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Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs. [ABSTRACT FROM AUTHOR]

In this work, the concept of peer-to-peer energy sharing in wildlife communication systems is explored. In this context, wild animals can share energy wirelessly besides their data communications as they opportunistically come into range of each other. Our goal is to find a way to balance the energy among the nodes and minimize this energy loss. We propose a novel encounter-based energy-sharing scheme, called EBES, that utilizes single and multi-hop transmission to achieve energy balance, minimize energy losses, and maximize the lifetime of the wildlife communication system. EBES is based on a variety of parameters, including the amount of energy left in the system and the nodes' encounter rate, and buffer sizes. In the simulation studies, we considered a wildlife communication network that is involved in data communication and applied EBES over the opportunistic routing protocols such as EBR, Spray&Wait, and Epidemic resulting in a network lifetime increase of 35% and improving the routing protocols performance. Additionally, we compared EBES with the other well-known energy balancing techniques that also contribute to data communication such as EA-Epidemic, EERPFAnt, and OE-OLSR and the results show the remaining energy was improved by 31%, 26%, and 15%, respectively. [ABSTRACT FROM AUTHOR]

In existing schemes, there has been no analysis or steps built for balancing data integrity and energy consumption of clusters in the network, and providing security in WSN is difficult. In this research work, to overcome these troubles, we have used a Query-based Location-Aware Secure Multicast Routing for Wireless Sensor Networks (QLAMSR) is proposed to attain energy efficiency and security. There are three modules involved here. The first module is about the network model and the system overview to obtain more network lifetime. In the second module, an advanced encryption standard and RC6 algorithms is used here to provide authentication and data integrity. In the third module, efficient energy routes are established and demonstrated to illustrate network reliability. Node trust and reliable route energy efficiency are calculated to produce efficient data integrity through the idea of a fuzzy decision model. The proposed protocol is simulated with the Network Simulator tool (NS 2.34) to analyze the network performance metrics and finally its shown that proposed QLAMSR attains route energy efficiency by 39–92% and data integrity rate by 18–89(packets/sec). [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary In this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN). [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' randomness that can cause a continual change of network topology, thus finding correct paths among the nodes is the fundamental task of routing protocols. There is a rise in protocols that depend on cross-layer communication across several layers to increase the MANET performance. In this manuscript, a Hybrid Clustering Approach (SG-MFOA) using a Multipath Cross-Layer Design in a MANET Network is proposed. Here, multiple routes are selected for data packet transmission with the help of hybrid routing. Furthermore, a cross-layer metric is acquired depending upon Expected Transmission Time (ETT), Residual Energy and Load Balancing Factor. It is necessary to select a Cluster Head (CH) depending on this trade for proficient routing. So, the cluster head is optimally chosen to utilize SG-MFOA. Thus, the multipath route selection is performed by the multi-objective functions containing bandwidth, congestion delay, transmission delay and to ensure the prolonging of network lifetime, queue delay at each connection Access Category from the available routes to a destination. When compared with the existing methods, like a proficient self-attention-based conditional variational auto-encoder generative adversarial networks-based multipath cross-layer design routing paradigm for MANET (SCVAGAN-MCDR-MANET), Energy Aware Cross Layer Routing Protocol to Increase Route Reliability in MANETs (EACLRP-RR-MANET), Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector Routing Protocol for MANET (CLAF-DVRP-MANET), respectively. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This study leverages the Deep Double Q-Learning Network (DDQN) algorithm to optimize the traditional Greedy Perimeter Stateless Routing (GPSR) protocol, resulting in a multi-objective optimized GPSR routing protocol (DDQN-MTGPSR). By constructing a multi-objective routing optimization model through cross-layer data fusion, the proposed approach aims to enhance UAV network communication performance comprehensively. In addition, this study develops the above DDQN-MTGPSR intelligent routing algorithm based on the NS-3 platform and uses an artificial intelligence framework. In order to verify the effectiveness of the DDQN-MTGPSR algorithm, it is simulated and compared with the traditional ad hoc routing protocols, and the experimental results show that compared with the GPSR protocol, the DDQN-MTGPSR has achieved significant optimization in the key metrics such as the average end-to-end delay, packet delivery rate, node average residual energy variance and percentage of node average residual energy. In high dynamic scenarios, the above indicators were optimized by 20.05%, 12.72%, 0.47%, and 50.15%, respectively, while optimizing 36.31%, 26.26%, 8.709%, and 69.3% in large-scale scenarios, respectively. [ABSTRACT FROM AUTHOR]

