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The paradigm of sensor networks involves connecting wireless electronic devices through small sensor nodes to gather and sense surrounding information. As these networks have limited resources, it is crucial to optimize their usage to enhance network performance. To achieve this, software-defined network technology is integrated into wireless sensor networks to efficiently utilize network resources. Furthermore, optimized clustering and energy-aware routing techniques are employed to evenly distribute network traffic and enable energy-efficient data transmission in SDN-enabled WSNs. However, the issue of hotspots or energy holes consistently persists in cluster-based routing protocols. This research aims to develop an energy-aware routing protocol incorporating a mobile sink, aiming to achieve energy consumption equilibrium and extend the network's lifespan. To address these concerns and ensure the longer sustainability of SDN-enabled WSNs, a novel approach called mobile sink-based energy-aware routing is proposed for energy-efficient data delivery. It utilizes optimized sink mobility to resolve the hotspot issue based on a proposed fitness function and centroid point approach. The fitness function considers essential parameters such as energy, control node density, and distances from control nodes. The flow rules are also generated based on the rank-based tree topology for multi-hop data transmission. The proposed approach is executed with an ONOS controller to implement SDN policies, and the performance of the heterogeneous network is evaluated through simulation using the ns-3 simulator. Furthermore, the proposed MS-EAR demonstrates significant improvements in the network lifespan compared to existing techniques such as GM-WOA, GMPSO, and FJAPSO, with increases of 18.0 % , 47.5 % and 94.0 % , respectively. It also outperforms the current state-of-the-art by considering various performance metrics, including stability period, number of alive nodes, network residual energy, packets transmitted to the control server, and average delay. [ABSTRACT FROM AUTHOR]

The Tree Multiparty Quantum Key Agreement Protocol (TMQKAP) is introduced as a novel solution for secure quantum key agreement among multiple participants, specifically tailored for tree topologies. Based on the BB84 protocol, TMQKAP employs hierarchical tree structures and XOR operations to facilitate efficient and secure key generation. Key elements are exchanged among participants in an equitable manner, ensuring that each participant contributes equally to the generation of the shared key. The protocol demonstrates robust security, effectively defending against both external and internal attacks, and achieves a quantum efficiency of 1/2 (N −1), where N is the number of participants. Thorough security analysis and simulations show TMQKAP’s robustness against various attacks while maintaining high efficiency. Additionally, the protocol is readily implementable with current quantum technologies, utilizing single-photon transmission to facilitate secure key distribution.

We investigated the evolutionary patterns of two forest-dwelling endemic terrestrial snails (Gittenedouardia spadicea and Maizania wahlbergi) in the Maputaland–Pondoland–Albany biodiversity hotspot in South Africa to examine the degree of phylogeographical congruence between the two species. Phylogenetic relationships and divergence time estimations within each species were inferred using the combined mitochondrial DNA (mtDNA) sequence dataset for the cytochrome c oxidase subunit I (COI) and large subunit ribosomal RNA (16S rRNA). In addition, the rapidly evolving COI datasets were used to infer intraspecific genetic structure and population differentiation within both species. Phylogeographical concordance factor (PCF) analysis was used to statistically estimate the degree of congruence between the two species at localities where both species were sampled. Phylogenetically, G. spadicea exhibited two clades that diverged during the Plio/Pleistocene, while M. wahlbergi formed a single shallow clade that showed Pleistocene divergence. The haplotype networks for the COI locus demonstrated evidence of geographical and genetic isolation within both species. PCF analysis demonstrated partial phylogeographical concordance, suggesting that the two species may have been affected by similar ancient climatic events. Traits other than poor dispersal capabilities and limited gene flow may also influence how a species responds to fluctuating climate change. [ABSTRACT FROM AUTHOR]

We address the problem of configuring a power distribution network with reliability and resilience objectives by satisfying the demands of the consumers and saturating each production source as little as possible. We consider power distribution networks containing source nodes producing electricity, nodes representing electricity consumers and switches between them. Configuring this network consists in deciding the orientation of the links between the nodes of the network. The electric flow is a direct consequence of the chosen configuration and can be computed in polynomial time. It is valid if it satisfies the demand of each consumer and capacity constraints on the network. In such a case, we study the problem of determining a feasible solution that balances the loads of the sources, that is their production rates. We use three metrics to measure the quality of a solution: minimizing the maximum load, maximizing the minimum load and minimizing the difference of the maximum and the minimum loads. This defines optimization problems called respectively min -M, max -m and min -R. In the case where the graph of the network is a tree, it is known that the problem of building a valid configuration is polynomial. We show the three optimization variants have distinct properties regarding the theoretical complexity and the approximability. Particularly, we show that min -M is polynomial, that max -m is NP-Hard but belongs to the class FPTAS and that min -R is NP-Hard, cannot be approximated to within any exponential relative ratio but, for any ε > 0 , there exists an algorithm for which the value of the returned solution equals the value of an optimal solution shifted by at most ε . [ABSTRACT FROM AUTHOR]

