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One of the key goals in the design of the networks is to increase the lifespan of wireless sensor networks (WSNs). Using different models of intelligent energy management could help designers achiseve this objective. By reducing the number of sensors required to collect data on the environment, these models can achieve higher levels of energy efficiency without sacrificing the quality of the readings. When battery power is an issue, wireless sensor networks (WSNs) are often employed for applications such as monitoring or tracking. Several routing protocols have been developed in the last several years as possible answers to this problem. Despite this, the issue of extending the lifetime of the network while considering the capacities of the sensors remain open. As a result of applying neural networks, Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy-Efficient Sensor Routing (EESR) can be improved in terms of their overall efficiency as well as their level of dependability, as is shown in this research EESR. Energy-Efficient Sensor Routing (ESR) and Low-Energy Adaptive Clustering Hierarchy (LEACH) are the names of the two protocols that are being utilized here EESR. The system incorporates a refined version of the Levenberg–Marquardt Neural Network (LMNN), which serves to enhance the efficiency with which it uses energy. The ability of an Intrusion Detection Systems (IDS) based on an artificial neural system to detect anomalies has also been proven. Anomalies can be identified using this system's optimum feature selection. Simulations showed that the proposed ANN-ILMNN model worked better, as shown by these results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) has made an indelible mark on the lives of everyone by assisting in agriculture, education, manufacturing, and environmental monitoring etc. One of the biggest problems for WSNs is achieving security and protecting data in a hostile environment against nodes. To address these problems, a novel Reliable Fuzzy based Generic Detection (RFGD) method has been proposed in this paper. The suggested RFGD framework consists of 2 stages. Initially, a reliable cluster head is selected by using the Parameter Optimized Fuzzy Model. Secondly, a Generic Detection method is used to detect compromised nodes to ensure secure transmission of data. The proposed RFGD technique is equated with state-of-arts techniques in terms of packet delivery ratio, energy consumption, number of alive nodes and detection accuracy. The proposed RFGD framework achieves 26.66%, 10.76%, 17.43% and 10.76% more detection accuracy than ACOPSO improved ABC, FGF and GAPSO-H methods. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a group of nodes combined with tiny sensors equipped with transceiver parts used for sensing, monitoring, and data collecting in various environments. It is not possible to support the battery support to the sensors while placed in an unmanned area. Hence, the sensor's energy should be vital, and it is very crucial. Combining the techniques of clustering and proper routing is one of the efficient schemes to save the energy of the sensor nodes. During the clustering process, cluster heads can be chosen with utmost care for load balancing. Along with cluster head selection, this proposed work also focussed on two major things. First, the appropriate relay node can be selected using the fuzzy inference system with sensor energy level as one input and the distance between the relay node and the cluster head node as another input. Secondly, the mobile sink collects the aggregated data from the relay node and sends it to the base station. On combining these two schemes, mobile sink and fuzzy based relay node routing Protocol is proposed. Simulation result shows that the proposed protocol has an improved number of alive nodes and a better network lifetime in terms of energy consumption compared with the existing schemes. [ABSTRACT FROM AUTHOR]

The emerging Wireless Body Area Networks (WBANs) open various challenges and opportunities in the healthcare industry for monitoring the patient’s health. In modern healthcare applications, the monitoring is done by placing/implanting heterogeneous intelligent sensing devices on the human body. These devices are limited by power, storage, and computational capability. Two crucial issues of WBAN design are power consumption and reliable data transmission from sensors to the coordinator. Since transmission consumes significant energy, minimizing power depletion of the sensors in the transmission is a prime challenge to enhance the WBAN lifetime and network performance. Over time, various forwarder selection protocols have been proposed to enhance the energy efficiency of the WBAN, but they have several shortcomings. This paper proposes an Energy Aware Forwarder Selection Technique (EAFST), which enhances the stability period and network lifetime of WBAN by selecting efficient forwarder nodes. EAFST organizes the sensors into concentric rings centered at the coordinator. The high-priority sensor directly transmits data to the coordinator and does not relay other’s data. However, EAFST selects a forwarder for low or medium-priority nodes that fall in a lower ring based on residual energy, queue size, and path loss. This significantly reduces the energy burden on high-priority nodes and data transmission delay. Moreover, the forwarder’s role is shuffled among low and medium-priority nodes to minimize energy usage when the residual energy of the forwarder goes below the threshold value. The effectiveness of the proposed scheme is validated using extensive simulations, and the results show that EAFST has an improvement of 56%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$56\%$$\end{document} and 51%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$51\%$$\end{document} over the existing routing protocols in terms of network stability and throughput, respectively. Similarly, the proposed EAFST improves the network lifetime by 68%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$68\%$$\end{document} and has 38%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$38\%$$\end{document} less energy consumption than existing routing protocols. [ABSTRACT FROM AUTHOR]

