Energy efficient routing algorithm on the target tracking in Wireless Sensor Network

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user Routing (EPFMR) framework in WSN. EPFMR is deployed in the WSN environment using the instance time. The request time sent for the route discovery is the foremost step designed in the EPFMR framework to reduce the energy consumption rate. The proportional fairness routing in WSN selects the best route path for the packet flow based on the relationship between the periods of requests between different SN. Route path discovered for packet flow also measure energy on multi-user route path using the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR develops node dependent energy-efficient localized route path, improving the throughput. The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing, Throughput, RST, node density and average energy per packet in WSN. The Route Searching Time (RST) is reduced using the Boltzmann Distribution (BD), and as a result, the energy is minimized on multi-user WSN. Finally, GIFM applies an instance time difference-based route searching on WSN to attain an optimal energy minimization system. Experimental analysis shows that the EPFMR framework can reduce the RST by 23.47% and improve the throughput by 6.79% compared with the state-of-the-art works.

Finding the All-Pairs Shortest Paths (APSP) in a graph is the key for various domains. Motivated by the graphs are sparse in most real-world applications, we store the whole graph as a compressed storage format in each process of the distributed computing clusters and combine the Floyd algorithm with the Dijkstra algorithm to solve the APSP problem in this work, which leads to the novel Fast APSP algorithm. In contrast to the state-of-the-art Part APSP algorithm, our algorithm adds some memory overhead to store the original sparse graph and uses local Floyd and global Dijkstra algorithms simultaneously. The payoff is the circumvention of expensive global communication, reducing one local FW operation, simplifying the Minplus function, and making its data access continuous. Furthermore, we propose a parallel framework to solve the problem of mismatch between the number of GPUs and the number of divisible blocks of a graph. The Fast APSP algorithm exhibits an average speedup of 16.97x compared to the CPU Dijkstra algorithm, 7.09x compared to the GPU Dijkstra algorithm, 7.09x compared to the Part APSP algorithm, and 4.6x compared to the decentralized Part APSP algorithm. It also shows good scalability in our experiments. It takes about 12.45 minutes to solve the APSP problem for the graph with 11,548,845 vertices by engaging 2048 GPUs.

Geographic information systems related to the position of objects in the earth's surface such as the position of the attractions that are connected with roads. Streets that are interconnected to form a complex network. Usually people use our manual to find the shortest route of an object toward a particular object but it is less than optimal because it requires precision and a long time. The shortest route search problem has been solved by several algorithms such as Dijkstra's algorithm, Astar, Floyd Warshall, Ant Colony and others. Based on this final task is to discuss the implementation of the shortest route to the geographic information system of tourism in a city using the distance parameter. City to be addressed is Sawahlunto has 12 attractions that are connected with road network. Shortest path search algorithm used is Dijkstra's and Astar algorithm. Geographic information system of tourism is displayed in a web form, with the programming language PHP, database PosgreSQL and support PostGIS and pgRouting. Based on the testing of these applications can provide the shortest route along the travel distance to and from 12 tourist sites in Sawahlunto. Astar algorithm is faster in the process of finding the shortest route instead of Dijkstra's algorithm with an average gap of 40 ms. Application also provides information supporting the tourism profile information, attractions, culture, and contacts. Keyword : shortest path, Dijkstra algorithm, Astar algorithm, geographic information system

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and locating a target in a large deployment area but most systems require high transmission power levels and a large volume of messages, much time for neighbor discovery operations, and many sensors to detect the target at a given time. We propose a two‐level cooperative and energy‐efficient tracking algorithm (CET) that reduces energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and perform sensing for target tracking in wireless sensor networks. It is expected that only the nodes adjacent to the target are responsible for observing the target to save the energy consumption and extend the network lifetime as well by using a wakeup mechanism and a face‐aware routing. Through performance analysis and simulation studies, we demonstrate that CET improves target capturing speed and outperforms some existing protocols of target tracking with energy saving under certain ideal situations. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Monte Carlo Methods for Node Self-Localization and Nonlinear Target Tracking in Wireless Sensor Networks

Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations.

