Sensor deployment

This paper proposes a Billiards algorithm for two sensor deployment problems, random and deterministic deployment. The deployment considers both homogenous and heterogeneous sensors. The algorithm aims to maximize the coverage of a given monitored field with obstacles. The coverage is maximized by avoiding sensors overlapping and minimizing the uncovered areas. By adjusting the expansion ratio, the algorithm was able to find the best sensing ranges that maximize the field coverage.

This paper addresses a problem of sweep coverage by deploying a network of autonomous mobile robots. We propose a decentralized control algorithm for the robots to accomplish the sweep coverage. The sweep coverage is achieved by coordinating the robots to move along a given path that is unknown to the vehicles a priori. The motion coordination algorithm is developed based on simple consensus algorithms. The effectiveness of the algorithm is demonstrated via numerical simulations. The proposed algorithm would have applications to military and civilian operations.

Sensors are tiny electronic devices having limited battery energy and capability for sensing, data processing and communicating. They can collectively behave to provide an effective wireless network that monitors a region and transmits the collected information to gateway nodes called sinks. Most of the applications require the operation of the network for long periods of times, which makes the efficient management of the available energy resources an important concern. There are three major issues in the design of sensor networks: sensor deployment or the coverage of the sensing area, sink location, and data routing. In this work, we consider these three design problems within a unified framework and develop two mixed-integer linear programming formulations. They are difficult to solve exactly. However, it is possible to compute good feasible solutions of the sink location and routing problems easily, when the sensors are deployed and their locations in the sensor field become known. Therefore, we propose a tabu search heuristic that tries to identify the best sensor locations satisfying the coverage requirements. The objective value corresponding to each set of sensor locations is calculated by solving the sink location and routing problem. Computational tests carried out on randomly generated test instances indicate that the proposed hybrid approach is both accurate and efficient.

Network surveillance systems provides real time monitoring of target area and target objects. The development of wireless communication and sensing technology make it possible to deploy networked surveillance systems in various environments. Networked surveillance systems provide an extended perception and distributed sensing capability in monitored environments through the use of multiple networked sensors. We consider a surveillance network where the sensors are static. The task of tracking targets in a surveillance network is challenging because of the following reasons: (1) The location of the sensors need to be optimally deployed. (2) the view of the sensors need to be optimized so that at a given time the targets are shown with a discernable resolution for feature identification. (3) it is important to devise stable control algorithms for accomplishing the surveillance task. When the target moves, it is important to switch the sensing task between sensors to maintain the visibility of the target with adequate resolution. This paper presents a novel method to deploy static sensors given a target region and a dynamic programming method to optimally switch sensors when the target moves. Finally, simulation results demonstrate the efficacy of the proposed approach for tracking targets over an area.

The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.

In a wireless sensor network (WSN), nodes are battery powered, therefore, there are energy constrained. Network lifetime is one of the most important characteristics of performance for wireless sensor networks. A number of routing schemes, energy efficient and energy-aware, have been proposed to maximize the network lifetime. These schemes typically try to find the minimum energy path to optimize energy usage at a node. In this paper, we consider the lifetime maximization problem, we propose a routing protocols that take into account the battery residual energy in sensor nodes and the energy required for transmission along the path. For each transmission round, only the nodes which have their remaining energies greater than a threshold can participate as relays for other nodes data in addition to sensing the environment. This choice allows the distribution of energy load among any sensor nodes, thus extends network lifetime. Simulation results show that the network lifetimes is increased largely comparable to direct and MTE schemes.

Sensor networks have a wide range of potential, practical and useful applications. However, there are issues that need to be addressed for efficient operation of sensor network systems in real applications. Energy saving is one critical issue for sensor networks since most sensors are equipped with non-rechargeable batteries that have limited lifetime. To extend the lifetime of a sensor network, one common approach is to dynamically schedule sensors' work/sleep cycles (or duty cycles). Moreover, in cluster-based networks, cluster heads are usually selected in a way that minimizes the total energy consumption and they may rotate among the sensors to balance energy consumption. In general, these energy-efficient scheduling mechanisms (also called topology configuration mechanisms) need to satisfy certain application requirements while saving energy. In this paper, we provide a survey on energy-efficient scheduling mechanisms in sensor networks that have different design requirements than those in traditional wireless networks. We classify these mechanisms based on their design assumptions and design objectives. Different mechanisms may make different assumptions about their sensors including detection model, sensing area, transmission range, failure model, time synchronization, and the ability to obtain location and distance information. They may also have different assumptions about network structure and sensor deployment strategy. Furthermore, while all the mechanisms have a common design objective to maximize network lifetime, they may also have different objectives determined by their target applications.