In the wireless sensor networks (WSN), the cluster head node receives and aggregates the information collected by the sensors and forwards it to the base station (BS). Appropriate cluster head nodes help reduce the energy consumed for information transmission and prolong the network's lifetime. The fish migration optimization (FMO) algorithm is an emerging meta-heuristic algorithm, that imitates the grayling foraging and breeding in nature. This paper proposes a modified FMO (modFMO) algorithm, which uses an opposition learning based on the elimination principle and Cauchy-based mutation to enhance the FMO algorithm. The improved modFMO is compared with some excellent algorithms on CEC 2014 function sets, and the Friedman ranks test demonstrates the effectiveness of the improvement. In this paper, the FMO algorithm and the modFMO algorithms with the centralized control algorithm are applied to select the optimal cluster head respectively. This paper takes the average energy consumption of the cluster head nodes as the objective function. Compared with other classic algorithms, the simulation results demonstrate that the modFMO algorithm can extend the lifetime of wireless networks, reduce energy consumption, improve information transmission efficiency and improved the ability of FMO algorithm. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a set of interrelated devices on the Internet platform. It can receive and send data to make human life more efficient and convenient. The main challenge in the IoT network is energy consumption in nodes. Clustering is a proper data collection method in the IoT that selectively reduces energy consumption by forming IoT nodes into clusters. The Cluster Head (CH) can control all Cluster Member (CM) nodes, and all intra-cluster and inter-cluster connections are made through it. Today, metaheuristic algorithms solve many problems, including clustering, because they have good performance and are noticeable practical effects. This paper uses the artificial fish swarm algorithm, an effective algorithm to solve optimization problems based on imitation of fish behavior. The cost function contains the residual energy of the nodes, the sum of the distances, and the degree of each node. The simulation results on the dataset showed that the proposed method increases network lifetime value by at least 12.5% and reduces latency by at least 14%. [ABSTRACT FROM AUTHOR]

WSN (Wireless Sensor Network) is an emerging and exigent technology being used in various applications such as health monitoring, GPS tracking, security, environmental monitoring etc. The energy resource of WSN is limited because of the reduced battery power. Also, it is difficult for the sensor nodes in WSN to recharge their batteries in hostile environments. Therefore, the idea of heterogeneous WSN (H-WSN) is introduced in this work which offers extra energy to the nodes based on energy heterogeneity. Here, a hybrid RDA-BWO (Red Deer Algorithm-Black Widow Optimization) method is presented to perform energy efficient data transfer. Multiple Mobile Sinks (MMSs) are employed in the network to avoid multi-hop communication among CHs and sink. In H-WSN, energy efficient data transfer and MSLP (Mobile Sink Location Prediction) with MMSs integrates the strategies namely FCM (Fuzzy C Means) clustering, RDA based CH (Cluster Head) selection, Data collection and aggregation mechanism, BWO based MSLP, hot-spot elimination and MSTP (Mobile Sink Traversal Path). The entire H-WSN is clustered using FCM algorithm. The CH selection make use of distance parameter, residual energy, average energy, number of node neighbours and ECR (Energy Consumption Rate) for the proposed energy efficacy. The proposed H-WSN is implemented in NS2 platform. Simulation results outperform the baseline protocols on different metrics, such as throughput, network lifetime, network's residual energy, number of dead nodes, stability period, and number of alive nodes demonstrate the superiority of the proposed RDA-BWO method. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network's lifetime is an area of constant study and innovation. The battery units of the sensor nodes cannot be recharged or replaced. Therefore, the need for energy efficiency in WSNs is ever-present. This paper proposes a Firework inspired Clustering Algorithm (FCA) to generate well defined and load-balanced clusters with the sensor nodes and gateways. The gateway works as cluster head (CH) for each cluster. The algorithm considers each cluster as a firework where the CH is the center of the firework and each sensor node is a 'spark' emitted by the firework. The goal of the FCA is to maximize the lifetime of the sparks which in turn will maximize the lifetime of the network. Simulations of the proposed algorithm are performed and compared with a few existing algorithms. The results show that the proposed algorithm outperforms under different evaluation metrics such as average energy consumed by sensor nodes vs number of rounds, number of active sensors vs number of rounds, first gateway die and half of the gateways die. [ABSTRACT FROM AUTHOR]

The most important quality-of-service metric for wireless sensor networks (WSNs), arguably, is the lifetime. Estimating the network lifetime under optimal operation conditions can be done by casting the problem as a mixed integer programming (MIP) model and solving the problem instances to optimality. Yet, solution times of such models are excessively high. Therefore, it is not possible to work with large problem instances within an acceptable solution time. Adopting learning based algorithms has the ability to produce near-optimal results much more rapidly in comparison to MIP models. In this study, we propose a deep neural network (DNN) based model to determine the WSN lifetime near-optimally virtually instantly. The proposed model is able to predict the lifetime of a randomly deployed WSN over a predetermined area with an average accuracy more than 98.5%. An interesting outcome of the study is that the DNN based model is able to estimate the lifetime of WSNs with higher number of nodes successfully even if it is trained with a dataset obtained with lower number of nodes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consist of a large number of nodes deployed randomly in an area of interest. Theses nodes have sensing, computation, and wireless communications capabilities. In another hand nodes have energy constraints since they are equipped by non-rechargeable batteries. To handle this issue, routing protocols consist of designing the network in order to collect and transmit data with less energy consumption. In this paper we presented a new Hybrid Energy Efficient Static routing protocol (HEESR), combining between clustering and multi-hop routing techniques. HEESR is dividing the network into several levels. For each round, it creates clusters, route the collected data through gateways called Independent Nodes, elected using a new dynamic approach and introduces Dormant nodes. Finally, the proposed HEESR have improved network's life time, throughput and other compared metrics, in both homogeneous and heterogeneous networks, and prolonged network's stability zone up to 98.4% compared to LEACH, 98% compared to DEEC and up to 40.5% compared to SMR. [ABSTRACT FROM AUTHOR]

