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The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility) and widely distributed network need to be carefully addressed. This paper therefore focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new Non-linear Levy Brownian Generalized Normal Distribution Optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and inter-node distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

Underwater Wireless Sensor Networks (UWSNs) are widely used in many fields, such as regular marine monitoring and disaster warning. However, UWSNs are still subject to various limitations and challenges: ocean interferences and noises are high, bandwidths are narrow, and propagation delays are high. Sensor batteries have limited energy and are difficult to be replaced or recharged. Accordingly, the design of routing protocols is one of the solutions to these problems. Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs, this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing (HAECR) strategy. First, this strategy divides hierarchical regions based on the depth of the sensor node in a three dimensional (3D) space. Second, sensor nodes form different competition radii based on their own relevant attributes and remaining energy. Nodes in the same layer compete freely to form clusters of different sizes. Finally, the transmission path between clusters is determined according to comprehensive factors, such as link quality, and then the optimal route is planned. The simulation experiment is conducted in the monitoring range of the 3D space. The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption, extending the network lifetime, and increasing the number of data transmissions.

With the growth of wireless sensor networks (WSN), new technologies like the Internet-of-Things (IoT) are being created. There may be challenges that come because when implementing these application areas in practice. The primary issue is energy utilization while data transmission between these resource restricted sensors. In this work, we present a cluster-based routing protocol for IoT to anticipate energy utilization. Furthermore, for cluster head selection and cluster updation, we presented a multi-population ensemble particle swarm optimizer. The simulation was carried out using the MATLAB platform and demonstrates its superiority over different approaches.

With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted a lot of attention for provisioning the required Quality of Service. IoT perception layer is responsible for data dissemination of the “Things”, and energy efficient clustering protocols play an important role in providing them with long-time battery operation. Clustering routing protocols are among the most efficient methods in large scale IoT networks and using location-based decision support can highly simplify the routing problem. Existing literature either assume that the nodes’ location is known, or rely on the expensive and energy consuming GPS modules which are not practical in most IoT use cases. Developing a low-cost and low-energy localization solution is an ongoing challenge. In this paper, an IDSS based clustering routing protocol, named GAPSO-SVM, is proposed for the IoT perception layer utilizing a Support Vector Machine (SVM) based algorithm to estimate the nodes’ locations, and a hybrid Genetic Algorithm-Particle Swarm Optimization (GAPSO) based mechanism for clustering optimization. Simulation results show that, although the exact location of the nodes is not available, compared with recent similar works the convergence rate and network lifetime is enhanced by up to 80% and 11%, respectively.

In underwater communication, establishing a communication link between the sensor in the sea bed and the surface sinks is a daunting task. Further, the data has to be transmitted with minimum delay and maximum reliability. Therefore, the present study proposes a biobjective routing protocol for underwater wireless sensor networks. The existing protocols are reviewed and it is found that the traditional depth based and vector-based routing protocols are not able to tackle these conflicting objectives and hence suffer transmission failures with high delay. A biobjective optimization of delay and reliability of routes is proposed to obtain pareto-optimal routes employing uninformed search technique and a modified greedy best first search heuristic. Through simulation experiments, it is found that the biobjective protocol performs better than depth based, delay based and reliability-based routing protocols. However, since the biobjective routing problem in underwater wireless sensor networks is known to be NP-hard and dynamic in nature, the computational effort of uninformed search in yielding the exact solutions increases as the network size increases. The modified greedy best first search heuristic is employed to yield sub-optimal routes with less computational effort without compromising on the quality of the solutions and hence suitable for larger networks.

In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.

Mobile adhoc network (MANET) is an autonomous network, comprising several hosts which are linked to one another via wireless connections. Since the nodes in MANET are mobile in nature, clustering and routing become a difficult task. Security is also a major issue which needs to be considered in the design of MANET protocols. The design of effective clustering and routing techniques helps to improve the network lifetime. Clustering and routing processes can be considered as an NP hard problem, which can be solved by evolutionary algorithms (EAs). With this motivation, this study presents an energy efficient clustering with secure routing protocol named EECSRP using hybrid EAs for MANET. The goal of the EECSRP technique is to cluster the nodes and elect optimal routes for energy efficient and reliable data transmission. The EECSRP technique involves two major stages. In the first stage, the cluster head selection and cluster construction process takes place using the niche mechanism with monarch butterfly optimization algorithm. Next, in the second stage, $$\beta$$ -hill climbing with grasshopper optimization algorithm is applied for optimal selection of routes in MANET. The performance validation of the proposed EECSRP model is assessed using NS2 tool and the results are inspected under several aspects. The experimental results show the promising performance of the EECSRP model over the other compared methods interms of different evaluation parameters.