Recent advances in integrated electronic devices motivated the use of Wireless Sensor Networks (WSNs) in many applications including target surveillance and tracking. A number of sensor nodes are scattered within a sensitive region to detect the presence of intruders and forward subsequent events to the analysis center(s). Obviously, the sensor deployment should guarantee an optimal event detection rate. This paper proposes a tracking framework based on Voronoi tessellations. Two mobility models are proposed to control the coverage degree according to target presence. The objective is to set a non-uniform coverage within the monitored zone to allow detecting the target(s) by multiple sensor nodes. We show how the proposed algorithm adapts to the situation where multiple targets move in the monitored zone. Moreover, we introduce an algorithm to discover redundant nodes (which do not provide additional information about target position). This algorithm is shown to be effective in reducing the energy consumption using an activity scheduling approach. Simulations are carried out to underline the efficiency of the proposed models.

The compensation heat pulse method (CHPM) is of limited value for measuring low rates of sap flow in woody plants. Recent application of the CHPM to woody roots has further illustrated some of the constraints of this technique. Here we present an improved heat pulse method, termed the heat ratio method (HRM), to measure low and reverse rates of sap flow in woody plants. The HRM has several important advantages over the CHPM, including improved measurement range and resolution, protocols to correct for physical and thermal errors in sensor deployment, and a simple linear function to describe wound effects. We describe the theory and methodological protocols of the HRM, provide wound correction coefficients, and validate the reliability and accuracy of the technique against gravimetric measurements of transpiration.

Purpose -The purpose of this paper is to develop a new rectilinear branch pipe-routing algorithm for automatic generation of rectilinear branch pipe routes in constrained spaces of aero-engines. Design/methodology/approach -Rectilinear branch pipe routing that connects multiple terminals in a constrained space with obstacles can be formulated as a rectilinear Steiner minimum tree with obstacles (RSMTO) problem while meeting certain engineering rules, which has been proved to be an NP-hard and discrete problem. This paper presents a discrete particle swarm optimization (PSO) algorithm for rectilinear branch pipe routing (DPSO-RBPRA) problems, which adopts an attraction operator and an energy function to plan the shortest collision-free connecting networks in a discrete graph space. Moreover, this paper integrates several existing techniques to evaluate particles for the RSMTO problem in discrete Manhattan spaces. Further, the DPSO-RBPRA is extended to surface cases to adapt to requirements of routing pipes on the surfaces of aero-engines. Findings -Pipe routing numeral computations show that, DPSO-RBPRA finds satisfactory connecting networks while considering several engineering rules, which demonstrates the effectiveness of the proposed method. Originality/value -This paper applies the Steiner tree theory and develops a DPSO algorithm to plan the aero-engine rectilinear branch pipe-routing layouts.

One of the most fundamental problems in wireless sensor network is coverage problem. Higher coverage contributes to improving the quality of services provided by a sensor network. In this paper, we have investigated the coverage problem in heterogeneous sensor network from an intruder's point of view. We argue that a well covered area may still be vulnerable if there exist some paths that can be traversed without being discovered by any sensors. After defining vulnerable paths and weak points and demonstrating their properties we present an efficient algorithm with O(n log n) time complexity to find the longest vulnerable path from a given boundary point in non overlapping sensing regions. We claim that further sensor deployment based on the algorithm reduces vulnerability of the system and improves the quality of service significantly. We present simulation results to support our claim.

Sensing coverage is an important issue in wireless mobile sensor networks. The strategy of how to deploy sensor nodes in an environment, especially in unknown large environment, will affect the utility of the network just like the quality of communication. We present an efficient method for sensor deployment assuming that global information is not available. Our algorithm (Self-Deployment by Density Control, SDDC), uses density control by each node to deploy sensor nodes concurrently. We also make nodes to form clusters ...

