Lifespan Of Wireless Sensor Networks Research Articles

Fuzzy based multi-level multi-constraint multi-path reliable routing in wireless sensor network

In the direction of keeping up longer life time of network, there is a limitation on the life-span of Wireless Sensor Network. In numerous applications, data to be sensed should be transmitted commencing from sources towards a sink in a timely way. The low effectiveness of data transmission is capable of routing with low quality of service and needed to guarantee network transmission. The proposed scheme Fuzzy Based Multi-level Multi-constraint Multi-path Reliable Routing contains the energy, delay and transmission reliability. Clustering is utilized for efficient aggregation of residual energy. Super Cluster Head (SCH) selection algorithm among the CHs based fuzzy concept is proposed. The parameters are Attempt Rate (AR), Residual Energy of Sensor Nodes, and distance to the base station) (Dist). Further cost function (CF) for the average residual energy and average end-to-end (ENE) delay, average transmission reliability (AR) for multipath routing network is proposed. The sensed data from SCH to a sink by assures minimum end-to-end delay and maximum transmission reliability and maximum residual energy with assuring reliable routing. Also it is utilized an optimization technique to adjust the parameters used in fuzzy clustering levels to optimize the performance. The work also includes performance comparisons with some selected algorithms. The results of a simulation are shown in NS2 tool and reflect that proposed work performs better than other existing protocols considering different metric used for comparing delay, throughput, PDR, Packet Drop and control overhead.

Extending the Lifespan of Wireless Sensor Networks: A Survey of LEACH and Non-LEACH Routing Protocols

Extending the Lifespan of Wireless Sensor Networks: A Survey of LEACH and Non-LEACH Routing Protocols

Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks

In this paper, novel energy-aware and reliable routing protocols are proposed. The aim is to maximize the lifespan of wireless sensor networks (WSNs) subject to predefined reliability constraints by using multi-hop routing schemes, in which the source node forwards the packet to the base station (BS) via other nodes as relays. In the first proposed protocol, energy efficiency is achieved by maximizing the minimum residual energy of the path subject to fulfilling a predefined reliability constraints. The second protocol is an optimized version of the first one with respect to lifespan and complexity. The optimal path is the one in which the residual energy distribution of the nodes along the path is as close to uniform as possible and the packet arrives at the base station with a given success probability. To measure the uniformity of the residual energy distribution, we use an entropy like measure. The information about the current energy state of the network is maintained by using a look-up-table from which the optimal routes are computed on the BS. The BS broadcasts the updated optimal paths to each node after each round of packet transmission.

An efficient approach to preserve the network connectivity for prolonged lifespan of wireless sensor networks by cautiously removing the crossing edges using COLS

In recent times, wireless sensor networks (WSN) have been widely used in various applications which have led to the deployment of enormous numbers of sensors leading to the complexity of the network. Extensive research has been carried out for monitoring these sensors for connectivity, coverage, load balancing, network structure etc. Study on these complex networks to maintain connectivity for longevity is a challenging task. WSN when modelled on graphs exhibit the properties of non-planarity. In this paper, we would like to propose an algorithm COLS to reduce a non-planar graph to a planar graph by removing the crossing edges carefully. The proposed algorithm preserves the topological structure without compromising QoS of the original network and finds its application as a load balancer on WSN. Experiments have shown that the algorithm accurately converts the graph on multi-dimension to a two-dimension without considering cross edges and the time complexity of the given algorithm is O(n2).

An efficient approach to preserve the network connectivity for prolonged lifespan of wireless sensor networks by cautiously removing the crossing edges using COLS

In recent times, wireless sensor networks (WSN) have been widely used in various applications which have led to the deployment of enormous numbers of sensors leading to the complexity of the network. Extensive research has been carried out for monitoring these sensors for connectivity, coverage, load balancing, network structure etc. Study on these complex networks to maintain connectivity for longevity is a challenging task. WSN when modelled on graphs exhibit the properties of non-planarity. In this paper, we would like to propose an algorithm COLS to reduce a non-planar graph to a planar graph by removing the crossing edges carefully. The proposed algorithm preserves the topological structure without compromising QoS of the original network and finds its application as a load balancer on WSN. Experiments have shown that the algorithm accurately converts the graph on multi-dimension to a two-dimension without considering cross edges and the time complexity of the given algorithm is O(n2).

