Adaptive routing protocol for large-scale power internet of things based on edge computing and federated learning.

An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks

In the few last years, there is an increasing need of QoS guarantees for multimedia and real time applications in mobile ad hoc networks. While several QoS routing protocols were proposed, adaptive routing has not be enough exploited. We propose in this paper a new delay oriented adaptive routing protocol for mobile ad hoc networks. We integrate a mean delay estimation model at each node in our protocol in order to avoid synchronization problem. Adaptive mean delay routing (AMDR) protocol proposed in this paper uses two kinds of exploration packets having the task of gathering delay information of available paths and updating probabilistic routing tables. To reduce the overhead generated by AMDR protocol, we propose the use of a new MPR selection algorithm called Flooding Optimization Algorithm (FOA) based on mean delay.

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

Vehicular Ad hoc Networks (VANETs) pose many challenges to cope with, such as large network size, rapid topology changes and channel capacity limitations. To address these problems, we propose VACO (Vehicular routing protocol based on Ant Colony Optimization), a new adaptive multi-criteria VANET routing protocol. VACO combines both reactive and proactive components to respectively establish and maintain best routing paths. Reactive forward and backward ants are sent between source and target intersection (closest intersection to the destination vehicle) to explore and set up best routes consisting of a list of intersections. Routing decision is then realized at each intersection to opportunistically select best next intersection. The key feature of the route selection is to rely on a periodically estimated road segment relaying quality which is expressed in terms of three combined QoS parameters (latency, bandwidth and delivery ratio). The derived simulation results indicate that VACO shows better performance than a basic geographical routing protocol (GPSR) and a min-delay routing protocol (CAR).

For vehicle-to-vehicle (V2V) communication, suitable routing protocols are needed. An adaptive cross-layer multi-path routing protocol, R-S-AOMDV, is proposed. The routing metric combines hop-count, link-quality and vehicle-motion information. To make use of advantages of multi-path routing protocol, such as decrease of route discovery frequency, the protocol is designed based on the improvement of AOMDV (Ad hoc On-demand Multipath Distance Vector) routing protocol. Compared with AOMDV with minimum hop-count metric, simulation results show that R-S-AOMDV achieves better performance, no matter in sparse or dense urban VANET (Vehicular Ad hoc NETwork).

Wireless Body Area Network (WBAN) technologies are emerging with extensive applications in several domains. Health is a fascinating domain of WBAN for smart monitoring of a patient's condition. An important factor to consider in WBAN is a node's lifetime. Improving the lifetime of nodes is critical to address many issues, such as utility and reliability. Existing routing protocols have addressed the energy conservation problem but considered only a few parameters, thus affecting their performance. Moreover, most of the existing schemes did not consider traffic prioritization which is critical in WBANs. In this paper, an adaptive multi-cost routing protocol is proposed with a multi-objective cost function considering minimum distance from sink, temperature of sensor nodes, priority of sensed data, and maximum residual energy on sensor nodes. The performance of the proposed protocol is compared with the existing schemes for the parameters: network lifetime, stability period, throughput, energy consumption, and path loss. It is evident from the obtained results that the proposed protocol improves network lifetime and stability period by 30% and 15%, respectively, as well as outperforms the existing protocols in terms of throughput, energy consumption, and path loss.

Using the available but limited resources of sensors more efficiently has been the recent interest in designing the routing protocols for Wireless Sensor Network (WSN). The major concern includes energy, storage and computing resources. Accordingly, we propose an adaptive energy-aware Multi-path routing protocol with load balance (AEMRP-LB). It introduces a concept named direction-angle to overcome the deficiency of broadcast and takes into account the tradeoff between the residual energy and hop count to establish multiple node-disjoint paths. The traffic load is balanced over the selected paths by using a weighted traffic scheduling algorithm considering their transmitting capacity, and then AEMRP-LB uses the advantages of Multi-path Source Routing to report the acquired data as saving the computing and storage resources of the sensors. In the scene with multiple Source-Sink pairs, AEMRP-LB can adaptively adjust the available residual energy of shared nodes so as to make reasonable use of them. The simulation results show that AEMRP-LB outperforms the two comparative schemes, and the sensors consume the energy in a more equitable way which ensures a more graceful degradation of service with time.

The analysis of real social, biological and technological networks has attracted a lot of attention as technological advances have given us a wealth of empirical data. For, analysis and investigation time varying graphs are used to understand the relationship, contact duration, repeated occurrence of contact. It is under exploring in intermittently connected networks. Now, by extending the same concept in intermittent networks, the efficiency of the routing protocol can be improved. This paper discusses about the temporal characterizing algorithm. Such characterization can help in accurately understanding dynamic behaviors and taking appropriate routing decisions. Therefore, the present research provokes exploring different possibilities of utilizing the same time varying network analyses and designing an Adaptive Routing protocol using temporal distance metric. The adaptive routing protocol is implemented using ONE simulator and is compared with the Epidemic and PropHET for delivery ratio, overhead and the number of dropped messages. The result reveals that Adaptive routing performs better than Epidemic and PropHET for real and synthetic datasets.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node’s battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and ’Dijkstra’ algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

The Internet of Things (IoT) is transforming healthcare and other critical infrastructure domains; however, conventional routing protocols such as the Routing Protocol for Low-Power and Lossy Networks (RPL) fall short of meeting the stringent Quality of Service (QoS) demands of such applications.This paper introduces RPLMQoS, an enhanced routing protocol that incorporates a four-level priority queuing mechanism, a reinforcement learning-based adaptive routing agent, and a composite metric combining Expected Transmission Count (ETX), residual energy, and end-to-end latency.The protocol is evaluated through extensive simulations using the Cooja emulator and validated in a real-world deployment within a hospital simulation facility.Results demonstrate significant improvements over standard RPL, including a 92.7% packet delivery ratio for high-priority traffic, a 22.1% reduction in latency, and a 15.7% gain in energy efficiency.The paper also addresses security, scalability, and protocol limitations, providing a comprehensive assessment of RPLMQoS for reliable, large-scale IoT healthcare applications.

