Residual Energy Level Research Articles

Scheduling based on residual energy of sensors to extend the lifetime of network in wireless sensor network

The maximum amount of sensor energy is consumed during broadcasting. Hence in order to make delay with the network lifetime of a wireless sensor network (WSN), there is a need of optimum utilization of sensor energy. Scheduling in media access control (MAC) layer plays a critical role in the designing of WSN to avoid collision and conserve more energy. The Distributed energy aware MAC (DE-MAC) is one such efficient and feasible MAC protocol that addresses the energy management issues in WSN. This work proposes an extension of DE-MAC framework by allocating varying period slots to the nodes based on their residual energy level. Energy of all the nodes is exchanged at regular intervals. Accordingly, the nodes with low energy are allocated less time slots for broadcast and are made to sleep for more time. Simulation is carried out using NS2 (network simulator 2) and the efficiency and performance results are compared with DE-MAC for analysis. Improvement in performance for various parameters such as residual energy, throughput, and packet delivery ratio can be observed.

Adaptive Data Transmission Protocols for Energy Harvesting WSNs Used in Agriculture

Energy consumption is a major concern in wireless sensor networks (WSNs) as it affects the lifespan of sensor nodes. Battery-based WSNs have a short operating period, which makes them impractical for real-time applications, for instance in agriculture. Energy harvesting and suitable medium access control (MAC) protocols have been used to extend the lifetime of nodes. Receiver-initiated protocols have been proved to be the best solution for energy harvesting WSNs. However, they suffer from a key disadvantage, i.e. an increase in collision rate. These collisions need to be reduced using a multi-layer protocol structure. In such a context, a new solar-based hybrid MAC (SHMAC) protocol relying on receiver-initiation and characterized by a multi-layer structure is proposed. It is an adaptive protocol capable of adapting to changing weather conditions. The nodes with a high energy harvesting rate have a higher level of residual energy and are active for longer time periods compared with those with low energy harvesting characteristics. The proposed work has shown improvements in two major MAC layer parameters, i.e. collision rate and energy neutrality operation ratio (ENO).

Determination of the Worst Case for the Ballistic Test of the Soft Armour System Using the 9mm FMJ Bullets Differing in the Structure

Abstract Ballistic tests require significant rigor and the development of a worst case model during the research processes. The purpose of this study was to evaluate the effect of bullet type (manufacturer) on V50 and Behind Armor Blunt Trauma (BABT) results for two ballistic applications: p-aramid and UHMWPE fibre. The results confirmed the thesis that the source of the bullets implies the test results obtained in terms of the number of penetrated layers in the ballistic system, backface signature deformation profiles (p-BFS) and the level of residual energy transferred to the user of the personal protection.

Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.

AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks.

The Ad Hoc On-demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANETs) and other wireless ad hoc networks. The vanilla AODV protocol is simple and easy to implement because it only uses the hop count as a routing metric. Single-metric route determination also causes problems, such as network congestion and energy exhaustion, which limit the usage of AODV in resource-limited applications. To solve these problems, the authors propose a new routing protocol that combines the analytic hierarchy process (AHP), the entropy weight method (EWM), and AODV. The proposed protocol uses energy, congestion, and the hop count as metrics and weights these three metrics using AHP and EWM. To address the importance of energy in applications, such as drones, the proposed protocol chooses different comparison matrices for AHP at different node residual energy levels. Finally, the node chooses the best route link according to the score (sum of weighted metrics). It is also suitable for wireless sensor networks because the proposed protocol considers the residual energy of the node. The simulation results show that the improved routing protocol can effectively reduce the average end-to-end delay and energy consumption and prolong the lifetime of the whole network.

Outlier detection based energy efficient and reliable routing protocol using deep learning algorithm

AbstractWireless sensor network have also played a vital role in the observation and management of agricultural land in terms of climate, water usage, crops, etc. Due to the open communication system and low battery power of sensors, the agricultural sector still faces issues with energy consumption, information forwarding, and privacy. Thus, an energy‐efficient routing during transmission in WSN‐based smart agriculture is suggested in this study applying a feed‐forward neural network to detect outliers. Outlier identification, CH‐selection, and Relay Node (RN) selection are the three phases of this suggested method. Outlier detection is performed in the deployed nodes for categorises attack nodes from the normal nodes. CH‐selection is performed using a chaotic moth‐flame optimization technique according to distance, node degree, centrality factor and residual energy level, these parameters determine which node will become a Cluster Head. Then reliable routing protocol is designed using NB‐based probability method for RN selection. MATLAB software is used to test the proposed Outlier Detection based Energy Efficient and Reliable Routing Protocol and verify its performance. The effectiveness of the proposed‐model is tested with some prior wireless sensor network routing protocols environment‐fusion multipath routing protocol, dynamic Multi‐hop Energy Efficient Routing Protocol, SEMantic CLustering, and Reliable and energy efficient routing protocol. Outlier Detection based Energy Efficient and Reliable Routing Protocol algorithm attained a 0.91 (%)Packet Delivery ratio, 0.08% of packet loss, 0.91% of Average residual energy, 2.8 (Mbps) throughput, and 26 (sec) Delay.

