A neuro-fuzzy multi-topology adaptive routing framework for QoS-aware healthcare IoT communications

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

SummaryIn healthcare IoT, there is a vogueing need for better performing monitoring and reporting services of both medical and non‐medical applications. Off late, the solutions are stemming from wireless body area networks (WBAN). Keen, fast, and reliable management of clinical information at every level of the network opens room for researchers to work on the network level of WBAN. Being in the era of Implant Medical Devices, the challenges are versatile. Obtaining the desired throughput, combating node heating, and improvement in energy efficiency are a few challenges. Interestingly, the heterogenous nature of nodes in a WBAN thrusts the next hop selection at the intra‐WBAN level advancing into a non‐persistent hard problem. To this, we present a Joint Power and Temperature Aware Routing (JPTAR) Scheme with two elements. First is an analytic hierarchy process–based next hop selection method that accommodates, link quality, residual energy, closeness to sink, and node heating for the process. To this an imperceptibly, low computation algorithm for received signal strength indicator (RSSI) estimation‐based transmission power control is also added to ensure an optimized energy expenditure. Requisite elements of WBAN network management like QoS goals of emergency data and redundancy removal in transmissions are inherently part of the proposed work. The performance metrics like network lifetime, node temperature rise, throughput, end‐to‐end delay, and computation time are acquired under the worst‐case scenario. The obtained results depict that the proposed JPTAR scheme outperforms state‐of‐the‐art reactive multi‐hop routing algorithms (MATTEMPT and SIMPLE).

The Routing Protocol for Low Power and Lossy Networks (RPL) is operated by gadgets comprised of many devices of embedded type with limited energy, memory as well as resources that do their process. The improvements in the life of the network and energy conservation are the key challenging features in Low Power and Lossy Networks (LLN). Obviously, the LLN has a key strategic part in routing. The Internet of Things (IoT) device is expected to make the apt choice. In LLN, the poor routing choice leads to traffic congestion, reduction in power as well as packet loss ratio. The task in the proposal analyzes Delay (D), Load (L) and Battery Discharge Index (BDI) pivoted Energy Efficient Composite Metric Routing (EECMR) protocol for LLN. The performance of the work in the proposal is evaluated by the COOJA simulator. It outperforms with respect to Network Lifetime (NL), Delay as well as Packet Delivery Ratio (PDR) contrasted to the routing metrics like Traffic Load (TL), Link Quality (LQ), Residual Energy (RE), RE-Battery Discharge Index (RE-BDI) and Hop Count (HC).

ABSTRACTAs a fundamental supporting technology of 5th Generation (5G) systems, wireless sensor networks (WSN) are handling a new challenge to enhance its energy‐efficient reliable transmission. However, energy usage and network lifetime are observed as challenging tasks because of limited battery capacity and open environments. The cyclone foraging strategy with beluga whale optimization (CFS‐BWO) is proposed for energy‐efficient reliable data transmission for 5G WSN. The CFS is applied for improving the exploitation phase of traditional BWO, where population transfers in spiral orientation among the best solutions. CFS‐BWO optimizes the routing and data aggregation process, which minimizes the energy usage and enhances the network lifetime. The average delay, residual energy, communication cost, and distance are adopted as fitness functions for optimizing the best solution in both CH and route path selection. The performance was calculated by the metrics of residual energy, packet delivery ratio (PDR), and delay across 200, 400, 600, 800, and 1000 rounds. The CFS‐BWO reaches residual energy of 0.87 J, PDR of 0.98, and delay of 15 ms for 1000 rounds when compared to optimal cluster‐based routing (Optimal‐CBR).

Optimal relaying nodes selection for IEEE 802.15.6-based two-hop star topology WBAN

Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring, healthcare, disaster management, and smart infrastructure. However, the limited energy resources of sensor nodes remain a pressing challenge, particularly in data aggregation and transmission processes, where redundancy and inefficient routing can significantly shorten network lifetime. To address this problem, we propose a Hybrid Deep Reinforcement Learning (HDRL) framework that optimizes data aggregation while balancing energy consumption and communication overhead. The method integrates the decision making capability of reinforcement learning with the representational power of deep neural networks, enabling adaptive node selection and dynamic routing based on real-time energy and network states. The proposed HDRL model employs a dual-agent mechanism: the first agent focuses on cluster head selection for balanced energy distribution, while the second agent optimizes multi-hop routing paths to minimize redundant transmissions. A reward function is designed to jointly consider residual energy, data latency, and transmission reliability. Simulation results show that the HDRL-based approach outperforms traditional clustering and reinforcement learning methods in terms of network lifetime extension, reduced packet loss, and improved throughput. Notably, the proposed method achieves up to 30% improvement in energy efficiency and 25% reduction in end-to end delay, making it highly suitable for large-scale, real-time WSN applications.