This paper proposes a parallel shortest path-searching algorithm and implements it on a newly structured parallel reconfigurable processor, DAPDNA-2 (IPFlex Inc). Routing determines the shortest paths from the source to the ultimate destination through intermediate nodes. In open shortest path first (OSPF), Dijkstra's shortest path algorithm, which is the conventional one, finds the shortest paths from the source on a program counter-based processor. The calculation time for Dijkstra's algorithm is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) when the number of nodes is N. When the network scale is large, calculation time required by Dijkstra's algorithm increases rapidly. It's very difficult to compute Dijkstra's algorithm in parallel because of the need for previous calculation results, so Dijkstra's algorithm is unsuitable for parallel processors. Our proposed scheme finds the shortest paths using a simultaneous multi-path search method. In contrast with Dijkstra's algorithm, several nodes can be determined at one time. Moreover, we partition the network into different groups (network groups) and find the all-node pair's shortest path in each group using a pipeline operation. Networks can be abstracted, and the shortest paths in very large networks can be found easily. The proposed scheme can decrease calculation time from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(N) using a pipeline operation on DAPDNA-2. Our simulations show that the proposed algorithm uses 99.6% less calculation time than Dijkstra's algorithm. The proposed algorithm can be applied to the very large Internet network designs of the future.

Wireless Sensor Networks (WSNs) is the vast area and developing very speedily now-a-days. This paper gives some of the major applications and critical limitations of WSNs like power and lifespan, communication bandwidth, memory size. Sensor nodes are working by node energy so the energy consumption is the essential issue in WSN. Routing in WSN consumes more energy than any other phase. Various routing techniques in WSNs are discussed and different routing protocols are illustrated. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the popular and feasible hierarchical clustering routing protocols in WSN. Working of LEACH is explained in brief. Furthermore, the descendants of LEACH e.g. LEACH-C (Centralized LEACH), LEACH-B (Balanced LEACH), LEACH-F, Multihop LEACH, MS-LEACH (Multihop and Single hop LEACH), MH-LEACH etc. are discussed in terms of the energy utilization. By analyzing the variants of LEACH, it is observed that the energy requirement in the cluster set up phase or data transmission phase is minimized to minimize the total energy consumption of the WSN. To elect the proper cluster head node, centralized approach is followed. Afterwards, data transmission will use the multihop communication technique. This will help to save some energy when base station is located far away from the cluster head node. Proposed protocol aims to decrease the energy overhead of the sensor nodes. The new approach is proposed entitled PV-LEACH which will save the energy consumption in cluster setup phase and also in data transmission phase of the conventional LEACH protocol. So, this will enhance the performance ratio in terms of energy consumption and life time of the sensor nodes.

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.

This paper presents a Multihop Routing Algorithm for Wireless Sensor Networks (MBEEC) based on the Dijkstra Algorithm. An important challenge in wireless sensor networks is to balance the energy consumption. The Bio-Inspired Algorithm for Energy Efficient Routing for Continuous Data Dissemination Networks Wireless Sensor (BEE-C) can provide important savings on energy consumption but does not provide routing calculations that ensures a long lived topology. Lifetime is an extremely important factor in a Wireless Sensor Network, because of the difficulty of maintenance, limited battery, memory and processing that sensors have. MBEEC inherits the properties of BEE-C but uses Dijkstra Algorithm for calculating the routes. To ensure the longer lifetime of the network, MBEEC addresses a multihop algorithm in terms of consumption in a matter more energy efficient than BEE-C using Dijkstra to calculate routes, thus balancing the entire network.

This paper proposes a light-weight sensing node selection scheme, which incorporates both information utility measure and energy consumption measure for distributive target tracking in wireless sensor networks. By adopting this scheme, the target tracking algorithm could maintain lower energy consumption while still achieve desirable tracking accuracy. Simulation results are provided in this paper.

Target tracking in wireless sensor networks (WSNs) requires efficient collaboration among sensors to achieve the tradeoff between energy consumption and tracking accuracy requirements. In this paper, we present a sensor selection measure based on the Fisher information matrix (FIM) of the Kalman filter for target tracking in wireless sensor networks. After obtaining the target state estimate using the combination of maximum likelihood estimation and the Kalman filter, the leader of the current tracking cluster selects the most informative cluster of sensors based on the FIM-based measure to track the moving tracking at the next time. Simulation results show that the improved tracking performance of our proposed collaborative tracking approach compared to other existing methods in terms of tracking accuracy.

Fault tolerance has been identified as key challenges in the design and operations of Wireless Sensor Network (WSNs). Failures are inevitable in wireless sensor networks due to inhospitable environment and unattended deployment. Therefore, it is necessary to detect the networks for recovery from the failure to sustain it. WSNs are self-organized using clustering algorithms to conserve energy. The sensor nodes are organized in several small clusters where there are cluster heads in each cluster. These CHs (Cluster Heads) gather data from their local clusters aggregate them & send them to the base station with shortest distance as a forwarding metric using Dijkstra algorithm. The proposed scheme is supposed to be an efficient fault detection and recovery mechanism to make the network fault-tolerant and achieve reliability, latency and energy efficiency. Performance analysis and evaluation will be made using several scenarios of wireless sensor networks.