Hierarchical Routing Research Articles

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Design a patient monitoring system in health care using cluster-based hierarchical routing for green communication

To achieve a healthcare patient monitoring system, this paper presents a clustering-based hierarchical approach. A group-based controlled structured routing architecture is used in the proposed method to group the various health monitoring sensors. A merged center will select a leader for each cluster and delegate the management responsibilities to that individual. Collecting information, organizing, and communicating with leading platforms are all the responsibilities of the cluster leader. The goal of this rotation is to keep the system under a constant load and to optimize the efficiency of the support equipment. A large number of simulations were used to validate our quantitative approach, and we compared our results with other methods already in use. Our study confirms that our approach not only reduces the energy required to maintain the network in various configurations, but also significantly increases their lifetime. The QoD of our proposed method was 4.9 %, compared with the QoD of 7.45 % and 9.82 % for DEEC and LEACH, respectively. Demonstrated that cluster-based hierarchical roads can enable a healthcare patient monitoring system.

Targeted V1 comodulation supports task-adaptive sensory decisions

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.

Effect of Energy-Efficient Routing for Packet Splitting using Chinese Remainder Theorem (CRT) for Wireless Sensor Networks (WSNS)

The most popular Wireless Sensor Network (WSN) protocol utilized for energy optimization of WSN nodes is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, which is a basic energy-efficient routing protocol of hierarchical routing protocol’s family, where the sensor nodes remain static. There is a need to extend the lifetime with respect to reducing the energy consumption of nodes, and this was accomplished using the Chinese Remainder Theorem (CRT. In this research large data packets are broken into smaller packets. The focus of the study is to develop an energy routing for splitting packets in Wireless Sensor Networks (WSNs) using Chinese Remainder Theorem (CRT) techniques. A further improvement in WSN lifetime is by utilizing fuzzy logic. Factoring node parameters such as residual energy of nodes, their centrality within clusters, and their distance to the base station and improving upon the selection of nodes as cluster heads are required. The developed application can be used to reduce the communication cost of Energy efficiency of WSN. The forwarding technique for this research is to split the packet sent by the source node of a WSN so that the maximum number of bits per packet that a node has to transmit is significantly minimized.

Computational intelligence techniques for energy efficient routing protocols in wireless sensor networks: A critique

AbstractWireless Sensor Networks (WSNs) play a pivotal role in modern data acquisition systems, but their energy‐efficient operation remains a significant challenge. This work provides a concise overview of the key challenges and proposed solutions discussed in the context of energy‐efficient routing protocols for WSNs based on a systematic literature review. The need for fault tolerance to ensure network resilience is vital, given the limited resources and environmental challenges faced by sensor nodes. This work emphasizes the importance of addressing several open research questions, including the integration of diverse topologies, cross‐layer routing, and the use of multiple mobile sinks. Additionally, it highlights the need for QoS‐aware routing for real‐time applications, mathematical modeling for protocol evaluation, and the challenges of testing routing protocols on real hardware. Furthermore, this article suggests leveraging the RPL routing protocol with the Node‐based Metric RNE for enhanced energy efficiency and explores the promising field of hierarchical routing protocols, exemplified by the LEACH protocol and its variants. This work serves as a roadmap for researchers and practitioners, identifying critical challenges and proposing directions for future research to achieve energy‐efficient routing protocols in WSNs, ultimately enabling effective communication in resource‐constrained environments.

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.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance Analysis in Surveillance Systems.

