Lifetime Maximization Of Sensor Networks Research Articles

Large Scale Energy Efficient Sensor Network Routing using a Quantum Processor Unit.

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than the heuristic algorithm using a classical computer. In this manuscript, the technical context for the significance of the method is presented and justified. Then the experimental steps are demonstrated in an operational sequence with illustrations if ever needed. The method has been validated by positive results across a randomly generated sample set of network topologies. The successful experimental results of this method have provided a better approach for sensor network lifetime maximization problems and demonstrated that the current state of art quantum processor has been able to solve large practical engineering problems with merits that override current methods in the literature. In other words, quantum advantage can be exploited to best efforts. It has gone beyond the stage of proof of concept to proof of feasibility.

Multi Duty Cycle Scheduled Routing in Wireless Sensor Network-lifetime Maximization

Cluster-based protocols are best for applications that require reliability and a continuous functioning environment with a sustainable lifetime of WSN. The dynamic nature of the sensor node makes energy conservation a challenging issue. Sensor node scheduled based on sensing error for energy conservation compromise the accuracy of prediction. The high data accuracy achieved using a single duty cycle controller at each node with compromised throughput and increased routing overhead. Duty Cycle Controller managing a more number of control messages at the network level leads to control packet interference with data packet transmission, increasing packet drop and minimizing throughput. Also, the single-duty cycle controller at the network level leads to increased control overhead. The proposed multilevel cluster-based approach focuses on the appropriate cluster design, selection of cluster head, and sensor nodes scheduling based on sensing error. The proposed method applies a multi-duty cycle controller at each cluster level, and control messages handled are related to nodes in a cluster. Thus has less interference and packet drop leading to maximum throughput than existing methods. The simulation results demonstrated that the proposed method with sensor nodes scheduled at individual cluster levels using a multi-duty cycle controller exhibited improved network lifetime, throughput, and reduced energy consumption compared with the state-of-the-art techniques.

A Lagrangean relaxation approach to lifetime maximization of directional sensor networks

AbstractWe consider the directional sensor network lifetime maximization problem (DSLMP). Given a set of directional sensor and target locations, the problem consists in assigning, at each time unit of a given time horizon, the action radius, the aperture angle, and the orientation direction to all sensors. The objective is to maximize the number of time units when all targets are covered, under certain constraints on sensor available energy. We present a mixed integer nonlinear programming formulation and tackle it by Lagrangean decomposition and subgradient optimization. The algorithm is equipped with a repairing heuristics aimed at finding good‐quality feasible solutions to DSLMP. The results of the application of the proposed approach to a number of problem instances are also reported.

Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people's lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems.

A hybrid clustering and evolutionary approach for wireless underground sensor network lifetime maximization

Wireless Underground Sensor Networks (WUSNs) have attracted significant interest in recent years because of their applications in various fields. The major difference between WUSNs and terrestrial wireless sensor networks is that their signals travel through multiple layers: soil, air and a medium interface. As communications in heterogeneous channels result in more transmission loss, a solution is to deploy relay nodes to relay traffic from sensors to base stations/sinks. However, this poses several new challenges, including load balancing and transmission loss minimization in heterogeneous environments. This paper considers the problem of deploying relay nodes to prolong network lifetime under load balancing constraints. This problem can be formalized using a Mixed Integer Linear Programming model as a basis to achieve lower bound solutions. We show that the problem is NP-hard as it can be reduced to the Set Cover Problem. Two novel methods are proposed. The first is a hybridisation of a clustering heuristic and an exact algorithm using a maximum flow with min-max cost formulation. The second is an evolutionary approach to further improve our initial results. Experimental validation on a large set of benchmarks indicates that the proposed methods perform better than the existing methods.

Optimal Mobility Patterns of Multiple Base Stations for Wireless Sensor Network Lifetime Maximization

The success of widespread deployment and associated research efforts on wireless sensor networks (WSNs) is undisputed. Yet, there is still a large uncharted territory for exploring and improving many aspects of WSNs. As one of the most crucial design goals of WSNs, network lifetime (NL) maximization is one such area. Although energy balancing in data relaying toward a static base station (BS) prolongs NL, some nodes usually suffer from, what is generally known as, the hot-spot problem. BS mobility has been proposed in the literature against the hot-spots to mitigate the suboptimal energy dissipation. BS mobility increases the sensor NL significantly in certain network configurations. Furthermore, utilization of multiple mobile BSs extends WSN lifetime even further when compared with the single BS case. However, optimal mobility patterns of multiple mobile BSs should be employed for achieving the maximum WSN lifetime possible. In this paper, we investigate the characteristics of the optimal mobility patterns for WSN lifetime maximization by employing three representative patterns (i.e., grid, random, and spiral). We develop a novel mixed integer programming framework to characterize NL under different mobility patterns for multiple mobile BSs.