Due to rapid growth and tremendous advancement, the Internet of Things (IoT) has been considered as a superior technology in recent years and increased its demand over the internet world because of its smart services. IoT has a wide range of applications over the industry, business, and academia. Still, security and routing remain a huge challenging task because of some reasons, such as heterogeneity issues, large energy consumption, and uncontrollable environment. In order to cope up with such security issues, it is essential to develop an energy-efficient routing protocol. Hence, this article presents an Adaptive energy harvesting and Trust aware routing (EHTARA) algorithm for optimal routing such that it prolongs the lifetime of the network. The optimal secure routing path is chosen to exploit the cost metric function. Moreover, the classification of big data is performed at MapReduce framework using stacked autoencoder, which is trained using the proposed Adaptive $$E^{2}$$ -Bat algorithm. Moreover, the proposed Adaptive $$E^{2}$$ -Bat algorithm is derived by incorporating adaptive principle with $$E^{2}$$ -Bat algorithm. The proposed Adaptive EHTARA achieved an energy of 0.948 J that reveals the superiority of the proposed scheme.

In general, Wireless Sensor Networks (WSNs) is developed with a group of distributed and locative sensor nodes for sensing different environmental conditions. The primary challenges faced by WSN are: low network time and transmission data delay. In crucial applications like monitoring the ecosystem, military and disaster management, and data routing, the incorporation of WSN is very critical. Henceforth, a Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol was proposed but it was found to be uneconomical for energy management. Also, the optimization of Cluster Head (CH) is considered as NP hard problem. This research work deals the issues in optimal path selection in routing of wireless sensor networks to increase the network lifetime. Various techniques are available in metaheuristics, such as the Charged System Search (CSS), that effectively used to resolve the routing problem. Despite of this, most of the meta-heuristics suffer from local optima issues. A charged system search and harmony search algorithm based routing protocol is presented in this research work. Experimental results present the efficient performance of proposed HS model with increased cluster structures, improved network lifetime and reduced end-to-end delay and average packet loss rate.

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate}, compared to the state of the art Q-routing protocols, Comment: 13 pages, 7 figures, 5 tables

Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.

With an advent of Underwater sensor networks, underwater communication has reached its new dimension of research. These networks are characterized by the elongated end to end delay, high energy utility and most importantly dynamic network topologies. By incorporating these characteristics, numerous automated routing algorithms has been proposed to achieve the energy efficient and low latency data transmission. But still, short-comings still exists due to the above mentioned characteristics and the most comprehensive routing algorithms are badly desired. In this article, a novel routing scheme based on Q-learning framework and Deep Extreme Learning Machines aided with Adaptive Firefly Routing algorithm to address the above mentioned research constraints including energy efficiency and network unsteadiness in underwater communication , that practices the hybrid combination of reward function and adaptive fireflies to determine the optimal routing mechanism. In this algorithm, traditional q-learning mechanism has been replaced by the powerful q-deep extreme learning mechanism which uses the adaptive reward function for the varying underwater environment and to boost the packet-delivery ratio (PDR) and throughputs. Also the paper uses the powerful firefly aided routing mechanism to achieve the energy efficient data transmission and to avoid the void dilemma problems. The extensive experimentations has been conducted on the proposed algorithm and compared with other state of art schemes such as Q deep q-Learning energy aware routing protocol (DQLER), DELR Protocols and VBF protocols in which the proposed algorithm has outperformed than the compared existing algorithms in terms of complexity, energy consumption , packet delivery ratio and end to end delay.

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness.

The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.

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.