With technological advancements in networking and wireless communication, the flying ad hoc network has emerged as a cost-effective relay network. Networks of unmanned aerial vehicles (UAVs) can be cooperatively organized as an ad hoc network for effectively accomplishing complex tasks. Such a UAV network is deployed as a relay network that forwards and exchanges accurate and reliable end-to-end in-formation. However, the design of routing protocols is rendered difficult by flying constraints and extreme topology changes over a short time period. The performance of the routing protocols in such networks is considerably affected by high-speed nodes and extreme topology changes. Evaluating the routing protocol in a UAV network gives insight regarding the performance of the entire network. This paper evaluates, analyzes, and compares the performances of three routing protocols: Ad hoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination-Sequenced Distance-Vector (DSDV). In most of the simulated cases, the AODV outperformed the OLSR and DSDV. The number of nodes, i.e., the network size, largely affected the throughput, packet delivery ratio (PDR), and end-to-end delay (E2E) of the network. However, the OLSR improved the performance of the average E2E; therefore, it is suitable for delay-sensitive applications. However, AODV outperformed the other protocols in terms of throughput and PDR because it reacts to topology changes. DSDV delivered the poorest PDR and throughput. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system's lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%. [ABSTRACT FROM AUTHOR]

Summary: Recently, wireless sensor networks (WSNs) have been used for monitoring, sensing, processing, and communication purposes in real‐time applications. It is employed with a routing protocol that performs an effective data transmission process. However, while transmitting large data, there occurs an over fitting issue, which leads to determining a huge data leakage. Also, the delay is increased with heavy congestion in the network. Hence, a novel method is proposed to diminish the network congestion regarding distributed networks as well as cloud edge computing. Moreover, it diminished the data loss from an overloaded condition. However, the proposed technique controls congestion that resists the traffic in the network through lightweight, ultra‐dense label‐less federation and incorporates adaptive multi‐agent Markov reinforcement learning. Furthermore, a distributed energy‐efficient delay‐aware routing protocol is employed to analyze and regulate congestion control in the network. Also, it varies the network dynamically by adjusting the routing protocol that optimizes the congestion and implements the traffic mechanism. Moreover, the congestion in WSNs overwhelms the nodes and channels distributed in the packets. The evaluation of the proposed method is determined by various metrics such as queuing delay, network lifetime, energy efficiency, throughput, and packet delivery ratio. The experimental results revealed that the proposed method attained an enhanced performance by maximizing energy efficiency and packet delivery ratio by 94% as well as 89% and reducing the delay by 55%, respectively. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) is a type of wireless sensor network that plays a crucial role in monitoring patient healthcare. In this network, sensor nodes are typically placed inside or outside the patient's body. These nodes are capable of transmitting data to the sink node when any functional changes in the patient are detected. The routing and energy efficiency of the network nodes are challenging tasks in WBAN, as each sensor node operates on a battery. Therefore, in multi-hop routing designing a robust routing protocol has a significant impact on reducing energy consumption during the selection of the next hop. In this paper, a simple novel routing protocol named simple energy-aware and reliable (SEAR) routing protocol is proposed to transmit reliable data packets in a WBAN. The proposed routing scheme considers the remaining energy of sensor nodes, priority data, and hop count to the sink node as significant metrics for dynamically selecting the best forwarder node. Furthermore, the proposed protocol utilizes the route reliability factor (RRF) to select the optimal route among all possible routes between the source sensor node and the sink node. RRF selects the route with the maximum route residual energy and minimum hop count. As a result, SEAR has the ability to provide effective single-hop and multi-hop routing data transmission to improve the reliability of data transmission, decrease the energy consumption of the sensor nodes, and prolong the lifetime of the network. The simulation results show that the performance of the SEAR routing protocol outperforms the existing routing protocols for the metrics: packet loss ratio, throughput, end-to-end delay, normalized routing load, energy consumption, and network lifetime. The results indicate that the proposed protocol improves total energy consumption by 18.76% and 10.89% when compared to EERR-RLFL and AMCRP respectively. Meanwhile, SEAR reduces the average end-to-end delay by 17% and 9%, packet loss ratio by 29.48% and 17.69%, and normalized routing load by 31.17% and 20.91%. Additionally, SEAR achieves up to 16% and 9.71% higher throughput compared to EERR-RLFL and AMCRP routing protocols respectively. Overall, the results obtained indicate that the proposed SEAR routing scheme significantly enhances the overall performance of the network. [ABSTRACT FROM AUTHOR]

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP. [ABSTRACT FROM AUTHOR]

Energy resource limitations, data packet traffic congestion, mobility, and communication link failure of relay nodes are vital challenges in establishing a reliable and efficient path between end-to-end users in IoT Communication toward 6G. These factors can severely affect routing and network performance. Hence, it is imperative to design a routing approach to adapt to such heterogeneity to improve the quality of service (QoS) performance toward 6G networks. This paper proposes a multi-criteria-aware integrated decision-making (MCAIDeM) routing protocol in which relay nodes' residual energy, queue length, mobility, and link quality are integrated into a single routing approach for efficient and reliable route selection. In particular, the MCAIDeM routing protocol utilizes an analytic hierarchy process (AHP) approach for multi-criteria decision-making, which determines appropriate weights according to the parameter values of the nodes to select suitable relay nodes during the route selection process. Extensive simulations are performed to evaluate the performance, and the obtained simulation results showed that the proposed MCAIDeM routing protocol outperforms conventional routing protocols in terms of network throughput, latency, and energy consumption with various network parameters. The end-to-end delay obtained by MCAIDeM was 9.03% and 11.26% lower than that of MBMQA, 11.21%, and 13.67% lower than that of MEQSA-OLSRv2, and 19.15% and 16.61% lower than that of the MP-OLSRv2 routing protocols when the node speed increased from 5 m/s to 30 m/s, respectively. [ABSTRACT FROM AUTHOR]

In flying Ad hoc networks (FANETs), traditional geographic routing protocols such as greedy perimeter stateless routing (GPSR) protocol, usually adopts a periodic Hello mechanism and a greedy forwarding mechanism based on maximum advanced distance. However, the traditional periodic Hello mechanism ignores node mobility, network connectivity, and traffic type, thus causing temporary communication blindness (TCB). Additionally, the traditionally geographic routing protocols choose the next-hop node based on the maximum advanced distance. The greedy forwarding mechanism based on maximum advanced distance towards the destination ignores the impact of network dynamics on links, leading to short link lifetime and frequent link breakage. To address these problems, a novel airborne greedy geographic routing (AGGR) protocol for FANETs is proposed in this paper. It includes an adaptive Hello mechanism and a greedy forwarding mechanism based on the relative motion of nodes. In the adaptive Hello mechanism, the Hello period of working nodes is calculated according to the real-time relative characteristic values between the node and its upstream node, and the Hello period of idle nodes adopts a fixed value according to the movement characteristics relative to all neighbor nodes. In the greedy forwarding mechanism based on the relative motion of nodes, in order to adapt to the high dynamic changes of network topology, the time to enter the communication range of the destination is taken as routing criteria to choose the next-hop node. Moreover, the AGGR protocol is compared with the original GPSR in simulation. The results show that it significantly mitigates the TCB problem and gains significant improvement in network performance. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) are an emerging field in the domain of healthcare which are typically composed of biomedical sensors. These sensors are implanted inside or attached to the human body for monitoring a patient's condition and providing accurate treatment to patients. In WBANs, energy efficiency is a critical concern due to the restricted battery capacity of the sensors. Extending the network lifetime and reducing the energy consumption of these sensor nodes can significantly impact the reliability and effectiveness of WBANs in monitoring patients' health. An efficient routing protocol based on energy-related parameters is crucial in designing these networks. Although many routing protocols have been proposed for routing in WBANs, sufficient features have not been properly handled in these methods. To overcome these issues, a novel routing protocol named Simple Energy Efficient and Bandwidth Aware (SEBA) routing protocol is proposed for routing in WBANs. The proposed scheme considers multiple metrics of the network node, such as remaining energy, energy harvesting, draining rate energy, available bandwidth, and number of hops in a route selection to minimize energy consumption, increase network lifetimes, and enhance the reliability of data transmission in WBANs. Additionally, the SEBA uses a novel mechanism to change the route dynamically based on energy consumption. This mechanism plays a significant role in reducing the number of route errors, route discoveries, and distributing energy consumption among sensor nodes. The experimental results reveal that the proposed scheme performs well in terms of average network throughput, packet delivery ratio, normalized routing load, average end-to-end delay, energy consumption, and network lifetime compared with the existing AMCRP, EEMP, and EAR protocols. [ABSTRACT FROM AUTHOR]

