Terms Of Network Stability Research Articles

Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks

Inter-cell interference in dense cellular networks causes severe network performance degradation because the base stations (BSs) and devices cannot fully utilize all the network status information in practice. Reinforcement learning enables the wireless network to find an effective transmission strategy with limited information and the network entities to operate in a distributed manner. We herein propose a medium access algorithm based on multi-agent reinforcement learning by exchanging knowledge of transmissions from other cells. Specifically, a state mapping function between the device and the BS and a transfer value iteration are proposed. The BS transfers reward processes derived from inter-cell interference so that the partially observed medium of devices is expanded to monitor entire networks. Extensive simulation results show that simple knowledge transfer can help performance improvement in terms of network stability and throughput.

TEZEM: A new energy-efficient routing protocol for next-generation wireless sensor networks

The design and implementation of energy-efficient routing protocols for next-generation wireless sensor networks is always a challenge due to limited power resource capabilities. Hierarchical (clustering) routing protocols appeared to be a remarkable solution for extending the lifetime of wireless sensor networks, particularly in application-aware (threshold-sensitive) and heterogeneity-aware cluster-based routing protocols. In this article, we propose a protocol, namely, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol. It is a heterogeneity-aware and threshold-based protocol that provides a better solution to existing problems in next-generation wireless sensor networks. During execution, the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol splits the entire network area into several zones to manage network traffic efficiently. In the first step, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is designed for a homogeneous network where the initial energy of all the nodes is the same. Thereafter, we bring in heterogeneity in the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol execution environment to optimize its energy consumption. By investigating the performance of the various numbers of divisions, it is proved that the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol with 9 zonal divisions has higher stability and throughput. The performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is compared with those of Stable Election Protocol, Low-Energy Adaptive Clustering Hierarchy, Modified Low-Energy Adaptive Clustering Hierarchy, and Gateway-Based Energy-Efficient Routing Protocol through computer simulations. Simulation results verify the improved performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol in terms of network stability, lifetime, and throughput.

HSCR: Hierarchical structured cluster routing protocol for load balanced wireless sensor networks

AbstractThe robustness of wireless sensor networks (WSNs) has made it a preferred choice for many applications. WSNs should perform the intended sensing task with a limited amount of energy. Clustering methodology is used to reduce sensor nodes' energy consumption and increase the network lifetime by organizing nodes into independent groups. In this article, a hierarchical structured cluster routing (HSCR) protocol based on the principle of an m‐way balanced tree has been proposed for multi‐hop intra‐cluster communication and offload the cluster head. It enhances the network performance by providing an underlying architecture that reduces the intra‐cluster communication distance and the amount of data sent to the cluster head. In the proposed HSCR protocol, an m‐way tree‐based hierarchical structure has been proposed in which balanced structure is formed within each cluster so that data aggregation is performed at each intermediate parent node. The proposed approach can be implemented over any communication protocol used in WSNs. Simulation results prove that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, and network lifetime. Simulation results affirm that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, average intra‐cluster communication distance and network lifetime by 105.32%, 49.23%, 14.98%, 109.31%, and 35.75%, 15.32%, 17%, 24.31%, respectively.

Performance prediction of tomato leaf disease by a series of parallel convolutional neural networks

We present a new approach of classification decision of tomato (Solanum lycopersicum) leaf disease based on the summarization of the output from a series of parallel convolutional neural networks with different configurations. The use of Swish, LeakyReLU-Swish, ReLU-Swish, Elu-Swish, and ClippedReLU-Swish activation layers, as well as the Batch Normalization-Instance Normalization layer significantly improves the network performance, achieving classification accuracy over 99.0% with training, 97.5% with validation and 98.0% with testing dataset. Despite various performance metrics observed, none of the proposed networks overfitted on the validation dataset. Furthermore, we use various techniques to visualize the network performance. This demonstrates how the networks (Network 1, Network 2, Network 3, Network 4, Network 5) learn from the training dataset and can show diseased areas of leaves with high confident scores under real conditions. Network 1 shows the best performance in terms of network stability and visualization of the disease location. By computing, summarizing, and scoring the output of a series of parallel convolutional neural networks, the weakness of Network 4 and Network 5 in predicting the Healthy class can be overcome. This research will inspire and encourage further use of deep learning techniques to automatically detect and classify plant diseases under real conditions and improve financial condition of the farmers worldwide.