One of the most fundamental problems in wireless sensor network is coverage problem. Higher coverage contributes to improving the quality of services provided by a sensor network. In this paper, we have investigated the coverage problem in heterogeneous sensor network from an intruder's point of view. We argue that a well covered area may still be vulnerable if there exist some paths that can be traversed without being discovered by any sensors. After defining vulnerable paths and weak points and demonstrating their properties we present an efficient algorithm with O(n log n) time complexity to find the longest vulnerable path from a given boundary point in non overlapping sensing regions. We claim that further sensor deployment based on the algorithm reduces vulnerability of the system and improves the quality of service significantly. We present simulation results to support our claim.

This paper considers the problem of monitoring cane toads in Kakadu National Park using a large scale wireless sensor network deployment. Cane toads were mistakenly introduced in Australia in 1935. Their uncanny ability to survive in diverse climates and lack of natural predators in the Australian ecosystem have promoted unhindered growth of cane toads for the last 68 years. This application is of tremendous importance to Australia because cane toads are endangering native species and the ecosystem. We comment on how wireless sensor network technology can address long-term research challenges for cane-toad monitoring, and propose a novel framework for planning sensor deployment to meet application, economic and networking objectives.

Purpose -The purpose of this paper is to develop a new rectilinear branch pipe-routing algorithm for automatic generation of rectilinear branch pipe routes in constrained spaces of aero-engines. Design/methodology/approach -Rectilinear branch pipe routing that connects multiple terminals in a constrained space with obstacles can be formulated as a rectilinear Steiner minimum tree with obstacles (RSMTO) problem while meeting certain engineering rules, which has been proved to be an NP-hard and discrete problem. This paper presents a discrete particle swarm optimization (PSO) algorithm for rectilinear branch pipe routing (DPSO-RBPRA) problems, which adopts an attraction operator and an energy function to plan the shortest collision-free connecting networks in a discrete graph space. Moreover, this paper integrates several existing techniques to evaluate particles for the RSMTO problem in discrete Manhattan spaces. Further, the DPSO-RBPRA is extended to surface cases to adapt to requirements of routing pipes on the surfaces of aero-engines. Findings -Pipe routing numeral computations show that, DPSO-RBPRA finds satisfactory connecting networks while considering several engineering rules, which demonstrates the effectiveness of the proposed method. Originality/value -This paper applies the Steiner tree theory and develops a DPSO algorithm to plan the aero-engine rectilinear branch pipe-routing layouts.

Watershed characterization requires well-planned sampling to track simultaneous time-variable fluxes and flowpaths of water, nutrients, sediments, and energy. In this research project legacy hydrologic, meteorologic and water quality data from the Santa Fe basin in the Suwannee river watershed will be assembled into a web- accessible digital watershed. These data, together with predictions from physically-based hydrologic models, will be used to develop a probabilistic algorithm to predict surface water stage and flux throughout the Santa Fe river basin and evaluate the accuracy of these predictions. Information on prediction uncertainty will be used to design a spatial network of new conductivity-temperature-depth (CTD) sensors to improve the predictions. The adequacy of the assembled data and the utility of the optimal estimation algorithm will be evaluated by comparing resulting predictions with observations of surface water stage and flux from the newly deployed CTD sondes. Field data collection will include evaluations of interferences and applicability of off-the-shelf optical and cadmium reduction continuous nitrate sensors. The best performing sensor will be identified and calibrated to conditions in the Santa Fe watershed. At least two nitrate sensors will be linked with the CTD sondes into sensor packages and deployed at selected locations in the Santa Fe watershed. The high frequency data collected by the sensors will be transmitted continuously via cell-phone technology to a central university computer. These new data will be used to improve local relationships among flow, stage, conductivity and nitrate. Information collected with the sensors will lay the groundwork for developing a general methodology to augment continuous measurement of nitrate with correlated surrogates (i.e., in this case flow and conductivity) to decrease the density of sensors needed to accurately predict the nitrate in the system over space and time. Furthermore, the data will also lay the groundwork for developing improved understanding of the chemical and physical controls of nitrate and water fluxes through watersheds required to address science questions that cannot be answered with current data sampling and monitoring programs. Development of the network design algorithm and deployment of the sensor network represent a good initial test at a small scale of how to develop cost-effective water and environmental research networks. The communication ports will be configured for testing of any newly developed or off-the-shelf sensors, and outside users will be welcome to test instrumentation at these sites. The probabilistic algorithms to be developed as part of the work will be sufficiently generic that they will be useful for planning sensor deployments for a variety of variables in any watershed. Data collected and collated for the work will be archived in a web-accessible geodatabase to be made available to non-local researchers.

