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The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively. [ABSTRACT FROM AUTHOR]

A wireless sensor network is a wireless communication network, and each sensor node has several sensors to collect environmental information. Wireless sensor network nodes have limited energy resources and need optimal routing protocols to reduce energy consumption. Failure to reduce energy consumption by sensor nodes reduces network life and efficiency. The main problem in routing is finding optimal paths for sending packets by reducing energy consumption in sensor nodes. This paper proposes an optimal routing method to reduce energy consumption in wireless sensor networks. In the first step, wireless sensor nodes are clustered with the Water strider algorithms (WSA), and cluster heads are selected for routing. In the second step, a mobile sink collects the packets from the cluster heads and sends them to the base station. The mobile sink uses the Ant colony optimization (ACO) algorithms to travel a shorter path between the cluster heads. The authors contribute to presenting a discrete version of the WSA algorithm for cluster head selection to reduce energy consumption. The authors contribute by providing a more comprehensive objective function for clustering network nodes considering error rate, energy consumption, PDR rate, and Euclidean distance. Cluster head traversal with a version of the ACO algorithm to reduce energy consumption and cluster head traversal coding like the TSP problem is the contribution of other authors. The paper aims to reduce energy consumption, reduce the error rate of sending packets and increase the lifetime of the wireless sensor network. Experiments are simulated on several simulated scenarios in Matlab. Criteria such as energy consumption, Packet delivery ratio (PDR), package loss rates, and the number of alive nodes to evaluate the proposed method are used. Experiments show that the proposed algorithm reduces the energy consumption and loss rates of packages of the wireless sensor network by optimally selecting cluster heads and increasing the PDR and number of alive nodes. Comparisons show In terms of energy consumption, Packet delivery ratio (PDR), Loss rates of packages, and the number of alive nodes, the proposed method is more efficient than Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Information-centric wireless sensor networks, and Cluster based routing (CBR) routing methods. The PDR index in the proposed method is equal to 97.3% and is higher than PSO, GWO, and CS algorithms. The delay of the proposed method in routing is 25.97%, 5.78%, and 17.98% less than HHO, WOA, and GWO algorithms, respectively. [ABSTRACT FROM AUTHOR]

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Abstract Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

Contrary to homogeneous WSNs, heterogeneous WSN protocols make use of sensor fork with a variety of capabilities to extend the network's life, improve cluster stability, and assure accurate information transfer. Even though numerous authors have put forth various protocols, none of them have been able to successfully balance power consumption among basic fork, advanced fork, and cluster heads according to application needs and localization. Improving system longevity and performance requires reducing power consumption by sensor fork. While the location of the base station is known, each protocol arranges the sensor fork at random. Cluster head selection, set-up, and steady state phases are the standard three processes in the protocols. Depending on the network design, each protocol's decision-making process considers the node's remaining power as well as the system's total power. This study examines partitioning or cluster strategies based on power remaining and contrasts them in terms of a numerals of facets, including power effectiveness and stability duration. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios. [ABSTRACT FROM AUTHOR]

The widespread employment of wireless sensor networks in various fields necessitates the urgent creation of methods for prevailing over known shortcomings of this network category. Energy shortage as one of the most restrictive deficiencies of the employed sensors in this network category has encouraged many researchers from both academic and industry communities to propose efficient solutions to contribute to efforts done with the aim of decreasing energy consumption and consequently increasing the wireless sensor networks’ lifetime. Among bunches of schemes proposed in this regard, cluster-based routing protocols have demonstrated promising results so far. Plenty of these schemes have improved network communication and minimized delay, however, they still need to be improved in the crucial aspects for which they were proposed, namely energy consumption reduction and network lifetime prolongation. Considering all these pivotal points, a novel cluster-based hierarchical routing protocol, named Pizzza, is introduced in this paper. Pizzza is creatively designed by forming minimum spanning trees among communicating nodes in each sector-shape cluster, where only eligible nodes from the first level of the architecture can undertake cluster head leading role. Employment of this innovative scheme has concluded in the prolongation of the network lifetime through the reduction in energy wastage resulting from the elimination of reverse data flow from BS, data transmission to the nearest neighbors, and balanced energy consumption in the network. The efficient energy consumption in Pizzza has resulted in a 65.52% prolongation in the network lifetime and a 77.05% enhancement in the residual energy of the network compared to a selected set of popular and efficient protocols.