HGC: HyperGraph based Clustering scheme for power aware wireless sensor networks

Due to the energy constraints of sensors owing to the limitation of their built-in batteries, the lifespan of Wireless Sensor Networks (WSNs) are significantly affected. These particular ad-hoc networks have a huge number of applications including surveillance and target tracking. Unfortunately, since sensor nodes are limited in terms of power resources, efficient utilization of these resources is an important goal to design power-aware WSNs. This led researchers to propose numerous methods, such as clustered WSNs, in order to effectively manage the power resources. In this work, we proposed a heuristic clustering based on the hypergraph theory, and called HyperGraph Clustering (HGC) that aims at optimizing the energy of sensor nodes. Theoretical evaluation highlighted that this clustering protocol consumed less energy during the cluster formation phase and the selection of the cluster head. In addition, we evaluated the performance of the proposed HGC and the results showed the effectiveness of our scheme to those we compared in terms of the number of nodes alive, residual energy and the total consumption of the network.

An energy‐efficient and balanced clustering approach for improving throughput of wireless sensor networks

SummaryMaximizing the lifespan of wireless sensor networks is currently drawing a lot of attention in the research community. In order to reduce energy consumption, sensor nodes that are far from the base station avoid sending data directly. As a result, several disjoint clusters are formed, and nodes within a cluster send their data through the cluster head to avoid long transmissions. However, several parameters related to transmission cost need to be considered when selecting a cluster head. While most of the existing research work considers energy and distance as the most stringent parameters to reduce energy consumption, these approaches fail to create a fair and balanced cluster. Consequently, unbalanced clusters are formed, resulting in the degradation of overall performance. In this research work, a cluster head selection algorithm is proposed that covers all parts of the sensing area in a balanced manner, saving a significant amount of energy. Furthermore, a capture effect–based intracluster communication mechanism is proposed that efficiently utilizes the time slot under various traffic conditions. A Näive Bayes classifier is used to adapt the window size dynamically according to the traffic pattern. Finally, a simulation model using OMNeT++ is developed to compare the proposed approach with the pioneer clustering approach, LEACH, and the contemporary LEACH‐MAC protocol in terms of performance. The results of the simulation indicate that the proposed approach improves the overall performance in terms of network lifetime, energy efficiency, and throughput.

A survey on data aggregation techniques in IoT sensor networks

There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

ODECS: An On-Demand Explosion-Based Compressed Sensing Using Random Walks in Wireless Sensor Networks

In this paper, a new data acquisition scheme is proposed based on compressed sensing (CS) with on-demand explosion of random walks (RWs) for improving the lifespan of wireless sensor networks (WSNs) without compromising its detection capability. From the perspective of sensor networks, the lifetime largely relies upon communication efficiency and load distribution on the nodes. CS with RW is proven to be an efficient mechanism for data gathering for sparse recovery, but requires minimum O(s × log(n/s)) measurements. To lift this assumption, we have exploited the rate of change of data, which works in a similar fashion as sparsity does. Moreover, it also yields a way for opportunistic initiation of RWs which ensures explicit event detection in the target region. Therefore, an on-demand explosion-based compressed sensing (ODECS) is proposed to reduce the required measurements dynamically and limits the required measurements to a constant in case of no events. Simulation and experimental analysis show that ODECS outperforms the existing schemes in terms of data transmission, energy consumption, and lifetime by reducing communication which depends upon the probability of occurrence of event in each data acquisition round.

On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction

A useful approach to increase the lifetime of wireless sensor networks is clustering. Exchange of messages due to successive and recurrent reclustering burdens the sensor nodes and causes power loss. This paper presents a modified clustering methodology that diminishes the overhead in clustering and message exchanges thereby effectively scheduling the clustering task. The network is clustered subject to the remaining energy of sensor nodes. Energy based parameters decide cluster head nodes and ancillary nodes and the member nodes are linked with them. The roles of the head nodes of the cluster are interchanged depending on the nodes’ states. Reclustering of nodes is accomplished to achieve minimum energy consumption by calculating the update cycle using a fuzzy inference system. The average sensed data rate of cluster members, the distance at which the member nodes are from the sink and the power of cluster head nodes are counted to achieve better energy saving. Cluster member nodes apply machine learning at regular intervals to classify data based on their similarity. The classified data are transmitted to the cluster head after a reduction in the number of message transfers. The proposed method improves the energy usage of clustering and data transmission.

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

A novel algorithm which combined the merits of the clustering strategy and the compressive sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the cluster head (CH), and the corresponding proofs were presented first. In addition, to alleviate the “hot spot problem” and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of backup CH (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the energy-efficient compressive sensing-based clustering routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving energy efficiency and extending the lifespan of wireless sensor networks.

Mode-oriented hybrid programming of sensor network nodes for supporting rapid and flexible utility assembly

A number of utility programs (utilities for short) have been developed to fulfill application requirements and to assist user operations such as deployment validation and fault diagnosis throughout the lifespan of wireless sensor networks (WSNs). Because these utilities exhibit diverse behaviors and are required to be loaded at various stages, utility integration is a non-trivial task for developers. To support the rapid and flexible assembly of utilities into sensor node firmware, we formulate the utility management problem as a mode-scheduling procedure, in which utilities are grouped into multiple modes and the sensor node switches between modes to run only the corresponding utilities to address application and user needs. The key contribution of this article is a novel mode-oriented programming model, in which code is organized around modes and modules for the clear abstraction of the mode-scheduling procedure. To facilitate utility assembly, the proposed model supports hybrid programming, i.e., simple declarative configuration descriptions can be used to specify the mode-scheduling logic and to load the utility modules implemented with general-purpose imperative languages. To improve system flexibility, the developer’s descriptions are compiled into a compact configuration profile that will be used by a mode scheduler embedded in a WSN operating system to automatically control the mode scheduling. We have developed a Draft platform to support the proposed programming model, including a development environment and WSN middleware implementing the mode scheduler. Our evaluation results show that Draft achieves reduced development complexity and node reconfiguration cost.

Increasing Energy Efficiency in Wireless Sensor Networks Using GA-ANFIS to Choose a Cluster Head and Assess Routing and Weighted Trusts to Demodulate Attacker Nodes

Demodulating harmful nodes and diminishing the energy waste in sensor nodes can prolong the lifespan of wireless sensor networks (WSNs). In this study, a genetic algorithm (GA) and an adaptive neuro fuzzy inference system were used to diminish the energy waste of sensors. Weighted trust evaluation was applied to search for harmful nodes in the network to prolong the lifespan of WSNs. A low-energy adaptive clustering hierarchy method was used to analyze the results. It was discovered that searching for harmful nodes with GA-ANFIS using weighted trust evaluation significantly increased the lifespan of WSNs. For evaluation of the proposed method we used the mean of energy of all sensors against of the round, data packets received in base station, minimum energy versus rounds and number of alive sensors versus rounds. Also, in this paper we compared the proposed method results with LEACH, LEACH-DT, Random, SIF and GA-Fuzzy methods. As results the proposed method has high life time than other methods. A representation of the overall system was implemented using MATLAB software.

Reliability-Based Cluster Head Selection Methodology Using Fuzzy Logic for Performance Improvement in WSNs

Prolonging the network lifetime is one of the vital challenges in wireless sensor networks (WSNs). Typically, the lifespan of WSNs can be increased by a technique called clustering, which plays a significant role in simplifying intra-domain routing. The clustering method accounts only a small number of nodes, which are randomly selected as cluster heads (CHs). The main responsibility of CHs is to receive collected data or information from its member nodes and to aggregate the received data and convey the received data to the sink (Sk) or base station (BSn). In this paper, we have proposed a method namely “reliability-based enhanced technique for the ordering of preference by similarity ideal solution (RE-TOPSIS)” combining with fuzzy logic which uses multi-criteria decision making (MCDM) approach aiding in the effective and reliable selection of CHs. It also uses the conventional LEACH protocol to enable one-time CH selection or scheduling in each cluster based on RE-TOPSIS rank index value. This process completely eliminates the need of CH selection process in each round of LEACH’s setup state cycle. We have accounted for various criteria such as 1) residual energy; 2) distances between adjacent nodes; 3) energy utilization rates; 4) availability of neighboring nodes; 5) distances between the sink and CHs as well as distances between CHs to member nodes; and 6) the reliability index for completely devising the new scheme. The simulations are accomplished to assess or suggest the performances of the proposed RE-TOPSIS and to compare its performances with the performances of the existing protocols. The results show that the proposed scheme enhances the network lifespan, conserves energy, and introduces a considerable reduction in the frequency of CH selection per cycle by about 20%–25% as compared with the contemporary fuzzy-TOPSIS and LEACH protocols and finally the metrics of the proposed RE-TOPSIS are highlighted.

