Network Throughput Research Articles

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

EVRP: A novel geometrical based energy efficient eye vision routing protocol for wireless sensor networks based on the k-means algorithm

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

Biphase routing scheme for optimal throughput in large-scale optical satellite networks

In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.

An adaptive MAC-agnostic ranging scheme for underwater acoustic mobile networks

In the last ten years several simulation studies on Autonomous Underwater Vehicle (AUV) swarm fleet formation have been performed, and some preliminary sea demonstrations of proof-of-concept prototypes were carried out. However, their actual realization is hindered by the challenges imposed by the underwater acoustic channel and the difficulties of keeping track of the vehicles’ positions due to the long latency required by traditional Two-Way Travel-Time (TWTT) ranging measurements, that require a specific signaling, hence limiting the throughput of the underwater network. Although One-Way Travel-Time (OWTT) halves the latency, it requires a high precision oscillator, such as an atomic clock or an oven controlled crystal oscillator (OCXO), to be installed in each modem processing unit: while atomic clocks are still very expensive, OCXO are very power demanding, making their application to underwater acoustic networks not always possible, especially in the case of low cost vehicle swarms.In this paper we present a network protocol stack able to perform ranging and localization within the communication task in underwater acoustic networks, limiting the network overhead. Specifically, new layers have been added to the preexisting DESERT Underwater protocol stack to perform the ranging tasks without compromising the correct operation of the communication network. A ranging layer is placed on top of the Medium Access Control (MAC) scheme, allowing the latter to be changed according to the network topology and requirements. This MAC-agnostic ranging layer is further optimized by adapting the amount of data transmitted according to the channel state, and the ranging entries inserted in the data packets according to the least recent information transmitted, hence minimizing the Age of Information. Simulation results obtained with the DESERT Underwater Framework show how this layer allows all AUVs in the swarm to know their distance from every other node in the network, thus limiting the probability of vehicle collisions and allowing better mission coordination.

Development of a routing method for ground-air Ad-Hoc network of special purpose

The object of the study is the process of forming control decisions to ensure the operation of the ground-air communication network routing subsystem based on neural network algorithms. The carried-out research is based on the application of the numerical-analytical approach to the selection of modern scientific and applied solutions for building management models for promising Ad-Hoc communication networks. In the Google Collab simulation environment, using the Python programming language, it was possible: firstly, to simulate the operation of a ground-to-air communication network based on previously obtained models and a routing process management system based on the FA-OSELM algorithm. Secondly, in accordance with the scenario of route construction and maintenance described in the article, to experimentally determine the communication metrics of the proposed method of intelligent routing of the ground-air Ad-Hoc special-purpose network, in order to assess its efficiency, adequacy and reliability of the results obtained. Thus, in order to evaluate the effectiveness of the proposed solutions, a comparative analysis of the application of three existing routing methods (FLCA, Q-Routing, Neuro Routing) used in Ad-Hoc networks relative to the developed method was conducted. The result of the experiment showed that the proposed routing method MAODV-FA-OSELM provides significant advantages over analogs. Thus, the method exhibits the best network throughput (2.12e+06), the lowest average network latency (0.12), the lowest packet loss (6.32), the lowest bit error rate (2.41), and the lowest overhead (0.10e+06). However, it should be noted that a promising direction of further research may be the study of the computational complexity of the routing management process and the determination of the minimum allowable representative sample of initial data to ensure online decision-making.

T‐RESIN: Throughput‐aware dynamic resource orchestration for Internet of Everything‐enabled software‐defined edge networks

SummaryIn this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes.

Power allocation scheme for sum rate and fairness trade-off in downlink NOMA networks

Non-orthogonal multiple access (NOMA) is an essential enabler technology that is expected to help satisfy the key requirements of increased system throughput in future wireless networks. However, another equally important aspect that should go hand-in-hand with system throughput is user fairness for any network. But, to the best of our knowledge, even though there have been works that look at system throughput or user fairness maximization for NOMA-based networks, they looked at these as a single objective optimization problem, where one is the objective and the other is one of the constraints. However, quite often, joint optimization of both system throughput and user fairness is required to make optimized decisions in the face of trade-offs between these two equally important but conflicting objectives. In this regard, this paper formulates a multi-objective optimization problem to jointly maximize the sum rate and user fairness in a downlink transmission NOMA system, through optimize power allocation (PA), under system-imposed constraints. A weighted sum approach is used to turn the multi-objective optimization problem into a single-objective optimization problem to make it analytically tractable. The optimized PA is then obtained using Lagrange dual decomposition method and Karush–Kuhn–Tucker (KKT) conditions. Using our derived expressions, we propose an iterative PA algorithm that converges fast enough to be employed in practical NOMA networks. We also present simulation results to highlight the effectiveness of the proposed solution. Further, the performance of the proposed method is compared with that of the benchmark methods.