With huge cheap micro-sensing devices deployed, wireless sensor network (WSN) gathers information from the region and delivers it to the base station (BS) for further decision. The hotspot problem occurs when cluster head (CH) nearer to BS may die prematurely due to uneven energy depletion resulting in partitioning the network. To overcome the issue of hotspot or energy hole, unequal clustering is used where variable size clusters are formed. Motivated from the aforesaid discussion, we propose an enhanced fuzzy unequal clustering and routing protocol (E-FUCA) where vital parameters are considered during CH candidate selection, and intelligent decision using fuzzy logic (FL) is taken by non-CH nodes during the selection of their CH for the formation of clusters. To further extend the lifetime, we have used FL for the next-hop choice for efficient routing. We have conducted the simulation experiments for four scenarios and compared the propound protocol’s performance with recent similar protocols. The experimental results validate the improved performance of E-FUCA with its comparative in respect of better lifetime, protracted stability period, and enhanced average energy.

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span.

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Wireless sensor networks accommodating the mobility of nodes will play important roles in the future. In residential, rehabilitation, and clinical settings, sensor nodes can be attached to the body of a patient for long-term and uninterrupted monitoring of vital biomedical signals. Likewise, in industrial settings, workers as well as mobile robots can carry sensor nodes to augment their perception and to seamlessly interact with their environments. Nevertheless, such applications require reliable communications as well as high throughput. Considering the primary design goals of the sensing platforms (low-power, affordable cost, large-scale deployment, longevity, operating in the ISM band), maintaining reliable links is a formidable challenge. This challenge can partially be alleviated if the nature of link quality fluctuation can be known or estimated on time. Indeed, higher-level protocols such as handover and routing protocols rely on knowledge of link quality fluctuation to seamlessly transfer communication to alternative routes when the quality of existing routes deteriorates. In this article, we present the result of extensive experimental study to characterise link quality fluctuation in mobile environments. The study focuses on slow movements (-1 ) signifying the movement of people and robots and transceivers complying to the IEEE 802.15.4 specification. Hence, we deployed mobile robots that interact with strategically placed stationary relay nodes. Our study considered different types of link quality characterisation metrics that provide complementary and useful insights. To demonstrate the usefulness of our experiments and observations, we implemented a link quality estimation technique using a Kalman Filter. To set up the model, we employed two link quality metrics along with the statistics we established during our experiments. The article will compare the performance of four proposed approaches with ours.

The Zone Routing Protocol of Mobile Ad Hoc Networks is one of the most reliable and efficient routing protocols. However, maintaining Quality of Service, energy-efficiency and optimal resource management is of utmost importance to provide timely and reliable communication services. In this paper, a highly robust and efficient reinforcement learning based Dynamic Power Management (DPM) and Switching control strategy is developed. Unlike classical DPM models, our proposed model employs both system layer information and PHY layer information to perform stochastic prediction to schedule PHY switching. Here, we have applied both known and unknown node/network parameters such as node’s holding period, Bit Error Probability to perform stochastic prediction. Our proposed model intends to maintain minimum BEP and holding period while assuring maximum resource utilization. To achieve it, the overall DPM model is formulated as Controlled Markov decision process, where employing hidden Markov model with Lagrange relaxation and cost function we achieved optimal resource allocation without compromising transmission quality, latency or computational costs. Through simulation-based evaluations, the proposed model outperforms the classical learning models by 50% reduction in PHY Transmission Action, 94% lower cost consumption, 83% decrease in buffer cost/delay and 94% reduction on packet overflow.

Wireless underground sensor networks (WUSNs) facilitate remote monitoring and control of various underground environments, which are suffering from a significant reliability problem. To address this problem and relieve the ongoing networking challenges, we propose a new concept, called the magnetic induction (MI)-assisted wireless powered underground sensor network (MI-WPUSN), which integrates the advantages of MI communication techniques with those of wireless power transfer mechanisms. MI-WPUSN offers a unique platform consisting of seven envisioned devices and four distinct communication modes to provide significant reliability potential, which is constrained by its complex and challenging data collection. To unlock the potential of MI-WPUSN, we provide a systematic research roadmap for MI-WPUSN data collection, spreading from sensor deployment to multiple channel access control and to frequency-selective routing establishment. Specifically, we first summarize the state-of-the-art research advances on sensor deployment, based on which we propose a bidirectional projection-based deployment topology that achieves high network reliability and discuss the sensor numerical determination problem resorting to weighted Voronoi diagrams. We then introduce the principles for multiple channel access control and review the existing research. Finally, after introducing kernel regression methods that can easily handle the frequency-selective property of MI-WPUSN, we develop a modified Q-learning-based routing protocol. In particular, we list and analyze the underlying challenges and related future issues in each direction.