UAV Based Data Gathering in Wireless Sensor Networks

Recently, Wireless sensor networks (WSNs) have turned out to be a dominant subject of research due to its application areas. Moreover, with the integration of Unmanned Aerial Vehicle (UAV) in the loop, WSN becomes more valuable and interested topic among research community. With the addition of UAV in WSN, life span of WSN is increased and communication between WSN and Network Control Centre becomes more efficient. UAVs not only increased the life of the network, but also reduce the data processing and memory related issues in the network. In this paper, we address the problem of data gathering from a WSN with altitude controlled UAV. Simulation results verify the effectiveness of our proposed method.

3D grid clustering scheme for wireless sensor networks

Advances in wireless sensor networks (WSNs) technology have made a remarkable impact in the society. Since WSNs solely depend on the battery power of the tiny sensor nodes, maximizing the lifespan of the wireless sensor networks is a critical issue in the design and analysis of WSNs. Cluster-based routing prolongs the lifetime of the network. Most of the existing cluster-based routing protocols are applicable for two-dimensional network region. However, 2D deployment is not applicable if sensors are deployed in 3D space such as atmosphere or ocean. The proposed work presents a 3D grid clustering scheme which considers the deployment area as three-dimensional grids. The algorithm constructs optimal and load-balanced clusters at each grid cell with an initial cluster head (CH). As early energy depletion is a major design issue in clustering protocols, the proposed algorithm provides local remedy by using substitution CHs for replacing energy suffering cluster heads and nodes are independently distributed among clusters. The obtained simulation results show that our proposed scheme considerably outpaces the other existing schemes in terms of prolonging the lifespan of WSNs

Energy Consumption in Wireless Sensor Networks Using Multi Level Clustering Energy Efficient Protocol

A wireless sensor network (WSN) consists of a huge number of sensor nodes that are inadequate in energy, storage and processing power. One of the major tasks of the sensor nodes is the collection of data and forwarding the gathered data to the Base Station (BS). Hence, the network lifetime becomes the major criteria for effective design of the data gathering schemes in WSN .In this paper , every sensor nodes can converse straight with every other or with the base station. This develops into impracticable when the size of the region of interest enlarge. The anticipated protocol uses a multi-hop communication among the cluster-heads to preserve energy and cover up a huge area of interest. To diminish the quantity of information to be sent to the base station, we incorporated data aggregation. Furthermore, the rotation of cluster-heads and the utilize of the low-power sleep mode by the sensor nodes that do not contribute in routing permit to balance the load and reduce energy utilization considerably. Apply the MAT LAB Simulation software Tool to confirm the proposed design, comprehensive simulation has been approved. The NARRATIVE-LEACH schemes recommend reliable wider experience area and longer life span of WSN. To compare the other modules N-LEACH is the best enhanced in terms of stability period while compromise on lifetime.

An energy-balanced multi-sensor scheduling scheme for collaborative target tracking in wireless sensor networks

Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.

Energy-aware bee colony approach to extend lifespan of wireless sensor network

Wireless sensor networks (WSNs) consist of an immense number of tiny wireless sensor nodes deployed in a limited areas. The sensor nodes collect information from the areas and send to a central base station. The sensor nodes in such networks bring limited energy sources and in many situations, it is so difficult to recharge or replace the limited energy source of these tiny sensor nodes. For that reason, it is essential that these networks function under energy-efficient protocols and arrangements. This paper presents a novel Energy-Aware Bee Colony Approach to extend lifespan of WSN. The paper proposes an efficient clustering approach which chooses the best cluster-head with the help of bee colony approach. Through simulations, we demonstrate that our proposed solution uses the smallest amount of energy during the clustering procedure and chooses the best cluster-heads in contrast to advanced clustering approaches in WSN.

Maximization of WSN Life Using Hybrid Evolutionary Programming

Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. Evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.