Mobile Sensors

The recent innovations in wireless technology and digital electronics have opened many areas of research in Wireless Sensor Networks. In the last few years these networks have been successfully used in many applications such as localization, tracking, surveillance, battlefield monitoring, structural health monitoring, routing etc. Most of these applications need localization i.e. estimating location information either relative or absolute. In this paper we propose a computationally efficient algorithm namely, Light weight Multidimensional Scaling. This approach takes advantage of Nystrӧm approximation for estimating location of unknown sensor nodes, using the information of available distances between neighbours and anchors. Various node densities, noise factors and radio ranges are considered for simulation. The performance of the algorithm is obtained with Monte Carlo Simulation.

Cooperative behavior algorithms utilizing swarm intelligence are being developed for mobile sensor platforms to inspect jet engines on-wing. Experiments are planned in which several relatively simple autonomous platforms will work together in a coordinated fashion to carry out complex maintenance-type tasks within the constrained working environment modeled on the interior of a turbofan engine. The algorithms will emphasize distribution of the tasks among multiple units; they will be scalable and flexible so that units may be added in the future; and will be designed to operate on an individual unit level to produce the desired global effect. This proof of concept demonstration will validate the algorithms and provide justification for further miniaturization and specialization of the hardware toward the true application of on-wing in situ turbine engine maintenance.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

The Max-Sum algorithm is a solution method for the Distributed Constraint Optimization Problem (DCOP) which is a fundamental problem in multiagent cooperation. Particularly, we focus on the case of Max-Sum on a spanning tree, where the algorithm is an exact solution method. In this case, all agents simultaneously compute globally optimal objective values as erootf nodes of the tree that represents the problem. On the other hand, a tiebreak is generally necessary in order to determine a unique optimal solution among the agents. While top-down post-processing is a well-known solution, one can prefer to design the solver as a bottom-up computation that is simply integrated to pre-processing. To address this issue, we investigate a technique that employs a preference ordering based on spanning trees for the optimization algorithms. With this technique, top-down processing to choose a unique optimal solution can be embedded into bottom-up optimization via small weight values for the preference ordering. We also evaluate an integrated algorithm that maintains both tree structures and quasi-optimal solutions using the bottom-up approaches.

The Max-Sum algorithm is a solution method for the Distributed Constraint Optimization Problem (DCOP) which is a fundamental problem in multiagent cooperation. Particularly, we focus on the case of Max-Sum on a spanning tree, where the algorithm is an exact solution method. In this case, all agents simultaneously compute globally optimal objective values as erootf nodes of the tree that represents the problem. On the other hand, a tiebreak is generally necessary in order to determine a unique optimal solution among the agents. While top-down post-processing is a well-known solution, one can prefer to design the solver as a bottom-up computation that is simply integrated to pre-processing. To address this issue, we investigate a technique that employs a preference ordering based on spanning trees for the optimization algorithms. With this technique, top-down processing to choose a unique optimal solution can be embedded into bottom-up optimization via small weight values for the preference ordering. We also evaluate an integrated algorithm that maintains both tree structures and quasi-optimal solutions using the bottom-up approaches.

We propose an efficient method that applies directed soft arc con- sistency to a Distributed Constraint Optimization Problem (DCOP) which is a fundamental framework of multi-agent systems. With DCOPs a multi-agent system is represented as a set of variables and a set of constraints/cost functions. We focus on DCOP solvers that employ pseudo-trees. A pseudo-tree is a graph structure for a con- straint network that represents a partial ordering of variables. Most pseudo-tree-based search algorithms perform optimistic searches using explicit/implicit backtracking in parallel. However, for cost functions taking a wide range of cost values, such exact algorithms require many search iterations, even if the constraint density is relatively low. Therefore additional improvements are necessary to reduce the search process. A previous study used a dynamic programming-based preprocessing technique that estimates the lower bound values of costs. However, there are opportunities for further impr...

Strobing optimization in a pursuit problem with dynamic linear objects in discrete time involving mobile sensor of radar or sonar type is considered. This problem is formally represented as that of maximizing confidence probability of the pursuer-target system state. Through recasting the latter problem into that of stochastic optimal control, a recursive procedure is obtained that provides an optimal solution.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

In this paper, we propose a semiparametric regression model that relates the received signal strength indicators (RSSIs) to the distances separating stationary sensors and moving sensors in a wireless sensor network. This model combines the well-known log-distance theoretical propagation model with a nonlinear fluctuation term, estimated within the framework of kernel-based machines. This leads to a more robust propagation model. A fully comprehensive study of the choices of parameters is provided, and a comparison to state-of-the-art models using real and simulated data is given as well.