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the low data transmission efficiency of the blockchain network, an optimization method of blockchain transmission efficiency was proposed to optimize the network topology and forwarding strategy.First, the trusted value function was designed to calculate the trusted value of blockchain nodes, and a tree topology was constructed by comprehensively considering the trusted value and transmission time.Furthermore, the forwarding path selection strategy was designed based on the tree topology, with the minimum overall concurrent transmission time as the goal, and the forwarding table of the node about the forwarding order of its neighbor nodes was established.To reduce the impact of the node changed on the tree topology, a topology dynamic optimization strategy was proposed to adjust tree topology locally.The forwarding path selection strategy enabled the entire data transmission process to have the minimum transmission time, and the dynamic optimization strategy avoided reconfiguration of the entire network topology and effectively shortened the data transmission time.The simulation results show that, compared with the weight-first algorithm, the transmission time of the proposed method is reduced by about 20%, and the data transmission efficiency is significantly improved.

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Slowdowns in lineage accumulation are often observed in phylogenies of extant species. One explanation is the presence of ecological limits to diversity and hence to diversification. Previous research has examined whether and how species richness (SR) impacts diversification rates, but rarely considered the evolutionary relatedness (ER) between species, although ER can affect the degree of interaction between species, which likely sets these limits. To understand the influences of ER on species diversification and the interplay between SR and ER, we present a simple birth-death model in which the speciation rate depends on the ER. We use different metrics of ER that operate at different scales, ranging from branch/lineage-specific to clade-wide scales. We find that the scales at which an effect of ER operates yield distinct patterns in various tree statistics. When ER operates across the whole tree, we observe smaller and more balanced trees, with speciation rates distributed more evenly across the tips than in scenarios with lineage-specific ER effects. Importantly, we find that negative SR dependence of speciation masks the impact of ER on some of the tree statistics. Our model allows diverse evolutionary trajectories for producing imbalanced trees, which are commonly observed in empirical phylogenies but have been challenging to replicate with earlier models., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Slowdowns in lineage accumulation are often observed in phylogenies of extant species. One explanation is the presence of ecological limits to diversity and hence to diversification. Previous research has examined whether and how species richness (SR) impacts diversification rates, but rarely considered the evolutionary relatedness (ER) between species, although ER can affect the degree of interaction between species, which likely sets these limits. To understand the influences of ER on species diversification and the interplay between SR and ER, we present a simple birth–death model in which the speciation rate depends on the ER. We use different metrics of ER that operate at different scales, ranging from branch/lineage-specific to clade-wide scales. We find that the scales at which an effect of ER operates yield distinct patterns in various tree statistics. When ER operates across the whole tree, we observe smaller and more balanced trees, with speciation rates distributed more evenly across the tips than in scenarios with lineage-specific ER effects. Importantly, we find that negative SR dependence of speciation masks the impact of ER on some of the tree statistics. Our model allows diverse evolutionary trajectories for producing imbalanced trees, which are commonly observed in empirical phylogenies but have been challenging to replicate with earlier models. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has very high spectral efficiency and system capacity. NOMA has become one of the most competitive access solutions in 5G systems. In this article, the principle of NOMA is discussed first. Then, the NOMA system capacity optimisation problems are studied. Signal to interference plus noise ratio (SINR) is an important factor which affects the system capacity. The SINR of current user n is only related to the power allocated to users n+1 to N with high signal-to-noise ratio (SNR) but not interfered by users with low SNR. Therefore, a tree topology power allocation (TTPA) algorithm is introduced. When users are allocated to each layer of the tree structure, the current power allocation of each layer will not be affected by the previous layer. Through theoretical analysis, TTPA can achieve the same performance as the full search power allocation algorithm; however, its computational complexity is reduced from exponential to constant. It can be seen from the numerical simulation results that the proposed algorithm can achieve higher system capacity and has lower computational complexity.