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy. [ABSTRACT FROM AUTHOR]

With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are network of the large number of sensors having lesser batteries. Such networks and clustering of the nodes for energy efficiency go hand in hand. Clustering protocols like low energy adaptive clustering hierarchy, stable election protocol, improved low energy adaptive clustering hierarchy etc. allow the nodes to grouped together, form clusters and transmit their information to the base station via their leader, i.e. cluster head. Selection of cluster head follows various approaches and vary from protocol to protocol. While some of the approaches use single hop communication of the cluster head with the base station, others opt for multi-hop communication. This paper describes energy effective procedure for heterogeneous network consisting of super, advance and normal nodes. The protocol operates according to the type of the nodes. For the high energy super nodes, the protocol follows centralized procedure and for other nodes it follows decentralized approach. The selection of cluster head among the super nodes is optimized by the use of cost function depending upon distance of the nodes from the centroid of their residual energies and their signal to interference plus noise ratio. The nodes other than super nodes achieve lesser energy consumption by using the mobile agents to transfer their data to the base station. The proposed protocol has shown improvements in terms of throughput and number of dead nodes as equated to other state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Wireless body area network (WBAN) routing protocols are primarily designed for improvement of network performance parameters such as network lifetime, throughput and latency. However, these designs do not take into account the health hazards posed due to WBAN radiation. The focus of implementing IEEE 802.15.6 standards is to develop an energy-efficient, reliable and health-conscious wireless communication system around human body. This paper presents the implementation of a clustering-based routing protocol for WBAN that targets in achieving overall optimization of WBAN performance parameters including network lifetime, throughput, latency, node signal power and specific absorption rate (SAR) of human body for emf radiation. The suggested protocol applies a multi-objective NSGA-II optimization heuristic for cluster formation while taking into account various IEEE 802.15.6 standard constraints. A unique chromosome structure has been proposed for NSGA-II population in which each chromosome represents a random network topology for determining the node clusters, cluster heads and node transmission power. Furthermore, three system-level models for conducting multi-objective fitness evaluation of each chromosome have been proposed. These models predict the global transmission energy consumption; the average received signal strength (RSSI) and end to end network latency respectively. The developed NSGA-II model evolves into a non-dominated set of Pareto-optimal network topologies that renders the desired objectives of minimization of network energy consumption and network latency besides maximization of average RSSI. Enhancement in RSSI further leads to a tremendous improvement in the throughput rate. The node transmission power specifications of Pareto-optimal network topologies meet the standard SAR constraints too. The performance results shows that the suggested protocol strictly meets IEEE 802.15.6 compliance. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have a large number of existing applications and is continuously increasing. Thus it is envisioned that WSN will become an integral part of our life in the near future. Direct propagation, chain formation, cluster creation are various techniques by which data is communicated by sensor nodes to the sink. It has been proved that Clustering is an efficient and scalable method to utilize the energy of sensor nodes efficiently. Optimal election of cluster heads is an NP (non deterministic polynomial time)-Hard problem. In our proposed work, a multi-objective optimization algorithm, non dominated sorting genetic algorithm-II based clustering in wireless sensor networks has been proposed. Energy conservation, network lifetime, coverage and load balancing are the four conflicting objective functions used. Our proposed algorithm handles all of these multiple objectives simultaneously. To reduce the computational complexity of the algorithm, efficient non-dominated level update mechanism for sorting has been used, which eliminates the need of applying non dominated sorting from scratch every time. The algorithm returns a solution set consisting of multiple non dominated solutions, wherein every solution is a best solution according to some objective function, in a single run, from which any solution can be chosen based on user preferences. According to our simulation carried on MATLAB, the proposed approach outperforms the established clustering algorithms in terms of network characteristics such as network lifetime, energy consumption and number of packets received. [ABSTRACT FROM AUTHOR]

The effectiveness of wireless sensor network (WSN) in Internet of Thing (IoT) based large scale application depends on the deployment method along with the routing protocol. The sensor nodes are an important component of WSN-assisted IoT network running on limited and non-rechargeable energy resource. The performance of WSN-assisted IoT is decreased, when network is deployed at large area. So, developing robust and energy-efficient routing protocol is a challenging task to prolong the network lifetime. In contrast to the state-of-the-art techniques this paper introduces Scalable and energy efficient routing protocol (SEEP). SEEP leverages the multi-hop hierarchical routing scheme to minimize the energy consumption. To achieve scalable and energy efficient network, SEEP employs a multi-tier based clustering framework. The network area in SEEP is divided into various zones with the help of proposed subarea division algorithm. The number of zones in the network are increased as the network size increases to avoid long-distance communication. Every zone is divided into certain number of clusters (sub-zones) and the number of clusters are increased towards the base station, whereas the zone width is decreased. In every cluster, some of the optimal nodes are promoted as a Relay Node (RN) and Cluster Head (CH). Normal nodes send their sensed data to the base station via local RN and CH in a multi-hop way. Furthermore, propose protocol provides a trade-off between distance and energy to prolong the network lifetime. In the proposed framework, static and mobile scenarios have been considered by applying Random walk and Random waypoint model for node mobility in simulation to make it more realistic as the various application of WSN-assisted IoT. The effectiveness of SEEP is examined against LEACH, M-LEACH, EA-CRP, TDEEC, DEEC, SEP, and MIEEPB, and result indicates that SEEP performs better for different network metrics: network lifetime, scalability, and energy efficiency. [ABSTRACT FROM AUTHOR]

Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new Grey Wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new Grey Wolf and reversible Greedy based Reconstruction Algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error. [ABSTRACT FROM AUTHOR]

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head election and cluster formation. It divides the WSN into two-levels of hierarchy and three-levels of energy heterogeneity of sensor nodes. The simulation results reveal that the proposed approach outperforms existing baseline algorithms in terms of energy consumption, stability period, and the network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consisting of numerous nodes function in such a manner that each node that collects data from its physical environment, conveys them to the base station for rigorous analysis. Maintaining the functional coverage, energy consumption and the lifetime are among the most challenging factors for WSN. The aim of this paper is to provide an effective coverage with a simultaneous reduction in energy consumption using mobile nodes and evolutionary firefly algorithm. The methodology involves dividing network area into equal cells though smaller size, in order to facilitate the estimation of the physical coverage. In areas with desirable and sufficient coverage, the operational scheduling of nodes is set in such a manner to activate some sensors to ensure necessary coverage and deactivate others. This, to a great extent, decreases the demand for energy. Under the circumstances where the cells are deprived of appropriate or sufficient coverage, mobile nodes are used to improve the coverage. This is achieved by using firefly algorithm to determine the optimum deployment of mobile nodes to perform the job. Results of simulation show that the proposed method has greater superiority over its comparative techniques, particularly on aspects such as the rate of network field area, energy consumption and network lifetime to mention a few. [ABSTRACT FROM AUTHOR]

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN. [ABSTRACT FROM AUTHOR]

Mobile sinks have advantages such as solving the hotspot problem which occurs because some sensor nodes deplete energy faster than others and enhancing energy balance among the sensor nodes. However, sink mobility raises two challenges, frequent location updates and packets delivery delay. To tackle these two problems, this work proposes a protocol that involves two mechanisms. First, to reduce the frequent location updates of the mobile sink to all sensor nodes, we proposed an accessible virtual structure (Double Ring) that acts as an intermediate structure between the sink and the sensor nodes when exchanging metadata and query packets. Second, to accelerate packets delivery, we proposed a heuristic data dissemination protocol, called HDD, in which the heuristic function is based on four values, direction, transmission distance, perpendicular distance, and residual energy. The experimental results showed that our proposed protocol outperforms the state of art protocols in terms of energy consumption, delivery rate, latency, and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime. [ABSTRACT FROM AUTHOR]

Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly. [ABSTRACT FROM AUTHOR]

An important challenge in wireless sensor networks is energy conservation. Recently, several hybrid, dynamic and static clustering protocols have been proposed to solve this challenge. In this paper, a hybrid unequal energy efficient clustering is proposed to improve previous methods and increase lifetime of the network. In the proposed protocol, a new mechanism called clustering strategy is used. This mechanism, based on arrangement of nodes in a network, determines whether nodes should use information of their neighbors or should not use this information. This strategy helps to reduce overhead considerably. On the other hand, clustering is unequal so that nodes closer to base station (BS) have more energy to receive and relay data towards BS. In order to reduce overhead, clustering is designed as hybrid static–dynamic so that transmitting control message for clustering is not required at each round. Two new techniques are proposed for routing. First, assistance to cluster heads mechanism which allows cluster heads to get help from some of its member nodes which have suitable energy and distance to help sharing cluster's load. In other words, a new intra-cluster multi-hop routing is proposed. Second new technique is discretion license which is performed in real time and allows the nodes to prevent transmissions of packets that may arrive at a destination in an incomplete form. In addition, inter-cluster routing use a new technique based on layering is proposed. Simulation results show that the proposed method has reduced network overhead, increased network stability, energy balance and lifetime of the network. [ABSTRACT FROM AUTHOR]

