Energy-aware Node Research Articles

Optimizing environmental monitoring in IoT: integrating DBSCAN with genetic algorithms for enhanced clustering

In our study, we introduce an advanced clustering method designed for IoT-based environmental monitoring. We’ve combined two powerful techniques, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithms (GA), to create a specialized approach called EC-GAD (Enhanced-Clustering using Genetic Algorithms and DBSCAN). This integrated system model relies on DBSCAN, a robust clustering algorithm capable of handling irregular shapes and varying data densities, to group sensor nodes based on their physical proximity. To improve clustering performance, we’ve harnessed Genetic Algorithms to optimize the parameters of DBSCAN. Through a repetitive process involving selection, crossover, and mutation, GA refines parameter settings based on the quality of environmental clustering as assessed by fitness metrics. Our approach is tailored specifically for IoT deployments in environmental monitoring, which involve collecting data from sensor nodes and integrating DBSCAN and GA. We’ve paid special attention to choosing an appropriate distance metric and fine-tuning DBSCAN parameters such as epsilon (ε) and minPts to match the unique needs of environmental monitoring applications. Furthermore, we’ve taken energy efficiency into account by implementing energy-aware node selection and optimizing cluster formation to minimize energy consumption.

An adaptive congestion and energy aware multipath routing scheme for mobile ad-hoc networks through stable link prediction

Advancement in mobile ad-hoc network (MANET's) has offered solutions to wide range of real-time applications. MANET's consist of group of battery operated wireless mobile nodes that forms a dynamic topology and functions autonomously without infrastructure. Mobile nodes are limited to resource, bandwidth, energy and memory. Due to mobile nature of the nodes, mobility and resource limitations are the two major factors that affect MANET's performance. Frequent topology changes and mobility affects the link stability between two nodes, frequent link interruption results in network congestion, lead to packet drop and increase retransmission delays. During multihop data transmissions, the wireless channel is shared among neighbour nodes. Determining the channel status and predicting the quality of link is still a challenging issue. Existing MANET's routing scheme considers optimal hop count as a metric for data transmission and selects the shortest path. Due to nodes movement, frequent link failure occurs, resulting in packet loss and retransmission of packet, which restablished path avoids packets loss, and alternate paths can be discovered within time such that congestion is controlled and delay is minimised. Therefore, it is required to design routing scheme for MANET's that can adapt to network changes and conditions to support quality of service (QoS). In this paper, we propose an Adaptive Congestion and Energy Aware Multipath Routing Scheme (ACEAMR) to provision QoS requirements in MANET's. ACEAMR discovers stable, energy aware, and congestion free paths towards destination. The congestion level at node is determined by computing queue utilization through adaptive feedback mechanism. ACEAMR integrates stable link prediction (SLP) to estimate the link quality and channel availability for reliable data transmission. Energy aware nodes are computed using fitness function of nodes having sufficient energy to extend network lifetime. Simulation results show that proposed ACEAMR achieves 30 % of higher throughput, 25 % more in packet delivery ratio, 27 % less delay and 28 % energy efficient compared to LLECP-AOMDV, OLEADM and AOMDV schemes.

Energy-aware message distribution algorithm for enhance FANET pipeline surveillance reliability

Features such as the communication scheme, energy awareness, and task distribution amongst others are the key component that characterizes the Flying Ad-hoc Network (FANET). The operational efficiency in FANET surveying a specific region is affected by the nature of the UAVs' node placement, routing protocol, energy-aware task distribution, and node interaction amongst others. In this paper, Drone 1 (D1), Master Drone (DM), and Drone 2 (D2) were used to survey a pipeline of length 12.2 m. This paper aims at minimising energy use by drones during surveillance using energy-aware node exchange technique, task interaction and distribution scheme for each UAV. Due to fast energy depletion of DM due to packets aggregation, its election is based on the UAV with the highest energy before take-off. For two different simulations, 14,697.0 J and 14,836.6 J were obtained for DM. To avoid system failure due to fast energy loss of DM, the drones swapped positions and status. First swapping command comes up when DM loses 50% of its energy, while the second command occurs when it further loses 15%. Return to base threshold energy is computed for the three UAVs to avoid crash due to insufficient energy during surveillance. DM returns to base threshold energy for both single and double swapping simulation were 658.105 J and 652.456 J respectively. From the results obtained the algorithms were able to exchange nodes to maximize energy usage and perform an interaction-based task distribution for cooperative task sharing during surveillance. This translates into longer surveillance time and effective telemetry data aggregation.

A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA.

Energy-Aware Virtual Network Migration for Internet of Things Over Fiber Wireless Broadband Access Network

The virtualized fiber wireless broadband access networks (V-FiWi) paradigm, effectively embedding heterogeneous virtual networks (VNs) originated from the service provider (SP) into shared substrate network (SN) provided by infrastructure provider (InP), plays a tremendous role in meeting the differentiated requirements between wireless frontend subnetwork and fiber backhaul subnetwork to achieve the interoperability of heterogeneous resource allocation. However, most of the existing V-FiWi integration systems mainly focused on the virtual network request (VNR) acceptance ratio, InP revenue, and substrate resource utilization, and they ignored the crucial issue called higher energy consumption cost, which was resulted from the imbalanced consumption of the substrate resource between the arrival and departure of VNR. In this article, we devote to exploring the energy-aware virtual network migration (EA-VNM) problem over the FiWi access technology, aiming to reoptimize the energy consumption while maintaining the high InP revenue and the large substrate resource utilization. In response to this issue, we first represent the service-oriented V-FiWi broadband access network architecture from the perspective of computing, storage, and network resource constraints, in which a migration model consisting of migration node and migration time is explained in detail. Then, we propose an enhanced KM-based energy-aware node migration (EKM-ENM) algorithm to economize on more bandwidth resource. More specially, the EA-VNM technology consists of network topology attributes and global network resources-based node-ranking measurement (NRM) phase, maximum weight matching-based node migration phase, and energy-aware link migration phase via Dijkstra shortest path algorithm. Finally, a rather large number of simulations are analyzed and evaluated numerically. Simulation results suggest that the proposed EKM-ENM algorithm outperforms the traditional embedding algorithms in terms of saving energy cost, decreasing time complexity, and improving VNR acceptance ratio.

Designing a Energy Efficient Node Disjoint Multipath Routing technique to achieve energy efficiency in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks (MANETs) are wireless networks which comprise of mobile nodes with limited energy resources. Every node cooperates to perform routing and expends energy on a frequent basis. Nodes are mobile thus link breakages are common and new routes need to be established quickly. Traditional routing protocols tend to find shortest routes to destination providing best-effort delivery service. However due to limited energy and bandwidth resources shortest path routes may not suffice and may usually degrade the performance of the network. In this paper an Energy aware Node Disjoint routing technique END-AODV is proposed that is based on the traditional AODV protocol. The technique designed is based on the argument that node disjoint multipath routing can conserve energy more efficiently as compared to link disjoint routing. Link Disjoint routing leads to overuse of a subset of nodes thus decreasing the overall network lifetime. The technique proposed incorporates energy drain rate metric to establish energy aware routes which are node disjoint in nature. Simulation results with ns2.34 simulator show efficiency of the proposed technique in terms of packet delivery ratio, average energy consumption per data bit delivered, network lifetime and average end to end delay.

Energy-aware node selection scheme with friendly jamming technique for enhancing the secrecy of wireless powered sensor networks

This work studies the secrecy performance of wireless powered sensor networks (WPSNs) under eavesdropping attacks by proposing an energy-aware node selection algorithm. The non-identical N sensor nodes harvest the required energy from a dedicated power station and use it for data transmission to a certain destination. The nodes selection procedure takes place over two phases. In the first phase, K nodes out of the total number of N nodes are selected under the condition that they succeed to harvest sufficient energy for transmission from the power station. In the second phase, among the selected K nodes from the first phase, two nodes are chosen in each time slot. The node with best channel conditions is selected for data transmission to the base station, while the other node that has the worst channel state acts as a cooperative jammer for the eavesdropper. We derive exact and asymptotic closed-form expressions for the secrecy outage probability (SOP) over Nakagami-m fading channels. Moreover, a power allocation problem is formulated to get the optimal uplink power values by minimizing the exact SOP expression using particle swarm optimization (PSO). Numerical results are obtained to validate our proposed model, followed by the conclusion to show its effectiveness.

GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks

The constant growth of the energy crisis within the ICT Sector has persistently gained importance thereby prompting endeavors to curb growing energy demands and associated expenditures. This paper attempts to propose an intelligent energy aware task allocation and resource provisioning technique running in GreenSched model. The GreenSched model tends to exploit the heterogeneity of tasks and multi-core capacity of the varied nodes in the cloud environment and attempts to proactively schedule the deadline-and budget- constrained tasks on identified less energy consuming or energy aware nodes. It implements a Forward-only Counter Propagation Network (CPN) based intelligent scheduler unit that runs a scheduling technique to identify the best nodes for the task allocation process, one with least energy consumption and deadline- and budget -fulfilling capability. The nodes are clustered and classified by comparing their energy consumption values. The proposed algorithm has been implemented using the CloudSim toolkit and Kohonen and CP-ANN Toolbox with the help of MatlabTM platform. The experimental results exhibit that the proposed technique offers reduced energy consumption along with an overall improvement in the performance by meeting the deadline-and-budget constraints imposed by the users.

A novel optimal path selection strategy in MANET using energy awareness

Today, most ad hoc mobile devices operate on batteries. To maximise the lifetime of mobile networks, the power consumption rate of each node must be evenly distributed, and the overall transmission power for each connection request must be minimised. But many proposed routing protocols do not consider energy as an important factor for selecting routes. This causes earliest exhaustion of nodes and network in trouble. Here, a new approach for path selection strategy is introduced by using energy factor is known as energy aware ad-hoc on-demand multipath distance vector (EA-AOMDV) routing protocol. The proposed scheme attempts to solve the above-mentioned problems in the way of selecting energy aware nodes on the path. During the route selection process, the node is considered only when the node is having above the minimum energy threshold value. The proposed model is tested with the NS2 simulator and the result we obtained is significant.

Improved routing metrics for energy constrained interconnected devices in low-power and lossy networks

The routing protocol for low-power and lossy networks (RPL) is an internet protocol based routing protocol developed and standardized by IETF in 2012 to support a wide range of applications for low-power and lossy-networks (LLNs). In LLNs consisting of resource-constrained devices, the energy consumption of battery powered sensing devices during network operations can greatly impact network lifetime. In the case of inefficient route selection, the energy depletion from even a few nodes in the network can damage network integrity and reliability by creating holes in the network. In this paper, a composite energy-aware node metric (RERBDI ) is proposed for RPL; thismetric uses both the residual energy ratio (RER) of the nodes and their battery discharge index. This composite metric helps avoid overburdening power depleted network nodes during packet routing from the source towards the destination oriented directed acyclic graph root node. Additionally, an objective function is defined for RPL, which combines the node metric RERBDI and the expected transmission count (ETX) link quality metric; this helps to improve the overall network packet delivery ratio. The COOJA simulator is used to evaluate the performance of the proposed scheme. The simulations show encouraging results for the proposed scheme in terms of network lifetime, packet delivery ratio and energy consumption, when compared to the most popular schemes for RPL like ETX, hop-count and RER.

Establishing trust between energy aware nodes in MANETs: a family relationship-based approach

Mobile ad-hoc networks (MANETs) solely depends on cooperation between neighbour nodes in the network. The characteristics like topology changes, node mobility and node failure distinguish it from traditional networks and make it more vulnerable to attacks. Establishing trust between nodes plays a major role in MANETs as most of the attacks are performed by selfish and malicious nodes inside the network. There were many security schemes proposed based on cryptography, but all rely on centralised trust authority and pre-established infrastructure which is not possible in MANETs. Resource limitation parameters like energy constraint and signal strength will have to be considered in establishing trust. So we have proposed a family relationship routing protocol (FRRP) which assigns different relationship and privileges to nodes in the network based on direct and indirect trust by considering resource limitation parameters. This paper aims at providing decentralised and distributed trust management scheme to MANETs which does not depend on any predefined assumption.

Exploiting user metadata for energy-aware node allocation in a cloud storage system

Cloud computing has gained popularity in recent years delivering various services as cost-effective platforms. However, the increasing energy consumption needs to be addressed in order to preserve the cost-effectiveness of these systems. In this work, we target the storage infrastructure in a cloud system and introduce several energy efficient storage node allocation methods by exploiting the metadata heterogeneity of cloud users. Our proposed methods preserve load balance on demand and switch inactive nodes into low-energy modes to save energy. We provide a mathematical model to estimate the outcome of proposed methods and conduct theoretical and simulative analyses using real-world workloads.

Energy-aware node and link reconfiguration for virtualized network environments

Energy consumption in information and communications technology (ICT) accounts for a large portion of the total energy consumed in industrial countries, which is rapidly increasing. Virtualized network environments (VNEs) have recently emerged as a solution in this technology to address the challenges of future Internet. VNEs also play a fundamental role toward virtualizing data centers. Consequently, it is essential to develop novel techniques for reducing the energy consumption of VNEs. In this paper, we discuss multiple energy-saving solutions that locally optimize the node and link energy consumption of VNEs during the off-peak period by reconfiguring the mapping of already allocated virtual nodes and links. The proposed reconfigurations enable the providers to adjust the level of the reconfiguration and accordingly control possible traffic disruptions. An integer linear program (ILP) is formulated for each solution. Because the defined ILPs are NP-hard, a novel heuristic algorithm is also proposed. The proposed energy-saving methods are evaluated over random VNE scenarios. The results confirm that the solutions save notable amounts of energy in physical nodes and links during the off-peak period.

Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node's power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.