Energy Aware Routing Algorithm Research Articles

Energy Aware On-Demand Routing Protocol Scheme of DSR Protocol (EAORP)

Recently, there has been a need for connectivity in places with no infrastructure. In order to meet this need, new technology known as MANET is used to fulfil the market demand. Despite the many benefits that MANET will provide, a number of shortcomings still need to be further studied, especially the energy consumption problems, that stand in the way of not allowing widespread acceptance of this technology. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management.

Improving Quality of Service (QoS) in Wireless Multimedia Sensor Networks using Epsilon Greedy Strategy

Abstract Wireless Multimedia Sensor Networks (WMSNs) are networks consisting of sensors that have limitations in terms of memory, computational power, bandwidth and battery life. Multimedia transmission using Wireless Sensor Network (WSN) is a difficult task because certain Quality of Service (QoS) guarantees are required. These guarantees include a large quantity of bandwidth, rigorous latency requirements, improved packet delivery and lower loss ratio. The main area of research would be to investigate the process of greedy techniques that could be modified to guarantee QoS provisioning for multimedia traffic in WSNs. This could include optimization of routing decisions, dynamic allocation of resources and effective congestion management. This study introduces a framework called Epsilon Greedy Strategy based Routing Protocol (EGS-RP) for multimedia content transmission over WSN. The framework focuses on energy efficiency and QoS by using reinforcement learning to optimize rewards. These incentives are determined by a number of variables, including node residual energy, communication energy and the effectiveness of sensor type-dependent data collection. Experimental analysis was conducted to evaluate the effectiveness of the proposed routing strategy and compare it with the performance of standard energy-aware routing algorithms. The proposed EGS-RP achieves a throughput of 217 kbps, a bandwidth of 985 bps, a packet delivery ratio of 94.45% and an energy consumption of 32%.

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.

Enhancing Intrusion Detection Systems through Distributed Ledger Technology for Secure Logging

Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.

SDN-Improved LEACH: A new energy-aware routing algorithm to increase life time in software defined wireless networks

SDN-Improved LEACH: A new energy-aware routing algorithm to increase life time in software defined wireless networks

The Fuzzy-IAVOA Energy-Aware Routing Algorithm for SDN-based IoT Networks

The Fuzzy-IAVOA Energy-Aware Routing Algorithm for SDN-based IoT Networks

The fuzzy-IAVOA energy-aware routing algorithm for SDN-based IoT networks

The fuzzy-IAVOA energy-aware routing algorithm for SDN-based IoT networks

DCGCR: Dynamic Clustering Green Communication Routing for Intelligent Transportation Systems

For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7% less failure rate.

Fractional Competitive Fruitfly Optimized Secure Routing Protocol under Mobile Sink Based WSN with Deep QNN Based Prediction

Wireless Sensor Networks (WSN) consists of numerous of low cost and less-energy sensor nodes that are responsible to gather and transmit the data packets from one node to destination point. WSN has a wide range of applications over agriculture, military, traffic monitoring, instrument surveillance, and security monitoring. In WSN, the nodes are located in a specific region to create a wireless network. The effective data communication among sensors is a challenging task because of different complex parameters. Typically, clustering is a well-preferred methodology to provide the effective communication by partitioning the nodes into different clusters. Every cluster possesses individual cluster head that transmits the data to other sensor nodes. Therefore, it is substantial to choose optimal cluster head and optimal route for effective transmission with less energy consumption and less delay. To increase the network efficiency and sink utilization, an energy aware routing algorithm called Fractional Competitive Fruit Fly Optimizer (FrCFFO) is designed, which is an integration of Fractional concept into the Competitive Fruit Fly Optimizer (CFFO). Here, the energy prediction is performed using Deep Quantum Neural Network (QNN). Effective CH selection and routing is done using the proposed FrCFFO and the fitness parameter is considered depending upon the factors like energy, distance, link lifetime, trust, and delay. Moreover, the developed FrCFFO has achieved effective performance with minimum delay of 0.098sec, maximum energy of 0.233J, and maximum PDR of 90.81%.

Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things

Recent improvements in nanotechnology and Internet of Things (IoT) have led to the emergence of the concept of Internet of Nano-Things (IoNT). This concept provided ease of access and communication facilities for the nano-devices in applications of different fields operating at the molecular level such as health, industry, agriculture, robotics and military. These distributed devices interconnect over the Wireless Nano-Sensor Networks (WNSNs) in nano-scale which have limited energy and capacity. Therefore, these devices need to use their energy efficiently when communicating with each other to ensure network continuity and maintain all processes of IoNT applications. In this paper, a novel energy-efficient distributed routing algorithm, namely DEEPNT, is proposed in order to extend the lifetime of WNSNs in IoNT applications. The communication backbone of WNSNs constructed by DEEPNT by selecting cluster head nodes can be used in all types of IoNT applications in nano-scale in order to provide continuous data flow in critical areas such as healthcare running in vivo. This novel energy-efficient distributed protocol is compared with the traditional flooding based method and with an energy aware routing algorithm. According to the results, DEEPNT prolongs the WNSNs average lifetime respectively up to 10.78 and 5.95 times compared to mentioned algorithms despite the larger network sizes in nano-scale.

An Optimized Energy Aware Routing Algorithm Based on Multi-Objective in Wireless Sensor Networks

An Optimized Energy Aware Routing Algorithm Based on Multi-Objective in Wireless Sensor Networks

A Multi-Objective Routing Mechanism for Energy Management Optimization in SDN Multi-Control Architecture

This paper proposed and implemented an energy-aware routing multi-level and mapping problem (EARMLP) algorithm to minimize the overall power consumption in Software-Defined Networking (SDN)-based core networks. To enforce network utilization toward green policies design for Data Centers (DCs), SDN leverages protocol configurations for routing available in the infrastructure. Therefore, the proposed mechanism aimed to design an optimal routing strategy that considers system configuration and traffic demand between the data and control planes in networks. The problem is then addressed from the perspective of the policy-based EARMLP technique, which is used to carefully determine the optimal assignment between controllers and their switches to optimize network energy savings. Hence, a controller placement problem (CPP) is established to select the optimal locations and number of controllers in core networks and create an optimal mapping and resource allocation between switches and controllers. Since the formulated energy-aware routing algorithm is designed as a multi-objective NP-hardness of the problem, a heuristic approach is developed to find optimal solutions for traffic routing between inter-controllers and controller-switch in terms of energy-aware consumption strategies. Consequently, the proposed optimal routing mechanism can rearrange traffic to meet provisioning criteria by utilizing the capacity-aware design. Remarkably, the energy saved in networks by our suggested method can approach up to 70% of the energy saved in SDN-based networks compared to other methods.

Protruder Optimization-Based Routing Protocol for Energy-Efficient Routing in Wireless Sensor Networks

WSNs find valuable application in monitoring purposes, but they suffer from energy-efficiency issues that affect the network lifetime. The energy-efficiency problem is addressed using the cluster head (CH) formation, data aggregation, and routing techniques. Therefore, an energy-aware routing algorithm named protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC), and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures, such as energy, alive nodes, and throughput.

Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs

Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the energy levels of the node battery by utilizing various energy harvesting techniques in order to extend the network lifetime. In this paper, a new energy-aware clustering algorithm (EHEARA) has been proposed. The proposed algorithm is based on a dynamic clustering function and adopts a solar energy harvesting scheme in order to improve network lifetime. Furthermore, the active-sleep mechanism was used to distribute node activity and balance communication among nodes within clusters and cluster heads with the base station. The proposed algorithm is simulated using matrix laboratory (MATLAB), and the results show that it outperforms the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC), and stable election protocol (SEP) algorithms in terms of network lifetime, energy consumption, and network throughput.

An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things

Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However, in a clustered IoT supported WSNs, some of the Cluster Heads (CHs) bear more traffic load than the others and therefore die sooner leading to decrease the network lifetime. To overcome this problem and maximize the network lifetime, the load of the CHs must be balanced. This research work suggests a new clustering method to balance the traffic load imposed on the cluster heads in IoT supported WSNs. The proposed clustering method uses a 1.2-approximation algorithm. In addition, we introduce an energy-aware routing algorithm for transmitting data packets from the CHs to their destination. The proposed routing algorithm distributes the communication load of the data packets among more nodes near the destination by a proper segmentation of the area. The simulation results show that the proposed clustering and routing algorithms in addition to being practical for large-scale IoT supported WSNs, cause the network to have a better performance compared to other similar algorithms.

Energy-Aware Routing for Software-Defined Multihop Wireless Sensor Networks

In this paper, we propose an energy-aware routing algorithm and a control overhead reduction technique for prolonging the network lifetime of software-defined multihop wireless sensor networks (SDWSNs). This is an effort to optimize the energy consumption of WSNs that provide services to the Industrial Internet of Things (IIoT). A centralized controller grants a global view of the sensor network by introducing extra control overhead in the network, but this leads to extra energy costs. However, our new algorithm takes advantage of this global view and balances the network energy by selecting paths with the highest remaining energy level among multiple paths for each sensor node. We also identify key functions draining energy from the SDWSN and minimize their impact by implementing a data packet aggregation function, and minimizing the control overhead by keeping track of the sensor nodes' routing tables using a simple checksum function. We show that the proposed approach prolongs the network lifetime of the WSN by 6.5% on average compared to the standard shortest-path algorithm, and that the control overhead is reduced by approximately 12% while still maintaining a very high packet delivery ratio.

Trust and energy aware routing algorithm for Internet of Things networks

AbstractThe expansion in the Internet of Things (IoT) has led to a shift towards smart technologies. IoT focuses on integrating networks to facilitate smooth services to humans. The interface between the mobility patterns and the routing protocols is considered to increase the performance of the network. However, incorporating security in the IoT network has been a major issue that continues to nurture with increasing IoT devices. This article addresses this issue by developing a novel technique, namely energy harvesting trust aware routing algorithm (EHTARA) for initiating a trust‐based routing model in the IoT network in the presence of ambient energy sources. The cost metric is newly devised by considering energy, distance, and trust parameters for determining the best path. At the base station, big data classification is performed using the adaptive exponential‐Bat (adaptive E‐Bat) algorithm based deep belief network (DBN). The training of DBN is performed using the adaptive E‐Bat algorithm, which is the combination of adaptive concept, exponential weighted moving average (EWMA), and Bat algorithm (BA). Here, the optimization‐based map‐reduce framework helps to deal with the imbalanced data by adapting the deep learning in classification. The proposed EHTARA outperformed other methods with a maximal energy of 0.927.

Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks

The energy consumption of the routing protocol can affect the lifetime of a wireless sensor network (WSN) because tiny sensor nodes are usually difficult to recharge after they are deployed. Generally, to save energy, data aggregation is used to minimize and/or eliminate data redundancy at each node and reduce the amount of the overall data transmitted in a WSN. Furthermore, energy-efficient routing is widely used to determine the optimal path from the source to the destination, while avoiding the energy-short nodes, to save energy for relaying the sensed data. In most conventional approaches, data aggregation and routing path selection are considered separately. In this study, we consider the degrees of the possible data aggregation of neighbor nodes when a node needs to determine the routing path. We propose a novel Q-learning-based data-aggregation-aware energy-efficient routing algorithm. The proposed algorithm uses reinforcement learning to maximize the rewards, defined in terms of the efficiency of the sensor-type-dependent data aggregation, communication energy and node residual energy, at each sensor node to obtain an optimal path. We used sensor-type-dependent aggregation rewards. Finally, we performed simulations to evaluate the performance of the proposed routing method and compared it with that of the conventional energy-aware routing algorithms. Our results indicate that the proposed protocol can successfully reduce the amount of data and extend the lifetime of the WSN.

Centralized Q-Learning based Routing in EH-WSNs with Dual Alternative Batteries

Energy harvesting wireless sensor network (EH-WSN) uses a large number of sensor nodes to conduct environmental perception, data collection and data transmission through wireless links. However, the battery capacity of traditional energy harvesting wireless sensor is limited. In this paper, a kind of energy collection wireless sensor of dual-battery system is studied, and a centralized energy-aware routing algorithm based on reinforcement learning is proposed. Considering the energy of the overall link, each source node chooses a good path at the beginning of data transmission. After all the data is transmitted to the sink node, it selects the path according to the whole network state, and learns several times until it finds the best path choice. The simulation results show that the end-to-end delay of this protocol is lower than that of random packet forwarding.

E-CGR: Energy-Aware Contact Graph Routing Over Nanosatellite Networks

Satellite constellations are envisioned as meaningful transport networks to forward data throughout the world, both as a solution for areas where there are no other telecommunication infrastructures or as a backup solution to support the terrestrial network. Low Earth Orbit (LEO) satellites are the most appealing for this purpose due to the achievable performance keeping low design and deployment costs. Mega-constellation made of thousands of small LEO satellites are planned to be employed to cover the entire Earth's surface. However, smaller is the size and weight of satellites, higher are the technological challenges and stricter are the hardware constraints which affect the data forwarding process and have to be taken into account. Energy is one of them. Telecommunication hardware energy consumption is considerable, especially in case of high traffic volumes, while energy storage capacity and battery recharge rate are limited due to the small battery size and solar panel surface area, respectively. In this paper, we propose a novel energy-aware routing algorithm based on the Contact Graph Routing (CGR) called E-CGR. E-CGR exploits static and known a priori information about contacts to compute routing paths from sources to destinations which are then “validated” and “confirmed” from the energy viewpoint.