In multi-hop wireless networks (MWNs), hierarchical structures are important to achieve scalable routing control, as well as clustering algorithms for creating such structures and so have been extensively studied. Due to the constraints of MWNs as distributed systems, these clustering algorithms must be implemented in an autonomous and decentralized manner. In particular, ensuring transparency to network topology and node mobility is important. Spectral clustering is a technique for appropriately creating clusters regardless of the network topology. However, since this technique requires information on the entire network, it is difficult to implement it autonomously and in a decentralized manner. Therefore, autonomous and decentralized spectral clustering has only succeeded in simple grid networks. This paper proposes a spectral clustering algorithm that can be implemented in an autonomous and decentralized manner for any network topology. In this method, a certain spatial structure is generated in an autonomous and decentralized manner by using differential equation-based temporal evolution equations; clusters are then formed using the spatial structure yielded by the temporal evolution equations. We demonstrate that this method can realize spectral clustering in an autonomous and decentralized manner in a static network model and also can realize a stable cluster structure that responds to node movement in a dynamic network model.

Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period.

The minimal energy sensor nodes are critical to the long-term viability of any wireless sensor network WSN). Clustering is used for this purpose. Choosing an efficient Cluster Head (CH) is critical in such cluster-based networks, as they are accountable for aggregating and transmitting data from their associate nodes to the base station (BS). A Generative Adversarial Networks (GAN) is proposed in this work to improve the selection of CH. As part of the fitness function, nodes' residual energy, average energy, and inter-cluster distance are all considered. In an effort to further reduce energy consumption, a GAN routing method is proposed for use at the base station level for Efficient Energy. Simulations are used to evaluate the proposed ideal.The WSNs which require long life time with minimum cost sensors demand the proposed work. The research about the human unattainable places can be fit to necessitate this work. This method supports the maintenance of mines and petroleum refineries. In terms of energy consumption and network life expectancy, the results demonstrate a substantial improvement. And also, the proposed technique is analyzed and compared along with the existing approaches as Low-Energy Adaptive Clustering Hierarchy (Security based (S-LEACH), Cluster based (C-LEACH, More Energy Efficient-LEACH) (ME-LEACH) schemes. The proposed method detects the best location of storage-nodes for the sensor network. There is no need of agitation on battery drain up of storage-nodes (because of wireless recharge) which is a highly energy spending unit. The proposed method improves the network lifetime by a significant level. The proposed method is best fit to mines, petroleum refineries, forest department and military. The proposed method behaves as not only better storage scheme but also best fit to retrieval schemes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period. [ABSTRACT FROM AUTHOR]

There are a variety of applications for wireless sensor networks (WSNs), such as military, health monitoring systems, natural disasters, smartphones, and other surveillance systems. While the primary purpose of sensor nodes is to collect unattended data in hostile environments, many are placed in large numbers and operate independently. Due to limited capabilities, power is often limited. Therefore, these nodes are grouped into clusters to increase communication efficiency. In WSNs, two different routing protocols are possible: apartment and hierarchical or clustering protocols. Due to their significant role in minimizing energy consumption, hierarchical methods have become very popular in clustering. In cluster-based methods, nodes are organized into clusters, and the sensor node with the most resources is appointed as the cluster head (CH). In this paper, we present a Systematic Literature Review (SLR) explaining the difficulties in developing cluster-based methods, critical factors for clustering, and hierarchical clustering protocols. The most important factor of a routing protocol for WSN is the energy consumption and lifetime of a network. Focusing on energy consumption, different cluster-based methods were analyzed to determine which technology should be deployed by analyzing specific criteria to support the selection process. Additionally, the pros and cons of different protocols are listed with their relevance in specific scenarios. To identify these protocols, a systematic literature review was conducted using research studies published from 2010 to 2021, with 30 papers analyzed in the final phase. Based on the results of this SLR, several issues need to be further investigated with respect to the interaction of the potential technology with the Internet of Things (IoT) and Vehicular Ad-Hoc Networks (VANETs). [ABSTRACT FROM AUTHOR]

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed. [ABSTRACT FROM AUTHOR]

Group communications is central to information dissemination in tactical edge environments as an approach to delivering information to multiple nodes in a bandwidth efficient manner. The NATO IST-161 Research Task Group on Efficient Group and Information Centric Communications in Mobile Military Heterogeneous Networks has been exploring and evaluating multiple approaches, including specific middleware capabilities that provide group communications. Some of these middleware implementations incorporate their own capabilities for information routing / forwarding, which raises the question of layering them on top of existing IP multicast forwarding mechanisms. This paper examines the benefits and tradeoffs of using an underlying network forwarding approach, Simplified Multicast Forwarding (SMF) aided by the OLSRv2 unicast routing protocol, in combination with group communications middleware. The experiments are carried out in the context of disseminating Blue Force Data in the Anglova scenario with and without the assistance of SMF and OLSRv2. The current results show that further integration is necessary between the middleware protocols and the underlying routing protocols in order to achieve the desired performance. [ABSTRACT FROM AUTHOR]