The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

This study aims to explore the clustering routing algorithm of a wireless sensor network (WSN) based on swarm intelligence (SI) and improve the energy efficiency and transmission efficiency of WSN, thus optimizing the network performance. To achieve this goal, this study adopts the SI method, combines the characteristics of WSN, and proposes a WSN clustering algorithm by the chaos particle swarm optimization (CPSO). Firstly, by grouping network nodes to form clusters, the clusters’ optimal number is obtained with the minimum network energy, reducing the networks’ communication overhead. Secondly, the CPSO is used in each cluster to select the appropriate cluster head nodes to undertake data aggregation and transmission tasks. Meantime, these nodes are also responsible for maintaining and managing the energy consumption of the nodes within the cluster to extend the network’s lifetime. Finally, the energy of WSN's clustered nodes is rotated using global and local rotation methods to maintain the stability of WSN nodes. The research results reveal that compared with traditional WSN routing algorithms, the stable working time of the CPSO-based SI of the clustering routing algorithm can be extended by 30.10%. The lifecycle of WSN can be extended by 55.20%, significantly reducing energy consumption and improving data transmission efficiency. This study proposes a WSN clustering routing algorithm of SI based on CPSO, which has remarkable advantages in improving network energy efficiency and transmission efficiency. The results of this study have important practical application value for improving the energy efficiency of WSN, extending network lifetime, and improving data transmission quality. Meanwhile, this study also offers a useful reference for further exploring the application of SI in other network fields. [ABSTRACT FROM AUTHOR]

One big contributor in the future of the Internet of Things is the Periodic Sensor Networks (PSNs) because it has been used by many applications in real life. The main challenge in this type of networks is to gather the huge amount of data periodically in an energy saving way and then transmit them to the base station in order to extend the lifetime of PSN. Since the limited nature of the sensors batteries power, therefore, an energy-efficient data aggregation method is needed to optimize both energy and lifetime in PSNs. This article proposes a Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks. TLDA works in a periodic way. Each period consists of two data aggregation levels. The first level of data aggregation is applied at the sensor node. This level includes data collection, the sliding window to generate a varying number of segments with different lengths, and data aggregation using Adaptive Piecewise Constant Approximation technique to reduce the amount of data collected by each sensor. The second level is applied at the aggregator. It includes grouping received data sets based on the chaining hash table with SAX quantization method, finding and lowering the duplicate sets, finding and merging the duplicate readings, and transmit the aggregated data to the sink. Extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods. [ABSTRACT FROM AUTHOR]

In a wireless sensor network (WSN), there is always the possibility of failure in sensor nodes. Quality of Service (QoS) of WSNs is highly degraded due to the faulty sensor nodes. One solution to this problem is to detect and reuse faulty sensor nodes as much as possible. Accordingly, QoS of WSNs can be improved. This paper proposes a distributed cellular learning automata faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. The proposed method uses cellular learning automata to assign a status to each node based on hardware conditions, which makes the nodes do one of the network's operations. The proposed algorithm is experimented extensively and the results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged network lifetime. The proposed algorithm, divides the Region of Interest into virtual zones, each having some designated cluster head nodes. In the entire process, a node can either be a part of a cluster or it may remain as an independent entity. A non-cluster member transmits its data to next hop node using IRP-Intelligent Routing Process (based on the trade-off between the residual energy of itself as well as its neighbor, and the required energy to transmit packets to its neighbor). If on the transmission path, some cluster member is elected as a next hop, it rejects IRP and transmits the packets to cluster head, which later forwards them to sink (adopting multihop communication among cluster heads). Routing is not solely performed using clusters, rather they aid the overall routing process, hence this protocol is named as Cluster Aided Multipath Routing (CAMP). CAMP has been compared with various sensor network routing protocols, viz., LEACH, PEGASIS, DIRECT TRANSMISSION, CEED, and CBMR. It is found that the proposed algorithm outperformed them in network lifetime, energy consumption and coverage ratio. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impacts of mobility on the characterization of energy consumption in wireless networks. Considering a linear wireless network deployed for an information-collecting purpose, which includes a fixed sink node and a number of mobile nodes, the analytical expressions of the energy consumption are derived for each mobile node, which either adopts multi-hop or opportunistic routing for packet transmission to the sink node. The derived expressions are applied to analyze the network lifetime. We also compare the multi-hop routing and the opportunistic routing in terms of energy consumption and network lifetime. Our results provide several insights into the interplay between mobility, routing strategy and energy consumption. Specifically, we find that a certain degree of mobility has a significant benefit to the efficient and balanced energy usage, and consequently improving the network lifetime. [ABSTRACT FROM AUTHOR]