This article aims to investigate the performance of the IEEE 802.11-based tree-topology network, where a wireless node is within the others' carrier sensing ranges. In such a network, the concurrent transmission is a dominant cause of frame collisions. Moreover, the relay nodes (RN) (i.e., in the tree) likely cause the coexistence of non-saturated and saturated nodes in the networks. Those conditions have not been addressed in the previous works yet. As a solution, this work proposes new analytical expressions of delay and throughput in the investigated scenario. The presented analytical model incorporates the Bianchi model and airtime concept to formulate operations of the IEEE 802.11 nodes. First, by leveraging Bianchi-based analysis, the proposed model gives the frame collision probability caused by concurrent transmission. Second, by using airtime concept analysis, the ratio of frame numbers of each flow in a relay node (RN) is expressed to represent the buffer state of RN. As a result, we can obtain the network throughput, the throughput, and the delay of each flow. The validity of the analytical expressions is confirmed by the quantitative agreement between the analytical and simulation results.

In the industrial Internet of things (IIoT), there is always a strong demand for real-time information transfer. Especially when deploying wireless/wired hybrid networks in smart factories, the requirement for low delay interaction is more prominent. Although tree routing protocols have been successfully executed in simple networks, more challenges in transmission speed can be observed in the manufacturing broadband communication system. Motivated by the progresses in deep learning, a smart collaborative routing protocol with low delay and high reliability is proposed to accommodate mixed link scenarios. First, we establish a one-hop delay model to investigate the potential affects of Media Access Control (MAC) layer parameters, which supports the subsequent design. Second, forwarding, maintenance, and efficiency strategies are created to construct the basic functionalities for our routing protocol. Relevant procedures and key approaches are highlighted as well. Third, two sub-protocols are generated and the corresponding implementation steps are described. The experimental results demonstrate that the end-to-end delay can be effectively cut down through comprehensive improvements. Even more sensor nodes and larger network scale are involved, our proposed protocol can still illustrate the advantages comparing with existing solutions within IIoT.

Introduction: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient., Objectives: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem., Methods: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm; shift density estimation is introduced as a mechanism for providing selection pressure for population evolution; and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to Big-Opt problems., Results: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms., Conclusion: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Production and hosting by Elsevier B.V.)

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR. [ABSTRACT FROM AUTHOR]

FleCSPH is a smoothed particle hydrodynamics simulation tool, based on the compile-time configurable framework FleCSI. The asynchronous distributed tree topology combined with a fast multipole method allows FleCSPH to efficiently compute hydrodynamics and long range particle–particle interactions. FleCSPH provides initial data generators, particle relaxation techniques, and standard evolution drivers, which can be easily modified and extended to user-specific setups. Data input/output uses the H5part format, compatible with modern visualization software.

The backhaul of hundreds of light fidelity (LiFi) base stations (BSs) constitutes a major challenge. Building on an indoor wireless optical backhauling approach, this paper presents the top-down design of a multi-hop wireless backhaul configuration for multi-tier optical attocell networks by proposing super cells. Such cells incorporate multiple clusters of attocells that are connected to the core network via a single gateway. Consequently, new challenges arise for managing the bandwidth and power of the bottleneck backhaul. By putting forward user-based bandwidth scheduling (UBS) and cell-based bandwidth scheduling (CBS) policies, the system-level modeling and analysis of the end-to-end multi-user sum rate is elaborated. In addition, optimal bandwidth scheduling under both UBS and CBS policies are formulated as constrained convex optimization problems, and solved by using the projected subgradient method. Furthermore, the transmission power of the backhaul system is opportunistically reduced using a fixed power control (FPC) strategy. The notion of backhaul bottleneck occurrence (BBO) is introduced. An accurate approximate expression of the probability of BBO is derived, and verified using Monte Carlo simulations. Several insights are provided by studying different aspects of the performance of super cells including the average sum rate, the BBO probability and the backhaul power efficiency (PE). [ABSTRACT FROM AUTHOR]

The implementation of Wireless Sensor Networks (WSNs) is a challenging task due to their intrinsic characteristics, e.g., energy limitations and unreliable wireless links. Considering this, we have developed the Disjoint path And Clustering Algorithm (DACA) that combines topology control and self-healing mechanisms to increase the network lifetime with minimum loss of coverage. Initially, DACA constructs a tree that includes all nodes of the network by using the Collection Tree Protocol (CTP). This tree is an initial communication backbone through which DACA centralizes the information. Then, DACA builds a set of spatial clusters using Kmeans and selects the Cluster Heads (CHs) using Particle Swarm Optimization (PSO) and a multi-objective optimization (MOO) function. Subsequently, DACA reconstructs the tree using only the CHs. In this way, DACA reduces the number of active nodes in the network and saves energy. Finally, DACA finds disjoint paths on the reconstructed tree by executing the N-to-1 multipath discovery protocol. By doing so, the network can overcome communications failures with a low control message overhead. The simulations on Castalia show that DACA considerably extends the network lifetime by having a set of inactive nodes and disjoint paths that support the communication when active nodes die. Besides, DACA still maintains a good coverage of the area of interest despite the inactive nodes. Additionally, we evaluate the shape of the tree (i.e., the average number of hops) and the risk of connection loss of the network. [ABSTRACT FROM AUTHOR]

