Obstacle-Aware Wireless Video Sensor Network Deployment For 3D Indoor Space Monitoring

In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%

Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime

Optimization of Self-Directed Target Coverage in Wireless Multimedia Sensor Network

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor’s current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm’s performance and the effect of number of targets on the resulting subset

Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas

In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues

DETERMINISTIC DEPLOYMENT for DIRECTIONAL SENSOR NODES

Porezni sustav Republike Hrvatske se sastoji od više poreznih oblika koji se primjenjuju po različitim poreznim stopama na različite vrste poreznih osnovica. Porez na dobit je jedan od tih oblika i ima značajnu ulogu u poreznom sustavu. Obzirom da se porez na dobit primjenjuje na ostvareno dobit iz gospodarske djelatnost, njegova stopa i primjena utječe na događanja u gospodarstvu i jedan je od najčešćih poreza kod kojih se pojavljuje porezna evazija. Kako postojeći zakoni ne stimuliraju reinvestiranje dobiti poduzeća, često se kod poduzetnika pronalaze različiti načini izbjegavanja plaćanja ovog poreza, kroz donacije, stipendije ili kroz ulaganja u obrtna sredstva poduzeća. Porez na dobit ima i ulogu u privlačenju investicija u gospodarstvo, pa je zbog toga jako bitno po kojoj stopi se ovaj porez obračunava i na koji način se plaća. Kako bi se privukle investicije, bilo domaće bilo inozemne, bitno je što konkurentnije odrediti poreznu stopu i poreznu osnovicu za primjenu ovog poreza. Republika Hrvatska se izvjestan vremanski period nalazi u gospodarskoj krizi, a izlazak iz nje i pokretanje gospodarstva se traži u stranim investicijama. Obzirom da je gospodarstvo Republike Hrvatske otvoreno i nalazi se na globalnom tržištu, države u okruženju su mu konkurencija. U ovom radu će se analizirati stope poreza na dobit u nekim zemljama članicama Europske Unije, ali i zemalja koje to nisu, ali su izravna konkurencija Republici Hrvatskoj u privlačenju inozemnih investicija

Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol