Routing Protocol For Sensor Networks Research Articles

A trust management-based secure routing protocol with AUV-aided path repairing for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks (UASNs) are widely applied in marine based applications, but traditional security mechanisms are not feasible due to the underwater sensor nodes’ limited constraints and capabilities. Moreover, few existing routing protocols for underwater sensor networks considered underwater transmision limited constraints. In this paper, we propose T-SAPR, a trust management-based secure routing protocol with AUV-aided path repairing that addresses these challenges. T-SAPR uses an attention-based long short-term memory (LSTM) to build a trust model that considers node trust, communication trust, and environment surrounding’s trust. T-SAPR evaluates trust evidence to measure the trust level of the sensor nodes in the network and uses energy, packet delivery ratio, and latency jointly to seek optimal routing policies in the reward function of Q-Learning. Additionally, we propose a repairing mechanism with the assistance of AUVs to improve the reliability of UASNs in cases of multiple sensor node failures or the detection of malicious nodes. Evaluation results show that T-SAPR identifies malicious nodes by detecting their anomalous behavior while improving the packet delivery ratio and energy efficiency by 13.3% and 48.1% as compared to existing algorithms such as GEDAR, TADR-EAODV, and AFSA-ACOA-SCP.

QoS-based routing protocol and load balancing in wireless sensor networks using the markov model and the artificial bee colony algorithm

QoS-based routing protocol and load balancing in wireless sensor networks using the markov model and the artificial bee colony algorithm

An Efficient LEACH Clustering Protocol to Enhance the QoS of WSN

The development of an energy-efficient routing protocol for wireless sensor networks has been a challenging task for academics. It is very different from the conventional routing protocols, which are based on I.P. addresses. These conventional routing protocols are not preferable for WSNs since conventional routing protocols depend heavily on the routing tables, which often require updates. Also, the WSN varies from thousands to ten thousand, making the task of managing routing tables not easy and economical in terms of hardware resources. There is always research going on to develop an efficient routing protocol in terms of energy for WSNs. LEACH protocol is one of them. Energy consumption in WSNs became a vital factor to be focused on in enforcing an efficient routing strategy. Numerous LEACH protocol variations propose improvements to the current Protocol. In our study, we looked at different LEACH protocol iterations and adopted a modified LEACH protocol to extend the lifetime of wireless sensor networks. We adopted various power levels for transmission between the cluster node, cluster head, and base station, as well as a novel method for choosing the cluster head. Our modified Protocol performs better when compared to parameters like network lifetime, dead nodes per round, Cluster heads formed, packets sent to the base station, etc.

Energy conservation using PISAE and cross-layer-based opportunistic routing protocol (CORP) for wireless sensor network

In wireless sensor networks (WSNs) with constrained sensor node energy, data compression is crucial. Generally speaking, data communication uses up energy. By providing and receiving less data, a sensor node's lifespan is often increased. In this paper, provide an unique unsupervised neural network architecture called Partly-Informed Sparse Autoencoder (PISAE) that tries to reconstruct all sensor readings from chosen prime numbers. This architecture is used to implement the sensor selection approach that we propose. A key challenge for WSN is the selection of the cluster head (CH). Utilizing the K-Medoid, the clustering of sensor nodes is improved. The effect on quality of service (QoS), the location of the sensor nodes, the distance involved, and the energy status requirements are important factors. For the best choice of cluster heads in WSN with regard to distance and energy, the hybridization of two well-known optimization methods, namely Bacteria Foraging Optimization and Harmony Search Algorithm (HSA), is carried out in this study. Opportunistic routing protocol for WSN is proposed in the current work as a cross-layer based opportunistic Routing protocol (CORP). The suggested CORP approach is utilised to identify the best course of action to cut down on computation time and energy usage while enhancing data transmission dependability. Future generations of ubiquitous sensor networks won't have a single AI solution to the problems with energy and load. In addition, this study puts forth the idea of an energy-efficient routing using Fuzzy neural network (ERFN), which can increase the lifespan of WSNs by lowering energy consumption while maintaining energy usage balance. Based on the simulation findings, it can be stated that the proposed CORP approach increases QoS performance metrics such energy consumption, packet delivery rate, packet delay, network lifetime, throughput, and packet loss rate. The performance of the suggested method outperforms that of already-in-use algorithms like FRLDG, MOBFO-EER, and FEEC-IIR.

Energy‐efficient multipath clustering with load balancing routing protocol for wireless multimedia sensor networks

AbstractWireless multimedia sensor networks (WMSNs) has the capability of collecting scalar information, as well as multi‐media events like moving object tracking and traffic fatalities. Because energy in WMSNs is a constrained resource, several researchers offered cluster routing approaches, and most protocols generally do not offer an average resource usage of the network that is balanced and minimised. To address this issue, this paper proposes an Energy‐Efficient Multipath Clustering with Load Balancing Routing Protocol for WMSNs (EEMCL) to prolong the network lifetime. The suggested protocol, which segments the network into layers of clusters, would be implemented using multi‐hop. Sensing data from the sensor is transmitted to the sink by the main cluster heads in each layer, cooperating with the cluster heads in the upper layers. The proposed protocol improves energy dissipation, network life‐times, and network stability according to simulation results when compared with SEP, SEP‐E, and SEPFL protocols. The last node dead for the proposed protocol is at round 5833, 4027 for SEPFL, 2828 for SEP‐E, and 2325 for SEP.

An Energy-Efficient Routing Protocol for Wireless Sensor Networks Using AI Techniques

In the past many years different types of routing protocols, single-path and Multipath, have been designed for wireless sensor networks. We have also found various multipath routing protocols available that send similar data to the base station via multiple paths to increase reliability. We have found that a new simulation environment Castalia is developed based on OMNET++. They have provided implementation of Multipath rings routing protocol. We found that it is based on a technique called Synopsis Diffusion. We observed that although the implementation of the protocol is there, a detailed understanding of the technique is not available in the literature. Hence, in this paper, we have discussed the Multipath rings routing protocol for wireless sensor networks implemented by Castalia Simulator. Here, we have also, evaluated the performance (and hence verified its working) of the Multipath rings routing protocol through simulation. We have taken a view that the Multipath rings routing protocol can route the packet from source to Sink via multiple paths so the network becomes more robust and fault tolerant than the Single path routing. Simulation results also show that the Multipath rings routing protocol is more robust than the Proposed EE routing technique.

MMSCM: A multiple mobile sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

NMSFRA: Heterogeneous routing protocol for balanced energy consumption in mobile wireless sensor network

Wireless sensor networks are widely used in various fields and consist of a large number of sensor nodes that are powered by batteries. As a result, energy efficiency is crucial for the effective operation of these networks. With the advancement of cross technology communication (CTC), the presence of heterogeneous wireless nodes, especially mobile nodes, in wireless sensor networks has increased, making traditional routing protocols for homogeneous wireless sensor networks inapplicable. To overcome the above issues, this paper proposes a heterogeneous wireless sensor network routing protocol for a balanced energy consumption, NMSFRA (NGO based mean shift fuzzy clustering routing algorithm). In order to mitigate the impact of uneven cluster distribution and unstable network links due to the mobility of nodes, the proposed protocol first divides the network into balanced clusters using the Mean Shift (MS) method. The Mamdani fuzzy inference system is then used to take node mobility into account. The protocol dynamically selects different parameters as inputs to the fuzzy logic, enabling it to choose cluster heads (CHs) based on the overall location of the clusters. Additionally, to extend the network lifetime, the protocol generates multi-hop forwarding paths using the Northern Goshawk Optimization (NGO) method. Finally, the paper presents experimental simulations carried out on three different network topologies and compares the results with existing protocols such as LEACH, EETPF, E-FUCA, and DE-SEP. The results show that the NMSFRA protocol extends the survival period of nodes by 235 rounds, 240 rounds, 364 rounds, and 190 rounds, on average, compared to the LEACH, EETPF, E-FUCA, and DE-SEP protocols, respectively. The network throughput also increased by 154.92%, 48.74%, 84.77%, and 106.77%, respectively. The NMSFRA protocol also showed better performance in terms of total residual energy, FND, HND, and average speed difference between nodes in the first 5 rounds. Therefore, the proposed NMSFRA protocol demonstrates superior performance in terms of network lifetime, energy utilization, throughput, and network stability.

CMSTR: A Constrained Minimum Spanning Tree Based Routing Protocol for Wireless Sensor Networks

How to extend the network lifetime with given limited energy budget is always one of the main concerns in Wireless Sensor Networks (WSNs). However, imbalanced energy consumption and overlong intra-cluster communication paths are prevalent in the hierarchical routing protocols, which shortens the network lifetime inevitably. To this end, an energy-efficient routing Protocol based on Constrained Minimum Spanning Tree (CMSTR) is proposed in this paper. To be specific, a new multichain routing scheme to balance the energy consumption for intra-cluster communications is presented. Based on the multichain routing scheme, the problem of establishing intra-cluster routing is transformed into a shortest Hamiltonian path problem on the basis of a graph-theoretic analysis model, which is solved through a Constrained Minimum Spanning Tree (CMST) algorithm proposed in this paper, with the aim to obtain the initial path for intra-cluster communications. In order to shorten the initial path length to obtain higher energy-efficient chain routes, a Neighbor Node Replacement (NNR) algorithm and a Link Intersection Detection and Elimination (LIDE) algorithm are proposed, in which the problem of potential long links and intersections is to be effectively alleviated. With shorter chain routes, unnecessary intra-cluster communication energy depletion can be reduced accordingly. In order to evaluate the performance of CMSTR, extensive simulation experiments are conducted. The results show that CMSTR can greatly prolong the network lifetime with regard to the metrics of FND and HND. To be specific, compared with LEACH, R-LEACH, and DCMSTR, the value of FND increased by 800%, 540% and 57%, that of HND increased by 322%, 286% and 22%, and overall network lifetime (AND) increased by 29%, 10% and 5%, respectively. Besides, CMSTR has a stable and lowest packet loss percentage (0.4%). In summary, CMSTR has excellent performance in terms of energy efficiency and network stability.

Parametric analysis of dynamic routing protocol for wireless sensor networks

Parametric analysis of dynamic routing protocol for wireless sensor networks

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

Multi-hop clustering routing protocols are potential solutions to achieve effective and energy-efficient data delivery in cognitive radio sensor networks (CRSNs). Current clustering routing protocols for CRSNs generally assume perfect spectrum sensing, i.e., false alarm and missed detection are ignored, which may result in collisions with primary users and transmission failure or constrained spectrum usage. To alleviate the impact of imperfect spectrum sensing on network performance, an imperfect spectrum sensing-based multi-hop clustering routing protocol (ISSMCRP) is proposed for CRSNs in this paper. Cluster head (CH) and relay selection criteria are defined based on detection level function of available channels to help select CHs and relays with high spectrum sensing capability. Idle detection accuracy-based intra-cluster and inter-cluster channel selection criterion is proposed to promote successful intra-cluster and inter-cluster data delivery. In addition, control overhead introduced by CHs selection and cluster formation is strictly controlled, and reasonable cluster radii are set to manage the range of control information exchange, so as to reduce the energy consumption caused by control overhead. Simulation results show that compared with the existing clustering routing protocols for CRSNs, ISSMCRP gains obvious advantages in expanding the network lifespan and enhancing the network surveillance capability.

Cluster based energy efficient routing protocol for heterogeneous wireless sensor networks

SummaryWith the advancement in recent technologies, wireless sensor networks (WSNs) have emerged to become one of the key research areas. Sensor nodes in WSN are deployed randomly to sense the events that occur in an area. Clustering of sensor nodes in a wireless sensor network will result in better energy efficiency and topology management. In many scenarios, wireless sensor networks are deployed in remote areas and in hostile environments where batteries cannot be recharged or replaceable. In real world applications, heterogeneous sensor nodes with different initial energies in a WSN are more preferable to prolong stability period, network lifetime, and throughput. The main objective of this article is to design and develop a cluster‐based energy efficient routing protocol for heterogeneous wireless sensor networks (CEER). The proposed CEER routing protocol is based on the residual energy of all the sensor nodes in each round, distance of each sensor node from the base station, weighted election probabilities and reliability of a sensor node and the network. It is found from the results that, the proposed CEER routing protocol provides better stability, network lifetime, and throughput as compared with the existing routing protocols of heterogeneous wireless sensor networks.

An efficient routing protocol for wireless body sensor networks using reinforced learning algorithm in clusters

Wireless body sensor networks are intelligent enough to efficiently sense the signals for vital parameters of the patient, which aids in offering a better healthcare facility to the patients. Wearable bio-sensors with networking capability have led to the possibility of implementing WBSN and thus promising health care facility can be offered to the community with this upcoming technology. These WBSN basically consist of few sensors or nodes that observe the vital parameters of the patient and communicate them to the required destination with the help of the intermediate nodes, through the best possible paths. This paper proposes a Cluster-based routing Protocol using reinforcement learning with Q-Learning approach to achieve optimal route. Simulations are carried with a set of biomedical sensors covering an area of 1000 × 1000 m2. The simulation is carried out for … seconds. The reinforcement algorithm has been found to route the packets faster when compared with other algorithms.

Optimized Energy-Efficient Routing Protocol for Wireless Sensor Network Integrated with IoT: An Approach Based on Deep Convolutional Neural Network and Metaheuristic Algorithms

Wireless sensor networks (WSNs) have emerged as a significant architecture for data collection in various applications. However, the integration of WSNs with IoT poses energy-related challenges due to limited sensor node energy, increased energy consumption for wireless data sharing, and the necessity of energy-efficient routing protocols for reliable transmission and reduced energy consumption. This paper proposes an optimized energy-efficient routing protocol for wireless sensor networks integrated with the Internet of Things. The protocol aims to improve network lifetime and secure data transmission by identifying the optimal Cluster Heads (CHs) in the network, selected using a Tree Hierarchical Deep Convolutional Neural Network. To achieve this, the paper introduces a fitness function that takes into account cluster density, traffic rate, energy, collision, delay throughput, and distance from the capacity node. Additionally, the paper considers three factors, including trust, connectivity, and QoS, to determine the best course of action. The paper also presents a novel optimization approach, using the hybrid Marine Predators Algorithm (MPA) and Woodpecker Mating Algorithm (WMA), to optimize trust, connectivity, and QoS parameters for optimal path selection with minimal delay. The simulation process is implemented in MATLAB, and the developed method’s efficiency is evaluated using several performance metrics. The results of the simulation demonstrate the effectiveness of the proposed method, which achieved significantly lower delay (99.67%, 98.38%, 89.34%, and 97.45%), higher delivery ratio (89.34%, 89.34%, 83.12%, and 88.96%), and lower packet drop (93.15%, 91.25%, 79.90%, and 92.88%) in comparison to existing methods. These outcomes indicate the potential of the optimized energy-efficient routing protocol to improve network lifetime and ensure secure data transmission in WSNs integrated with IoT.

Deep Q-Network-Based Intelligent Routing Protocol for Underwater Acoustic Sensor Network

This article proposes a deep Q-network (DQN)-based intelligent routing (DQIR) protocol for the underwater acoustic sensor networks (UASNs). The routing decision problem is modeled as a Markov decision process (MDP). The DQN is applied to solve the MDP, in which the agent is trained to select the forwarder with the highest reward as the next hop. The optimal policy for the agent is to choose a routing that balances the residual energy of different nodes while minimizing the routing distance, thereby improving the network lifetime and decreasing the average time delay. To evaluate its performance, we developed the proposed algorithm on an Aqua-Sim Next Generation (Aqua-Sim NG) platform and using the artificial intelligence (AI) framework. According to the simulation results, DQIR consumes less energy than both the depth-based routing (DBR) protocol and DQN-based energy and latency-aware routing (DQELR). Furthermore, compared with DBR and DQELR, DQIR increases the network lifetime and reduces the average time delay.

A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches

A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches

LQETA-RP: link quality based energy and trust aware routing protocol for wireless multimedia sensor networks

LQETA-RP: link quality based energy and trust aware routing protocol for wireless multimedia sensor networks

Secure Internet of Things based hybrid optimization techniques for optimal centroid routing protocol in wireless sensor network

SummaryThe recent survey depicts internet growth has been reached to billion of people and Internet of Things (IoT) as another milestone. IoT is wired or wireless communication technologies to establish a communication channel between devices and services available over the Internet. The wireless sensor networks (WSNs) is one of the most essential technologies assisted in IoT for real‐time applications. The energy efficiency is a major issue in IoT and it becomes more complex due to large scalability and the WSNs cannot be applied directly to the IoT. Moreover, routing is a very challenging aspect that takes place in such platform because of it is intrinsic properties. In this article, we propose an optimal centroid‐based routing protocol (OCRP) for WSN assisted IoT based on hybrid optimization techniques, which aim is to enhance the energy efficiency and network lifetime. IoT network is composed by serving nodes and end users. In OCRP, a multi‐objective swarm optimization (MSO) algorithm is used to perform the clustering, which reduce the chaotic in nature of energy consumption. Then, multiservice queue based ant optimization algorithm is proposed for next neighborhood selection for inter‐cluster routing. Finally, our OCRP protocol is experimented with different simulation in network simulator (NS‐2) tool with wireless body sensor network (WBSN) application. The simulation results show the effectiveness of OCRP protocol in terms of energy consumption, average data transmission rate and network lifetime.

IATLR: Improved ACO and TOPSIS-Based Layering Routing Protocol for Underwater Acoustic Networks

Aiming at the challenges of underwater acoustic networks (UANs) such as low bandwidth, long propagation delay, dynamic network topology, and energy limitation, an improved ant colony optimization (IACO) and technique for order preference by similarity to an ideal solution (TOPSIS)-based layering routing (IATLR) protocol for UANs is proposed. In the IATLR protocol, the transfer probability of the ant colony optimization (ACO) is optimized by comprehensively considering multiple factors such as energy consumption, propagation distance, and forwarding angle. Moreover, the ACO is improved and the optimized ACO is combined with the TOPSIS method by using the pheromone concentration of ACO as the decision basis of TOPSIS. Furthermore, the multifactor decision ranking of the TOPSIS method is optimized by the transfer probability of the ACO. Consequently, the best next-hop node is determined. In addition, the energy consumption is balanced through the whole network by the node silence mechanism in the IATLR protocol. Simulation results show that the IATLR protocol has significant advantages compared with the depth-based routing for underwater sensor networks (DBR), layer-based energy-efficient routing (LEER), and depth and distance based layering routing protocol for underwater sensor networks (DDLR) routing protocols in terms of packet delivery rate (PDR), energy consumption, and end-to-end delay (EED). The PDR increases by 5%–30%, the EED decreases by about 1–7 s, and the average energy consumption (AEC) reduces by 0.5–6 J.

Energy-efficient routing protocol in wireless sensor networks based on bacterial foraging optimization

Energy-efficient routing protocol in wireless sensor networks based on bacterial foraging optimization