A Sector-Based Energy-Efficient Lightweight Clustering Algorithm

Wireless sensor networks consist of a large number of nodes with constrained energy supply. Energy efficiency is hot and challenging issue in wireless sensor networks. Existing studies have shown that clustering is one of the efficient techniques to improve energy efficiency. An easy-to-implement and flexible method for even clustering and cluster head selection is beneficial to optimize network stability and energy efficiency. In this study, a sector-based lightweight and flexible clustering algorithm is developed to reduce node energy dissipation and optimize energy efficiency. Our proposal divides the area into virtual sectors. In the meanwhile, an even sector cluster is created using the sector decomposition approach. Based on the total communication distance, residual energy, and local node density, each node in the cluster calculates its own priority. The node with the highest priority is selected as the cluster head. Our proposal is compared with TSC,MH-TSC,SEECP,DREEP and LEACH. Experimental results show that the proposed algorithm outperforms these algorithms in terms of network stability and network lifetime.

Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications

Wireless Sensor Networks (WSNs) play an important role in underground mining applications. In particular, they help to collect information using sensors and provide monitoring of complex mine environments to avoid potential risks and manage operations. Despite the importance of WSNs, they face the problem of energy consumption and the difficulty of replacing the batteries of the sensor nodes. The distributed energy efficient aggregation protocol (DEECP) helps to reduce the power consumption of the WSN. This protocol enables an increase in the lifetime of a WSN. The DEECP algorithm uses the clustering concept and selects cluster heads (CHs) according to the election probability based on the ratio between the residual energy and network average energy of each node. However, this method does not provide an optimum solution because it does not take into account the different sensor energy levels. In addition, the algorithm does not consider the effect of the distance between the base station and sensor node likely be chosen to become a CH. This can significantly affect the performance of the WSN. This paper proposes an optimization threshold for CH selection based on three energy levels of a sensor, namely, low, high, and super as well as the measurement of the distances between base stations and possible nodes be selected as CHs to optimize the CH selection method. The proposed approach is evaluated and compared with DEECP in terms of dead nodes, alive nodes, and network throughput. The results show that the proposed approach outperforms DEECPs in terms of network stability and lifetime.

Energy-Efficient k-Hop Clustering in Cognitive Radio Sensor Network for Internet of Things

The design and development of energy and spectrum-efficient solutions are important in the success of the Internet of Things (IoT). Due to the presence of an enormous number of smart devices, such as sensors, actuators, and different household devices achieving such scalable and efficient solutions are challenging. A wireless sensor network (WSN) with dynamic spectrum access (DSA) capability, known as the cognitive radio sensor network (CRSN) is recently introduced to deal with spectrum scarcity problem. Although the spectrum scarcity is reduced with DSA paradigm, the energy-efficient solutions are still required to be addressed due to the involvement of energy constrained devices in CSRN. Clustering is one of the efficient ways to optimize the energy consumption in the networks. Due to combination of both WSN and cognitive radio network (CRN), existing solutions of WSN and of CRNs are not applicable to CRSN. In this article, we propose a neighbor discovery algorithm and two greedy k-hop clustering schemes (k-SACB-WEC and k-SACB-EC) for CRSN with the aim focusing on IoT applications, which require constant intracluster and intercluster communications. We focus on achieving bichannel connectivity while maximizing network life. In our clustering different parameters, such as nodes' residual energy, spectrum awareness, appearance probability of primary users (PUs) of channels, channel quality, robustness on PUs' arrival, and the Euclidean distance between nodes are taken into consideration to select the hop count and common channels for clusters. Through simulation, we have highlighted the performance improvements of our proposed schemes in terms of the lifetime of the network, number of clusters, stability of networks, and frequency of reclustering over recently reported clustering algorithm in CRSN. The simulation results show that k-SACB-WEC generates at least 40% less number of clusters as compared to k-SACB-EC, network stability-aware clustering (NSAC), Prolong-SEP (PSEP), SAC-WCM, and Cognitive LEACH (CogLEACH). Also, in terms of network stability, the k-SACB-WEC achieves at least approximately 100% higher number of rounds before the first node dead than the compared competitive approaches.