Wireless body area network (WBAN) has made a significant contributions in health monitoring. However, due to the limited energy supply, sensor nodes must achieve physiological measurements with minimum energy consumption in order to maintain long-term monitoring. In this article, we design a kinetic energy harvestion device to provide energy for sensor nodes and propose a energy level classification routing protocol (ELC) to simultaneously optimize network lifetime, transmission reliability and energy efficiency. The protocol comprehensively considers several WBAN parameters such as residual energy, transmission distance and link reliability of the sensor node as the factors of the multi-objective optimal function, and adjusts the weights of these factors based on the entropy method according to the actual measured data. After extensive simulation comparisons with other existing routing protocols the results show significant improvement in network lifetime, transmission reliability and energy efficiency.

Most Internet of Things (IoT) devices use IPv6’s Routing Protocol for Low-power and Lossy Networks (RPL). However, its performance endures due to high network congestion and the limited energy capacity of nodes, especially in large-scale networks. Clustering is a widely used technique for better energy utilization. The existing cluster-based RPL routing protocols are not efficient in balancing energy consumption and are not fit for a scalable network. So, this work proposed a novel unequal clustering-based method that extends the network’s life of RPL by forming clusters of variable size based on the node and base station (BS) separation. This is accomplished in a tri-stage cluster setup, routing, and maintenance. The cluster head selection is based on the connectivity and residual energy, and then an unequal transmission range is given to CH. This paper emphasized data transfer in the direction of the base station. To achieve this, we have defined a grey region. This grey region contains the data forwarder node, which transfers data to the Base station. The selection of data forwarder nodes is based on composite metrics, namely, Expected Transmission Count(ETX), Congestion Score (Cs), and Residual Energy (RE). The simulation is performed using the COOJA simulator. When confronted with a clustered additive approach (CA-RPL) and the minimum rank with hysteresis objective function (MHROF-RPL), the proposed algorithm (SER-UC) delivers significant 8 . 10% and 27 . 21% improvement in packet delivery rate (PDR), 9 . 17% and 13 . 9% decrement in energy consumption and 11 . 16% and 19 . 62% decrement in end-to-end delay concerning CA-RPL and MHROF-RPL. Along with this, the proposed algorithm also shows improvement in network lifetime. The network lifetime is measured in terms of First node die (FND), Half node die (HND), and Last node die (LND). The proposed algorithms show 22 . 67% and 14 . 13% enhancement in FND, 29 . 44% and 17 . 08% in HND, and 30 . 27% and 16 . 16% in LND concerning CA-RPL and MHROF-RPL.

Introduction: For instance, in a static IoT network where all sensor nodes are installed in precisely determined spots, it is possible that just one of the nodes will serve as a central hub for all data flowing through the network. As all traffic is redirected to this node, congestion at this node dramatically increases, quickly consuming the node's energy, creating a hole in the network. It's possible that the network will fail as a result of the failure of this node and the subsequent breakdown in communications. In order to overcome this problem, we proposed an algorithm Articulation Node Based Mobile Node Routing protocol for LLNs AM-RPL to avoid network failure. This algorithm first locates the articulation node in a given network, and it then gives the dynamic node instructions to move in that direction. A communication link is established as soon as a new node enters the communication range, updating the new DAG to include the dynamic node. This connects two components and shares articulation nodes load, improving network connectivity and performance. The proposed algorithm is implemented in the Cooja simulator to test the protocol's performance. The experiment's findings indicate that, when compared to a typical scenario, the PDR, Radio Duty Cycle, Charge Consumption, and Parent Switches of nodes all improve as load is distributed using dynamic nodes. Objectives: This research aims to develop an efficient routing mechanism for Low-Power and Lossy Networks (LLNs) to address congestion-induced failures in static IoT networks. The Articulation Node Based Mobile Node Routing Protocol (AM-RPL) is proposed to dynamically identify articulation nodes and direct mobile nodes toward them, ensuring balanced network load distribution. By mitigating excessive energy depletion at central nodes, AM-RPL enhances network resilience, stability, and longevity. The study further evaluates the protocol’s effectiveness in optimizing key network parameters and explores its scalability across diverse IoT topologies for large-scale deployment. Methods: To achieve the research objectives, AM-RPL is designed to detect articulation nodes and guide mobile nodes to strategically reposition themselves within the network. The protocol is implemented and tested in the Cooja simulator, where various IoT network scenarios are created to assess its performance. Metrics such as Packet Delivery Ratio (PDR), Radio Duty Cycle, Charge Consumption, and Parent Switches are recorded and analyzed to determine improvements over conventional static routing protocols. A comparative analysis is conducted to evaluate AM-RPL’s effectiveness in reducing congestion, improving energy efficiency, and maintaining stable communication links. Results: The simulation results indicate that AM-RPL significantly enhances network stability and performance by reducing congestion at critical nodes. The introduction of mobile nodes enables dynamic load balancing, leading to improvements in PDR, energy consumption, and parent selection efficiency. Additionally, the protocol demonstrates better adaptability to varying network conditions, making it a viable solution for large-scale IoT deployments. While percentage improvements vary based on network scenarios, the overall trend consistently shows enhanced reliability, energy efficiency, and sustained connectivity, validating AM-RPL’s effectiveness in overcoming the limitations of traditional static routing methods. Conclusions: The deployment of the Articulation Node Based Mobile Node Routing protocol (AM-RPL) adeptly addresses the paramount challenge of network failure precipitated by congestion and energy depletion at pivotal central nodes in static IoT networks. By dynamically identifying articulation nodes and strategically directing mobile nodes to these loci, AM-RPL proficiently redistributes the load, thereby fortifying network resilience. The simulation outcomes within the Cooja environment reveal marked enhancements in Packet Delivery Ratio (PDR), Radio Duty Cycle, Charge Consumption, and Parent Switches. These results substantiate the effectiveness of the AM-RPL algorithm in preserving network stability and optimizing performance by mitigating congestion-induced failures. Prospective endeavors may encompass the validation of the protocol across varied IoT network topologies and the pursuit of further refinements to augment its scalability and robustness. While we did not measure performance improvement in percentage terms due to varying readings under the same network conditions, AM-RPL reliably showed enhancements in reliability, energy efficiency, and network stability. This consistent improvement underscores the protocol's potential for optimizing large-scale IoT networks.