In this paper, we propose a semiparametric regression model that relates the received signal strength indicators (RSSIs) to the distances separating stationary sensors and moving sensors in a wireless sensor network. This model combines the well-known log-distance theoretical propagation model with a nonlinear fluctuation term, estimated within the framework of kernel-based machines. This leads to a more robust propagation model. A fully comprehensive study of the choices of parameters is provided, and a comparison to state-of-the-art models using real and simulated data is given as well.

In this paper we study the problem of steering a team of Unmanned Aerial Vehicles (UAVs) toward a static configuration which maximizes the visibility of a 3D environment. The UAVs are assumed to be equipped with visual sensors constrained by a maximum sensing range and the prior knowledge on the environment is considered to be very sparse. To solve this problem on-line, derivative-free measurement-based optimization algorithms can be adopted, even though they are strongly limited by local optimality. To overcome this limitation, we propose to exploit the partial initial knowledge on the environment to find suitable initial configurations from which the agents start the local optimization. In particular, a constrained centroidal Voronoi tessellation on a coarse approximation of the surface to cover is proposed. The behavior of the agent is so based on a two-step optimization approach, where a stochastic optimization algorithm based on the on-line acquired information follows the geom...

In this paper, we propose a methodology to optimize the trajectory of mobile sensors whose dynamics contains fractional derivatives to find parameter estimates of a distributed parameter system. The problem is to maximize the determinant of the Fischer information matrix representing the amount of information gathered on parameters by the sensors. The introduced method transforms the problem to a fractional optimal control one in which both the steering of the sensors and their initial positions are optimized. The resulting fractional optimal control problem is reformulated into an integer order optimal control one which is then solved using the Matlab PDE toolbox and the RIOTS optimal control toolbox which handles various constraints imposed on the sensor motions. The effectiveness of the method is illustrated with a two-dimensional diffusion equation for different numbers of sensors and different orders.

This paper presents design details of SEAL, a generalpurpose low-cost spatial-temporal data logger. The SEAL module includes GPS, Lithium-ion polymer battery, high-density flash memory and a USB interface. The main features include: low cost, very large size XML-based data logs, low power consumption, flexible sensor attachment, and an optional Wi-Fi interface for Internet-enabled data. It is compact, self-contained and light-weight-suitable for a UAV (unmanned aerial vehicle) payload. In fact, our general-purpose SEAL module can turn any mobility platform into a mobile sensor. For example, we have interfaced CO2 and NH3 sensors with a SEAL module and attached the pack to a car which can be driven to map the CO2 and NH3 in a region of interest. The XML-based data logs allow interoperable post-processing in every modern computer operating system and display in desktop mapping applications such as Google Earth and NASA WorldWind.

Intrusion detection, area coverage and border surveillance are important applications of wireless sensor networks today. They can be (and are being) used to monitor large unprotected areas so as to detect intruders as they cross a border or as they penetrate a protected area. We consider the problem of how to optimally move mobile sensors to the fence (perimeter) of a region delimited by a simple polygon in order to detect intruders from either entering its interior or exiting from it. We discuss several related issues and problems, propose two models, provide algorithms and analyze their optimal mobility behavior.

The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras. On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network. A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network. This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system. Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 hours. Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them. The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants. The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring. Results suggest that a 2-3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow.

We consider several variations of the problems of covering a set of barriers (modeled as line segments) using sensors that can detect any intruder crossing any of the barriers. Sensors are initially located in the plane and they can relocate to the barriers. We assume that each sensor can detect any intruder in a circular area of fixed range centered at the sensor. Given a set of barriers and a set of sensors located in the plane, we study three problems: (i) the feasibility of barrier coverage, (ii) the problem of minimizing the largest relocation distance of a sensor (MinMax), and (iii) the problem of minimizing the sum of relocation distances of sensors (MinSum). When sensors are permitted to move to arbitrary positions on the barrier, the MinMax problem is shown to be strongly NP-complete for sensors with arbitrary ranges. We also study the case when sensors are restricted to use perpendicular movement to one of the barriers. We show that when the barriers are parallel, both the

Mobile wireless sensors need to know their localizations in many control and monitoring applications. Among all sensors, some know their exact position (i.e., they are equipped with GPS or they are positioned by human intervention). These sensors are called anchors. Some sensors can have different capabilities allowing them to calculate either distances or angles when they receive messages from others nodes. So, they only use anchor positions to obtain an estimated position. However, when sensors are mobile they cannot continuously calculate their position because of the energy constraints. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. We propose three techniques following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, we adapt the periods where nodes invoke their localization. Simulation result...

This paper presents a game theoretic approach for the management of multiple mobile sensors. Our approach can maintain tracks of smart targets under possibly adversarial environments. To ensure computational tractability, sensor management is divided into sensor assignment and sensor scheduling. In sensor assignment, covariance control and information theoretic sensor assignment are combined logically. In sensor scheduling, the targets are modeled as entities with different levels of intelligence, which will invoke different strategies of sensor scheduling. Simulation results demonstrate the effectiveness of the proposed sensor management scheme.

2005 CENS Summer Intern:NIMS (Networked Info-Mechanical Systems) is a sensor system utilizing fixed and actuated sensors to monitor environmental changes. A new, rapidly deployable NIMS (NIMS RD) allows for more mobility and versatility in the field. With short-term ...

The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras. On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network. A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network. This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system. Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 hours. Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them. The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants. The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring. Results suggest that a 2-3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

We describe in this paper a low-noise, low-power and low-voltage analog front-end amplifier dedicated to high resistive gas sensor detection. A mobile sensor system for very low level signals such as gas spikes detection is required to implement with a scaled CMOS technology. For a key circuit of these systems, a Chopper Stabilization Amplifier (CHS) which suppresses DC offset and 1/f noise figure of MOS devices is commonly used.

We prove a convergent strategy for a group of mobile sensors to generate contour plots, i.e., to automatically detect and track level curves of a scalar field in the plane. The group can consist of as few as four mobile sensors, where each sensor can take only a single measurement at a time. The shape of the formation of mobile sensors is determined to minimize the least mean square error in the estimates of the scalar field and its gradient. The algorithm to generate a contour plot is based on feedback control laws for each sensor platform. The control laws serve two purposes: to guarantee that the center of the formation moves along one level curve at unit speed; and to stabilize the shape of the formation. We prove that both goals can be achieved asymptotically. We show simulation results that illustrate the performance of the control laws in noisy environments.

Early mobiscopes arose directly from widely available sensing modalities in networked devices. Examples include image sensors in mobile phones, GPS in phones and vehicles, and the increasingly diverse telemetry available in vehicles. We consider these as representatives of two broad categories of mobiscope.

Indoor environments present opportunities for a rich set of location-aware applications such as navigation tools for humans and robots, interactive virtual games, resource discovery, asset tracking, location-aware sensor networking etc. Typical indoor applications require better accuracy than what current outdoor location systems provide. Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments. Further, typical indoor applications require different types of location information such as physical space, position and orientation. This dissertation describes the design and implementation of the Cricket indoor location system that provides accurate location in the form of user space, position and orientation to mobile and sensor network applications. Cricket consists of location beacons that are attached to the ceiling of a building, and receivers, called listeners, attached to devices that need location. Each beacon periodically transmits its location information in an RF message. At the same time, the beacon also transmits an ultrasonic pulse. The listeners listen to beacon transmissions and measure distances to nearby beacons, and use these distances to compute their own locations. This active-beacon passive-listener architecture is scalable with respect to the number of users, and enables applications that preserve user privacy. This dissertation describes how Cricket achieves accurate distance measurements between beacons and listeners. Once the beacons are deployed, the MAT and AFL algorithms, described in this dissertation, use measurements taken at a mobile listener to configure the beacons with a coordinate assignment that reflects the beacon layout. This dissertation presents beacon interference avoidance and detection algorithms, as well as outlier rejection algorithms to prevent and filter out outlier distance estimates caused by uncoordinated beacon transmissions. The Cricket listeners can measure distances with an accuracy of 5 cm. The listeners can detect boundaries with an accuracy of 1 cm. Cricket has a position estimation accuracy of 10 cm and an orientation accuracy of 3 degrees.

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. Our AMBROSia project focuses on a class of observing systems which are embedded into the environment, consist of stationary and mobile sensors, and react to collected observations by reconfiguring the system and adapting which observations are collected next. In this paper, we report on recent research directions and corresponding results in the context of AMBROSia.

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. A wide range of critical environmental monitoring objectives in resource management, environmental protection, and public health all require distributed observing systems. The goal of such systems is to help scientists verify or falsify hypotheses with useful samples taken by the stationary and mobile units, as well as to analyze data autonomously to discover interesting trends or alarming conditions. In our project, we focus on a class of observing systems which are embedded into the environment, consist of stationary and mobile sensors, and react to collected observations by reconfiguring the system and adapting which observations are collected next. In this paper, we give an overview of our project in the context of a marine biology application.

