Routing Aggregation Research Articles

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

Evolution of the ionisation energy with the stepwise growth of chiral clusters of [4]helicene

Polycyclic aromatic hydrocarbons (PAHs) are widely established as ubiquitous in the interstellar medium (ISM), but considering their prevalence in harsh vacuum environments, the role of ionisation in the formation of PAH clusters is poorly understood, particularly if a chirality-dependent aggregation route is considered. Here we report on photoelectron spectroscopy experiments on [4]helicene clusters performed with a vacuum ultraviolet synchrotron beamline. Aggregates (up to the heptamer) of [4]helicene, the smallest PAH with helical chirality, were produced and investigated with a combined experimental and theoretical approach using several state-of-the-art quantum-chemical methodologies. The ionisation onsets are extracted for each cluster size from the mass-selected photoelectron spectra and compared with calculations of vertical ionisation energies. We explore the complex aggregation topologies emerging from the multitude of isomers formed through clustering of P and M, the two enantiomers of [4]helicene. The very satisfactory benchmarking between experimental ionisation onsets vs. predicted ionisation energies allows the identification of theoretically predicted potential aggregation motifs and corresponding energetic ordering of chiral clusters. Our structural models suggest that a homochiral aggregation route is energetically favoured over heterochiral arrangements with increasing cluster size, hinting at potential symmetry breaking in PAH cluster formation at the scale of small grains.

Delineating Source and Sink Zones of Trip Journeys in the Road Network Space

Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals’ journeys on road networks and give rise to relationships among human activities, road network structures, and land-use types. Therefore, this study developed a novel approach to delineate source–sink zones based on trip route aggregation on road networks. We first represented original trajectories using road segment sequences and applied the Latent Dirichlet Allocation (LDA) model to associate trajectories with route semantics. We then ran a hierarchical clustering operation to aggregate trajectories with similar route semantics. Finally, we adopted an adaptive multi-variable agglomeration strategy to associate the trajectory clusters with each traffic analysis zone to delineating source and sink zones, with a trajectory topic entropy defined as an indicator to analyze the dynamic impact between the road network and source–sink zones. We used taxi trajectories in Xiamen, China, to verify the effectiveness of the proposed method.

A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs

A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs

Energy-Efficient Data Aggregation and Cluster-Based Routing in Wireless Sensor Networks Using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods.

Mineralogical control on physically protected soil organic matter in a neotropical moist forest

Context Minerals and organic matter physically associate in many soils, yet the precise nature, either via a hierarchy of aggregate particles or by build-up of organo–mineral associations, remains obscure, especially in tropical forest environments. Aims We investigated physically protected organic matter by comparing soils with contrasting parent material, topography, and pedogenesis, but with similar tropical moist forest on Barro Colorado Island in the lowlands of Panama. Methods Bulk soil from 10 sites was separated by size into free-floating particles, macroaggregates (>250 μm), microaggregates (53–250 μm), and <53-μm particles. A subsample of macroaggregates was disintegrated and separated into coarse particles (>250 μm), occluded microaggregates, and occluded <53-μm particles. Concentrations and natural abundance of stable isotopes for carbon (C) and nitrogen (N) were determined for each fraction, and ratios (C:N, stable isotopes) were used to characterise organic matter for each fraction. Key results Macroaggregates were the largest fraction of bulk soil (71%) and were 15% greater in kaolinite- than smectite-dominated soils. Macroaggregates were composed of coarse particles (14%), occluded microaggregates (62%), and occluded <53-μm particles (24%). Concentrations of C and N widely varied among fractions but the variation was not related to clay mineralogy. The C:N ratio and stable N isotope ratio indicated more decomposed organic matter in kaolinite- than smectite-dominated soils. Conclusions and implications Macroaggregates composed of plant detritus and microaggregates in the Barro Colorado Island soils imply that the aggregate hierarchy route ultimately protects soil organic matter in this tropical forest environment.