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

Healthcare Internet of Things (IoT) systems play a key role in modern medical environments by supporting real-time patient monitoring, remote diagnostics, and emergency response. These systems depend on wearable devices and wireless sensors that collect and share vital health data. A common challenge in such networks is the limited battery life of the devices. Repeated data transmissions and the dynamic nature of the network often lead to quick energy drain, unstable communication, and increased delays. This becomes a serious issue in time-sensitive applications where consistent and timely data flow is critical. To address this problem, this paper proposes a new energy-aware opportunistic routing approach built on a hybrid combination of Fuzzy Logic and Genetic Algorithm (Fuzzy-GA). The fuzzy module is designed to assess uncertain input parameters such as residual energy, proximity to the sink, and communication reliability. These values are converted into a score that helps in evaluating the suitability of each node. The GA then uses this score to identify an optimal set of forwarding nodes by simulating natural selection, crossover, and mutation processes. The goal is to improve routing decisions in real time by adjusting to changing network conditions. Simulation experiments were conducted to test the performance of the proposed method against well-known protocols. The Fuzzy-GA approach achieved notable improvements in key metrics. Energy consumption (EC) was reduced by up to 35.77 %, while the packet delivery ratio (PDR) increased by 9.37 %. The average end-to-end delay (E2E Delay) dropped by 32.35 %, and the network lifetime (NLT) extended by 25.06 %. These results indicate that the proposed method supports more efficient and stable communication. The improved performance makes it suitable for healthcare systems where accuracy, timeliness, and energy conservation are essential for continuous patient care and reliable data exchange.

Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication.

QoS support is essential for differentiated data processing according to types and characteristics of traffics occurred from various applications such as environment monitoring, disaster control and data collection as well as efficient use of energy in order to transmit the multimedia data in Wireless Multimedia Sensor Network (WMSNs). This paper proposes a multipath routing method which may extend the life time of the whole network by supporting QoS of WMSNs and improving the energy efficiency. The proposed multipath routing method sets the path considering the distance to sink node, remaining energy of node and link quality. In addition, setting the path according to characteristics of packet in order to support the differentiation of service for guaranteeing QoS is based on the path cost. The proposed method improve the energy efficiency while securing the reliability by enhancing the packet transmission rate, reducing the packet loss rate and delay time preventing the concentration of energy consumption through the multipath and subsequent reorganization of the path.

Introduction— Smart irrigation systems require reliable and energy-efficient communication between the sensors in a Wireless Sensor Network (WSN) and the control system. Objective— To address this challenge, this paper presents several cluster-based selection protocols, based on the Stable Election Protocol (SEP) and Distributed Energy-Efficient Clustering (DEEC). Methodology— The presented protocols divide the agriculture field into sub-fields to reduce energy consumption between far sensors and the Base Station (BS). Results— Comparison with traditional protocols using evaluation metrics such as network throughput, stability, instability period, and lifetime, shows that the presented protocols outperform in terms of all metrics. Conclusions— The results indicate the effectiveness of the proposed protocols in prolonging the network’s lifetime and improving energy efficiency in heterogeneous WSNs, thus supporting the performance of smart irrigation systems. Numerically, using the proposed protocols, the network lifetime increased by 23% compared to conventional SEP.

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service