In recent years, the increasing sophistication of detection and tracking systems with multiple sensors has generated a great deal of interest in the development of new computational structures and strategies. The design of such spatially distributed systems involves the integration of solutions to the problems in data-association, hypothesis testing, data fusion, etc. More importantly, the design of efficient algorithms for these problems becomes an important issue as numerous distributed computational framework models are developed. In this paper, we present a survey of the recent research work conducted on the distributed sensor networks (DSNs) ranging from multi-sensor fusion methods, through target location, complexities of sensor deployment, probabilistic optimal surveillance to mobile agent based DSN (MADSN).

We study a surveillance wireless sensor network (SWSN) comprised of small and low-cost sensors deployed in a region in order to detect objects crossing the field of interest. In the present paper, we address two problems concerning the design and performance of an SWSN: optimal sensor placement and algorithms for object detection in the presence of false alarms. For both problems, we propose explicit decision rules and efficient algorithmic solutions. Further, we provide several numerical examples and present a simulation model that combines our placement and detection methods.

Coverage is a very important issue in wireless sensor networks. Current literature defines a point to be covered if it is within the sensing radius of at least one sensor. In this paper we argue that this is a conservative definition of coverage. This definition implicitly assumes that each sensor makes a decision independent of other sensors in the field. However, sensors can cooperate to make an accurate estimation, even if any single sensor is unable to do so. We then propose a new notion of information coverage and investigate its implications for sensor deployment. Numerical and simulation results show that significant savings in terms of sensor density for complete coverage can be achieved by using our definition of information coverage compared to that by using the existing definition.

Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The sensor coverage problem has received increased attention recently, being considerably driven by recent advances in affordable and efficient integrated electronic devices. This problem is centered around a fundamental question: How well do the sensors observe the physical space? The coverage concept is subject to a wide range of interpretations due to a variety of sensors and their applications. Different coverage formulations have been proposed, based on the subject to be covered (area versus discrete points) and sensor deployment mechanism (random versus deterministic) as well as on other wireless sensor network properties (e.g. network connectivity and minimum energy consumption). In this article, we survey recent contributions addressing energy-efficient coverage problems in the context of static wireless sensor networks. We present various coverage formulations, their assumptions, as well as an overview of the solutions proposed. q 2005 Published by Elsevier B.V.

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

This paper considers the problem of container inspection at a port-of-entry. Containers are inspected through a specific sequence to detect the presence of nuclear materials, biological and chemical agents, and other illegal shipments. The threshold levels of sensors at inspection stations of the port-of-entry affect the probabilities of incorrectly accepting or rejecting a container. In this paper, we present several optimization approaches for determining the optimum sensor threshold levels, while considering misclassification errors, total cost of inspection, and budget constraints. In contrast to previous work which determines threshold levels and sequence separately, we consider an integrated system and determine them simultaneously. Examples applying the approaches in different sensor arrangements are demonstrated. Note to Practitioners-Increased emphasis on container inspection at ports-of-entry prompted researchers to investigate methodologies that help practitioners in operating such inspection systems in order to reduce both the risk of accepting "undesired containers" as well as the cost of inspection. The threshold levels of the sensors can be adjusted by changing, for example, the power level of the X-ray machine or the count level of the sensors in order to make appropriate decisions. This paper provides methodologies for determining the optimum threshold levels of sensors and order of inspection that minimize the overall cost of the system. An approach for obtaining these values under budget and risk constraints is also included in this paper.

One of the most fundamental problems in wireless sensor network is coverage problem. Higher coverage contributes to improving the quality of services provided by a sensor network. In this paper, we have investigated the coverage problem in heterogeneous sensor network from an intruder's point of view. We argue that a well covered area may still be vulnerable if there exist some paths that can be traversed without being discovered by any sensors. After defining vulnerable paths and weak points and demonstrating their properties we present an efficient algorithm with O(n log n) time complexity to find the longest vulnerable path from a given boundary point in non overlapping sensing regions. We claim that further sensor deployment based on the algorithm reduces vulnerability of the system and improves the quality of service significantly. We present simulation results to support our claim.