Nowadays, Wireless Sensor Networks (WSNs) is enhancing for different applications. Simultaneously, energy consumption for processing the tasks in most of the applications has also been increased. The nodes in the network may die while utilizing more energy for executing the tasks. The death of nodes causes various issues in data transmission. In most cases, the nodes next to the sink may have more traffic and it makes the energy to drain quickly. Such situation can be handled with an optimization algorithm. In this paper, an energy efficient data aggregation approach is proposed by the combination of multi-objective clustering and optimization-based routing algorithm. Initially, the K-Means clustering technique is employed for cluster formation. Further, cluster head is selected using Adaptive Group Teaching Optimization Algorithm based on multiple objectives mainly considers on reducing the energy usage for preventing nodes from death. A minimum spanning tree algorithm is employed to create the communication route form sink node to cluster head. Finally, the path for transmitting the data to the base station is identified with the Improved Deer Hunting Optimization algorithm. Performance of presented routing algorithm is compared along with existing routing approaches to compare the performance and is found to be performing better than them. [ABSTRACT FROM AUTHOR]

Control and user (data) plane separation (CUPS) is a concept applied in various networking areas to scale network resources independently, increase the quality of service, and facilitate the autonomy of networks. In this study, we leverage this concept to design a plane-separated routing algorithm, CUPS-based hierarchical routing algorithm (CHRA), as an energy-efficient and low-latency end-to-end communication scheme for clustered ad-hoc networks. In CHRA, while cluster heads constitute the control plane to conduct network discovery and routing, ordinary nodes residing in the user plane forward packets according to the routing decisions taken by the control plane. Exploiting the CUPS, we avoid exhausting cluster heads by offloading packet-forwarding to ordinary nodes and improve the quality of service by utilizing alternative paths other than the backbone of cluster heads. Our simulation results show that CHRA offers a better quality of service in terms of end-to-end latency and data-to-all ratio, and promotes fairness in energy-consumption in both stationary and mobile scenarios. [ABSTRACT FROM AUTHOR]

Summary: A sensor network is a situation‐based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load‐balanced and fuzzy‐ACO (DLB‐fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree‐ and energy‐balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster‐based hierarchical routing. Communication is performed at two levels. In the first level, node‐to‐cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy‐integrated ACO method is applied for inter‐cluster route formation. The route optimization is here performed under fuzzy‐based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed load‐balanced protocol is analyzed against the LEACH, LEACH‐C, LEACH‐CC, M‐LEACH, Fuzzy‐PSO, Fuzzy‐ACO, Fuzzy‐Cuckoo, and FMCB‐ER protocols. The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network typically uses a large number of power‐constrained sensor nodes for sensing phenomenal data and for its processing. The lifetime of a sensor node is limited and this is a great challenge for researchers to sustain the network for a long time. Since, the data transmission from a sensor node to the base station is more power consuming, the hierarchical routing protocols play a major role in multihop communication and to prolong the network life time. It uses a clustering approach with an overhead of cluster head (CH) selection to attain this. This overhead needs proper addressing and to be reduced by some means. The work in this paper employs a new approach based on low‐energy adaptive clustering hierarchy (LEACH) and considers network load for electing CH in addition to number of links, residual sensor energy and distance. Here, energy and time are used for calculating load on a CH. The simulation results show that the proposed protocol performs better than LEACH, LEACH‐I, and NR‐LEACH algorithms on the parameters of average energy ratio throughput, end‐to‐end delay, packet delivery ratio, and a number of alive nodes. [ABSTRACT FROM AUTHOR]

A wireless sensor networks (WSN's) has stimulated significant research work among the researchers in monitoring and tracking tasks. It's a quite challenging task that needs to cope up with various conflicting issues such as energy efficiency, network lifetime, connectivity, coverage, etc. in WSN's for designing various applications. This paper explores the recent work and efforts done in addressing the various issues in WSN's. This paper focused on basic concepts regarding the WSN's and discusses meta-heuristics and heuristics algorithms for solving these issues with recent investigations. Various optimization algorithms in the context of WSN, routing algorithms, and clustering algorithms were discussed with details of earlier work done. This paper delivers various Multi-Objective Optimization approaches deeply for solving issues and summarizes the recent research work and studies. It provides researchers an understanding of the various issues, trade-offs between them, and meta-heuristics and heuristics approach for solving these issues. A glimpse of open research challenges has also been provided which will be helpful for researchers. This paper also gives an insight into various issues, open challenges that still exist in WSN's with their heuristics and meta-heuristics solutions and also focuses on various conflicting issues as well. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) based Communication has been devised for exchanging data with low cost, minimal maintenance, and for more convenience. These communications are used in various applications like monitoring, surveillance, defence, healthcare, automation etc. Several routing protocols had been proposed for energy efficient wireless communication and to prolong the network life time. In such protocols, the network design plays a vital role in improving the network performance. The communication strategy among the sensor nodes depend on network design criteria. The paper therefore, presents a novice hierarchical structure based network design and energy efficient routing method for WSN. Here the clusters are formed using fuzzy-multi-criteria decision approach and cluster heads are optimally selected using Analytical Hierarchy Process. The Penguin Search Optimization Algorithm is used for diversified and intensified strategic planning for both intra-cluster and inter-cluster communication. The proposed technique is compared with existing techniques on multiple parameters and found to perform better. It shows a throughput of 0.95 Mbps and minimal energy consumption of 0.35mj and proving it to be energy efficient. [ABSTRACT FROM AUTHOR]