Improving network efficiency in wireless body area networks using dual forwarder selection technique

Wearable computing has a great prospect in smart healthcare applications. The emergence of the Internet of Things, Wireless Body Area Networks (WBANs), and big data processing open numerous challenges and opportunities. In healthcare, the monitoring is done by placing/implanting sensor nodes (resource-constrained devices) on a patient’s body to communicate data to a resource-rich node called a sink. The data transmission energy consumption is directly proportional to the distance between the sensor and the sink node. Therefore, it is vital to reduce the energy consumption of the sensor node due to data transmission. In this article, a new Dual Forwarder Selection Technique (DFST) has been proposed to prolong the network lifetime by reducing energy consumption and ultimately improving the stability period and throughput of the network. The DFST works by grouping sensor nodes on a body where both forwarder nodes have been selected through a cost function for relaying data to the sink. The proposed scheme’s efficiency has been evaluated using simulation results in terms of network stability, lifetime, and throughput. Energy consumption of sensor nodes minimized, which, as a result, increased residual network energy. The number of dead nodes of the DFST is about 50% less than that of its counterparts RE-ATTEMPT and iM-SIMPLE. The average throughput of the proposed scheme is 51% and 8% higher than the methods. Similarly, the residual energy of the DFST is approximately 200% and 120% more than iM-SIMPLE and RE-ATTEMPT, respectively.

Dynamic pricing and fleet management for electric autonomous mobility on demand systems

The proliferation of ride sharing systems is a major drive in the advancement of autonomous and electric vehicle technologies. This paper considers the joint routing, battery charging, and pricing problem faced by a profit-maximizing transportation service provider that operates a fleet of autonomous electric vehicles. We first establish the static planning problem by considering time-invariant system parameters and determine the optimal static policy. While the static policy provides stability of customer queues waiting for rides even if consider the system dynamics, we see that it is inefficient to utilize a static policy as it can lead to long wait times for customers and low profits. To accommodate for the stochastic nature of trip demands, renewable energy availability, and electricity prices and to further optimally manage the autonomous fleet given the need to generate integer allocations, a real-time policy is required. The optimal real-time policy that executes actions based on full state information of the system is the solution of a complex dynamic program. However, we argue that it is intractable to exactly solve for the optimal policy using exact dynamic programming methods and therefore apply deep reinforcement learning to develop a near-optimal control policy. The two case studies we conducted in Manhattan and San Francisco demonstrate the efficacy of our real-time policy in terms of network stability and profits, while keeping the queue lengths up to 200 times less than the static policy.

An Energy Efficient and Reliable Routing Scheme to enhance the stability period in Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a wireless network of wearable sensing and computing devices connected through a wireless communication channel, thereby offering a plethora of enticing applications in the area of remote health monitoring, sports, and entertainment. However, WBANs nodes are highly resource-constrained; therefore, energy-efficient and reliable data transmission is very vital in the design and implementation of most of its applications. Furthermore, on time and accurate data delivery with minimum delay is also highly required. Over time, numerous energy-efficient routing solutions have been proposed for WBANs; however, the significant feature of reliability in these solutions has not been adequately addressed. Therefore, in this paper, we propose a new Energy-Efficient and Reliable Routing Scheme (ERRS) to enhance the stability period and reliability for resource-constrained WBAN. ERRS comprises two novel solutions, namely, the Forwarder Node Selection and Forwarder Node Rotation techniques. The proposed ERRS takes advantage of the adaptive static clustering routing technique and achieves enhanced stability period and longer network lifetime, ultimately maximizing reliability. Through extensive simulation-based evaluation using MATLAB, ERRS showed an improvement of 26% over the benchmark protocol in terms of network stability and throughput. Whereas the end-to-end delay of the proposed ERRS is improved by 17% and 40% than by SIMPLE and M-ATTEMPT protocols, respectively, which proves ERRS to be an efficient and reliable routing solution for WBANs.

Intra-clustering communication enhancement in WSN by using skillful methodologies

Member nodes in a wireless sensor networks are split into many virtual sets called cluster. Each cluster will be formed of a cluster head and cluster members. Clustering techniques are an energy efficient process that prolong the network lifetime. Intra-cluster communication is the energy efficient major factor in clustering protocols. The dissipated energy in intra-cluster will primary depends on the communication distance between member nodes and its cluster head. Therefore, reducing of energy that have been consumed in data transmission between ordinary nodes and its belonging cluster header, is consider as one of the important topics that have been taken into consideration by researchers. Clustering technique in WSN is one of the robust techniques that are utilizing to enhance consumed energy. Traditional clustering technique is not convenient with the animated nature of the WSN. A Fuzzy C-Mean Clustering (FCM) algorithm is proposed, this method has given momentum to improve network status and promote the state of sustainability of energy consumed by sensors nodes. Decision tree algorithm (DTA) which falls under the category of supervised machine learning algorithms is utilized to select Cluster Head (CH). The CH electing is done in such a way that reduces the intra-cluster communication distance; also, this method has limit the amount of consumed energy in the entire network.An event driven data model that depends on FCM with DTA which is named as Voronoi Fuzzy Clustering with Decision Tree Algorithm (VFDTA) is adopted in this study. The evaluation metrics the performance of VFDTA protocol is better than well-known protocols (LEACH, SEP and Z-SEP) in terms of network stability, consumed energy and network lifetime.

A Game Theory Based Clustering Protocol to Support Multicast Routing in Cognitive Radio Mobile Ad Hoc Networks

The frequent movement of nodes in cognitive radio mobile ad hoc networks (CRAHNs) causes challenges in scalability, stability, channel sensing and channel access problems that can be solved by using clustering technique. Game theory is a feasible approach to solve such problems by casting clustering problems as distributed optimization problems. The main contributions of this article are as follows. Firstly, we propose a minimum connected weighted inner edge spanning tree (MWIEST) game to find an approximate solution of a MWIEST problem in CRAHNs. In this game, a link-weight function of each link is designed based on a combination of link-stability and link-connectivity ratio functions. Secondly, we prove that the MWIEST game is an exact potential game that exists at least one Nash equilibrium ( NE ) point which is an approximate solution of the MWIEST problem. Besides, we also prove that best responses ( BR s) of the game converge to a NE in finite iterations. Thirdly, based on the MWIEST game, we propose four algorithms including the node information exchange (NIE), the best response selection (BRS), the intermediate nodes selection (INS) and the forming cluster (FC). Specifically, the algorithms NIE, BRS and INS provide a set of intermediate nodes (SetIN) which supports the FC algorithm to form clusters. Finally, we propose the game theory based clustering (GBC) protocol which is combination of the FC algorithm and the proposed cluster maintenance algorithm to construct high stable clusters supporting multicast routing in CRAHNs. Moreover, each obtained cluster includes most members having the same receiving channel which avoids the affected regions of licensed channels. For the performance evaluation, we implement the GBC protocol in OMNET++ platform to demonstrate its performance improvement over the state-of-the-art protocols in terms of network stability and control overheads.

Performance enhancement routing protocols for heterogeneous WSNs

SummaryOne of the main constraints in wireless sensor networks (WSNs) is the energy consumption. To mitigate this limitation and to prolong network life‐time, stability period and throughput, this paper proposes new cluster‐heads selection protocols, they based on Stable Election Protocol (SEP) and Distributed Energy‐Efficient Clustering (DEEC). We first propose Enhanced Zonal‐SEP (EZSEP) and Zonal Threshold‐DEEC (ZTDEEC) protocols, the proposed protocols are based on dividing the network field into certain zones, this will improve the connectivity of the far normal nodes with the base station (BS). Several evaluation metrics are used to compare between the proposed protocols and the conventional ZSEP and TDEEC protocols such as: network stability, instability period, life‐time and throughput. Considering the same total initial energy, the obtained results show that the proposed EZSEP slightly outperforms the conventional ZSEP in terms of network stability, instability period and life‐time, it achieves enormous improvements in terms of throughput as more nodes can transmit direct to BS. On the other hand, the proposed ZTDEEC provides huge improvements in terms of all the evaluation metrics mentioned above. To further improve the network life‐time and network throughput, we propose Threshold‐based EZSEP (TE‐ZSEP) and Enhanced‐ZTDEEC (EZ‐TDEEC) protocols, in these two new protocols we redefine the threshold formula used in EZSEP and ZTDEEC to consider both weighted energy and weighted distance parameters. By combining the idea of dividing the network field into certain zones and the new threshold formula, the proposed TE‐ZSEP and EZ‐TDEEC protocols can effectively improve the energy consumption in heterogeneous WSN and prolong its life‐time as proved by the obtained results.

An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks

Energy conservation is one of the most important challenges in wireless sensor networks (WSNs). Therefore, compared with the traditional networks, the WSNs not only need high-quality services with high throughput or low transmission delay, but also pay greater attention to energy utilization to extend network lifetime. The clustering routing algorithm is considered to be among the effective ways to collect and transmit data in WSNs. Cluster head (CH) plays a vital role in the cluster which is in charge of data aggregation and data transmission, so their energy consumption is higher than non-CH nodes. The traditional clustering algorithm tends to have the same size in each cluster. However, due to the randomness of the node distribution, the equal clustering mechanism obviously cannot reduce energy consumption. In order to solve this problem, this paper contributes a new unequal clustering algorithm, an energy-aware adaptive kernel density estimation algorithm (EAKDE), which aims to balance the energy dissipation among the CHs. EAKDE utilizes fuzzy logic to determine the priority of nodes competing for CH. In order to adapt the dynamic change of node conditions, adaptive kernel density estimation algorithm is utilized to assign the appropriate unequal cluster radius to sensor nodes. The simulation results demonstrate that, in different scenarios, EAKDE outperforms the other well-known algorithms in terms of network stability, network lifetime, and energy efficiency.

Energy-Efficient Harvested-Aware Clustering and Cooperative Routing Protocol for WBAN (E-HARP)

Wireless body area network (WBAN) is an interconnection of small bio-sensor nodes (BSNs) that are deployed in/on different parts of human body. It is used to sense health-related data, such as rate of heart beat, blood pressure, blood glucose level, electro-cardiogram (ECG), and electro-myography (EMG), of human body and pass these readings to real-time health monitoring systems. WBANs are the important research area and are used in different applications, such as medical field, sports, entertainment, and social welfare. BSNs or simply called sensor nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of the WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic cluster head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated cost factor (CF). The parameters used for calculation of CF are residual energy of SN, required transmission power, communication link signal-to-noise ratio (SNR), and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data are routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using the NS-2 simulation tool has been conducted. The results are compared with some latest techniques, named EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay, and packet delivery ratio.

SNIRD: Disclosing Rules of Malware Spread in Heterogeneous Wireless Sensor Networks

Heterogeneous wireless sensor networks (WSNs) are widely deployed, owing to their good capabilities in terms of network stability, dependability, and survivability. However, they are prone to the spread of malware because of the limited computational capabilities of sensor nodes. To suppress the spread of malware, a malware spread model is urgently required to discover the rules of malware spread. In this paper, a heterogeneous susceptible-iNsidious-infectious-recovered-dysfunctional (SNIRD) model was proposed, which not only considers the communication connectivity of heterogeneous sensor nodes but also reflects the characteristics of malware hiding and dysfunctional sensor nodes. Then, the fraction evolution equations of heterogeneous sensor nodes in different states in discrete time were obtained. Furthermore, the existence of equilibria for the heterogeneous SNIRD model was proved, and the malware spread threshold was obtained, which indicates whether malware will spread or fade out. Finally, the heterogeneous SNIRD model was simulated and it was contrasted with the conventional SIS and SIR models to validate its effectiveness. The results construct a theoretical guideline for administrators to suppress the spread of malware in heterogeneous WSNs.

Data-driven clustering for multimedia communication in Internet of vehicles

The main requirement of multimedia communication service is to improve ultra-reliable and low-latency data communication, so the challenge of providing multimedia communication service is to improve data sharing for making full use of network resources. In this paper, a data content based vehicle clustering model is designed to analyze the transmitted multimedia data correlation between the vehicles, and the vehicles with high correlation of transmitted multimedia data are classified into a cluster and share the same resources. The data sharing in network is regarded as a performance criterion for adjusting the self-organized multimedia communication structure. Based on these factors, in order to ensure the stability of the self-organized communication structure, this paper proposes a content-aware stable multimedia communication algorithm for Internet of vehicles, which controls the multimedia communication within a certain range and combines with the transmitted multimedia data correlation of the vehicles that need to be adjusted. Finally, a network clustering structure with data sharing maximization and stable multimedia communication is performed. Extensive simulation experiments are carried out to evaluate the performance of the proposed algorithm in terms of network stability, average end-to-end communication delay, and packet loss rate.

A Fuzzy Logic Based Method for Analysing Test Results

Network operators must perform many tasks to ensure smooth operation of the network, such as planning, monitoring, etc. Among those tasks, regular testing of network performance, network errors and troubleshooting is very important. Meaningful test results will allow the operators to evaluate network performanceof any shortcomings and to better plan for network upgrade. Due to the diverse and mainly unquantifiable nature of network testing results, there is a needs to develop a method for systematically and rigorously analysing these results.In this paper, we present STAM (System Test-result Analysis Method) which employs a bottom-up hierarchical processing approach using Fuzzy logic. STAM is capable of combining all test results into a quantitative description of the network performance in terms of network stability, the significance of various network erros, performance of each function blocks within the network. The validity of this method has been successfully demonstrated in assisting the testing of a VoIP system at the Research Instiute of Post and Telecoms in Vietnam.The paper is organized as follows. The first section gives an overview of fuzzy logic theory the concepts of which will be used in the development of STAM. The next section describes STAM. The last section, demonstrating STAM’s capability, presents a success story in which STAM is successfully applied.

DSCB: Dual sink approach using clustering in body area network

Wireless Body Area Networks (WBANs) is a revolutionary achievement in the field of health services. The field is envisioned to play an important role in medical, psychological, and even in non-medical applications. Different routing protocols are being designed in WBAN to enhance its performance, focusing on delay, energy efficiency, throughput, and network lifetime. Line-of-sight (LoS) and Non-Line-of-Sight (NLoS) communications and clustering are the least focused areas in the literature. This paper presents a routing protocol called Dual Sink approach using Clustering in Body Area Network (DSCB) which is more reliable in terms of network stability, and energy efficient in comparison to its counterparts. This protocol enhances network life-time by introducing the concept of clustering while using two sink nodes. The proposed scheme is compared with existing protocols named as SIMPLE and DARE. The cost function is established for selection of forwarder node, based on nodal distance from the sink, residual energy, and transmission power. Simulation results depict that the proposed protocol achieves significant performance improvement in network throughput by attaining 55% and 22% more efficient results compared to SIMPLE and DARE respectively. Furthermore, the results show improved performance of DSCB protocol, in terms of network stability and end-to-end delay performance metrics.

Energy Consumption Rate based Stable Election Protocol (ECRSEP) for WSNs

In recent few years Wireless Sensor Networks (WSNs) have seen an increased interest in various applications like border field security, disaster management and medical applications. So large number of sensor nodes are deployed for such applications, which can work autonomously. Due to small power batteries in WSNs, efficient utilization of battery power is an important factor. Clustering is an efficient technique to extend life time of sensor networks by reducing the energy consumption. In this paper, we propose a new protocol; Energy Consumption Rate based Stable Election Protocol (ECRSEP). Our CH selection scheme is based on the weighted election probabilities of each node according to the Energy Consumption Rate (ECR) of each node. We compare results of our proposed protocol with Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy Efficient Clustering (DEEC), Stable Election Protocol (SEP), and Enhanced SEP(ESEP). Our simulation results show that our proposed protocol, ECRSEP outperforms all these protocols in terms of network stability and network lifetime.