In the recent years, we have witnessed an explosion of networking applications due to the reasons such as the rapid development of cloud infrastructure, edge computing, and the Internet of Things. Furthermore, those applications become complex, the problem related to the large size of the state space and limited metric collection has emerged. This leads to an urging demand for adaptive management method in network routing. However, the complexity of traditional routing algorithms can be prohibited for practical systems. To overcome this challenge, we propose a novel tensor-based reinforcement learning method to route and schedule the packet flows, which is adaptive and model-free. Moreover, we improve the learning quality and efficiency by combining the Tucker decomposition technique within the learning process so that the machine learning direction can be obtained with low complexity. Finally, simulation results show that our proposed algorithm can achieve better performance under the same training episode and more stable results with less convergence time than conventional routing method, K-shortest path, traditional reinforcement learning approaches (i.e. Q-learning and SARSA) and comparable results to DQL.

Recent communication technologies in the Wireless Sensor Networks WSN enable us to implement and construct various physical sensing nodes with electronic circuits for transmitting and receiving tasks. Low-Energy Adaptive Clustering Hierarchy LEACH is a well-known routing protocol used and implemented in researches and articles; also, there are various attempts from the researchers to modify it to achieve the best results. Since almost all of the articles considered deployment either predefined or randomly depleted for the whole reign of interest RoI. A sophisticated random node deployment method is proposed, named Farmer Disseminating the Seeds FDS, the farmer walks with almost uniform steps and parallel lines to cover the whole RoI. A formation of a uniform grid with deviated random local distances from grid crossings considered as a predefined number of normal nodes with one advance node that has double battery energy. FDS is used to improve the importance of deployment methods as an additive parameter in estimating lifetime and energy consumption in routing protocols. Traditional random deployment and FDS methods are compared.

In wireless sensor networks (WSNs), various routing protocols have been proposed based on clustering to achieve energy efficiency. The performance of such routing protocols can further be improved by deploying heterogeneous sensor nodes since responsibilities can be divided among different sensor nodes according to their heterogeneity. In this paper, we propose a novel non-uniform hierarchical clustering with dynamic route adjustment scheme, termed as NHCDRA, for heterogeneous WSNs considering two mobile sinks moving around the periphery of the sensor field. NHCDRA divides the sensor field into several non-uniform sized hierarchical clusters. The size of boundary clusters (near to the sink’s mobility path) is kept relatively large to accommodate more number of sensor nodes so that the responsibilities of cluster heads can be rotated efficiently to distribute the load. Furthermore, a set of dynamic route adjustment rules are defined to manage the routing paths as a consequence of mobility of the sinks. These rules reduce the overhead of route adjustment as well as ensure data delivery to the sink in minimal number of hops. Simulation results show that NHCDRA significantly reduces the data delivery delay and improves network lifetime when compared with state-of-the-art.

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.

Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the most popular techniques which provide the required energy-efficient communication and scalability in large-scale WSNs. In clustering, WSN is divided into number of clusters, and cluster head is selected in each cluster. However, in the existing clustering protocols, CH’s near base station undergoes large number of receiving, aggregating and transmitting operations in comparison with far away CHs. This imbalance of load on CHs and lack of structured multi-level clustering framework leads to early death of WSNs. Moreover, resolving the issues of scalability and data reliability along with load balancing is a very tedious task. In this paper, a hierarchical clustering and routing (HCR) protocol is proposed to formulate a load-balanced approach for clustering while taking care of energy efficiency, reliability and scalability. Firstly, a hierarchical layered framework is created to split the WSN into virtual circular layers for efficient transmission of data in hierarchical fashion. Subsequently, an ant lion optimizer is employed for the selection of CHs to ensure reliable, energy balanced and scalable cluster formation. Simulation results demonstrate that HCR protocol outperforms existing state-of-the-art clustering protocols in terms of network lifetime, balanced clustering, throughput and energy efficiency.