Reinforcement learning-based dynamic load balancing in edge computing networks

Edge computing (EC) has emerged as a paradigm aimed at reducing data transmission latency by bringing computing resources closer to users. However, the limited scale and constrained processing power of EC pose challenges in matching the resource availability of larger cloud networks. Load balancing (LB) algorithms play a crucial role in distributing workload among edge servers and minimizing user latency. This paper presents a novel set of distributed LB algorithms that leverage machine learning techniques to overcome the three limitations of our previous LB algorithm, EVBLB: (i) its reliance on static time intervals for execution, (ii) the need for comprehensive information about all server resources and queued requests for neighbor selection, and (iii) the use of a central coordinator to dispatch incoming user requests over edge servers. To offer increased control, custom configuration, and scalability for LB on edge servers, we propose three efficient algorithms: Q-learning (QL), multi-armed bandit (MAB), and gradient bandit (GB) algorithms. The QL algorithm predicts the subsequent execution time of the EVBLB algorithm by incorporating rewards obtained from previous executions, thereby improving performance across various metrics. The MAB and GB algorithms prioritize near-optimal neighbor node servers while considering dynamic changes in request rate, request size, and edge server resources. Through simulations, we evaluate and compare the algorithms in terms of network throughput, average user response time, and a novel LB metric for workload distribution across edge servers.

High-fidelity entanglement routing in quantum networks

Quantum networks, endowed with their distinct quantum properties, are poised to emerge as indispensable platforms for the implementation of quantum information technology applications in the near future. Within the realm of quantum networks, the transmission of remote quantum information presents a pivotal and intricate challenge, commonly referred to as the entanglement routing problem. While previous efforts have delved into the intricacies of entanglement routing, there has been a notable oversight concerning the crucial parameter of entanglement fidelity. Hence, this paper introduces a novel Purification-enabled Entanglement Routing Algorithm (PERA). Initially, PERA utilizes the network throughput as the routing metric and ensures fidelity guarantees on all nodes in the link through a hop-by-hop purification process. Subsequently, acknowledging the probabilistic nature of entanglement purification, a strategy involving multiple attempts at purification is employed. Finally, a comprehensive utility function is deployed to address the path selection problem for multiple source–destination pairs. Numerical simulations confirm that PERA establishes entanglement connections with outstanding fidelity guarantees. Concurrently, PERA outperforms the baseline algorithm in terms of network throughput and resource utilization.

A 930 m/180 Gbps*User Underwater Coherent Optical Code-Division Multiple-Access Network Based on Hybrid 256-Differential Pulse Position Modulation and Weighted Modified Prime Code Sequence

Currently, there are three types of optical communication networks based on the communication channel between the transmitter and receiver: the optical fiber channel, visible light channel, and optical wireless channel networks. The last type has several advantages for underwater communication, wireless sensors, and military communication networks. However, this type of optical network suffers from weather conditions in free-space communications and attenuation owing to the scattering and absorption mechanisms for underwater communication. In this study, we present a new transceiver architecture of a coherent optical code-division multiple-access (OCDMA) system based on a hybrid M-ary differential pulse position modulation scheme and a spreading code sequence called weighted modified prime code for underwater communication to minimize channel dispersion, increase the transmission rate per second, enhance the network bit error rate (BER) performance, and improve network security. Using an OCDMA system, we can simultaneously expand the network coverage area and increase the number of users sharing the network over the same channel bandwidth. The simulation results in this study proved that the proposed system can accommodate 1310 active users and a network throughput of 180 Gbps*user over a transmission distance of 930 m without any repeater at a 10−9 BER performance, compared to the 45 Gbps*user network throughput and 100 m transmission distance reported in the literature.

Analysis of Coverage and Capacity for UAV-Aided Networks with Directional mmWave Communications

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

Hierarchical Cross Traffic Scheduling Based on Time-Aware Shapers for Mobile Time-Sensitive Fronthaul Network

To solve the problem of jitter and low network throughput caused by the impact of background flows on IQ traffic in mobile fronthaul network, this paper proposed a new scheduling model for background flows, named hierarchical crossover traffic scheduling mechanism based on time-aware shaper (HC-TAS) by improving the traditional counterpart. Then, in this new model, we designed an inbound scheduling algorithm based on frame length matching and an outbound scheduling algorithm based on queue status, making sure that smaller data frames will not be blocked by large data frames. This greatly improves the utilization of timeslots in the scheduling process and reduces the jitter impact of background flows. To verify its performance, we conducted experiments in a simulated fronthaul network conforming to IEEE 802.1CM. The experimental results show that, under the condition that the jitter is guaranteed to be zero, compared with two mainstream scheduling schemes, Comb-FITting and TAS + Preemption, our proposed scheme can achieve lower maximum end-to-end delay and higher link utilization. The proposed HC-TAS meets the requirements of low jitter and high bandwidth utilization in 5G fronthaul network, and the research results provide a technical basis for the application and development of general time-sensitive networks as well.

Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime.