In delaytolerant networks (DTN), energyefficient node operation is vital due to battery life constraints. Energy con sumption significantly impacts data forwarding, necessitating novel energyefficient routing protocols. We begin by comparing the performance of several protocols, including CASPaR, Direct Delivery, Epidemic, SprayandWait, and PRoPHETv2, using the Opportunistic Network Environment (ONE) simulator. Our findings demonstrate the superiority of CASPaR in terms of message delivery and latency. To further optimize energy usage, we propose EECASPaR, a combination of a threshold algorithm with CASPaR. EECASPaR is a singlecopy, congestionavoidance, energyefficient protocol suitable for energyconstrained applications, promoting sustainable communication. This protocol considers the remaining node energy to make informed decisions regarding message acceptance or forwarding to neighbor nodes. Through extensive simulations with varying energy thresholds, our results confirm that EECASPaR enhances remaining node energy and reduces the occurrence of dead nodes. [ABSTRACT FROM AUTHOR]

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility. [ABSTRACT FROM AUTHOR]

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

This FANETs (Flying Adhoc NETworks) are supported by UAVs (Unmanned Aerial Vehicles) which carry out the task of transmitting and receiving data. Transmission and routing are complicated by the highly mobile AUVs. Node links are temporary and may result in transmission related issues. FANETs are prone to network attacks, especially black hole attacks leading to heavy data loss. This may lead to packet drops which might compromise the transmitted data. The proposed Eagle Optimized Energy Efficient Optimal Route-Finding Protocol (EOEEORFP) selects an efficient route towards the destination. Factors like trust degree, priority queue, network delay, hop count and energy consumption levels are taken into consideration in designing the routing strategy. These factors help in identifying the nodes that form the trusted nodes in the routing path. This is achieved by employing the biological behavior of eagles in finding their prey. The suggested protocol works in two stages. In the initial stage, it sets up an optimal route with selective nodes (trusted nodes). In the second stage, the network security is fortified by eradicating black hole attacks by using RREP (Route Reply) packets strategically. The EOEEORFP protocol assures the successful data delivery by employing a trusted routing path. The proposed EOEEORFP protocol is assessed against Adhoc On demand Distance Vector (AODV) and AODV-V2 protocols to measure its performance. EOEEORFP achieves very good PDR and throughput while conserving the network energy by minimizing the network delay. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has witnessed remarkable growth, leading to the emergence of numerous applications and services. However, energy efficiency remains a critical concern in IoT networks due to the resource-constrained nature of many IoT devices. Energy-efficient routing protocols play a pivotal role in mitigating this challenge, optimizing energy consumption, and extending the operational lifetime of IoT deployments. This survey paper aims to provide a holistic understanding of the state-of-the-art approaches, their characteristics, strengths, limitations, and performance metrics. The survey encompasses a wide range of energy-efficient routing protocols proposed in the literature, including traditional routing protocols adapted for IoT scenarios and protocols specifically tailored for energy optimization. We discuss the impact of different network characteristics on the performance of energy-efficient routing protocols, such as network size, node density, mobility, and heterogeneity. Through this systematic survey, we aim to provide researchers, engineers, and practitioners with a comprehensive overview of energy-efficient routing protocols for IoT networks. The survey findings can serve as a valuable reference for protocol selection, performance evaluation, and the development of novel energy-efficient routing strategies in future IoT deployments. [ABSTRACT FROM AUTHOR]