In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the sensing range (SR) factor equipped on the robots. Conventional path-based MCPP strategies considering SR, primarily rely on naive approaches, generating nodes (viewpoints) to be visited and a global path based on these nodes. Therefore, our study proposes a general MCPP framework for surveillance by dealing with path-based and area-based structures comprehensively. As the traveling salesman problem (TSP) solvers, our approach incorporates not the naive approach but renowned and powerful algorithms such as genetic algorithms (GAs), and ant colony optimization (ACO) to enhance the planning process. We devise six distinct methods within the proposed MCPP framework. Two methods adopt area-based approaches which segments areas via a hierarchical max-flow routing algorithm based on SR and the number of robots. TSP challenges within each area are tackled using a GA or ACO, and the result paths are allocated to individual robots. The remaining four methods are categorized by the path-based approaches with global-local structures such as GA-GA, GA-ACO, ACO-GA, and ACO-ACO. Unlike conventional methods which requires a global path, we further incorporate ACO- or GA-based local planning. This supplementary step at the local level enhances the quality of the path-planning results, particularly when dealing with a large number of nodes, by preventing any degradation in global path-planning outcomes. An extensive comparative analysis is conducted to evaluate the proposed framework based on execution time, total path length, and idle time. The area-based approaches tend to show a better execution time and overall path length performance compared to the path-based approaches. However, the path-based MCPP methods have the advantage of having a smaller idle time than the area-based MCPP methods. Our study finds that the proposed area-based MCPP method excels in path planning, while the proposed path-based MCPP method demonstrates superior coverage balance performance. By selecting an appropriate MCPP structure based on the specific application requirements, leveraging the strengths of both methodologies, efficient MCPP execution becomes attainable. Looking forward, our future work will focus on tailoring these MCPP structures to diverse real-world conditions, aiming to propose the most suitable approach for specific applications.

HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).

Hierarchical Energy Harvesting Aware Adaptive Fuzzy Routing with Data Compression for Energy Harvesting WSN

In present scenario of the world moving towards the smart systems. Smart systems and applications are developed using sensor nodes for data collection, data aggregation and decision making process. Wireless sensor nodes are configured in the physical environment for communications between the devices and the user. The number of limitations affecting the performance of wireless sensor based application. The various factors affecting the performance of the WSN based applications, one the major factor is energy efficiency of the sensors. Sensor nodes are battery powered devices and the sensors are dropping their energy during the transmissions. So we need a solution to overcome the energy efficiency issue of WSN based applications. In this paper, we proposed a methodology called Hierarchical energy harvesting adaptive fuzzy routing algorithm with uniform data quality compression to manage the energy efficiency of the sensor nodes. It uses a new energy management framework and allocating the energy budget to sensors and reduces the consumption of each node. The existing energy harvesting approaches only concentrate on find the best path and forward the data to base station. The performance evaluation shows the effective use of proposed method in WSN based applications.

A Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud Content Delivery Networks (CDNs)

Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100.

Models and Algorithms for Multiagent Hierarchical Routing with Time Windows

Models and Algorithms for Multiagent Hierarchical Routing with Time Windows

Clustering Algorithms and Optimization Strategies in WSNs: A State-of-the-Art Review

Wireless Sensor Networks (WSNs) have emerged as a groundbreaking technology for collecting data on various physical phenomena, offering the potential to revolutionize infrastructure systems. The paper provides an exhaustive exploration of the existing literature on clustering algorithms within WSNs and the diverse optimization techniques proposed for cluster head selection. A comprehensive analysis of various routing protocols, mobility-based solutions, and energy conservation strategies is presented, shedding light on their respective strengths and limitations. The examination encompasses a wide spectrum of approaches, including hierarchical cluster-based routing, mobile sensor integration, and energy-efficient techniques inspired by industry standards like ZigBee. The paper further explores ecological monitoring applications, redundancy reduction mechanisms, and power- efficient hierarchical routing protocols, offering valuable insights into their contributions to the field. This review paper aims to consolidate and synthesize the wealth of knowledge in the domain of clustering algorithms and optimization techniques for WSNs. By assessing the strengths and weaknesses of these methodologies, it seeks to provide a comprehensive resource for researchers and practitioners in the field, ultimately contributing to the advancement of energy-efficient and reliable wireless sensor networks. Keywords— APTEEN, EARP, ECCP, GPS, HEED, LEACH, PEGASIS, TEEN, WSN.

Models and Algorithms for Multiagent Hierarchical Routing with Time Windows

The problem of modeling real logistics systems arranged in a hierarchical manner is considered. Clusters of lower level consumers are formed that meet the time window (TW) constraints for each consumer and the cluster as a whole. In each such cluster, a traveling salesman’s route is constructed and the vertex closest to the central node, which is the vertex of reloading goods from heavy vehicles (Vs) to light Vs serving consumer clusters, is selected. The transshipment vertices, in turn, are combined into higher level traveling salesmen’s routes, taking into account TWs for routes of this level. The software implementation is tested on well-known networks. The technique is applicable for the synthesis of the central distribution center and system distribution centers of the lower level, as well as for calculating the required number of vehicles (agents).

A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics.

Neuromorphic cognitive computing offers a bio-inspired means to approach the natural intelligence of biological neural systems in silicon integrated circuits. Typically, such circuits either reproduce biophysical neuronal dynamics in great detail as tools for computational neuroscience, or abstract away the biology by simplifying the functional forms of neural computation in large-scale systems for machine intelligence with high integration density and energy efficiency. Here we report a hybrid which offers biophysical realism in the emulation of multi-compartmental neuronal network dynamics at very large scale with high implementation efficiency, and yet with high flexibility in configuring the functional form and the network topology. The integrate-and-fire array transceiver (IFAT) chip emulates the continuous-time analog membrane dynamics of 65 k two-compartment neurons with conductance-based synapses. Fired action potentials are registered as address-event encoded output spikes, while the four types of synapses coupling to each neuron are activated by address-event decoded input spikes for fully reconfigurable synaptic connectivity, facilitating virtual wiring as implemented by routing address-event spikes externally through synaptic routing table. Peak conductance strength of synapse activation specified by the address-event input spans three decades of dynamic range, digitally controlled by pulse width and amplitude modulation (PWAM) of the drive voltage activating the log-domain linear synapse circuit. Two nested levels of micro-pipelining in the IFAT architecture improve both throughput and efficiency of synaptic input. This two-tier micro-pipelining results in a measured sustained peak throughput of 73 Mspikes/s and overall chip-level energy efficiency of 22 pJ/spike. Non-uniformity in digitally encoded synapse strength due to analog mismatch is mitigated through single-point digital offset calibration. Combined with the flexibly layered and recurrent synaptic connectivity provided by hierarchical address-event routing of registered spike events through external memory, the IFAT lends itself to efficient large-scale emulation of general biophysical spiking neural networks, as well as rate-based mapping of rectified linear unit (ReLU) neural activations.

In-Depth Analysis of the LEACH Routing Protocol for WSNs

Wireless sensor networks (WSNs) have broad use in various industries, including the military, healthcare, and automation, because of their ability to assume control without human intervention. Most WSN projects use ambient sensors that take power from various sources to avoid the hassle of constantly replacing or charging batteries. Power efficiency and long network life are directly tied to routing methods. Hierarchical routing has been studied in academia, with the Low Energy Adaptive Clustering Hierarchy (LEACH) being the most popular option for enhancing WSNs' utility. This study includes our review of LEACH and its offspring strategies for WSNs and a historical summary of the protocols developed from LEACH's hierarchical routing design.

Trust management based multidimensional secure cluster with RSA cryptography algorithm in WSN for secure data transmission

WSNs are now widely used for information gathering and transmission using WSN. Due to its low cost and simple communication, this type of network is widely used in many applications. Although hierarchical routing protocols may handle a variety of applications, choosing a Cluster Head (CH) and balancing network overload are difficult problems. This recommended strategy provides LEACH Protocol based on Novel Trust Management with Cryptographic RSA algorithm (NTM-LEACH-RSA) to extend the lifetime of network and to consume less energy. Here the proposed methodology includes two aspects for improving Security in WSN. Using the suggested NTM-LEACH technique, cluster formation and cluster head election are carried out in the first phase. Here, the threshold function value, the distance and density between nearby nodes, and the trust value are used to elect the cluster head. Based on the energy domain and the distance domain, the threshold function value is estimated in this case. The RSA cryptography technique is employed in the second phase to protect data transmission and guarantee data integrity. By using simulation tools, the proposed NTM-LEACH-RSA methodology's performance analysis is estimated. In comparison to other algorithms currently in use, it also offers higher performance results.