Vehicular delay tolerant networks (VDTNs) present an efficient platform for delivering delay tolerant contents in vehicular networks by utilizing vehicle to vehicle or vehicle to road side infrastructure communications. Routing protocols play an essential role in VDTNs environment to provide high routing performance. However, most of existing vehicular network routing methods suffer from scalability issues and cannot make a trade-off between different performance criteria. In this paper we introduce an innovative hierarchical VDTNs scheme, called Hierarchical and Intelligent Vehicular Delay Tolerant Content Routing (HI-VDTCR), to establish a scalable delivery routing mechanism and provide a balance between different performance criteria. HI-VDTCR consists of two layers. An upper layer is responsible for sending messages to the destination vicinities without imposing high overhead to the network and a lower layer is tasked with delivering messages to the destination node by introducing a Markov decision process model, as an efficient reinforcement learning algorithm, for forwarding messages to the destination rendezvous. The proposed hierarchical and intelligent design empowers HI-VDTCR to provide an acceptable scalability in terms of network operation area and number of vehicle nodes. Evaluation results confirm that HI-VDTCR method outperforms other well-known vehicular routing methods. [ABSTRACT FROM AUTHOR]

In the past few decades, the architecture of optical networks has undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) optical networks to elastic optical networks (EONs) and later to space-division multiplexing (SDM) EONs, to address the continuous growth of Internet traffic. By 2024, Pbps-level optical networks are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, a newly designed architecture for optical networks called the spatial channel network (SCN) architecture, which achieves high cost efficiency by means of practical hierarchical optical cross-connects, has recently been proposed. However, the evolution of optical network architectures will simultaneously present challenges related to network optimization. For instance, with the evolution from WDM optical networks to EONs, the kernel network optimization problem was transformed from the routing and wavelength assignment (RWA) problem into the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the network optimization problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the network optimization problem in SCNs. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks, have found plenty of applications, nowadays. Due to the presence of tiny and restricted batteries in these little sensors, deployment of a power-efficient routing protocol is a must. Between all the already-proposed routing protocols, the hierarchical ones are more efficient in energy conservation than flat routing protocols. In order to decrease energy consumption and increase the network lifetime, we proposed a new hierarchical routing protocol, by dividing the network area to sectors and two levels and choosing Cluster Heads from the lower level, which is more near to the Base Station. In order to minimize the reverse flow of the data from BS, we use a tree structure in each sector. In addition, the frontier between two levels can be moved during network lifetime and having dead nodes. The results of simulations show that our proposed TSBC protocol outperforms LEACH, Multi-Hop LEACH and many other conventional routing protocols in energy conservation and in network lifetime. One of the most important properties of our scheme that can distinguish it from any other scheme is reverse flow from BS cancellation or at least deduction. The special formula, which is used in our protocol for CH selection, in order to prevent battery depletion in a special spot, can also be adapted to any other hierarchical clustering protocols to achieve higher energy-efficiency. It is still noteworthy to mention that thanks to devising special measures we highly prevent fragmentation occurrence in routing process in the network.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Recently fuzzy based hierarchical routing protocols such as F-MCHEL and F-SCH were proposed to improve the lifetime of a wireless sensor network. In certain cases F-MCHEL results in several small sized clusters increasing the inter cluster communication cost. This is due to the negligence of node degree in cluster head selection. The performance of F-SCH is observed to be better than F-MCHEL because super cluster head selection is based on centrality and mobility. Though it minimized inter cluster energy, the randomness in LEACH based cluster head selection inhibits the benefits of multilevel routing. Hence, with an objective to minimize the total energy consumed by the network, fuzzy based two-level hierarchical protocol is proposed. The objective is achieved by appropriate selection of cluster head and super cluster head. Cluster head selection is based on battery power, centrality and node degree while super cluster head selection is based on battery power, centrality and mobility of cluster heads. Experiments are carried out both in simulation and hardware to validate the proposal. The performance of the network in terms of first node, half node and last node death is studied and the proposal is compared with F-MCHEL and F-SCH. The results reveal the efficiency of the proposal. [ABSTRACT FROM AUTHOR]