Saving energy in Wireless Sensor Networks (WSNs), is critical in different applications, such as environment monitoring, keeping human awareness and etc. Many studies have investigated energy consumption and improved the WSN lifetime longevity by reducing the energy consumption. Still, proposed approaches overlook the nodes’ distribution role in energy model and routing protocol, which is a key factor in a WSN. In this work, we propose a novel approach; namely GDECA; which assumes nodes’ distributions are mixtures of Gaussian distribution, as an assumption applied in real world. So GDECA rely on a distribution estimation borrowed from Machine Learning (ML) to fit the Gaussian Mixture Model (GMM) to the nodes and calculate the parameters for these distributions. Next, the estimated parameters are employed in Cluster Head CH selection policy. Besides, sinks routing is determined based on nodes distribution. Results showed the improvement close to 40–50% in energy consumption. As another outcome, GDECA keeps all the nodes active until end of the simulation. Observations also demonstrate that sinks path calculation using this approach is optimum, and randomly changing number of sinks increases energy consumption.

Wireless sensor networks have been used widely in environmental tracking and monitoring. Nevertheless, WSNs are facing a number of challenges, such as unreasonable cluster head selection and energy‐hole problems while nodes have unbalanced energy consumption. Thus, the paper aims to propose an energy‐efficient non‐uniform clustering routing protocol to enhance nodes energy efficiency and balance the energy consumption in WSNs. In addition, a non‐uniform clustering network partition is introduce to reduce the probability of energy hole occurrences and optimize cluster heads dynamical selection method to suggest an improved shuffled frog leaping algorithm. Ultimately, the simulation experiment has demonstrated a 20% gain of energy efficiency to extend network lifetime by the proposed protocol and enhanced algorithm.

Wireless sensor networks is one of the important parts in modern-day communication that employing low-cost sensor devices with different environmental and physical parameters. The communication path between the base station and sensor nodes are built with the help of an efficient routing protocol. In the past years, the existing protocols met few difficulties in terms of higher computational complexity, poor cluster head selection performance, higher energy consumption, expensive in cluster head selection, scalability management, and uneven load distribution, and so on. In this paper, we proposed BM-BWO with fuzzy logic based HEED protocol (BMBWFL-HEED). In BMBWFL-HEED, we use the combination of the boosted mutation based black widow optimization (BM-BWO) algorithm with HEED protocol to select the higher residual energy. Particularly, the mutation phase of the Black Widow Optimization (BWO) algorithm is improved with the help of direction average strategy (BM-BWO). The fuzzy logic system selects the most relevant and optima cluster heads. Different kinds of experimental analysis, benchmark functions are applied to evaluate the performance of proposed BMBWFL-HEED protocol and it is compared with some existing algorithms like ICFL -HEED, HEED, and ICHB-HEED. In the case of residual energy, a variation of energy consumption and the number of cluster head formation for both homogeneous and heterogeneous environments. The proposed BMBWFL-HEED method demonstrates optimal performance output among all other methods.

The emerging needs of innovative services in different areas led to the development of advanced intelligent systems using the heterogeneous technologies, devised by Internet of Things (IoT). IoT focuses on integrating the networks to facilitate smooth services to the humans. The interface between mobility patterns and the routing protocols contributes significantly to alter the performance of network. This paper proposes routing protocol based on Sunflower based grey wolf optimization (SFG) algorithm for improving the network lifetime. The first step is the simulation of IoT and then, the multipath routing is initiated in the IoT network. The SFG algorithm selects the best path from the multipath available for routing, based on Context awareness, Network lifetime, Residual Energy, Trust, and Delay. Finally, the multipath routing takes place in the IoT network through optimal routing path selected using the proposed SFG algorithm. The proposed SFG algorithm is designed by integrating sun flower optimization (SFO) and the grey wolf optimizer (GWO) such that the optimal routes are selected. The proposed SFG outperformed other methods with minimal delay of 0.779 s, maximal energy of 0.203 J, maximal network lifetime of 98.039%, and maximal throughput of 47.368%, respectively.