Environmental monitoring is often performed through a wireless sensor network, whose nodes are randomly deployed over the geographical region of interest. Sensors sample a physical phenomenon (the so-called field) and send their measurements to a sink node, which is in charge of reconstructing the field from such irregular samples. In this work, we focus on scenarios of practical interest where the sensor deployment is unfeasible in certain areas of the geographical region, e.g., due to terrain asperities, and the delivery of sensor measurements to the sink may fail due to fading or to transmission collisions among sensors simultaneously accessing the wireless medium. Under these conditions, we carry out an asymptotic analysis and evaluate the quality of the estimation of a d-dimensional field (d ≥ 1) when the sink uses linear filtering as a reconstruction technique. Specifically, given the matrix representing the sampling system, V, we derive both the moments and an expression of the limiting spectral distribution of VV H , as the size of V goes to infinity and its aspect ratio has a finite limit bounded away from zero. By using such asymptotic results, we approximate the mean square error on the estimated field through the η-transform of VV H , and derive the sensor network performance under the conditions described above.

Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. In this preliminary work, we propose a hybrid approach to the detection of faults and we illustrate its performance on data coming from a real sensor deployment. The proposal is a first step to have a hybrid method towards automated on-line fault detection and classification in context-aware WSNs middleware framework.

In the resource over-constrained environment of sensor networks, techniques for storing data locally in sensor nodes have been proposed to support efficient processing of adhoc queries. These data-centric storage (DCS) techniques differ in the method of mapping events to sensors, but all of them fail to deal with storage hot-spots due to irregular sensors deployments, and(or) data distributions. In this paper, we present Zone Sharing (ZS), a novel distributed scheme for the decomposition of storage hot-spots. We apply ZS to the DIM scheme, which has been shown to be the best among all DCS schemes. ZS locally detects hot-spots and tries to evenly distribute their loads among the sensor nodes in the network. Simulations have shown the efficiency of ZS in decomposing small to moderate sized hot-spots without imposing an additional energy load to the network nodes.

Sensors are tiny electronic devices having limited battery energy and capability for sensing, data processing and communicating. They can collectively behave to provide an effective wireless network that monitors a region and transmits the collected information to gateway nodes called sinks. Most of the applications require the operation of the network for long periods of times, which makes the efficient management of the available energy resources an important concern. There are three major issues in the design of sensor networks: sensor deployment or the coverage of the sensing area, sink location, and data routing. In this work, we consider these three design problems within a unified framework and develop two mixed-integer linear programming formulations. They are difficult to solve exactly. However, it is possible to compute good feasible solutions of the sink location and routing problems easily, when the sensors are deployed and their locations in the sensor field become known. Therefore, we propose a tabu search heuristic that tries to identify the best sensor locations satisfying the coverage requirements. The objective value corresponding to each set of sensor locations is calculated by solving the sink location and routing problem. Computational tests carried out on randomly generated test instances indicate that the proposed hybrid approach is both accurate and efficient.

In this work we propose and investigate a novel research topic in Wireless Sensor Networks (WSNs): sensor deployment in order to maximize the interest of the gathered information. The target areas are characterized by zones with different interest levels. We model the interest variable as an “importance function” that assigns a quantitative reference to each point. Due to the nature of WSNs, the sensor deployment must guarantee that the information of all nodes can reach certain control nodes or sinks. Otherwise, the events captured by the nodes are lost. This condition is equivalent to ensuring the existence of a communication path from every node to at least one of the sinks. We propose a global optimization model that fulfills all the conditions and we solve it with a simulated annealing algorithm that determines optimal node placement. Moreover, we also characterize the effects of an aerial deployment, that is, the effect of node placement inaccuracy in network behavior and performance. We have found that small placement variations may lead to strong fluctuations in the quality of the connectivity properties of the network topology, and thus to a significant performance degradation in terms of captured interest.