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. [ABSTRACT FROM AUTHOR]

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network's lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability. [ABSTRACT FROM AUTHOR]

In the world of connected devices with enormous application market, wireless sensor network comes with inherent challenges. Energy consumption balancing among the sensors affects its lifetime and connectivity. A hybrid approach for the randomly deployed sensor network is proposed in this paper with better consumption energy balance through the lifetime of the sensor network. An optimized threshold probability is analyzed for the improvement of network lifetime in comparison with stable election protocol. Simulation results provide improved consumption energy distribution and prolonged network lifetime of heterogeneous wireless sensor network. The proposed protocol is reactive to the change in the network, making it suitable for the Internet of Things applications. [ABSTRACT FROM AUTHOR]

Due to the characteristics of self‐organization, flexibility, and low cost, Mobile ad hoc networks (MANETs) have been applied in many fields, such as disaster rescues and military actions. However, in reality, the successful route establishment based on routing protocols is the prerequisite of data transmission. Besides, the transmission reliability of MANETs is affected by resource constraints, such as channel capacity and energy, and environmental factors. Hence, how accurately modeling and evaluating the transmission reliability of MANETs is very important to ensure their application. Whereas, existing studies ignore the impact of routing protocols and do not fully consider the influence of some factors on transmission reliability, such as environmental randomness, channel capacity, and energy consumption. To solve the above problems, we first propose a signal‐to‐noise‐ratio‐Capacity connection model to better describe the characteristics of wireless links, and the probability of one hop connection is deduced considering the capacity demand randomness. Then, considering the influence of routing protocol on the MANET link establishment, we take the ad hoc on‐demand distance vector routing protocol as an example, and propose an evaluation algorithm of the MANET transmission reliability based on algebraic graph theory and Monte Carlo simulation. Finally, the effectiveness of the proposed method is demonstrated by a numerical example of an unmanned aerial vehicle search and rescue scene. [ABSTRACT FROM AUTHOR]

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution's simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol's significance with enhanced quality of service performance. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal "Energies" that focuses on energy-efficient wireless communication and networking. The issue includes 18 articles covering various domains within this field. The articles discuss topics such as power domain integration in 5G networks, secure and energy-efficient routing protocols for wireless sensor networks, energy-efficient scheduling algorithms, energy-efficient cross-layer design, energy issues in device-to-device communication, energy harvesting techniques, and more. The authors highlight the significance of energy optimization in wireless systems and present innovative approaches, algorithms, and technologies for sustainable and efficient wireless communication. The document expresses appreciation to the authors and reviewers for their contributions and hopes that the insights provided will inspire further advancements in this field. [Extracted from the article]

Summary: In the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section. [ABSTRACT FROM AUTHOR]

With the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme. [ABSTRACT FROM AUTHOR]

The design of underwater wireless sensor networks (UWSNs) faces many challenges, including power consumption, storage, battery life, and transmission bandwidth. UWSNs usually either use node clustering or multi-hop routing as their energy-efficient optimization algorithms. The cluster optimization technique will organize the sensor nodes into a cluster network, with each cluster led by a cluster head (CH). In contrast, the multi-hop optimization algorithm will create a multi-hop network by sending data to the base station (BS) while switching between different sensor nodes. However, the overburdens of CH nodes impact the performance of the cluster optimization method, whereas the overburdens of nodes close to the BS impact the performance of the multi-hop optimization algorithm. Therefore, clustering and routing procedures can be considered as a simultaneous NP-hard problem that metaheuristic algorithms can address. With this motivation, this paper proposes an energy-efficient clustering and multi-hop routing protocol using the metaheuristic-based algorithm to increase energy efficiency in UWSNs and lengthen the network life. However, the existing metaheuristic-based methods use two separate algorithms for clustering and multi-hop routing, increasing computational complexity, different initialization, and difficulty in hyperparameters' tuning. In order to address the mentioned shortcomings, this paper proposes a novel hierarchical structure called hierarchical chimp optimization (HChOA) for both clustering and multi-hop routing processes. The proposed HChOA is validated using various metrics after being simulated using an extended set of experiments. Results are compared to those from LEACH, TEEN, MPSO, PSO, and IPSO-GWO to validate the impact of the HChOA. According to the findings, the HChOA performed better than other lifespan and energy usage benchmarks. [ABSTRACT FROM AUTHOR]