An improved blockchain based encryption scheme for secure routing in wireless sensor network using machine learning technique

AbstractA secure routing scheme plays an integral role in ensuring the secure routing and efficiency of wireless sensor networks (WSNs). Recently, many studies have been undertaken to improve the data aggregation process, data security, and routing security. However, these approaches are highly suffered due to major limitations like time complexity, malicious attacks, and data insecurity. This research aims to develop an improved blockchain based encryption scheme for secure routing in wireless sensor networks using machine learning techniques. An improved artificial quantum beetle swarm neural network approach is proposed to perform the data aggregation among the sensor nodes. The cosine equivalent function and packet density correlation degree approach is emphasized to perform clustering in sensor nodes. The cluster head (CH) is selected based on the similarity of each sensor node. The Kalman filtering technique is introduced to filter out the data from unwanted malicious. This technique helps to filter the data before it is sent to the CH. Blockchain‐based encryption scheme is emphasized for data security by preventing malicious attacks during data transmission. The enhanced differential evaluation based firefly routing protocol is presented to maintain secure routing by choosing the optimal routing for the protected data transmission. Performance metrics such as latency, throughput, routing performance, energy consumption, FPR, blockchain (BC) computational performance, dead sensor count and average hop count are analyzed and compared with existing techniques. In an experimental scenario, the proposed approach achieves a maximum packet delay of 500 slots, throughput of 581 times/s, a latency of 0.31 ms, a computation complexity of 0.6, and a routing complexity of 2081 ms with a BC computation time of 95 ms. The performance of the proposed approach shows a better result than other existing approaches.

CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic

CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic

The impact of the cost of road transport on the price of Brazilian soybean: the case of Mato Grosso do Sul and the practice of collaborative logistics

Of vital importance to the economy of Mato Grosso do Sul, soybean held the second position in the overview of the products exported in 2020, however, the state is distant from the main export terminals and with high dependence on the road modal. In view of this, the present study sought to evaluate the impact of the average cost of transportation on the price of Mato Grosso do Sul state soybean and the adoption of the practice of collaborative logistics as an alternative to reduce transportation costs. In order to calculate the average cost of road transport, the criteria established in the National Policy of Minimum Costs of Road Cargo Transport (PNPM – TRC) were followed as a guide. Analyzes were made for the vehicle compositions with 7 axles and 9 axles, originating in the municipalities of Maracaju, Ponta Porã and São Gabriel do Oeste and as destinations the port of Santos, Paranaguá and São Francisco do Sul. The results indicate that the vehicle compositions with 9 axles presented the lowest cost per ton transported for all routes analyzed and that, in the comparison between the current scenario and the ideal scenario that considers the adoption of the practice of collaborative logistics with the aggregate of routes Santos (route 1a), Paranaguá (route 1b) and São Francisco do Sul (route 1d) the reduction in the total financial cost for 7-axle compositions was R$ 49,936,261.64, representing a reduction of 5.24% and for 9-axle compositions the reduction in the total financial cost between the current scenario and the ideal scenario of the same aggregate route was R$ 43,462,818.45, representing also a reduction of 5.24%.

An Insight on Clustering Protocols in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSN) have drawn the attention of many researchers as well as general users in recent years. Since WSN has a wide range of applications, including environmental monitoring, medical applications, and surveillance, their usage is not limited. As energy is a major constraint in WSN, it is necessary to employ techniques that reduce energy consumption in order to extend the network’s lifetime. Clustering, data aggregation, duty cycling, load balancing, and efficient routing are some of the techniques used to reduce energy consumption. In this paper, we discuss in details about clustering, its properties, the existing clustering protocols. The clustering protocols that support data aggregation will also be discussed. The paper concludes with considering the impact of clustering and data aggregation in WSN.

