Coverage of Targets in Mobile Sensor Networks With Restricted Mobility

Abstract: The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB ROT ), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors.

Abstract: We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1-1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1-1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵-1 approximation ratio by using a colouring technique, where denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.

Abstract: The deployment of mobile and adaptive virtual force barrier coverage (MA-VFBC) classification scheme using a mobile emergency response and command interface (MERCI) platform that is functionally implemented to track and report incidences and consequent collateral damages to infrastructures within a region of interest (ROI) is proposed. Considering the enormous use of the global positioning system (GPS) devices for location data-gathering and processing, and its inherent limitations, the proposed GUI-based MA-VFBC platform is implemented using self-deploying and obstacle-avoiding scattered mobile sensor nodes. The GPS service is kept as alternatives, since only initial co-ordinates from where the deployed sensor starts to move and the maximum boundary location of the target location is considered. The practical experimentation work appraises the use and feel of the (MERCI) platform when integrated with the proposed novel MA-VFBC path-tracking classification schemes, while the simulation work investigates evident real-time system reliability issues as direction of node deployment with path distances, system computation time and system overheads in the presence of dissimilar multiple obstacles.

Abstract: Nowadays, it is a common ground to find an ehealth environment flooded by a large amount of data, which comes from several mobile devices/sensors, and could not represent hundred percent of useful information. In other words, a process to enhance this data scenario is an essential effort. Therefore, in this paper, we present an approach oriented to the context which targets to provide more dynamic and personalized services in an e-health environment. The proposal adopts a Quality of Context (QoC) paradigm which was conceived to improve an e-health IoT environment. The scenario was characterized by supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. Thereby, the objective was to demonstrate the use of the proposed QoC evaluation, appraising some parameters. Experiments considered the use of diverse types of sensors, such as pulse and oxygen in the blood, body temperature, blood pressure, patient’s position and falls, environment temperature and humidity. Results indicate the success of the proposal.

Abstract: So far, air quality monitoring is usually handled by monitoring stations located at fixed locations. However, due to the cost of installation, deployment, and operation, the number of monitoring stations deployed is often tiny; thus, the monitored area is limited. To deal with this problem, in this paper, we consider a mobile air quality monitoring system that relies on sensors mounted on buses to broaden the monitoring area. Specifically, we investigate the optimal buses to place the sensors as well as the optimal monitoring timings to maximize the number of critical regions that are monitored. We mathematically formulate the targeted problem and prove its NP-hardness. Then, we exploit the greedy and dynamic programming approaches to propose a polynomial-time 1/2-approximation algorithm. We use the data of real bus routes in Hanoi, Vietnam, for the experimentation and show that the proposed algorithm guarantees an average performance ratio of 72.68%.

Abstract: This paper has always been one of my favorite “children,” combining as it does elements of the duality of linear programming and combinatorial tools from graph theory. It may be of some interest to tell the story of its origin.

Abstract: Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category. Moreover, protocols using contemporary methodologies such as network flow and quality of service modeling are also discussed. The paper concludes with open research issues. � 2003 Elsevier B.V. All rights reserved.

Abstract: Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. We address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided by a particular sensor network. We first define the coverage problem from several points of view including deterministic, statistical, worst and best case, and present examples in each domain. By combining the computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks.

Abstract: Sensor deployment is an important issue in designing sensor networks. We design and evaluate distributed self-deployment protocols for mobile sensors. After discovering a coverage hole, the proposed protocols calculate the target positions of the sensors where they should move. We use Voronoi diagrams to discover the coverage holes and design three movement-assisted sensor deployment protocols, VEC (vector-based), VOR (Voronoi-based), and minimax based on the principle of moving sensors from densely deployed areas to sparsely deployed areas. Simulation results show that our protocols can provide high coverage within a short deploying time and limited movement.

Abstract: Wireless ad-hoc sensor networks will provide one of the missing connections between the Internet and the physical world One of the fundamental problems in sensor networks is the calculation of coverage Exposure is directly related to coverage in that it is a measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of timeIn addition to the informal definition, we formally define exposure and study its properties We have developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks The algorithm works for any given distribution of sensors, sensor and intensity models, and characteristics of the network It provides an unbounded level of accuracy as a function of run time and storage We provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation