Energy Aware Reliable Routing Protocol for Mobile Ad Hoc Networks

In wireless sensor networks, if sink node is stationary, the nodes close to sink will deplete energy faster than those in other areas. The unbalanced energy consumption among the nodes will lead to the energy hole problem, which shortens the network lifetime. To solve the problem, evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks is proposed in this article. In evolutionary game model, the average residual energy of each cluster, average intra-cluster energy consumption, and average inter-cluster energy consumption are used to design utility function. The sink will select the cluster with more utility value as its new location and move to the cluster head which has the largest residual energy and the shortest distance to other cluster heads. The simulation results show that the algorithm can effectively balance network load, reduce network energy consumption, prolong the network lifetime, and increase the number of data packets that sink receives.

In this paper, we propose an energy efficient data forwarding protocol called Energy Aware Geographic Routing Protocol (EAGRP) for wireless sensor networks to extend the life time of the network. In EAGRP, both position information and energy are available at nodes used to route packets from sources to destination. The routing design of EAGRP is based on two parameters: location and energy levels of nodes. Each node knows the location and energy level of its neighbors. The performance measures have been analyzed with variable number of nodes. The simulations were carried out for different number of nodes. The proposed protocol was compared with Greedy Perimeter Stateless Routing (GPSR). Simulation results show that EAGRP performs competitively against the (GPSR) in terms of packet delivery ratio, throughput, energy consumption, and delay. Consequently, it can be concluded that EGARP does efficiently and effectively extend the network lifetime by increasing the successful data delivery rate.

Clustering scheme enabling the efficient utilization of the limited energy resources of the deployed sensor nodes can effectively prolong the lifetime of wireless sensor networks. The most common technique in famous clustering schemes is a probabilistic clustering scheme based on a randomized cluster-head rotation for distributing the energy consumption among nodes in each cluster. Because most of those schemes utilize mainly the residual energy of each node as the criterion of cluster-head election, those schemes have demerit which the unbalanced energy consumption among cluster-heads is occurred. To overcome this demerit, we consider a distance from the base station to cluster-heads as well as the residual energy as the criterion of cluster-head election for balanced energy consumption among cluster-heads. Our scheme provides fully distributed manner by utilizing local information and good energy-efficiency by load balanced clustering scheme. Through simulation experiments, we showed that the proposed scheme is more effective than LEACH and EECS in prolonging the lifespan of wireless sensor networks.

Network lifetime is always a challenging issue in battery-powered networks due to the difficulty of recharging or replacing nodes in some scenarios. Clustering methods are a promising approach to tackle this challenge and prolong lifetime by efficiently distributing tasks among nodes in the cluster. The present study aimed to improve energy consumption in heterogeneous IoT devices using an energy-aware clustering method. In a heterogeneous IoT network, nodes (i.e., battery-powered IoT devices) can have a variety of energy profiles and communication capabilities. Most of the existing clustering algorithms have neglected the heterogeneity of energy capacity among nodes and assumed that they are of the same energy level. In this work, we present HetEng, a Cluster Head (CH) selection process that extended an existing clustering algorithm, named Smart-BEEM. To this end, we proposed a statistical approach that distributes energy consumption among highly energetic nodes in the network topology by constantly changing the CH role between the nodes based on their real energy levels (in joules). Experimental results showed that HetEng resulted in a 6.6% increase of alive nodes and 3% improvement in residual energy among the nodes in comparison with SmartBEEM. Moreover, our method reduced the total number of iterations by 1% on average.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Cluster-based routing scheme is able to prolong the network lifetime and save the energy consumption. In this field, many cluster-based routing protocols are proposed such as LEACH, TEEN, APTEEN, and so on. On the other hand, the chain-based routing protocols construct the chain such as PEGASIS and concentric-clustering routing scheme. The concentric-clustering routing scheme is the multiple-chain among sensor node to transmit the data to the base station or lower cluster head. However, a problem of this scheme is that the number of sensor nodes in the each level is unbalanced as assigning the level based on total number of level. Thus, the unbalanced energy consumption among sensor nodes is occurred. Also, as this scheme selects the next cluster head to be closely located at current cluster head, energy of cluster head dissipates quickly on the multipath among cluster heads. To overcome these problems, we propose the advanced concentric-clustering routing scheme adapted to large-scale sensor networks. Our scheme fixes the number of sensor node on level for the same number of sensor nodes in each level. In addition, we consider the residual energy as the criterion of cluster head election for balanced energy consumption as well as the distance from lower cluster head or base station to upper cluster head. As the simulation results, our scheme shows a better performance of energy consumption than other multiple-chaining schemes about 24.4%.

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service

In this paper, we consider the problem of unbalanced energy consumption for nodes in wireless sensor networks (WSN). Current routing protocols divide the network into clusters and route data packets by electing cluster heads, but ineffective network energy utilization leads to the death of nodes very soon. We show that the behavior of sensor networks becomes very unstable once the first node dies. We propose NSEEAR - network stability enhancement by energy aware routing, an adaptive routing protocol which leads to a better energy utilization of the network to prolong its stability period (the time interval before the death of the first node) and the significant network lifetime (the time interval before half of the network is dead) which is crucial for many sensing applications in which the feedback from the network should be reliable. NSEEAR organizes the network into concentric tiers around the base station and routes aggregated data packets by forwarding them from one tier to another in the direction of the base station. Relaying nodes are elected by considering their distance from the transmitting node and the base station, as well as their residual energy. We evaluate NSEEAR through analysis and simulations and show that NSEEAR results in much longer stability periods compared to those obtained by the current clustering protocols.