In recent years, wind power is emerging as one of the most promising renewable energy sources. Yet, reliability is less due to the unplanned downtimes caused by faults in the wind turbines. Monitoring of turbine elements with existing wired technology is not economical and hence replacement with smart wireless sensor devices are rapidly emerging as the next evolution. In such a wireless scenario, maximizing network lifetime and reliability is essential. The recently proposed hybrid multihop routing protocol aims to improve the lifetime of a WSN deployed in a widely distributed network. Though its performance is revealed to be better, the chain based CH selection and routing of data through MST increases the energy consumption thereby reducing the network lifetime. With an objective to circumvent this, three novel multihop routing strategies namely, Shortest Grid Routing, Partitioned Super Grid Direct Routing, and Shortest Super Grid Routing are proposed. For different network scenarios, the proposals are analyzed through simulation and validated via real time experimentation. The performance of the proposals is evaluated using metrics such as first node death, energy consumption, stability, and network reliability and are compared with the recently proposed protocol. The results reveal enhanced network lifetime and reliability which proves the applicability of the proposed routing protocols for CPS based infrastructure network.

Future Internet-of-Things (IoT) scenarios and applications are envisioned to be supported by emerging 5G networks. In this context, complex routing schemes for pervasive infrastructures are highly simplified, as every hardware element may stablish its own communication link with a 5G base station. However, this situation also introduces new risks, especially in the security field where innovative cyber-physical attacks and distributed denial of service attacks are becoming more popular and dangerous each day. Thus, data authentication, protection and anonymization in those new applications and schemes is a key challenge to be addressed. Besides, most devices in future IoT systems will be resource constrained, so traditional solutions based on private keys stored in devices’ memory and computationally heavy cryptographic algorithms will turn unsecure, inefficient or, directly, impossible to run. Therefore, in this paper we propose a new mechanism to protect, authenticate and anonymize data in IoT systems supported by future 5G networks. The proposed solution employs both digital watermarking techniques and lightweight cryptographic technologies. To generate keys in a secure and simple manner, physical unclonable functions are employed. Besides, to reduce as much as possible the computational cost of algorithms, chaotic dynamics will be considered. In order to evaluate the performance of the proposed solution an experimental validation based on simulation techniques is also carried out.

Wireless Body Area Networks (WBANs) are emerging in the livestock industry for remote monitoring of cattle using wireless body sensors (WBS). The random mobility of animals acting as nodes causes the network’s topology to change rapidly, originating from scalability and reliability issues. Stable transmission of acquired data to the base station requires an intelligent clustering mechanism that reduces the energy consumption and fulfills the network’s constraints. Several clustering techniques are available as a solution, but these techniques yield numerous cluster heads, resulting in more energy utilization. Higher energy utilization lessens the effective life of WBSs and increases monitoring costs. This paper presents a metaheuristic approach for selecting optimal clusters in WBANs to realize an energy-efficient routing protocol for livestock health and behavior monitoring. The proposed approach employs Ant Lion Optimizer (ALO) to select the optimal clusters for different pasturage sizes using sensors of different transmission ranges considering user’s preferences about cluster density. The proposed technique with ALO is compared with other recent techniques such as Ant Colony Optimization, Grasshopper Optimization, and Moth Flame Optimization. The comparison results show the proposed technique’s effectiveness in realizing energy-efficient protocols of WBANs for remote monitoring applications.

The Unmanned Aerial Vehicles (UAVs), organized as a Flying Ad-hoc NETwork (FANET), are used to make effective remote monitoring in diverse applications. Due to their high mobility, their energy consumption is increasingly affected leading to reduced network stability and communication efficiency. The design of node clustering of a FANET needs to consider the number of UAVs in the vicinity (transmission range) in order to ensure an adaptive reliable routing. Novel clustering schemes have been employed to deal with the highly dynamic flying behavior of UAVs and to maintain network stability. In this context, a new clustering algorithm is proposed to address the fast mobility of UAVs and provide safe inter-UAV distance, stable communication and extended network lifetime. The main contributions of this paper are first to extend and improve important metrics used in two well-known algorithms in the literature namely: The Bio-Inspired Clustering Scheme for FANETs (BICSF) and the Energy Aware Link-based Clustering (EALC). Then, exploiting the improved metrics, an Energy and Mobility-aware Stable and Safe Clustering (EMASS) algorithm, built upon new schemes useful for ensuring stability and safety in FANETs, is proposed. The simulation results showed that the EMASS algorithm outperformed the BICSF and the EALC algorithms in terms of better cluster stability, guaranteed safety, higher packet deliverability, improved energy saving and lower delays.