Improved Energy Based Multi-Sensor Object Detection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are spatially distributed to independent sensor networks that can sense physical characteristics such as temperature, sound, pressure, energy, and so on. WSNs have secure link to physical environment and robustness. Data Aggregation (DA) plays a key role in WSN, and it helps to minimize the Energy Consumption (EC). In order to have trustworthy DA with a rate of high aggregation for WSNs, the existing research works have focused on Data Routing for In-Network Aggregation (DRINA). Yet, there is no accomplishment of an effective balance between overhead and routing. But EC required during DA remained unsolved. The detection of objects belonging to the same event into specific regions by the Bayes Node is distributed through the Sensor Nodes (SNs). Multi-Sensor Data Synchronization Scheduling (MSDSS) framework is proposed for efficient DA at the sink in a heterogeneous sensor network. Secure and Energy-Efficient based In-Network Aggregation Sensor Data Routing (SEE-INASDR) is developed based on the Dynamic Routing (DR) structure with reliable data transmission in WSNs. Theoretical analysis and experimental results showed that in WSN, the proposed Bayes Node Energy Polynomial Distribution (BNEPD) technique reduced Energy Drain Rate (EDR) by 39% and reduced 33% of Communication Overhead (CO) using poly distribution algorithm. Similarly, the proposed MSDSS framework increased the Network Lifetime (NL) by 15%. This framework also increased 10.5% of Data Aggregation Routing (DAR). Finally, the SEE-INASDR framework significantly reduced EC by 51% using a Secure and Energy-Efficient Routing Protocol (SEERP).

An enhanced secure data aggregation routing protocol for sensor networks

An enhanced secure data aggregation routing protocol for sensor networks

An enhanced secure data aggregation routing protocol for sensor networks

An enhanced secure data aggregation routing protocol for sensor networks

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

Towards energy- and interference-aware health monitoring by using WBANs in medicine services

We study the problems of energy consumption and interference for Wireless Body Area Networks (WBANs). Our model relies on a real medical monitoring, where the patient’s vital signals such as EEG, ECG, and EMG are regularly sampled at equal intervals to send to a medical center. For this purpose, an efficient scheme called Energy- and Interference-aware Routing and Scheduling (EIRS) is presented, which is based on three phases. EIRS first uses an energy-aware scheme to develop a data aggregation routing tree for each WBAN. In phase 2, it finds a set of appropriate channels for developed tree in terms of interference. Phase 3 employs a greedy algorithm to assign interfering transmissions to distinct time slots, while scheduling non-interfering transmissions at the same time slot. To realize energy efficient and reliable communication, EIRS jointly employs Euclidean distance, residual energy, channel diversity, multi-radio technology, and spatial reuse. As another advantage, the proposed scheme reduces inter-WBAN interference as well as intra-WBAN interference. Numerical results confirm the superiority of EIRS in terms of network lifetime, consumption of radio resources, interference, and delay.

A secure aggregation routing protocol with authentication and energy conservation

AbstractWireless sensor networks (WSNs) have emerged in various applications. However, the uneven resource allocation of sensor devices seriously affect the lifetime of wireless sensor networks. Moreover, the privacy leakage problem in wireless sensor networks is becoming increasingly serious. To solve the imbalance of energy consumption and privacy disclosure of nodes, we propose a secure aggregation routing protocol with authentication and energy conservation. Firstly, a new cluster head and cluster gateway selection method is proposed to balance the energy consumption of each node. By designing cost function and node density function, a cluster head and cluster gateway selection indicator is defined, and furthermore, a cluster head and cluster gateway node selection method is proposed. Secondly, a key negotiation scheme with authentication function is proposed. By introducing a trusted authorized node, a node authorization method is proposed to distribute the authorized keys. Finally, by introducing the proposed cluster head selection method and key negotiation scheme into WSN, a secure aggregation routing protocol with authentication and energy conservation is proposed. Performance analysis shows the proposed protocol obtains more executable rounds with less total energy consumption and extend the lifetime of wireless sensor networks.

Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment.

Forest fire monitoring is very much needed for protecting the forest from any kind of disaster or anomaly leading to the destruction of the forest. Now, with the advent of Internet of Things (IoT), a good amount of research has been done on energy consumption, coverage, and other issues. These works did not focus on forest fire management. The IoT-enabled environment is made up of low power lossy networks (LLNs). For improving the performance of routing protocol in forest fire management, energy-efficient routing protocol for low power lossy networks (E-RPL) was developed where residual power was used as an objective function towards calculating the rank of the parent node to form the destination-oriented directed acyclic graph (DODAG). The challenge in E-RPL is the scalability of the network resulting in a long end-to-end delay and less packet delivery. Additionally, the energy of sensor nodes increased with different transmission range. So, for obviating the above-mentioned drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) is proposed. The CAA-ERPL is compared with E-RPL, and the performance is analyzed resulting in reduced packet transfer delay, less energy consumption, and increased packet delivery ratio for 10, 20, 30, 40, and 50 nodes. This has been evaluated using a Contiki Cooja simulator.