This paper explores how the integration and performance of Wireless Body Area Networks (WBANs) contribute to sustainable healthcare practices. WBANs play a crucial role in reducing the environmental footprint of healthcare systems by providing continuous monitoring that can prevent unnecessary hospitalizations and reduce the consumption of disposable medical supplies. Additionally, the design of WBANs using environmentally friendly materials, low-power devices, and recyclable components is discussed to optimize the sustainability of healthcare practices. To enhance infrastructure, services, and quality of life, new technologies have been integrated to improve WBANs. Patients' essential health data may be continuously and instantly collected thanks to these networks, and healthcare providers can receive the data right away for quick analysis and action. WBANs have healthcare benefits, but there are also major drawbacks. Technologies that can combine low power consumption, minimal delay, and high enough data rates are needed for WBANs in order to support a variety of medical applications. The effectiveness of the LoRaWAN and IEEE 802.15.6 technologies in WBAN is assessed in this paper. With the use of seven crucial metrics which are throughput, arrival rate, delay, energy consumption, residual energy, network lifetime, and packet delivery ratio (PDR), this paper seeks to study which technology is appropriate with WBANs using NS3 as a simulation program. With network parameters, 1000 seconds, and 50 nodes, the paper evaluated the performance of each technology. The results show that IEEE 802.15.6 is superior in terms of throughput, PDR, and arrival rate whereas LoRaWAN is superior in terms of energy consumption, residual energy, network lifetime, and delay.

To monitor the large area covered by water desirably, underwater acoustic networks have grown rapidly in the recent decade. However, due to the differences between terrestrial radio propagation and underwater acoustic propagation, it is crucial to design energy efficient routing protocols for underwater acoustic network. In this paper, we propose a power efficient clustered routing protocol named CRSP (Clustered Routing based Shortest Path). In CRSP, the monitoring area is divided into small cylinders called a layer, which has the same radius and height. The nodes located in the same layer were grouped into clusters, where the node with the lowest priority determined by residual energy and distance is selected as cluster head. In order to find the optimal routing among the cluster headers, the shortest path algorithm with redistributive heap is proposed, where the transmission energy consumption and the residual energy are taken into consideration. The experimental results show that CRSP performs better than the existing representative protocols in terms of energy consumption, network lifetime and end-to-end delay.

As the short-distance and multiple-hop communication way in wireless sensor networks, the closer a sensor node lies to the sink, the more it will be shared by communication paths, which make its energy drained more quickly. Such unbalanced energy consumption among nodes will lead to network performance decline. Sink relocation can effectively balance energy consumption, and thus improve the network performance and prolong the network lifetime. We propose a hexagonal grid based sink relocation method (HGSR), which utilizes the mathematical properties of the honeycomb mesh and adjust the transmission radius according to residual energy state. Simulation results show the proposed HGSR reach better performance comparing to several existing sink relocation methods.

Stable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing (ENSR) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality (SBC) is introduced. In ENSR, at first, some nodes will be selected as the stable forwarding nodes, usually with maximum SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including SBC, to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric (SPRM). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing (GLRR) and reliable energy-aware routing protocol (RER), in terms of network efficiency and reliability.

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

Unbalanced energy consumption is an inherent problem in wireless sensor networks where some nodes may be overused and die out early, resulting in a short network lifetime. In this paper, we investigate the problem of balancing energy consumption for data gathering sensor networks. Our key idea is to exploit the tradeoff between hop-by-hop transmission and direct transmission to balance energy dissipation among sensor nodes. By assigning each node a transmission probability which controls the ratio between hop-by-hop transmission and direct transmission, we formulate the energy consumption balancing problem as an optimal transmission probability allocation problem. We discuss this problem for both chain networks and general networks. Moreover, we present the solution to compute the optimal number of sections in terms of maximizing the network lifetime. Numerical results demonstrate that our methods outperform the traditional hop-by-hop and direct transmission schemes and achieve significant lifetime extension especially for dense sensor networks.

Clustering techniques are employed to optimize energy consumption corresponding to different node activities and thus to conserve the limited energy of the sensor nodes. Different locations and different loads cause unbalanced energy consumption in the network nodes, consequently, the network lifetime is shortened. In this paper, a clustering method to improve energy conserving and to balance the energy consumption of cluster head is proposed. The area and the dimensions of the clusters are calculated by the algorithm in such a way that the rates of energy consumption of all the cluster heads be equalized. Also, all the intra-cluster communications are performed in single hop manner. The proposed approach presents a solution for the challenge of short lifetime of heavy burdened cluster heads around the base station. A straightforward routing scheme is proposed corresponding to the proposed clustering model to deliver the data sensed by cluster members to the base station. The performance evaluations indicate that the proposed method not only prolongs more effectively the lifetime of both cluster heads and cluster members than the other works, but also, outperforms them in balancing the energy level of the cluster heads. Consequently, the duration of activity of the cluster members in different clusters is also balanced and prolonged. Moreover, the proposed method improves the network throughput, comparing with the other works.