Energy efficient scheduling of sensor nodes is one of the most efficient techniques to extend the lifetime of the wireless sensor networks (WSNs). Instead of activating all the deployed sensor nodes, a set of sensor nodes are activated or scheduled to monitor the targeted region. While scheduling with lesser number of sensor nodes, coverage and connectivity of the network should be taken care due to the limited sensing and communication range of the sensor nodes. In this paper, we have proposed an improved genetic algorithm (GA) based scheduling for WSNs. An efficient chromosome representation is given and it is shown to generate valid chromosome after crossover and mutation operation. The fitness function is derived with four conflicting objectives, selection of minimum number of sensor nodes, full coverage, connectivity and energy level of the selected sensor nodes. We have introduced a novel mutation operation for better performance and faster convergence of the proposed GA based approaches. We have also formulated the scheduling problem as a Linear Programming. Extensive simulation is performed on various network scenarios by varying number of deployed sensor nodes, target point and network length. We also perform a popular statistical test, analysis of variance followed by post hoc analysis. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with sink to monitor physical or environmental conditions. In cluster-based WSNs, the Cluster Head is treated as the gateway and gateways perform the multiple activities, such as data gathering, aggregation, and transmission etc. Due to improper clustering some sensor nodes and gateways are heavily loaded and dies early. This decreases lifetime of the network. Moreover, sensor nodes and gateways are constrained by energy, processing power and memory. Hence, to design an efficient clustering is a key challenge in WSNs. To solve this problem, in this paper we proposed (1) a clustering algorithm based on the shuffled complex evolution of particle swarm optimization (SCE-PSO) (2) a novel fitness function by considering mean cluster distance, gateways load and number of heavily loaded gateways in the network. The experimental results are compared with other state-of-the-art load balancing approaches, like score based load balancing, node local density load balancing, simple genetic algorithm, novel genetic algorithm. The experimental results shows that the proposed SCE-PSO based clustering algorithm enhanced WSNs lifetime when compared to other load balancing approaches. Also, the proposed SCE-PSO outperformed in terms of load balancing, execution time, energy consumption metrics when compared to other existing methods. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period. [ABSTRACT FROM AUTHOR]

The improvement of sensor networks’ lifetime has been a major research challenge in recent years. This is because sensor nodes are battery powered and may be difficult to replace when deployed. Low energy adaptive clustering hierarchical (LEACH) routing protocol was proposed to prolong sensor nodes lifetime by dividing the network into clusters. In each cluster, a cluster head (CH) node receives and aggregates data from other nodes. However, CH nodes in LEACH are randomly elected which leads to a rapid loss of network energy. This energy loss occurs when the CH has a low energy level or when it is far from the BS. LEACH with two level cluster head (LEACH-TLCH) protocol deploys a secondary cluster head (2CH) to relieve the cluster head burden in these circumstances. However, in LEACH-TLCH the optimal distance of CH to base station (BS), and the choicest CH energy level for the 2CH to be deployed for achieving an optimal network lifetime was not considered. After a survey of related literature, we improved on LEACH-TLCH by investigating the conditions set to deploy the 2CH for an optimal network lifetime. Experiments were conducted to indicate how the 2CH impacts on the network at different CH energy levels and (or) CH distance to BS. This, is referred to as factor-based LEACH (FLEACH). Investigations in FLEACH show that as CHs gets farther from the BS, the use of a 2CH extends the network lifetime. Similarly, an increased lifetime also results as the CH energy decreases when the 2CH is deployed. We further propose FLEACH-E which uses a deterministic CH selection with the deployment of 2CH from the outset of network operation. Results show an improved performance over existing state-of-the-art homogeneous routing protocols. [ABSTRACT FROM AUTHOR]

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have grown excessively due to their various applications and low installation cost. In WSN, the main concern is to reduce energy consumption among nodes while maintaining timely and reliable data forwarding. However, most of the existing energy aware routing protocols incur unbalanced energy consumption, which results in inefficient load balancing and compromised network lifetime. Therefore, the main target of this research paper is to present adaptive energy aware cluster-based routing (AECR) protocol for improving energy conservation and data delivery performance. Our proposed AECR protocol differs from other energy efficient routing schemes in some aspects. Firstly, it generates balance sized clusters based on nodes distribution and avoids random clusters formation. Secondly, it optimizes both intra-cluster and inter-cluster routing paths for improving data delivery performance while balancing data traffic on constructed forwarding routes and at the end, in order to reduce the excessive energy consumption and improving load distribution, the role of Cluster Head (CH) is shifted dynamically among nodes by exploit of network conditions. Simulation results demonstrate that AECR protocol outperforms state of the art in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period. [ABSTRACT FROM AUTHOR]

Coverage is a significant performance indicator of wireless sensor networks. Data redundancy in k-coverage raises a set of issues including network congestion, coverage reduction, energy inefficiency, among others. To address these issues, this paper proposes a novel algorithm called complex alliance strategy with multi-objective optimization of coverage (CASMOC) which could improve node coverage effectively. This paper also gives the proportional relationship of the energy conversion function between the working node and its neighbors, and applies this relationship in scheduling low energy mobile nodes, thus achieving energy balance of the whole network, and optimizing network resources. The extensive simulation results demonstrate that CASMOC could not only improve the quality of network coverage, but also mitigate rapid node energy consumption effectively, thereby extending the life cycle of the network significantly. [ABSTRACT FROM AUTHOR]