Relay selection, clustering, and data aggregation routing in wireless body area networks

SummaryThis paper addresses the problems of relay selection, clustering, and routing to extend the lifetime of wireless body area networks. We first propose an efficient algorithm called “Energy‐aware Relay Selection and Cluster‐based Routing (ERSCR)” to develop a hybrid data aggregation tree in the network. ERSCR has three phases, including the relay selection, clustering and Cluster Head (CH) selection, and data transmission. It divides the biosensors into several clusters and selects an appropriate CH for each cluster. Each biosensor transmits data to its CH or relay node. The aggregated data are then routed to the sink through an energy‐balanced routing tree. The proposed scheme considers both residual energy and distance for routing data of biosensors. It not only reduces the energy consumption in the network but also balances the energy consumed by different biosensors. Subsequently, we improve the ERSCR algorithm and introduce the “Joint Relay Selection, Clustering, and Routing (JRSCR)” algorithm to achieve a better network performance. JRSCR benefits from the advantages of the ERSCR algorithm. Moreover, it reduces the number of transmissions with the direct use of the relay nodes as CH. As another advantage, both ERSCR and JRSCR algorithms are compatible with the natural physical states of the human body. Simulation results demonstrate the efficiency of the proposed algorithms in terms of energy consumption, network lifetime, and maximum hop count.

Aggregate structure evolution induced by annealing and subsequent solvent post-treatment for thermoelectric property enhancement of PEDOT:PSS films

• Effect of aggregation structure evolution on thermoelectric performance is studied. • Thermal annealing induces notably increased PEDOT crystallinity. • Solvent treatment affects nanofibril formation, edge-on stacking, phase separation. • Power factor of the treated film is 216 times of that of the pristine. Organic polymer thermoelectric (TE) materials have developed rapidly in recent years in terms of strategies to improve their TE performance. However, the effect of molecular mechanisms on their TE performance remains yet to be unveiled. Insights into the evolution of crystalline microstructures of conjugated polymers are critical in determining their aggregate structure and hence their TE performance. Herein, an effective approach has been developed to modify the aggregate structures of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS). During annealing and subsequent solvent post-treatment, phase separation between the conductive PEDOT and the insulating PSS occurs and the excess PSS in the skin layer is effectively removed. As a result, carrier mobility and carrier concentration are synergistically boosted via the formation of well-crystallized molecular morphology and the increase of PEDOT doping level, leading to significantly enhanced TE performance with an unprecedented power factor that is 216 times of that of the pristine samples. The corresponding molecular mechanisms are discussed in detail and the aggregate structure evolution route is thoroughly proposed. It is believed that the aggregate structure evolution proposed in this work and its influential mechanisms on TE performance can be extended to other polymers and potentially direct the future design of high-performance polymer-based TE materials.

Q-learning based routing for in-network aggregation in wireless sensor networks

In-network data aggregation is an inherent paradigm that extends the lifetime of resource-constrained wireless sensor networks (WSNs). By aggregating sensor data at intermediate nodes, it eliminates data redundancy, minimizes the number of transmissions and saves energy. A key component of in-network data aggregation is the design of an optimal routing structure. However, when the monitoring environment is highly dynamic, the conventional in-network aggregation routing algorithms lead to unnecessary redesign, high overhead and inferior performance, and make in-network aggregation a challenging task. This paper proposes a novel adaptive routing algorithm for in-network aggregation (RINA) in wireless sensor networks. The proposed approach employs a reinforcement learning method called Q-learning to build a routing tree based on minimal information such as residual energy, distance between nodes and link strength. In addition, it finds the aggregation points in the routing structure to maximize the number of overlapping routes in order to increase the aggregation ratio. Theoretical analysis proves the feasibility of the proposed approach. Simulation results show that the aggregation tree constructed by RINA increases the network lifetime by achieving optimum data aggregation and outperform other state-of-the-art approaches in terms of different significant features under different simulation scenarios.