A swarm of unmanned aerial vehicles (UAVs) requires the transmission of mission-related data across the network. The resource constraints and dynamic nature of the swarm bring critical challenges to the design of UAV routing protocols. Most of the conventional ad hoc routing schemes are not intelligent and cannot adapt to the dynamic nature of UAV swarming networks. On the other hand, some artificial intelligence (AI)-based routing schemes may consume significant computational resources in the UAVs. In this article, a low-cost, adaptive routing protocol, namely skeleton-based swarm routing (SSR) , is proposed, which exploits an intelligent online learning algorithm and the topology features of the mission-driven UAV swarm to distribute the traffic over optimal routes. Here, the skeleton represents the most stable parts of the swarm formation. SSR architecture consists of three modules: 1) A geometric addressing module, which assigns geometric coordinates to each node based on the swarm skeleton structure; 2) A leaf-like routing pipe which allows the selection of multiple candidate routes around the shortest path; 3) An intelligent low-complexity learning model which determines how to distribute the packets inside the routing pipe to achieve load-balanced, high-throughput transmissions. The proposed skeleton-based scheme can also facilitate the UAV formation construction and morphing. The simulation results show that the proposed SSR protocol can noticeably improve the network performance (up to 100% throughput improvement) compared to the single path routing schemes, such as the ad-hoc on-demand distance vector (AODV) and link-quality and traffic-load aware optimized link state routing (LTA-OLSR) protocols.

Underwater wireless sensor networks (UWSNs) have emerged as a promising networking technology owing to their various underwater applications. Many applications require sensed data to be routed to a centralized location. However, the routing of sensor networks in underwater environments presents several challenges in terms of underwater infrastructure, including high energy consumption, narrow bandwidths, and longer propagation delays than other sensor networks. Efficient routing protocols play a vital role in this regard. Recently, reinforcement learning (RL)-based routing algorithms have been investigated by different researchers seeking to exploit the learning procedure via trial-and-error methods of RL. RL algorithms are capable of operating in underwater environments without prior knowledge of the infrastructure. This paper discusses all routing protocols proposed for RL-based UWSNs. The advantages, disadvantages, and suitable application areas are also mentioned. The protocols are compared in terms of the key ideas, RL designs, optimization criteria, and performance-evaluation techniques. Moreover, research challenges and outstanding research issues are also highlighted, to indicate future research directions.

In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order to explore the complex characteristics of IoT, machine learning methods, which rely on long training time to generate intrusion detection models, are proposed in the literature. Furthermore, these systems need to learn a new/fresh model from scratch when the environment changes. This study explores the use of transfer learning in order to generate intrusion detection algorithms for such dynamically changing IoT. Transfer learning is an approach that stores knowledge learned from a problem domain/task and applies that knowledge to another problem domain/task. Here, it is employed in the following two settings: transferring knowledge for generating suitable intrusion algorithms for new devices, transferring knowledge for detecting new types of attacks. In this study, Routing Protocol for Low-Power and Lossy Network (RPL), a routing protocol for resource-constrained wireless networks, is used as an exemplar protocol and specific attacks against RPL are targeted. The experimental results show that the transfer learning approach gives better performance than the traditional approach. Moreover, the proposed approach significantly reduces learning time, which is an important factor for putting devices/networks in operation in a timely manner. Even though transfer learning has been considered a potential candidate for improving IoT security, to the best of our knowledge, this is the first application of transfer learning under these two settings in RPL-based IoT networks.

Copyright © 2021 The Authors. Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

Future air-ground integrated vehicular network (AGVN) is a vital part of the expected ubiquitous communication system. The resources in AGVN are geographically separated and are often heterogeneous. Therefore, distributed management and cooperative management are key issues in a multi-tier based topology of AGVN. Recently, artificial intelligence (AI) shows advantages in facilitating the management of AGVN; however, adequate, real-time, and secure information for driving AI is still a big concern. In this article, we propose a conceptual enhanced AGVN by introducing a so-called surveillance plane to promote the management in the control plane. Thus, components such as virtual network functions (VNFs) and service function chaining (SFC) can get profits. Concepts and trials of utilizing the surveillance plane and side information are presented from three different aspects, namely networking, security, and application. We also summarize research directions in designing a more flexible management scheme by coupling the service, surveillance, and control planes of the enhanced AGVN.

The promising potential of distributed and interconnected lightweight devices that can jointly generate superior information-collecting and problem-solving abilities has long fostered various significant and ubiquitous techniques, from wireless sensor networks (WSNs) to Internet of Things (IoT). Although related applications have been widely used in different domains in attempting to collect and harness the ever-growing information flows, one major issue that impedes the further advancement of WSNs or IoT-based applications is the restricted battery power. Previous research mainly focuses on investigating novel protocols to save energy by reducing data traffic with the aid of optimal or heuristic algorithms. However, data packet behaviours and significant parameters involved are mostly preconfigured in a supervised-learning fashion rather than using an unsupervised learning paradigm and therefore may not adapt to uncertain or fast-changing environments. Hence, this paper concentrates on optimising the behaviours of data packets and significant parameters in a widely tested routing protocol, namely, Cognitive Packet Network (CPN), with the aid of several bio-inspired algorithms to increase the efficiency of energy usage and information acquisition. Two novel packet behaviours are introduced, and an on-line parameter calibration scheme is proposed to realise packet time-to-live (TTL) adjustment and rate adaptation. The simulation results show that the introduction of the bioinspired algorithms can improve the efficiency of information sharing and reduce the energy consumption.

The minimisation of energy consumption has become an emerging topic in wireless sensor networks (WSNs) as these networks enable a wealth of new applications. The internet of things (IoT) application is one of them and the current hype around the IoT is huge. Therefore, the development of efficient communication protocols for WSNs is a major concern. In this context, various research communities have triggered several optimisation techniques to provide energy-efficient solutions to WSNs. This study aims to apply the genetic algorithm (GA) in WSNs clustering and to evaluate its performance over another optimisation technique. The proposed protocol is analytically analysed and compared with a fuzzy logic (FL)-based routing protocol and traditional routing protocol like LEACH and K-means using a Java-based custom simulator. Simulation results show that there is a trade-off between GA-clustering and FL-clustering, but the overall performance of GA-clustering is very promising for obtaining optimal energy consumption.

In general, the conventional routing approaches in mobile ad hoc networks (MANETs) provide a route between the source and the target with a minimum hop count. Dynamic source routing (DSR) as a typical prototype of routing protocols relies on the minimum hop count parameter to provide the path without considering any other factors such as energy consumption and node energy level, which significantly affect the routing algorithm performance. To enhance the performance of the DSR, a novel and efficient routing mechanism based on a hybrid approach using the minimum execution time (MET) scheduling and moth flame optimization (MFO) scheme is proposed. This hybridization version of DSR is called the (MET-MFODSR) algorithm, and it aims to improve the routing mechanism through establishing an optimal route with minimum energy consumption, which increases the network lifetime and reduces route failure issues. The proposed MET-MFODSR protocol is implemented using the MATLAB platform, analyzed and evaluated in different simulation environments. The simulation results demonstrate that the suggested routing algorithm is applicative and practicable, and its performance exceeds the performance of the existing Bee DSR (BEEDSR) and Bee-inspired protocol (BeeIP) algorithms.

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

In this article, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts – routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a large-scale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme.

Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices during their activity. In this work we focus on a new type of data, available thanks to the definition of the novel SRv6 paradigm, referred to as SRv6 Traffic Counters ( SRTCs ). SRTCs provide aggregated measurements related to forwarding operations performed by SRv6 routers. In this work a detailed description of different SRTCs types (SR.INT, PISD, PSID.TM and POL) is provided and their relationships is formalized. The theoretical framework deployed is used to identify, on the basis of network configuration parameters of both SRv6 and IGP protocols, the minimum set of independent SRTCs to characterize the Network Status : we show that about the 80% of counters can be neglected with no information loss. We also apply our framework to two use cases: i) Traffic Matrix (TM) Assessment and ii) Traffic Anomaly Detection. For the TM assessment, we show that in a partially deployed SRv6 scenario a specific type of SRTCs , i.e., PSID, is more reliable than other ones; on the contrary, in a fully deployed scenario POL and PSID.TM counters provide the full TM knowledge. For the Traffic Anomaly Detection case, we show that known solutions based on link load measurements can be improved when integrating SRTCs information.

With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.