ log log N , we show that a leader among at most n devices can be elected deterministically in O ( k ċ n 1+ε ) + O ( k ċ N 1/k ) time and O ( k ) energy if each device can simultaneously transmit and listen, where ε > 0 is any small constant. This improves upon the previous O ( N )-time O (log log N )-energy algorithm by Chang et al. [STOC 2017]. We provide lower bounds to show that the time–energy tradeoff of our algorithm is near-optimal. Dense instances: For the dense instances where the number of devices is n = Θ ( N ), we design a deterministic leader election algorithm using only O (1) energy. This improves upon the O (log* N )-energy algorithm by Jurdziński, Kutyłowski, and Zatopiański [PODC 2002] and the O (α ( N ))-energy algorithm by Chang et al. [STOC 2017]. More specifically, we show that the optimal deterministic energy complexity of leader election is \(\Theta (\max \lbrace 1, \log \tfrac{N}{n}\rbrace)\) if each device cannot simultaneously transmit and listen, and it is \(Θ (\max \lbrace 1, \log \log \tfrac{N}{n}\rbrace)\) if each device can simultaneously transmit and listen.

A Robust, Preference-Based Coordinator Election Algorithm for Distributed Systems

A Robust, Preference-Based Coordinator Election Algorithm for Distributed Systems

An election algorithm combined with support vector regression for estimating hydrological drought

An election algorithm combined with support vector regression for estimating hydrological drought

Election of MPR Nodes and Detection of Malicious Nodes Based on a Byzantine Fault in the OLSR Protocol Case of a Scale-Free Network

V2X (Vehicle-to-Everything) communications play a crucial role in enabling the efficient and reliable exchange of information among vehicles, infrastructure, and other entities in smart transportation systems. However, the inherent vulnerabilities and dynamic nature of V2X networks present significant challenges for ensuring secure and trustworthy communication. By enhancing the security of the OLSR (Optimized Link State Routing) protocol through secure MultiPoint Relays (MPRs) Selection, this research aims to provide a robust approach that enhances the overall security posture of V2X networks, enabling safe and secure interactions between vehicles and their environment. The proposed method is based on the Byzantine general’s problem, which is the principle used in blockchain. Compared to the classical flooding mechanism, this technique greatly reduces network traffic overhead and improves the efficiency of bandwidth utilization. The results demonstrated that the proposed algorithm performed better than the well-used UM-OLSR implementation. The outcome proved that our MPR election algorithm guarantees a better packet delivery ratio, and it also performs very well in the detection and isolation of malicious nodes, leading to increased security of the OLSR protocol control plane.

Proof of Karma (PoK): A Novel Consensus Mechanism for Consortium Blockchain

In blockchain-based systems, participants can be malicious. Therefore, this work first characterizes several properties expected in systems where the honest behaviour of involved parties plays significant role in the success. Considering these properties, a new consensus mechanism, Proof of Karma (PoK), is proposed based on karma (actions) of nodes. PoK incorporates a self-stabilizing leader election algorithm based on karma score to ensure consistency in the system. In PoK, both new and existing nodes get a fair chance to earn profit by becoming a leader and adding a valid block to the blockchain. PoK gives incentives and imposes penalties to encourage and discourage the nodes' honest and malicious actions. PoK is analyzed with respect to the CAP theorem. The work provides security analysis to demonstrate the resistance of PoK against various blockchain specific attacks and karma specific attacks. Several experiments are also performed to assess the performance of PoK and compare it with the baseline model. The results show the feasibility, effectiveness, usability and scalability of PoK. PoK is also compared based on the characterized properties with various existing consensus mechanisms that consider malicious actions of nodes. PoK achieves consensus finality, decentralization and fairness, outperforming existing works.

AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS

Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.

Self-stabilizing systems in spite of high dynamics

We initiate research on self-stabilization in highly dynamic identified message-passing systems where dynamics is modeled using time-varying graphs (TVGs). More precisely, we address the self-stabilizing leader election problem in three wide classes of TVGs: the class TCB(Δ) of TVGs with temporal diameter bounded by Δ, the class TCQ(Δ) of TVGs with temporal diameter quasi-bounded by Δ, and the class TCR of TVGs with recurrent connectivity only, where TCB(Δ)⊆TCQ(Δ)⊆TCR. We first study conditions under which our problem can be solved. We introduce the notion of size-ambiguity to show that the assumption on the knowledge of the number n of processes is central. Our results reveal that, despite the existence of unique process identifiers, any deterministic self-stabilizing leader election algorithm working on the class TCQ(Δ) or TCR cannot be size-ambiguous, justifying why our solutions for those classes assume the exact knowledge of n. We then present three self-stabilizing leader election algorithms for Classes TCB(Δ), TCQ(Δ), and TCR, respectively. Our algorithm for TCB(Δ) stabilizes in at most 3Δ rounds. However, we show that stabilization time cannot be bounded for the leader election problem in TCQ(Δ) and TCR. Nevertheless, we circumvent this issue by showing that our solutions are speculative in the sense that their stabilization time in TCB(Δ) (⊆TCQ(Δ)⊆TCR) is O(Δ) rounds.