A tree topology based Byzantine fault tolerance system was designed and the calculation of the Byzantine node bound based on depth and width was given.The consistency protocol was designed through divide the tree into groups by communication and work,which guaranteed the safety of system.The access of a node was determined with joint signature mechanism.The Byzantine nodes were placed to the leaves by view change protocol,which guaranteed the liveness of the system.On the foundation of traditional protocol,the new protocol support multiple tasks operating at the same time,and communication between nodes is largely decreased,as well as the space complexity,hence the feasibility being promoted,and the gap between Byzantine fault tolerance system and the non-Byzantine systems being narrowed.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms., Competing Interests: The authors declare that they have no competing interests., (© 2024 Liu et al.)

The medical monitoring system is widely used. In the medical monitoring system, each user only possesses one piece of data logging that participates in statistical computing. Specifically in such a situation, a feasible solution is to scatter its statistical computing workload to corresponding statistical nodes. Moreover, there are still two problems that should be resolved. One is how the server takes advantage of intermediate results obtained through statistical node aggregation to perform statistical computing. Statistical variable decomposition technique points out the direction for statistical projects. The other problem is how to design an efficient topological structure for statistical computing. In this paper, tree topology was adopted to implement data aggregation to improve aggregation efficiency. And two experiments were done for time consumption of statistical computing which focuses on encrypted data aggregation and encrypted data computing. The first experiment indicates that encrypted data aggregation efficiency of the scheme proposed in this paper is better than that of Drosatos' scheme, and the second indicates that improving computing power of the server or computational efficiency of the functional encryption scheme can shorten the computation time. [ABSTRACT FROM AUTHOR]

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.

This article proposes a scheme for selecting the best site for sink placement in WSN applications employing backbone assisted communications. By placing the sink at a specific position, energy scavenging and delay constraints can effectively be controlled. In contrast to the conventional scheme for base station placement at the geographical centre or random placement at the end of the region of interest, the proposed scheme places the base station at either the graph theoretical centre or centroid of the backbone connecting nodes in the region of interest. This strategy shows a considerable reduction in the total number of hops that each packet needs to travel to reach the sink. The proposed scheme is applied on all the families of graphs prevalent in backbone assisted sensor networks to confirm the performance consistency and improvement in network parameters of the communication backbone measured in terms of delay, the carried load and the total energy consumption, eventually affected by the average number of hops for the message to reach the sink.

This study, the fifth in a series investigating palynological characters in angiosperms, aims to explore the distribution of states for 19 pollen characters on five early diverging orders of Superasteridae (Berberidopsidales, Caryophyllales, Cornales, Ericales, and Santalales) plusDilleniales. To illustrate the character states found in the pollen of this group, we examined pollen grains of 15 species exemplifying 15 families across all studied orders using light, scanning, and transmission electron microscopy.We reconstructed the phylogeny of the early diverging Superasteridae and related taxa with eight genetic markers for 172 genera, using maximumlikelihood (ML) analysis. Nineteen pollen characters were coded for the genera used in this phylogeny and compiled into two morphological matrices using two coding strategies. The characters were then optimized on the newly generated ML tree plus two constrained trees differing in the position of Dilleniales, using three methods of inference. Taxa in this grade show a striking diversity of pollen morphologies, particularly in certain characters such as size, tectum sculpture, and aperture number. The plesiomorphic condition for the early diverging Superasteridae is unambiguously and consistently inferred to comprise monad-dispersed, isopolar, spheroidal, circular in outline, equatorially arranged, tricolpate pollen grains with granular aperture membranes, a smooth tectum, and endexine present. We identify diagnostic character states and synapomorphies for several monophyletic groups, and explore the palynological evidence thatmay shed light on some unresolved relationships. For example, the hypothesis that Dilleniales is sister to Superrosidae is better supported than alternative hypotheses, being consistent with a number of shared palynological state changes including transitions to presence of costae, reticulate tectum, and columellar infratectumstructure. Across this part of the angiospermphylogeny, most state transitions occur repeatedly, and their frequency varies among both clades and characters. We discuss the impact of optimization method, tree topology, and coding strategy upon ancestral state reconstruction. [ABSTRACT FROM AUTHOR]