Cooperative UAV networks are becoming increasingly popular in military and civilian applications. Alas, the typical ad-hoc routing protocols, which aim at finding the shortest path, lead to significant performance degradation because of the 3-dimension highly-dynamic nature of UAV networks and the uneven distribution of nodes across the network. This paper proposes OPAR, an optimized predictive and adaptive routing protocol, to face this challenging problem. We model the routing problem with linear programming (LP), where the goal is to maximize network performance, considering the path lifetime and path-length together. This model relies on a precise link lifetime prediction mechanism. We support the LP problem with a lightweight algorithm to find the optimized solution with a computation complexity of O(|E| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) , where |E| is the number of network links. We evaluate the OPAR performance and compare it with the well-known routing algorithms AODV, DSDV, and OLSR to cover a wide range of proactive and reactive protocols as well as distance vector and link-state techniques. We performed extensive simulations for different network densities and mobility patterns using the ns-3 simulator. Results show that OPAR prevents a high volume of routing traffic, increases the successful delivery by more than 30%, improves the throughput 25% on average, and decreases the flow completion time by an average of 35% <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

One of the major issues in current routing protocols for mobile ad hoc networks (MANETs) is to develop a routing protocol that can satisfy different applications’ needs and optimize routing paths to cope with protocol topology changes. Thus, developing a new routing protocol to optimize routing paths based on dynamic topology changes is a challenging task in MANET. Position-based routing protocols are more suitable for high node mobility due to their dependability on local position information rather a global network knowledge. However, most existing position-based routing protocols forward packets greedily which leads to inaccurate location information and results in frequent route failures. Furthermore, incorporating fixed-interval beaconing to distribute local positions introduces high overheads. In this paper an on-demand geo-casting routing scheme which makes use of geographical information to establish cost-effective and reliable paths for packets to reach their destination is proposed. Through the proposed scheme, search area during route discovery process includes only promising search paths, which will alleviate the effect of control message overhead. Moreover, each node is able to independently adjust protocol parameters based on network environment, data traffic conditions, and node’s congestion level. The simulation results show that the proposed protocol outperforms existing routing protocols under different network conditions such as various mobility and traffic loads.

The authors study generalized hypercube (GHC) interconnection networks. This network provides a rich interconnection topology, is symmetric, and potentially forms the basis of a very reliable architecture. They propose a general taxonomy for adaptive routing protocols, and identify three classes of protocols for generalized hypercubes in particular. These protocols are fully distributed, resilient to link failures or bottlenecks, and require comparatively little additional routing overhead over current fixed path routing protocols. The performance of the three protocols is evaluated for a 64 node circuit switched GHC. The authors propose metrics in addition to the commonly used metrics of latency and path length, to capture the unique behavior of such adaptive protocols.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The rapid surge of Mobile Ad Hoc Networks (MANETs) stimulates the need for adaptive, intelligent, and secure routing mechanisms to ensure seamless communication in dynamic environments. Traditional routing protocols are battling with security threats such as wormhole attacks that disrupt routing and degrade network performance. To address these challenges, this study outlines a state-of-the-art technique, the Reinforcement Learning-Based Secure Routing Protocol (RLSRP), which leverages adaptive k-hop clustering and deep Q-Networks (DQN) to fine-tune routing decisions dynamically while mitigating security risks. RLSRP unwaveringly measures network condition by evaluating latency variations and anomalies to spot suspicious nodes, thereby enhancing route stability. The protocol implements zone-related clustering where nodes within each zone collaborate to optimise routing paths based on real-time conditions, ensuring energy-efficient communication. Current research investigated deep reinforcement learning methodologies to improve security in zone-related MANETs and ensure efficient data routing in large-scale environments. A detailed simulation-based evaluation depicts the potency of the proposed RLSRP model when compared with other reinforcement learning-based routing protocols. Using a large-scale setup–scaling up to 10 million nodes–with Dask and TensorFlow, the results show that RLSRP consistently outperforms FSSAM, Cluster-RL, and Reputation-based Q-learning in terms of Packet Delivery Ratio (exceeding 99%), reduced latency, and improved energy efficiency. These findings reported RLSRP as a secure and scalable solution for practical MANET routing.

Increasingly, the services operations which perform data sensing and data propagation in a dynamic environment are important tasks of wireless sensor networks. Therefore, increasing the network lifetime is the main contribution of this investigation. We propose a novel power-aware chessboard-based adaptive routing (PCAR) protocol to support immobility management in wireless sensor networks. The paramount design challenge in this work is to scale-down the network energy consumption, thus maximizing the network lifetime. Our PCAR protocol utilizes vector-oriented propagation, power-consideration decision, and multi-path routing protocols to guide the propagating data to its destination. Moreover, the properties of the clusters are combined in the PCAR to form the cluster-plates in a chessboard-based clustered sensor network. The alternate usage of cluster-head nodes and sleep nodes increases energy efficiency. The opportune divide-and-conquer multi-path fusion mechanism slows down and balances energy consumption. Finally, a performance analysis shows that energy efficiency is achieved by the PCAR protocol.