Three‐dimensional scan‐based algorithm for directional neighbor discovery in ad hoc networks

SummaryIn recent years, neighbor discovery techniques using directional antennas have attracted widespread attention. Most currently available directional neighbor discovery techniques are designed based on two‐dimensional (2D) space. Although these algorithms can accomplish the discovery between nodes, the algorithm portability is poor for three‐dimensional (3D) platforms, and the application scenarios are limited since new communication scenarios such as unmanned aerial vehicle (UAV) groups and maritime fleets emerge where information needs to be delivered in real time and nodes are located in 3D space. This paper proposes a new deterministic directional neighbor discovery algorithm in 3D space named 3D scan‐based algorithm (3D‐SBA) to meet the needs of the line‐of‐sight (LOS) scenes. Four existing neighbor discovery algorithms, namely, SBA‐based random mode selection (SBA‐R), quorum, complete random algorithm (CRA), and SBA‐based leader election algorithm (LE), have been extended to our proposed 3D algorithm model for simulation and comparative analysis with 3D‐SBA. The simulation results show that the 3D‐SBA algorithm consumes 51.15%, 180.20%, 9.48%, and 17.49% of the time slots compared with the four existing algorithms mentioned above. However, the quorum algorithm has a very high node collision rate, up to 92.11%. Ultimately, the 3D‐SBA algorithm has the best performance considering the conflicts and the density of network nodes.

Proposal and comparative analysis of a voting-based election algorithm for managing service replication in MANETs

Proposal and comparative analysis of a voting-based election algorithm for managing service replication in MANETs

Optimal Space Lower Bound for Deterministic Self-Stabilizing Leader Election Algorithms

Given a boolean predicate $\Pi$ on labeled networks (e.g., proper coloring, leader election, etc.), a self-stabilizing algorithm for $\Pi$ is a distributed algorithm that can start from any initial configuration of the network (i.e., every node has an arbitrary value assigned to each of its variables), and eventually converge to a configuration satisfying $\Pi$. It is known that leader election does not have a deterministic self-stabilizing algorithm using a constant-size register at each node, i.e., for some networks, some of their nodes must have registers whose sizes grow with the size $n$ of the networks. On the other hand, it is also known that leader election can be solved by a deterministic self-stabilizing algorithm using registers of $O(\log \log n)$ bits per node in any $n$-node bounded-degree network. We show that this latter space complexity is optimal. Specifically, we prove that every deterministic self-stabilizing algorithm solving leader election must use $\Omega(\log \log n)$-bit per node registers in some $n$-node networks. In addition, we show that our lower bounds go beyond leader election, and apply to all problems that cannot be solved by anonymous algorithms.

Central node selection algorithm of minimizing maximum weighted response time based on dynamic programming

In order to minimize the maximum weighted response time for any node in the network to reach the central control node, a central control node election algorithm based on dynamic programming is proposed. Firstly, the response times of nodes and links in the wireless network are modeled as the node weights and edge weights in the network topology, and then the central control node election problem that minimizes the maximum weighted response time for any node in the network to reach the central control node is modeled as the central problem, where represents the number of central control nodes. Then, by using the interpolation method based on dynamic programming, the weighted response time between the two points can be obtained, and the K-central problem modeled is transformed into several R-control set problems. Then, several control R-set problems are transformed into several 0-1 integer programming problems, and each integer programming problem can be solved one by one by using the branch and bound method. Finally, a simplified implementation method based on the above algorithm is given when K=1, the optimality of the proposed algorithm is proved and the complexity of the algorithm is analyzed. Simulation results show that the proposed central control algorithm can minimize the maximum weighted response time of the network.

Major 3 Satisfiability logic in Discrete Hopfield Neural Network integrated with multi-objective Election Algorithm

<abstract> <p>Discrete Hopfield Neural Network is widely used in solving various optimization problems and logic mining. Boolean algebras are used to govern the Discrete Hopfield Neural Network to produce final neuron states that possess a global minimum energy solution. Non-systematic satisfiability logic is popular due to the flexibility that it provides to the logical structure compared to systematic satisfiability. Hence, this study proposed a non-systematic majority logic named Major 3 Satisfiability logic that will be embedded in the Discrete Hopfield Neural Network. The model will be integrated with an evolutionary algorithm which is the multi-objective Election Algorithm in the training phase to increase the optimality of the learning process of the model. Higher content addressable memory is proposed rather than one to extend the measure of this work capability. The model will be compared with different order logical combinations $ k = \mathrm{3, 2} $, $ k = \mathrm{3, 2}, 1 $ and $ k = \mathrm{3, 1} $. The performance of those logical combinations will be measured by Mean Absolute Error, Global Minimum Energy, Total Neuron Variation, Jaccard Similarity Index and Gower and Legendre Similarity Index. The results show that $ k = \mathrm{3, 2} $ has the best overall performance due to its advantage of having the highest chances for the clauses to be satisfied and the absence of the first-order logic. Since it is also a non-systematic logical structure, it gains the highest diversity value during the learning phase.</p> </abstract>

An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection

Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious network security threats, which use periodic slight multi-variate time series pulse flows to degrade network quality. Limited by the poor data in a single client, a powerful and satisfactory LDDoS attack detection model is hard to be trained. Federated Learning (FL) is a promising paradigm offering joint learning through multiple clients. We propose an asynchronous federated learning arbitration framework based on bidirectional LSTM (bi-LSTM) and attention mechanism (AsyncFL-bLAM). In the AsyncFL-bLAM, the leader node election algorithm is proposed for constructing the framework of asynchronous federated learning. The proposed bLAM model composed of feature extracter and arbitrator takes on the responsibility of LDDoS detection locally. Furthermore, the novel AsyncFL framework helps to upload and aggregate the bLAM models’ parameters asynchronously between leader node and client nodes. Experimental results show that the AsyncFL-bLAM outperforms the state-of-the-art models in accuracy, and reduces the overall communication rounds.