A survey of optimization algorithms for wireless sensor network lifetime maximization

Wireless sensor networks (WSN) consist of a set of sensors and collection sinks that gather and analyze environmental conditions. Spurred by the growing need for data collection and transmission, WSN research topics have gained interest in recent years. WSNs are often deployed in hostile or inaccessible locations in which sensor replacement or repair is impractical. This survey explores various research approaches and extensions to the problem, which include online routing, clustering approaches, and lifetime maximization on specially structured networks. We additionally consider the impact of having mobile and/or multiple sinks and delay-tolerant routing. Finally, we expand our analysis to examine multicriteria optimization problems, and outline future research challenges in the field.

Optimal Base Station Mobility Patterns for Wireless Sensor Network Lifetime Maximization

Maximization of network lifetime through the efficient utilization of energy is one of the main objectives in wireless sensor network (WSN) design. Although energy balancing throughout the network for relaying the data traffic generated by sensor nodes toward a static base station prolongs network lifetime, some of the nodes are required to dissipate their energies suboptimally, i.e., farther nodes transmit some of their data to extended distances so that nodes closer to the base station are not overburdened. Base station mobility is proposed as a remedy for countering the suboptimal energy dissipation trends in WSNs. As the base station relocates, the burden of relaying the data coming from all nodes can be shared by a larger set of nodes, and hence, suboptimal energy dissipation can be mitigated. In order to take advantage of base station mobility for prolonging WSN lifetime, determining the optimal mobility patterns is of utmost importance. In this paper, we built a mixed integer programming framework to characterize the impact of various mobility patterns on WSN lifetime. Our results reveal that optimal Gaussian and spiral mobility patterns give the highest network lifetime values throughout the parameter space we explored.

Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility

The longevity of Wireless Sensor Networks (WSNs) is a crucial concern that significantly influences their applicability in a specific context. Most of the related literature focuses on communication protocols aiming to reduce the energy consumption which would eventually lead to longer network lifetimes. On the other hand, a limited number of studies concentrate on providing a unifying frame to investigate the integrated effect of the important WSN design decisions such as sensor places, activity schedules, data routes, trajectory of the mobile sink(s), along with the tactical level decisions including the data propagation protocols. However, a monolithic mathematical optimization model with a practically applicable, efficient, and accurate solution method is still missing. In this study, we first provide a mathematical model which integrates WSN design decisions on sensor places, activity schedules, data routes, trajectory of the mobile sink(s) and then present two heuristic methods for the solution of the model. We demonstrate the efficiency and accuracy of the heuristics on several randomly generated problem instances on the basis of extensive numerical experiments.

Visual sensor network lifetime maximization by prioritized scheduling of nodes

Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable.The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations.

The Impact of Dynamic Voltage and Frequency Scaling on Multicore DSP Algorithm Design [Exploratory DSP

We connect to the two recent IEEE Signal Processing Magazine special issues on digital signal processing (DSP) on multicore processors (November 2009 and March 2010) and address an issue that was not addressed in the articles there, which we believe has important consequences for DSP algorithm design in the future. The basic observation that we start out with is that in DSP algorithm design, there is very often a tradeoff between the computational effort spent (in terms of the number of operations) and the quality/accuracy of the algorithm output. Herein we discuss the problems that emerge when optimizing algorithms for circuits that support the operation of several parallel cores that operate at different speeds. The understanding, formulation, and solution of these optimization problems require a cross-disciplinary approach that models the interplay between circuits, computer architecture, signal processing, and optimization. While allocation of computational and transmission resources for the purpose of saving energy is an established research topic in other fields (sensor network life time maximization being a notable example), there appears to be relatively little open literature on the type of problems that we discuss here. Taken together, we believe that the challenges we pose are important and that the signal processing algorithm design community is well positioned to tackle them.

Distributed algorithms for lifetime maximization in sensor networks via Min–Max spanning subgraphs

We consider the problem of static transmission-power assignment for lifetime maximization of a wireless sensor network with stationary nodes operating in a data-gathering scenario. Using a graph-theoretic approach, we propose two distributed algorithms, MLS and BSpan, that construct spanning trees with minimum maximum (minmax) edge cost. MLS is based on computation of minmax-cost paths from a reference node, while BSpan performs a binary search over the range of power levels and exploits the wireless broadcast advantage. We also present a simple distributed method for pruning a graph to its Relative Neighborhood Graph, which reduces the worst-case message complexity of MLS under natural assumptions on the path-loss. In our network simulations both MLS and BSpan significantly outperform the recently proposed Distributed Min---Max Tree algorithm in terms of number of messages required.