Routing protocols for Wireless Sensor Networks (WSN) are designed to select parent nodes so that data packets can reach their destination in a timely and efficient manner. Typically neighboring nodes with strongest connectivity are more selected as parents. This Greedy Routing approach can lead to unbalanced routing loads in the network. Consequently, the network experiences the early death of overloaded nodes causing permanent network partition. Herein, we propose a framework for load balancing of routing in WSN. In-network path tagging is used to monitor network traffic load of nodes. Based on this, nodes are identified as being relatively overloaded, balanced or underloaded. A mitigation algorithm finds suitable new parents for switching from overloaded nodes. The routing engine of the child of the overloaded node is then instructed to switch parent. A key future of the proposed framework is that it is primarily implemented at the Sink and so requires few changes to existing routing protocols. The framework was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The algorithm increased the lifetime of the network by 41 % as recorded in the testbed experiment. The Packet Delivery Ratio was also improved from 85.97 to 99.47 %. Finally a comparative study was performed using the proposed framework with various existing routing protocols. [ABSTRACT FROM AUTHOR]

Reducing the energy consumption of network nodes is one of the most important problems for routing in wireless sensor networks because of the battery limitation in each sensor. This paper presents a new ant colony optimization based routing algorithm that uses special parameters in its competency function for reducing energy consumption of network nodes. In this new proposed algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN), a new pheromone update operator was designed to integrate energy consumption and hops into routing choice. Finally, with the results of the multiple simulations we were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime. [ABSTRACT FROM AUTHOR]

Energy conservation of the sensor nodes is the most important issue that has been studied extensively in the design of wireless sensor networks (WSNs). In many applications, the nodes closer to the sink are overburdened with huge traffic load as the data from the entire region are forwarded through them to reach the sink. As a result, their energy gets exhausted quickly and the network is partitioned. This is commonly known as hot spot problem. Moreover, sensor nodes are prone to failure due to several factors such as environmental hazards, battery exhaustion, hardware damage and so on. However, failure of cluster heads (CHs) in a two tire WSN is more perilous. Therefore, apart from energy efficiency, any clustering or routing algorithm has to cope with fault tolerance of CHs. In this paper, we address the hot spot problem and propose grid based clustering and routing algorithms, combinedly called GFTCRA (grid based fault tolerant clustering and routing algorithms) which takes care the failure of the CHs. The algorithms follow distributed approach. We also present a distributed run time management for all member sensor nodes of any cluster in case of failure of their CHs. The routing algorithm is also shown to tolerate the sudden failure of the CHs. The algorithms are tested through simulation with various scenarios of WSN and the simulation results show that the proposed method performs better than two other grid based algorithms in terms of network lifetime, energy consumption and number of dead sensor nodes. [ABSTRACT FROM AUTHOR]

Cognitive radio sensor network (CRSN) is an intelligent and reasonable combination of cognitive radio technology and wireless sensor networks. It poses significant challenges to the design of topology maintenance techniques due to dynamic primary-user activities, which in turn decreases the data delivery performance of the network as well as it's lifetime. This paper aims to provide a solution to the CRSN clustering and routing problem using an energy aware event-driven routing protocol (ERP) for CRSN. Upon detection of an event, the ERP determines eligible nodes for clustering according to local positions of CRSN nodes between the event and the sink and their residual energy levels. Cluster-heads are selected from the eligible nodes according to their residual energy values, available channels, neighbors and distance to the sink. In ERP, cluster formation is based on relative spectrum awareness such that channels with lower primary user appearance probability are selected as common data channels for clusters. For data routing, ERP employs hop-by-hop data forwarding approach through the CHs and primary/secondary gateways towards the sink. Through extensive simulations, we demonstrate that the proposed ERP provides with better network performances compared to those of the state-of-the-art protocols under a dynamic spectrum-aware data transmission environment. [ABSTRACT FROM AUTHOR]

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime. [ABSTRACT FROM AUTHOR]

Although many applications use battery-powered sensor nodes, in some applications battery- and mains-powered nodes coexist. In this paper, we present a distributed algorithm that considers using mains-powered devices to increase the lifetime of wireless sensor networks for such heterogeneous deployment scenarios. In the proposed algorithm, a backbone routing structure composed of mains-powered nodes, sink, and battery-powered nodes if required, is constructed to relay data packets to one or more sinks. The algorithm is fully distributed and can handle dynamic changes in the network, such as node additions and removals, as well as link failures. Our extensive ns-2 simulation results show that the proposed method is able to increase the network lifetime up to 40 % compared to the case in which battery- and mains-powered nodes are not differentiated. [ABSTRACT FROM AUTHOR]

Clustering has been well known as an effective way to reduce energy dissipation and prolong network lifetime in wireless sensor networks. Recently, game theory has been used to model clustering problem. Each node is modeled as a player which can selfishly choose its own strategies to be a cluster head (CH) or not. And by playing a localized clustering game, it gets an equilibrium probability to be a CH that makes its payoff keep equilibrium. In this paper, based on game theory, we present a clustering protocol named Hybrid, Game Theory based and Distributed clustering. In our protocol, we specifically define the payoff for each node when choosing different strategies, where both node degree and distance to base station are considered. Under this definition, each node gets its equilibrium probability by playing the game. And it decides whether to be a CH based on this equilibrium probability that can achieve a good trade-off between minimizing energy dissipation and providing the required services effectively. Moreover, an iterative algorithm is proposed to select the final CHs from the potential CHs according to a hybrid of residual energy and the number of neighboring potential CHs. Our iterative algorithm can balance the energy consumption among nodes and avoid the case that more than one CH occurs in a close proximity. And we prove it terminates in finite iterations. Simulation results show that our protocol outperforms LEACH, CROSS and LGCA in terms of network lifetime. [ABSTRACT FROM AUTHOR]

Battery power is a critical resource of Wireless Sensor Networks (WSNs). Therefore, an effective operation of WSNs depend upon the efficient use of its battery resource. Cluster based routing protocols are proven to be more energy efficient as compared to other routing protocols. Most of the cluster based routing protocols, especially Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, follows Dynamic, Distributed and Randomized (DDR) algorithm for clustering. Due to the randomness present in clustering algorithms, number of cluster heads generated varies highly from the optimal count. In this paper, we present an approach which attempts to control the randomness present in LEACH's clustering algorithm. This approach makes the cluster head count stable. NS-2 simulation results show that proposed approach improved the First Node Death (FND) time and Last Node Death (LND) time by 21 and 24 % over LEACH, 10 and 20 % as compared to Advance LEACH (ALEACH) and 5 and 35 % over LEACH with Deterministic Cluster Head Selection (LEACH-DCHS) respectively. [ABSTRACT FROM AUTHOR]

In past decade, wireless sensor networks have gained attention by researchers, manufacturers as well as the users for remotely monitoring tasks and effective data gathering in diverse environment. The wireless sensor nodes are tiny battery powered devices having limited lifetime, hence for longevity and reliability, the foremost concern is minimizing energy consumption and maximizing network lifetime while designing protocols and applications. In this paper, we review the main design issues based on the model of wireless sensor networks: structure-free and structured for data collection and aggregation where role of clustering and routing is discussed for energy conservation and enhancing network lifetime. These design strategies are the foundation of any networking protocol from the energy saving point of view. A comprehensive tabular overview of different approaches under structure-free and structured wireless sensor networks for data collection and aggregation, clustering and routing is presented with key issues. [ABSTRACT FROM AUTHOR]

Balancing energy usage over a larger group of participating nodes in a sensor network extends the network's lifetime. Security protocols mainly focus on saving energy through early filtering of attacks; however, this does not necessarily prolong the network lifetime. In order to save more energy and extend the network lifetime, the underlying routing is equally important. Security protocols such as commutative cipher en-route filtering (CCEF), which considers shortest path routing, do not take advantage of the opportunity to save more energy by considering an energy efficient underlying routing at the design level. In this paper, we propose pre-deterministic key-distribution based CCEF (PKCCEF), which extends the network life significantly through pre-deterministic and random distribution of the witness keys with a modified underlying routing that considers the residual energy. For high FTR, our method selects a path with more filtering nodes to be able to detect more attacks. This needs further verification for legitimate reports and needs more energy as compared to original method, but it can save energy by filtering fabricated or false report earlier. For low FTR, our method selects an energy efficient path with fewer verifications of legitimate reports. Independent of the number of attacks, our method extends the network lifetime as compared to the original method i.e. CCEF. Our method achieves energy efficiency without compromising the security of the original scheme. The results show the effectiveness of the proposed method in terms of the energy efficiency (up to 16.05 %) and extending the network lifetime (up to 84.01 %). [ABSTRACT FROM AUTHOR]

In this paper the problem of maximum lifetime routing is investigated in asynchronous duty-cycled wireless sensor networks. To model this problem, a new energy balance based asynchronous MAC protocol is proposed called K-Persistent FRTS-RCTS, which stands for K-Persistent flooding of RTS and random sending of CTS. In this protocol, each transmitter in order to send a data packet, first transmits at most K packets of RTS to its neighbors by flooding, but if the transmitter does not receive any CTS packets, it transmits the RTS packet directly to the sink. The CTS packet is sent to the transmitter either by one of the waking neighbors via a uniform random mechanism or by the sink node in response to the received RTS packet. It is shown that by using K-Persistent FRTS-RCTS MAC, the problem is formulated as a mixed integer nonlinear programming problem. In this problem, the optimization variables consist of the maximum number of RTS flooding requests ( K) as the integer values and the flow rate of information on any route and the duty cycle of nodes both as the real values. To assess the performance of the proposed method, it is compared to the same maximum lifetime routing problem under the 1-Persistent FRTS-RCTS and the well-known X-MAC protocols. Evaluation results indicate that K-Persistent FRTS-RCTS MAC outperforms two mentioned MAC protocols in terms of both network lifetime and topology changes. [ABSTRACT FROM AUTHOR]