The measurement and monitoring of Quality of service based on security analysis in wireless sensor network using deep learning architecture

Wireless sensor networks comprise of unattended little sensor hubs having low energy and low scope of correspondence. Machine learning has demonstrated its effectiveness in creating proficient cycles to deal with complex issues in different organization perspectives. This exploration propose novel procedure in wireless sensor network routing and security improvement by network observing utilizing profound learning model. The organization routing is done utilizing cluster based scalable hierarchical energy routing protocol (CSHERP) and the organization checking is completed utilizing fuzzy Boltzmann adversarial learning (FBAL). This routing protocol adopts an information driven strategy, with moderate hubs collecting information and sending it to a sink hub. The arrangement gave is valuable to additional investigation of expanded WSN networks. The trial examination is completed as far as energy efficiency, throughput, end-end delay, latency, QoS. The proposed technique attained energy efficiency of 96%, throughput of 91%, end-end delay of 86%, latency of 85%, QoS of 96%.

An Efficient Routing Awareness Based Scheduling Approach in Energy Harvesting Wireless Sensor Networks

Wireless sensor networks (WSNs) have emerged as a promising technology for various Internet of Things (IoT)-based sensing applications. Generally, sensor nodes are powered by non-rechargeable and low-capacity batteries; they only last for a small amount of time. Due to their dispersed nature, sensor nodes cannot have their batteries replaced or maintained locally. The energy harvesting (EH) concept shows commitment as a method for addressing the current energy shortage. Another problem is the intra-energy control of various network load functions, including coverage preservation, routing, clustering, etc. This study proposes an ERAS (efficient routing awareness scheduling) algorithm to address the abovementioned issues. The ERAS method employs a solar harvesting system as a hierarchical clustering-based routing protocol. The presented scheme dynamically alters nodes between states and adjusts to new states based on the insides of sensed data packets. Synchronization-based scheduling ensures proper coordination between the cluster head (CH) and cluster members (CMs). It selects the CH based on both remaining and harvested energy and the number of active nodes to improve throughput. Surprisingly proposed EASR algorithm performs efficiently while considering various performance matrices, including network lifetime, packet delivery ratio, energy consumption, and network sustainability. Extensive simulation results exhibit the network lifetime of the proposed ERAS technique (79.8%) compared to ERA, SEED and CREST methods as 67.3%, 73.8%, and 75.2%, respectively.

Implementation of Blockchain-Assisted Source Routing for Traffic Management in Software-Defined Networks

The control and infrastructure layers are split into Software-Defined Networks (SDNs). With the control and infrastructure planes split, new network applications may be developed with more simplicity and greater independence. On the other hand, the disadvantages of SDN create a slew of questions. In large-scale networks, such as Wide Area Networks (WANs) covering huge areas, more propagation delays substantially contribute to network convergence time. In addition, traditional SDN restricts network design flexibility due to the influence of controller location on network performance in large-scale networks. SDN-based source routing (SR) has emerged as a viable solution to the issues above, where the packet header field is used to specify a packet's route. This study presents an SR-based End-to-End (E2E) traffic management framework called SoRBlock. In SoRBlock, inter-domain routing uses blockchain technology, while intra-domain routing relies on the SR technique in SDNs. The simulation results show that the proposed SR-based SoRBlock framework outperforms the traditional hierarchical routing approach, HRA, in SDN networks by lowering path setup time (PST) and the number of controller messages. While the same (i.e., identical origin and target) service requests were used for all runs in the simulations, the proposed SoRBlock architecture presents almost three times less total PST between 45ms and 65ms than the HRA method between 130ms and 200ms due to the HRA approach's increased node-controller and controller-controller latencies. On the other hand, SoRBlock shows two times less PST ([75ms – 90ms]) than HRA ([150ms – 175ms]) when different service requests (i.e., different origin and target) were used. Concerning Controller Messages Processed (CMP), the HRA deals nearly 50% more controller messages between 7 and 15 than the SoRBlock between 3 and 10 when the number of domains varies, while the CMP in the SoRBlock scheme ([10 - 17]) approaches that in the HRA framework ([15 - 20]) regarding the ratio while the count of nodes rises in domains.