Lifespan Of Wireless Sensor Networks Research Articles

Prolong life-span of WSN using clustering method via swarm intelligence and dynamical threshold control scheme

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

An Improved Hybrid Energy Efficient Clustering Technique to Enhance the Lifespan of Wireless Sensor Networks

Clustering is a significant mechanism used in Wireless Sensor Networks in order to have an efficient energy balance which is inevitable to prolong the lifetime. The concept of unequal clustering has proved to be an effective method for load balancing and thereby reducing hotspot issues in the energy constrained wireless sensor networks. This paper proposes an energy efficient clustering mechanism which can enhance the lifetime of Wireless Sensor Networks. The proposed protocol is an enhancement of the Hybrid Energy-Efficient Distributed clustering protocol where the clustering is performed using energy efficient varying sized clustering algorithm. An overview of the Hybrid Energy-Efficient Distributed clustering protocol, highlighting its advantages and drawbacks is also given in this paper. The performance of the proposed algorithm is evaluated through simulation using MATLAB based on the parameters delay in packet delivery, residual energy of the network and the number of live nodes during precise time periods. Extensive simulation results show that the proposed enhanced Hybrid Energy-Efficient Distributed clustering algorithm gives better performance compared to the energy efficient clustering protocols like Hybrid EnergyEfficient Distributed clustering protocol and Unequal Hybrid Energy-Efficient Distributed clustering protocol based on the above mentioned parameters.

IJARCCE - Computer and Communication Engineering

2 Abstract: Wireless sensor Networks are resource constrained systems that needs efficient utilization of all resources. With the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster based protocols (clustering protocols) should be compatible with these constraints. Clustering provides an effective method for enhancing the lifespan of wireless sensor network. Optimization of clustering process improves overall performance in terms of cluster head selection, cluster balancing, aggregation and communication.

Keeping Desired QoS by a Partial Coverage Algorithm for Cluster-Based Wireless Sensor Networks

Many studies were done on design algorithms to completely cover an area. We can call the issue as a complete coverage problem. Although there were many researches about proposing a partial coverage issue; almost all of the studies only focus on lengthening the lifespan of wireless sensor networks (WSNs) but ignore the truth that a wireless sensor network will inevitably lose its coverage because of exhausted energy during the operating time. Lifespan refers to wireless sensor network offering its service to meet the required coverage ratio. In this work, we propose a topology control algorithm for cluster-based wireless sensor networks to keep predefined Quality of Service (QoS) of coverage as long as the sensor nodes can stand. The algorithm forms a multi-hop cluster network with a required connectivity by using a novel cluster head competition scheme and proper transmission of power settings. It can organize sensor nodes into clusters actively to achieve required coverage ratio as long as possible. The algorithm is suitable for practical applications of large-scale or high-density wireless sensor networks due to its distributed processing and scalable cluster topology

Performance Evaluation of Improved Highest Remaining Energy First Based iLEACH

Power incompetence has been found to be major difficulty in wireless sensor network because of the inadequate battery of sensor nodes. Hence, the most significant focus of the existing work is to find the approaches to lessen the issue of power consumption and will bring about enhancement in the wireless sensor network constancy period and life time. A considerable measure of analysts have planned different kind of the conventions to additional enhance the wireless sensor network life span. This paper has assessed the issues which have been overlooked in the field of the wireless sensor network. In this paper, a new enhanced highest residual power initial based clustering method is planned to upgrade the effects of the existing iLEACH protocol. Planned technique is an augmentation to ileach utilizing sorting procedures to enhance the outcome additional. The proposed algorithm is planned and executed in MATLAB. It has been indicated in this paper that the proposed algorithm gives preferable comes about over current clustering protocols.

Energy-Balanced Scheduling for Target Tracking in Wireless Sensor Networks

A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.

Quality-of-Service Routing Protocol for Wireless Sensor Networks

In this paper, we develop new algorithms to find the optimal, energy preserving, paths from Source Node (SN) to the Base Station (BS) in Wireless Sensor Networks (WSNs). Optimality is defined in a constrained sense, in which the minimal energy route is sought (to maximize the lifespan of WSNs) under reliability constraint, meaning that each packet must reach the BS with a given probability. Energy efficiency is going to be achieved by selecting nodes for multi-hop packet forwarding under information, which yields the most evenly distributed energy state over the network after the packet has reached the BS. The new algorithm gave good results with any BS positioning in sensor networks. The simulation results will demonstrate that our algorithm is more efficient than the other routing protocols proposed before. There are many efficient protocols which increase the lifetime of sensor network such as LEACH, PEGASIS, PEDAP and PEDAP-PA, but they failed to provide energy ba-lancing under reliability constraints. In this paper, we propose a new algorithm under name HQRA (High Quality of service Routing Algorithm), which is able to find near-optimal paths in WSNs by minimizing the energy but guaranteeing a given level of reliability, as well.

The Research of Routing Algorithm Based on Shortest Path Tree in WSNs

This article poses an algorithm of the hierarchical topology in wireless sensor network based on shortest path tree algorithm to cover the shortage of LAC Routing Algorithm (LAC-T), which based on SPT(Shortest Path Tree). LAC-T algorithm elects cluster head by the remaining energy of nodes and the distance between node and base station. Meanwhile, it uses SPT to communicate among cluster heads in WSNs. The result of stimulation shows that LAC-T algorithm is not only easy to achieve, but could be better balance the energy consumption of each node, extend the lifespan of WSN (Wireless Sensor Network).

Restoring coverage area for WSN through simulated annealing

PurposeContinuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.Design/methodology/approachThe authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.FindingsThe experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.Practical implicationsThe proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.Originality/valueThe paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining

Wireless sensor nodes are congenitally limited by insufficient hardware resources, such as memory size and battery life. These factors influence the lifespan of wireless sensor networks and pose numerous challenges regarding the addition of security mechanisms to protect sensor nodes. As the number of applications using wireless sensor networks increases, protecting sensor nodes from malicious attacks becomes ever more important. In this paper, we propose a new intrusion detection system called the Markovian IDS, to protect sensor nodes from malicious attacks. The Markovian IDS incorporates game theory with anomaly and misuse detection to determine the best defense strategies. It also employs Markov decision processes with an attack-pattern-mining algorithm to predict future attack patterns and implement appropriate measures. Experimental results show that the proposed Markovian IDS has a higher defense success rate than game theory or Markov decision processes alone.

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsity of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network.