Wireless sensor networks consist of many wireless sensor nodes that enable the collection of sensor data from the physical world. A key requirement to interpreting the data is to determine the locations of the sensor nodes. The localization techniques developed can be divided into two categories: range-free and range-based. Range-free localization usually assumes isotropic networks where the hop count between two nodes is proportional to their distance. However, anisotropic networks are more realistic due to the presence of various anisotropic factors in practice, e.g. irregular radio propagation, low sensor density, anisotropic terrain condition, and obstacles which can detour the shortest path between two nodes. The previous anisotropy-tolerating solutions focused on only one anisotropic factor -the obstacles. We will propose a pattern-driven localization scheme to tolerate multiple anisotropic factors. Range-based localization assumes that the inter-node distances can be accurately measured by special ranging hardware. There are two important issues: (1) the ranging noise which affects the localization accuracy; (2) the collinearity of critical node sets which may produce unanticipated flip ambiguities and harm the localization robustness. However, the previous research does not fully address these two issues, i especially for patch merging, a powerful tool to localize sparse sensor networks. We will present our inflexible body merging algorithm to address these two issues for both patch merging and multilateration. Our algorithm can also improve the percentage of localizable nodes by nearly two times in sparse networks as compared with stateof-the-art work. Another critical issue for range-based localization is the existence of outliers in raw data (i.e. distance measurements and anchor positions) which strongly deviate from their true values. These outliers can severely degrade localization accuracy and need to be rejected. Previous studies have two inadequacies of (1) focusing on adding an outlier rejection ability to multilateration but neglecting patch merging; (2) rejecting only the outlier distances but neglecting outlier anchors which are more difficult to remove, because outlier anchors may collude by declaring positions in the same coordinate frame. We will present an algorithm to reject both outlier distances and colluding outlier anchors, in both dense networks and sparse networks. This work would not have been possible without the support of my colleagues, friends, and mentors. Specifically, I would like to thank my supervisor, Bin Xiao, who has given me all the support and guidance I needed as a doctoral student. He is not only a good researcher with broad knowledge but also a nice friend and kind person. I am very grateful to have had his trust in my ability, and I have often benefitted from his insight and advice. I am also thankful to Jiannong Cao, my co-supervisor, for his many suggestions and constant support during this research. He has sharp intuition, and grand insight about research, and taught me a lot about how to be a good researcher. This thesis was also made possible by collaboration and consultation with many colleagues. I would like to thank

Recent critical application sectors of sensor networks like military, health care, and industry require the use of mobile sensor nodes, something that poses unique challenges in aspects like handoff delay, packet loss, and reliability. In this paper we propose a novel mobility model that handles those challenges effectively by providing on-time mobility detection and handoff triggering. In that way soft handoffs and controlled disconnections are assured. The proposed solution uses crosslayer information from the MAC and Network layers. Our solution was implemented and evaluated in an experimental testbed, in the context of the European FP7 GINSENG project.

One of the most active research areas in wireless sensor networks is the coverage. The efficiency of the sensor network is measured in terms of the coverage area and connectivity. Therefore these factors must be considered during the deployment. In this paper, we have presented a scheme for homogeneous distribution of randomly distributed mobile sensor nodes (MSNs) in the deployment area. The deployment area is square in shape, which is divided into number of concentric regions centered at Base Station, these regions are separated by half of the communication range and further deployment area is divided in to numbers of regular hexagons. To achieve the maximum coverage and better connectivity MSNs will set themselves at the center of the hexagon on the instruction provided by the BS which is located at one of the corner in the deployment area. The simulation results shows that the presented scheme is better than CPVF and FLOOR schemes in terms of number of MSNs required for same coverage area and average movement required by MSNs to fix themselves at the desired location and energy efficiency.

We are surrounded by a sensor-enriched environment which is able to provide a broad spectrum of features from various sensor classes that can be utilised for situation and activity awareness. However, the probably most common sensor, shipped with nearly every ...

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.

Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. This paper investigates a new problem for two users. Each user has his / her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. The paper models it as minimum path pair (MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, it devises an algorithm by enumerating node pairs. An efficient algorithm based on point-to-point shortest path calculation is proposed to further improve query efficiency. It also presents a smart driving direction system IS proposed to model the dynamic traffic pattern so as to provide a user with the fastest route to a destination with edge failure situation.

The design of a cellular underwater network is addressed from the viewpoint of determining the cell size and the frequency reuse pattern needed to support a desired number of users operating over a given area within a given system bandwidth. By taking into account the basic laws of underwater acoustic propagation, it is shown that unlike in the terrestrial radio systems, both the cell radius R and the frequency reuse number N must satisfy a set of constraints in order to constitute an admissible solution (which sometimes may not exist). The region of admissible (R, N) defines the possible network topologies. It is determined by the user density and the system bandwidth (ρ, B), and by the required signal-to-interference ratio and per-user bandwidth (SIR0, W0). The range of admissible solutions also depends on the choice of operating frequency region. Moving to a higher frequency region than that dictated by SNR maximization, improves the SIR and yields a greater system capacity.

2. Overall Objectives 2.1. General objectives Recent evolutions in distributed computing significantly increased the degree of uncertainty inherent to any distributed system and led to a scale shift that traditional approaches can no longer accommodate. The key to scalability in this context lies into fully decentralized and self-organizing solutions. The objective of the ASAP project team is to provide a set of abstractions and algorithms to build serverless large-scale distributed applications involving a large set of volatile, geographically distant, potentially mobile and/or resource-limited computing entities.

The Distributed Constraint Optimization Problem (DCOP) is a fundamental formalism for multi-agent cooperation. With DCOPs, the agent states and the relationships between agents are formalized into a constraint optimization problem, which is then solved using distributed cooperative optimization algorithms. In the original DCOP framework, a set of objective functions is employed to represent the relationships between agents. However, constraints for resources that are consumed by teams of agents are not well supported. Resource constraints are necessary to handle practical problems including distributed task scheduling with limited resource availability. A dedicated framework called Resource Constrained DCOP (RCDCOP) has been recently proposed. RCDCOP models objective functions and resource constraints separately. A resource constraint is an n-ary constraint that represents the limit on the number of resources of a given type available to agents. Previous research addressing RCDCOPs employs the Adopt algorithm, which is an efficient solver for DCOPs. An important graph structure for Adopt is the pseudo-tree for constraint networks. A pseudo-tree implies a partial ordering of variables. In this variable ordering, n-ary constrained variables are placed on a single path of the tree. Therefore, resource constraints that have large arity augment the depth of the pseudo-tree. This also reduces the parallelism, and therefore the efficiency of Adopt. In this paper we propose another version of the Adopt algorithm for RCDCOP using a pseudo-tree that is generated ignoring resource constraints. The key ideas of our work are as follows: (i) The pseudo-tree is generated ignoring resource constraints. (ii) Virtual variables are introduced, representing the usage of resources. These virtual variables are used to share resources among subtrees. (iii) The addition of virtual variables increases the search space. To reduce this problem, the search is pruned using the bounds defined by the resource constraints. These ideas are used to extend Adopt. The proposed method reduces the previous limitations in the construction of RCDCOP pseudo-trees. The efficiency of our technique depends on the class of problems being considered, and we describe the obtained experimental results.

The positioning of sensors in Wireless Sensor Network WSN affects coverage, communication cost and resource management.In non-deterministic deployment, mobility feature of the mobile ad-hoc network can be best exploited to reposition the sensors so as to achieve the best network coverage. The Uncovered Region Exploration Algorithm (UREA) is one such algorithm proposed in [1] for redeployment of the homogeneous nodes after initial random deployment. In this paper we propose generalization of UREA andput forth, two important application scenarios. First, after final deployment of WSNs, they resume to their assigned tasks. The power of some over-utilized nodes in the Field of Interest FoI may get depleted over the time. The nodes get drained, decreasing the sensing radius of the sensors, resulting in the decrease in the network coverage, as a result decrease in Quality of Coverage QoC. A redeployment of sensors leading to an improvement in the coverage is of significant use in practical real life applications. Second, sensor nodes with varying sensing characteristics are often desirable, especially when only a subset of deployed sensors may be mobile. It is show that the generalized version of UREA presented here lead to significant improvement in the coverage. In each case, we present findings from a study of large scale deployment involving ten sets of random deployments over 40to 100 nodes. To the best of our knowledge, this work is the first to study, model simulate the intermediate non-deterministic post deployment scenario and non-deterministic nonhomogeneous sensor node deployments to improve the network coverage.

Technology progression in the last decades leads to many smartphone advancements, such as embedding variety of sensors for various measurement and applications. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant developments. Besides this, indoor positioning services on the smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; indoor navigation, asset tracking, controlling in warehouse, rescue operation, and entertainment application. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to an existing unpredictable error of the embedded sensors. To solve this issue, fusing multi-sensors measurements is proposed, in this paper, to reduce the existing sensors drifts and errors. Further, the sensors measurements with the previous estimated position are fused by using KALMAN Filter to determine the current position of the smartphone in each step of walking with higher accuracy. The obtained positioning accuracy through the proposed approach and based on trial experiments is 2 meters which is equivalent to 10% improvement in comparison with the state of the art.

In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors' measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors' measurements.