Abstract—Sensor deployments may be static, but researchers have recently been making a case for mobile collector nodes to enhance data acquisition. Since mobile nodes are often more privileged, their compromise can give the adversary a significant advantage. Hence, ...

We study a surveillance wireless sensor network (SWSN) comprised of small and low-cost sensors deployed in a region in order to detect objects crossing the field of interest. In the present paper, we address two problems concerning the design and performance of an SWSN: optimal sensor placement and algorithms for object detection in the presence of false alarms. For both problems, we propose explicit decision rules and efficient algorithmic solutions. Further, we provide several numerical examples and present a simulation model that combines our placement and detection methods. _____________________________________________ Litvak, N., Altaf, M.U., Barbu, A., Jain, S., Miretskiy, D.I., Mohammadi, L., Onur, E., Panhuis, J.C.H.W. in 't, Sumihar, J.H., Vellekoop, M., Wijk, A.C.C. van, Bisseling, R.H. (2008). Increasing detection performance of surveillance sensor networks. In O. Bokhove, et al. (Eds.), Proceedings of the sixty-third European Study Group Mathematics with Industry (SW...

In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.

A wireless sensor network with randomly deployed nodes can be used to provide an irregular sampling of a physical field of interest. We assume that a sink node collects the data gathered by the sensors and uses a linear filter for the reconstruction of a bandlimited scalar field defined over a d-dimensional domain. Sensors' locations are assumed to be known at the sink node, up to a certain position error. We then take the mean square error (MSE) of the reconstructed field as performance metric, and evaluate the effect of both uniform and quasi-equally spaced sensor layouts on the quality of the reconstructed field. We define a parameter that provides a measure of the regularity of the sensors deployment, and, through asymptotic analysis, we derive the MSE in the case of different sensor spatial distributions. For two of them, an approximate closed form expression is obtained. We validate our analysis through numerical results, and we show that an excellent match exists between analysis and simulation even for a small number of sensors.

Abstract—This paper considers the problem of container inspec-tion at a port-of-entry. Containers are inspected through a specific sequence to detect the presence of nuclear materials, biological and chemical agents, and other illegal shipments. The threshold levels of sensors at ...

One of the most fundamental problems in wireless sen-sor network is coverage problem. Higher coverage con-tributes to improving the quality of services provided by a sensor network. In this paper, we have investigated the cov-erage problem in heterogeneous sensor network ...

In the resource over-constrained environment of sensor networks, techniques for storing data locally in sensor nodes have been proposed to support efficient processing of adhoc queries. These data-centric storage (DCS) techniques differ in the method of mapping events to sensors, but all of them fail to deal with storage hot-spots due to irregular sensors deployments, and(or) data distributions. In this paper, we present Zone Sharing (ZS), a novel distributed scheme for the decomposition of storage hot-spots. We apply ZS to the DIM scheme, which has been shown to be the best among all DCS schemes. ZS locally detects hot-spots and tries to evenly distribute their loads among the sensor nodes in the network. Simulations have shown the efficiency of ZS in decomposing small to moderate sized hot-spots without imposing an additional energy load to the network nodes.

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruptiontolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.

In large scale applications like military surveillance, sensing coverage is essential for target detection and optimization of power consumption is important for lifetime extension of the network. In battle field, nodes are deployed randomly which results some places empty and some places dense. To assure the sensing coverage for the whole area of interest, sensors are deployed several times which represents random distribution with dense node deployment. This dense sensor deployment shows redundancy. These redundant traffics result collisions, packet losses and retransmissions. Consequently it consumes more energy and decreases the lifetime of the network. In order to use this dense sensor network efficiently to increase the lifetime of the network , it is necessary to find out a set of small number of working nodes that can cover the region of interest and reduce redundancy. In this paper, we propose a new energy efficient protocol where each node in the network takes decision to turn on or turn off in a distributed manner that results a set of small number of active nodes throughout lifetime of the network and covers sensing area of interest for target detection. It reduces redundancy, power consumption and increase the lifetime of a network. In this protocol, nodes balance their energy by going to working state based on their residual energy to keep the nodes alive to assure the coverage throughout the lifetime. The simulation results of this protocol shows that throughout network lifetime the coverage is preserved in a range from 84% to 100% and about a constant number of nodes are working.