In optical networks, increasing longevity is of critical importance. This article describes a cutting-edge routing protocol based on Q-learning techniques that have been meticulously constructed to extend the lifetime of optical networks by enhancing energy effectiveness and throughput. The protocol dynamically manages energy usage using Q-learning, a reinforcement learning approach. The primary objective is to choose routing algorithms that optimize long-term revenues for individual nodes while increasing energy efficiency. In a detailed study, the protocol's performance is compared to that of well-known rivals such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Low-Energy Adaptive Clustering Hierarchy (M-LEACH), and Balanced Low-Energy Adaptive Clustering Hierarchy (B-LEACH) (B-LEACH). The evaluation considers several factors, including network durability as measured by active/inactive node ratios, energy efficiency as measured by per-round energy consumption, quality of service as measured by throughput per round, and scalability as measured over networks with 40, 70, and 100 nodes. The complete examination for each network configuration spans over 5,000 cycles. M-LEACH outperforms LEACH and B-LEACH in all performance measures in the simulation results test, establishing a new benchmark. It is fascinating to compare the performance of the unique Q-learning-based protocol to that of LEACH, M-LEACH, and B-LEACH. Regarding network durability, energy efficiency, quality of service, and scalability, the proposed protocol outperforms. [ABSTRACT FROM AUTHOR]

One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the network performance if succeeded. By replying to a route request with the minimum routing cost and the maximum sequence number (SN) value, the malicious vehicle can fool the source vehicle that it has the "freshest" and best cost-effective route to the destination vehicle. As a result, all data packets intended to a legitimate destination are caught and destroyed by the malicious vehicle. This may also cause a serious risk for autonomous vehicle control system or traffic warning applications. Previous published malicious detection algorithms suffer high error rates when the malicious node uses a "close-to-legitimate" SN value to attack or actively change the information in the route reply packet. This paper proposes a Black Hole Attack Detection Algorithm (BADA) based on a machine learning approach. In VANETs, it is a challenge to differentiate the behavior of malicious vehicles as they try to avoid detection by imitating that of the normal vehicles. The BADA classification algorithm outperforms existing solutions because it relies on the history of each vehicle's route request and response behavior, and it deploys the k-Nearest Neighbors machine learning algorithm to identify malicious vehicles. The paper also proposes a Black hole Attack Detection Routing Protocol which utilizes the proposed BADA solution to realize a more secured and improved AODV-based protocol. Using the NS2 simulation system, the paper evaluates the performance of the proposed protocol and compares it with related solutions on the traffic network model in Ho Chi Minh city, Vietnam under black hole attacks using minimum SN values. Simulation results have shown that the proposed algorithm can correctly detect the malicious node higher than 99.0%, outperforming previously published algorithms. [ABSTRACT FROM AUTHOR]

Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as environmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Cooperation Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission. [ABSTRACT FROM AUTHOR]

Energy constraint has become the major challenge for designing wireless sensor networks. Network lifetime is considered as the most substantial metric in these networks. Routing technique is one of the best choices for maintaining network lifetime. This paper demonstrates implementation of new methodology of routing in WSN using firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor networks routing. For network cutoff avoidance while maximize net lifetime energy exhaustion must be balanced. Balancing energy consumption is the key feature for rising nets lifetime of WSNs. This routing technique involves determination of optimal route from node toward sink to make energy exhaustion balance in network and in the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods. [ABSTRACT FROM AUTHOR]