Mobile-Beacon Assisted Sensor Localization with Dynamic Beacon Mobility Scheduling

International audienceIn mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals with minimum delay. We propose a novel DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, beacon trajectory is defined as the track of depth-first traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to RSS (Received Signal Strength)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free. Then we suggest to apply node elimination and topology control (Local Minimum Spanning Tree) to shorten beacon tour and reduce delay. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path based scheduling method

Transmission Power Adjustment Scheme for Mobile Beacon-Assisted Sensor Localization

© 2005-2012 IEEE. Localization, as a crucial service for sensor networks, is an energy-demanding process for both indoor and outdoor scenarios. GPS-based localization schemes are infeasible in remote, indoor areas, and it is not a cost-effective solution for large-scale networks. Single mobile-beacon architecture is recently considered to localize sensor networks with the aim of removing numerous GPS-equipped nodes. The critical issue for the mobile beacon-Assisted localization is to preserve the consumed power to increase the lifetime. This paper presents a novel power control scheme, namely 'Z-power,' for mobile beacon traveling along a predefined path. The proposed scheme takes the advantage of deterministic path traveled by the single beacon to efficiently adjust the transmission power. Based on the extensive results, the proposed power control scheme could successfully improve the beacon and sensors energy consumption about 25.37% and 34.09%, respectively. A significant energy-Accuracy tradeoff was achieved using Z-power, which could successfully keep the same level of accuracy while providing lower energy consumption. Another group of results collected when obstacle-handling algorithm was applied at the presence of obstacles. In this scenario, Z-power improves energy consumption and localization accuracy with the same level of success

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Data collection of mobile sensor networks by drones

Data collection by autonomous mobile sensor arrays can be coupled with the use of drones which provide a low-cost, easily deployable backhauling solution. These means of collection can be used to organize temporary events (sporting or cultural) or to carry out operations in difficult or hostile terrain. The aim of this thesis is to propose effective solutions for communication between both mobile sensors on the ground and on the edge-to-ground link. For this purpose, we are interested in scheduling communications, routing and access control on the sensor / drone link, the mobile collector. We propose an architecture that meets the constraints of the network. The main ones are the intermittence of the links and therefore the lack of connectivity for which solutions adapted to the networks tolerant to the deadlines are adopted. Given the limited opportunities for communication with the drone and the significant variation in the physical data rate, we proposed scheduling solutions that take account of both the contact time and the physical flow rate. Opportunistic routing is also based on these two criteria both for the selection of relay nodes and for the management of queues. We wanted to limit the overhead and propose efficient and fair solutions between mobile sensors on the ground. The proposed solutions have proved superior to conventional scheduling and routing solutions. Finally, we proposed a method of access combining a random access with contention as well as an access with reservation taking into account the aforementioned criteria. This flexible solution allows a network of dense mobile sensors to get closer to the performance obtained in an oracle mode. The proposed solutions can be implemented and applied in different application contexts for which the ground nodes are mobile or easily adapted to the case where the nodes are static

Dynamic mobile anchor path planning for underwater wireless sensor networks

In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length

Z-path trajectory mechanism for mobile beacon-assisted localization in wireless sensor networks

A wireless sensor network consists of many sensors that communicate wirelessly to monitor a physical region. In many applications such as warning systems or healthcare services, it is necessary to enhance the captured data with location information. Determining the coordinates of the randomly deployed sensors is known as the problem of localization. A promising solution for statically deployed sensors is to benefit from a mobile beacon-assisted localization. The main challenge is planning an optimum path for the mobile beacon to ensure the full coverage, increase the accuracy of the estimated position and decrease the required time for localization of resource-constrained sensors. So, this research aims at developing a superior trajectory mechanism for mobile beacon-assisted localization to help unknown sensors to efficiently localize themselves. To achieve this purpose; first, a novel trajectory named Z-path is proposed to guarantee fully localized deployed sensors with higher precision since the path reduces collinear beacon positions and promises shorter localization time; second, Z-path transmission power adjustment scheme named Zpower is developed to dynamically and optimally adjust the transmission power for a reliable transmission while conserving the energy consumption for localization by mobile beacon and unknown sensors; third, Z-path obstacle-handling trajectory mechanism is designed to improve the effectiveness of the proposed path toward obstacles which obstruct the path. Finally, the proposed Z-path obstacle handling mechanism is integrated with the developed power adjustment scheme to improve the energy efficiency of the designed obstacle tolerance mechanism. The performance of the proposed trajectory is evaluated by comparing the efficiency with five benchmark trajectories in terms of localization success, accuracy, energy efficiency, time and ineffective position rate, which is a newly introduced metric by this research to measure the collinearity of the trajectories. Simulation results show that Z-path has successfully localized all 250 deployed sensors with higher precision by at least 5.88% improvement than Localization with a Mobile Anchor based on Trilateration (LMAT) trajectory and 58% improvement than random way point. It also serves as a benchmark path with 93 ineffective positions per node localization as compared with LMAT as a second efficient path by 100 collinear positions and faster trajectory for localization. Furthermore, results revealed that Z-power accomplishes better performance in terms of energy consumption as an average 34% for unknown sensors and 25% for mobile beacon than Z-path. In case of obstacle tolerance mechanism, it ensures higher localization performance in terms of accuracy, time and success around 37.5%, 13% and 11% respectively, as compared to Z-path at the presence of obstacles. The handling mechanism integrated with the power control scheme has reduced energy consumption and improved ineffective position rate compared with Z-path handling trajectory by 35.7% and 54.4%, respectively

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies

[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]紙

Wireless Sensor Technologies and Applications

Recent years have witnessed tremendous advances in the design and applications of wirelessly networked and embedded sensors. Wireless sensor nodes are typically low-cost, low-power, small devices equipped with limited sensing, data processing and wireless communication capabilities, as well as power supplies. They leverage the concept of wireless sensor networks (WSNs), in which a large (possibly huge) number of collaborative sensor nodes could be deployed. As an outcome of the convergence of micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics, WSNs represent a significant improvement over traditional sensors. In fact, the rapid evolution of WSN technology has accelerated the development and deployment of various novel types of wireless sensors, e.g., multimedia sensors. Fulfilling Moore’s law, wireless sensors are becoming smaller and cheaper, and at the same time more powerful and ubiquitous. [...