Communication architecture and operations for SDR-enabled UAVs network in disaster-stressed areas

The article presents a collaboration and communication architecture for disaster stressed areas where communication losses restrain disaster response and perplex rescue operations. In these areas, the fragile access networks such as cellular or Wi-Fi systems become congested or inaccessible, requiring a reliable emergency communication infrastructure. The proposed architecture involves Unmanned Aerial Vehicles (UAVs), equipped with wireless technologies i.e., 3G/4G/5G/Wi-Fi systems, and manifestly Software Defined Radios (SDRs), enabling self-forming, self-organizing, and self-healing capabilities. The architecture uses a Hybrid Connectivity Module (HCM) which empowers cooperative communication capacities and abridge the technologies to achieve reliable communication link between UAVs, mobile-to-UAV, and UAV-to-mobile towards Ground Control Station (GCS) in catastrophic areas. The seamless connectivity via next generation SDR waveforms and designed algorithms ensure an implementation of versatile multi-user UAVs network. This enables the formation of a backbone network assisting ground devices to communicate GCS. For cooperative communication, the architecture provides a MAC-centric cross-layer protocol which offers efficient multihop routing between UAVs. The data (i.e., text, images, or voice) is routed to devices or GCS via UAVs, which can notify rescue services reliably and help to trace the catastrophic zones. Furthermore, the protocol helps to allocate network resources by creating virtual sub-nets for simultaneous interference-free data transmissions which maximizes the network throughput. The experimental results show the data latency far better than any other existing ad hoc routing protocol (e.g., OLSR or AODV) based designs which usually add delays in the emergency response operations.

Upgrading strategies for long-haul quantum key distribution networks

Quantum key distribution (QKD) has become a promising option for transmitting sensitive data due to the increased maturity of QKD devices and the threat scalable quantum computers imposes on asymmetric public-key cryptosystems. By utilizing existing infrastructure, e.g., amplifier huts and dark fibers, the cost and complexity of deploying QKD networks (QKDNs) can be reduced. In this study, we develop a topology optimization algorithm that minimizes the cost of the QKDN deployment and maximizes the key capacity between any pair of nodes. We present three deployment upgrade strategies for facilitating the encryption of increasing throughput of QKD-secured optical transport networks and enabling a pay-as-you-grow approach. Comparing different strategies in a capacity-planning study allows operators to assess the scalability of deployments and upgrades. Depending on the availability requirements, our results compare the cost of protection measures. We confirm that adding trusted nodes is the most cost-efficient upgrading strategy based on our analysis.

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.

Toward Optimal Virtualization: An Updated Comparative Analysis of Docker and LXD Container Technologies

Traditional hypervisor-assisted virtualization is a leading virtualization technology in data centers, providing cost savings (CapEx and OpEx), high availability, and disaster recovery. However, its inherent overhead may hinder performance and seems not scale or be flexible enough for certain applications, such as microservices, where deploying an application using a virtual machine is a longer and resource-intensive process. Container-based virtualization has received attention, especially with Docker, as an alternative, which also facilitates continuous integration/continuous deployment (CI/CD). Meanwhile, LXD has reactivated the interest in Linux LXC containers, which provides unique operations, including live migration and full OS emulation. A careful analysis of both options is crucial for organizations to decide which best suits their needs. This study revisits key concepts about containers, exposes the advantages and limitations of each container technology, and provides an up-to-date performance comparison between both types of containers (applicational vs. system). Using extensive benchmarks and well-known workload metrics such as CPU scores, disk speed, and network throughput, we assess their performance and quantify their virtualization overhead. Our results show a clear overall trend toward meritorious performance and the maturity of both technologies (Docker and LXD), with low overhead and scalable performance. Notably, LXD shows greater stability with consistent performance variability.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

Multi-Objective ED2MT Strategy-based Clustering Algorithm for Large Scale WSN

Wireless Sensor Networks play a crucial role in everyday life, providing a wide range of networking structures to develop real-time applications. Energy consumption is limited by WSN devices, so reducing the energy consumption of malicious nodes is crucial to improving network performance. A main concern of WSNs from an application perspective is the prolonged lifetime of the network and reduced energy usage. To overcome these challenges a novel multi-objective ED2MT (Energy, Distance, Degree, Mobility, Time) driven strategy has been proposed which consists of three phases namely dual Cluster Head Selection, data compression, and routing. In the first phase, the proposed ED2MT uses a Fuzzy C-Means (FCM) clustering algorithm to select dual Cluster Head (CH) which has been performed in two stages, namely Inter-Cluster and Intra-Cluster. In the second phase, a Neighborhood Indexing Sequence (NIS) technique is utilized to compress the number of bits in the data before it is transmitted. In the third phase, a multi-objective shortest-path routing algorithm is utilized for route selection. The performance of the ED2MT method has been evaluated using network lifetime, delay, throughput, alive nodes, energy efficiency, and packet delivery ratio to provide a better result. The proposed ED2MT framework achieves 20.55%, 18.4%, and 15.2% more residual energy than C3HA, CAFL, and DHSCA algorithms.