BMUDF: Hybrid Bio-inspired Model for fault-aware UAV routing using Destination-aware Fan shaped clustering

Routing data between unmanned aerial vehicles (UAVs) involves identifying node locations, analyzing residual energy levels, evaluating temporal throughput and packet delivery performance, and identifying other network and node parameters. It assists in forming quality of service (QoS) aware routes. Existing routing models require large data samples to find the optimized path or are highly complex, increasing their computational requirements. Low-complexity models showcase low-performance routing QoS when deployed on large-scale networks. To solve these limitations, a hybrid bioinspired model is proposed for fault-aware UAV routing that uses destination awareness with a fan-shaped clustering process (BMUDF). The model initially collects data from different UAV nodes and their node-level and network-level constraints. These parameters are processed through the particle swarm optimization (PSO) model, which enforces fan-shaped clustering (FSC) for effective routing operations. Our scheme uses the PSO model to identify the initial routing paths cascaded with a genetic algorithm (GA) based destination-aware routing model. These routing paths are evaluated through the QoS matrices like maximum temporal throughput, packet delivery ratio (PDR), delay, and energy consumption. A grey wolf optimization (GWO) model further scrutinizes this routing performance, integrating fault tolerance and route optimization during continuous operations. The GWO model evaluates a trust-based fitness function, which helps to identify faulty nodes, and reconfigures the network with non-faulty nodes to improve its QoS performance under node failures and faults. Integrating the bioinspired models into the proposed system maximizes performance under different network scenarios. This performance compares and validated with an analysis of variance (ANOVA) test with various state-of-the-art models. It is observed that the proposed model showcased an 8.3% lower routing delay, 5.9% lower energy consumption, 1.5% higher packet delivery ratio, and 9.1% higher throughput, which makes it useful for a wide variety of real-time UAV routing scenarios.

RLS2: An energy efficient reinforcement learning- based sleep scheduling for energy harvesting WBANs

In the Energy-Harvesting Wireless Body Area Networks (EH-WBAN), one of the fundamental challenges is preserving the self-sustainability of sensors without compromising network reliability and connectivity. Determining the sleep/wake schedule of body nodes (BNs) is an efficient way to achieve self-sustainability. Sleeping nodes should be connected to at least one active node to reduce delay and keep the network connected. There are two fundamental problems with previous methods for determining BN's sleep/wake schedule: (1) BN suffers from emergency packet loss and unnecessary frequent sleeping and waking up, and (2) They do not guarantee network connectivity. Studies that have only examined connectivity in EH-WBAN also have two main issues: (1) BNs are considered homogenous in terms of energy harvesting and its consumption, (2) These methods cannot adapt to the time-varying behavior of energy-harvesting resources. This study proposes a new method for sleep/wake scheduling called Reinforcement Learning-based Sleep Scheduling (RLS2). RLS2 has the following innovative points: (1) To avoid emergency packet loss or unnecessary frequent sleeping and waking up, each BN has its own sleep/wake schedule based on its energy level and sensed data changes, (2) Lowest possible number of BNs are determined as relay nodes in each round to increase network reliability and connectivity; these BNs remain active in each round, while the others operate according to the determined schedule. In this part of the proposed method: (1) Heterogeneous BNs are considered, (2) As a first step in solving adaptability, the problem of finding the optimal active groups is formulated as a Markov decision process (MDP), followed by a Q-learning algorithm capable of learning time-varying behavior of energy harvesting resources, (3) The unavailable action space is removed to reduce the problem's complexity, (4) To achieve good Q-learning performance, a reward function based on residual energy level and neighborhood degree of BNs is defined. It can find an active group with the lowest cardinality in the current round, which is maximum in terms of the residual energy of its sensors. The performed simulations indicate the appropriate convergence of the proposed method. The results show that, on average, the proposed method improves network connectivity and energy efficiency by 50% and 31%, respectively, and reduces network delay by 27%.

A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA.

Genetic Algorithm with Random and Memory Immigrant Strategies for Solving Dynamic Load Balanced Clustering Problem in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), clustering is an effective method to distribute the load equally among all the nodes as compared to flat network architecture. Due to the dynamic nature of the network, the clustering process can be viewed as a dynamic optimization problem and the conventional computational intelligence techniques are not enough to solve these problems. The Dynamic Genetic Algorithm (DGA) addresses these problems with the help of searching optimal solutions in new environments. Therefore the dynamic load-balanced clustering process is modeled using the basic components of standard genetic algorithm and then the model is enhanced is using immigrants and memory-based schemes to elect suitable cluster heads. The metrics nodes’ residual energy level, node centrality, and mobility speed of the nodes are considered to elect the load-balanced cluster heads and the optimal number of cluster members are assigned to each cluster head using the proposed DGA schemes such as Random Immigrants Genetic Approach (RIGA), Memory Immigrants Genetic Approach (MIGA), and Memory and Random Immigrants Genetic Approach (MRIGA). The simulation results show that the proposed DGA scheme MRIGA outperforms well as compared with RIGA and MIGA in terms of various performance metrics such as the number of nodes alive, residual energy level, packet delivery ratio, end-to-end delay, and overhead for the formation of clusters.

Имитационная модель одноранговой беспроводной сенсорной сети

The article provides a description of the developed simulation model Mesh network. The simulation model is a tool for assessing the probabilistic and temporal characteristics of data delivery, as the main indicator of the quality of the service provided by the Mesh network. A mesh network is a complex object of modeling: the mobility of nodes, the lack of a fixed infrastructure and centralized control, the limitations of the wireless transmission medium and node resources are the main features that distinguish Mesh networks from wired communication networks. These features affect the choice of the data delivery route and the quality indicators of the service provided by the Mesh network. The simulation model takes into account the features of the Mesh network, in particular, the features of modeling the states and loading of network nodes, the length of the queue for service, and the change in the level of residual energy of the node are considered. With the help of a simulation experiment on a Mesh network model with given parameters and external influences, it is possible not only to evaluate the probabilistic and temporal characteristics of data delivery, but also, taking into account the acceptable values for the quality indicators of the provided service, recommend the structure of the Mesh networks.

An α-Fairness Approach to Balancing the Energy Consumption Among Sensors for UAV–IoT Systems

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

EECH/CF: An Energy-efficient Cluster Head Election and Cluster Formation Algorithms for WSNs

Background and Objective: Wireless sensor networks (WSN) consist of sensor nodes with a limited battery life and limited communication distance. The minimal energy expended by the sensor nodes and network can be achieved either by reducing the number of communications or by controlling the topology. Thus, energy consumption can be optimized by employing several techniques, such as clustering. Clustering allows the network to be divided into a set of clusters, each of which is managed by a cluster head. In a hierarchical cluster-based WSN, the cluster head receives the data from its sensor members, aggregates it and sends the result to the base station, which leads to an extra overload. So, the selection of appropriate cluster head plays a very important role in conserving the energy of the sensor nodes and extend the lifetime of the network. This paper introduces an energyefficient cluster head election and cluster formation algorithm for WSNs, called EECH/CF. To select cluster heads, our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Methods: Our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Results and Discussion: Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Conclusion: Results show that our clustering algorithms improve the lifetime of the network as we expected. Indeed, the delay before the death of the first node can be up to seven times longer with EECH/CF.

Data Collecting and Energy Charging Oriented Mobile Path Design for Rechargeable Wireless Sensor Networks

Energy efficiency is one of the most important concerns in wireless sensor networks (WSNs). As far as we know, almost all energy efficiency researches of WSNs focus on energy conservation in some respects such as wireless data transmission and minimal data collection. Recently, wireless energy transfer has been a promising technology to prolong the lifetime of microsensor nodes, and so the traditional WSNs can be extended to rechargeable WSNs. Rechargeable WSNs is a new type of wireless sensor networks, where each sensor node can replenish energy through wireless charging. For rechargeable WSNs, it is powered by reusable energy or harvested energy, so the energy efficiency problem can be completely solved. Furthermore, mobile data collection has been well recognized to have significant advantages over sensory data collection manner using static sinks. In this paper, by employing one or multiple recharging sinks to replenish energy for sensor nodes and collect sensory data concurrently, we propose a novel wireless charging and mobile data collecting method based on self-organizing map (SOM) unsupervised learning for rechargeable WSNs. In other words, the sink mobility and energy replenishment are jointly considered in this paper. Finally, we evaluate the performance of the proposed algorithms through software simulation. Extensive results verify that the performance of the proposed algorithm can reduce the travel cost of mobile sink and improve the residual energy level for sensor nodes. As a results, it is very promising in the field of data acquisition in wireless sensor networks.

An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks

Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.

Game theory based node clustering for cognitive radio wireless sensor networks

Cognitive radio wireless sensor networks (CRWSN) is a promising technology for developing bandwidth constrained applications. Future Internet of Things (IoT) applications may extensively use CRWSN. CRWSN consists of cognitive radio enabled sensor nodes which are energy constrained, in general. Hierarchical cluster based approach for overall network management is suitable for network stability and scalability. Thus node clustering is an important problem in CRWSN setup. Objective of this work is to develop a suitable node clustering algorithm for CRWSN, in which nodes are expected to be mobile. A node clustering protocol for CRWSN has been proposed in this paper. The proposed clustering protocol is based on evolutionary game theory (EGT). Initial clusters are formed through a simple partitioning approach. Eventually, initial clusters are merged to form the final clusters. After forming the clusters by the resourceful sink node, the cluster head nodes for respective clusters are determined. The sink node runs the EGT based algorithm to identify the most capable node as the cluster head. The strength of this approach is that while identifying the cluster head node, various parameters such as residual energy level, geographic location, mobility, and the probability of primary user (PU) arrival are considered. The clusters and therefore, the cluster head nodes are distributed uniformly in the geographical area. The proposed clustering protocol has been compared with LEACH, RARE and the spectrum aware clustering algorithm. The simulation results show that the proposed clustering protocol outperforms all these similar node clustering protocols. On average, the proposed protocol outperforms the benchmarks protocols by 25% in terms of number of high energy nodes selected as cluster head, by 37% in terms of uniform geographical distribution of cluster head nodes, by 23% in terms of total energy consumed during the simulation time, and by 27% in terms of network lifetime. The future scope of the work has been outlined.

A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.

Алгоритм роя пчел выбора головных узлов кластеров беспроводной сенсорной сети

The article discusses the actual problem of energy-efficient interaction of sensor nodes in a wireless sensor network. The limitations of wireless sensor networks are indicated, the main of which is the requirement of low power consumption, on which the service life of the network depends. Restrictions dictate to reduce the number of operations when organizing the interaction of network nodes. One of the effective approaches is the clustering of the sensory field created by the network, since the length of the data delivery route is reduced. The article proposes an algorithm for a swarm of bees that allows at the beginning of each cycle of the wireless sensor network to determine not only the heads of clusters, but also potential heads of clusters that can become them in the next rounds of the network. Cluster heads and potential cluster heads are chosen according to the Euclidean distance metric and the level of residual energy. It is shown that, unlike the well-known LEACH algorithm and its versions, in the bee swarm algorithm, the phase of choosing the head node of the cluster becomes unnecessary for at least one cycle, and the sensor nodes thus get rid of some calculations associated with choosing the head of the cluster. The conducted simulation experiments testify to the effectiveness of the proposed bee swarm algorithm in choosing the heads of wireless sensor network clusters.

Feature Selection Based on IoT Aware QDA Node Authentication in 5G Networks

The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an effective manner. However, security of 5G networks while data transmission was not improved. The IoT aware Quadratic Discriminant Analysis (QDA) Node Authentication Method is proposed in order to achieve safe data communication in the 5G network. The suggested method performs feature selection using Quadratic Discriminant Analysis to identify relevant properties of mobile nodes such as trust value, residual energy level, and node cooperativeness. Simulation can be used to test the data packets, the number of mobile nodes in a security level, the computation overhead, and the authentication accuracy. The observed output clearly demonstrates that the Quadratic Discriminant Analysis Method significantly enhances the authentication accuracy of each node and the security level with less overhead than state-of-the-art-methods.

Fault-Tolerant Ad Hoc On-Demand Routing Protocol for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) are particularly suited for scenarios that demand rapid deployment of a communication system without any existing network resources. For instance, a MANET can facilitate the intercommunication process between members of a rescue party in a natural disaster, where the underlying routing protocol is crucial to maintaining the dissemination capability of data-critical packets. However, the backbone of every MANET, i.e., their routing protocol, is limited by the communication range of nodes, their high-speed mobility, and the capacity constraints of energy. This study proposed a fault-tolerant ad hoc on-demand routing protocol (FT-AORP) that relies on these characteristics of MANET nodes to determine reliable paths for data transmission. Subsequently, two of the discovered paths were used to transmit the duplicates of an original data packet to maximize fault tolerance. Further, using the OMNeT++ network simulator, the performance of the proposed system was evaluated through extensive simulation experiments against three simulation parameters: the number of network nodes, node speed, and data packet sending rate. The simulation results demonstrated that FT-AORP greatly improved the packet delivery ratio, reduced end-to-end delay, and maintained a higher residual energy level of the transmission path, compared to other baseline routing protocols.