Employment of ground-based positioning systems has been consistently growing over the past decades due to the growing number of applications that require location information where the conventional satellite-based systems have limitations. Such systems have been successfully adopted in the context of wireless emergency services, tactical military operations, and various other applications offering location-based services. In current and previous generation of cellular systems, i.e., 3G, 4G, and LTE, the base stations, which have known locations, have been assumed to be stationary and fixed. However, with the possibility of having mobile relays in 5G networks, there is a demand for novel algorithms that address the challenges that did not exist in the previous generations of localization systems. This paper includes a review of various fundamental techniques, current trends, and state-of-the-art systems and algorithms employed in wireless position estimation using moving receivers. Subsequently, performance criteria comparisons are given for the aforementioned techniques and systems. Moreover, a discussion addressing potential research directions when dealing with moving receivers, e.g., receiver’s movement pattern for efficient and accurate localization, non-line-of-sight problem, sensor fusion, and cooperative localization, is briefly given.

A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers

Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper.

https://www.mdpi.com/1424-8220/18/1/112/pdf

Underwater Acoustic Target Tracking: A Review

Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.

Target Tracking for Sensor Networks: A Survey

Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.

/pdf/an-overview-of-next-generation-underwater-target-detection-53kp6pewku.pdf

An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

The wide availability of sensing modules and computing capabilities in modern mobile devices (smartphones, smart watches, in-vehicle sensors, etc.) is driving the shift from mote-class wireless sensor networks (WSNs) to the new era of crowdsensing WSNs. In this emerging paradigm sensors are no longer static and homogeneous, but are rather worn/carried by people or cars. This results in a new type of wide-area WSN-crowd-based and overlaid on top of heterogeneous communication technologies-that paves the way for very innovative applications. To this aim, the positioning of mobile devices operating in the network becomes crucial. Indeed, the pervasive, almost ubiquitous availability of smart devices brings unprecedented opportunities but also poses new research challenges in their precise location under mobility and dense-multipath environments typical of urban and indoor scenarios. In this paper, we review recent advances in the field of wireless positioning with focus on cooperation, mobility, and advanced array processing, which are key enablers for the design of novel localization solutions for crowdsensing WSNs.

https://www.mdpi.com/1424-8220/19/5/988/pdf

A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks.

Target tracking is one of the major applications of Wireless Sensor Networks (WSN). In traditional target tracking with directional sensors, the target can be detected only when it is in the field of view (FoV) of a sensor. The target's track is obtained with the help of filtering algorithms/techniques at the base station (BS). These techniques sometimes lead to incorrect location estimation of the target due to measurement errors and noise. It is ideally required that the main sensing lobe/FoV of the sensor follow the target, whenever the target is in the FoV of the sensor. Based on the above considerations, this paper proposes an efficient solution to target tracking with the help of beam steering enabled directional sensors mounted on the nodes for tracking the targets. At every step of beam steering, the sensor node updates the location of the target to the nearest cluster head or base station. Each sensor will continuously monitor the target till it crosses the FoV or sensing range of the sensor. The proposed method has been implemented in OMNeT++ and the performance of the algorithm has been evaluated with directional active and passive sensors. Different target mobilities and different sensor FoV angles have been considered. The results show that the proposed method successfully tracked the target and located the target at every instant of time. Directional active sensors yield more accurate results when compared to passive sensors. It was also seen that the location accuracy of the target was mainly dependent on the FoV angle and FoV/sensing radius of the sensor.

Target tracking in a WSN with directional sensors using electronic beam steering

This paper presents the throughput results obtained from a Wireless Sensor Network testbed, with single and multiple sources in different network deployments and routing architectures. The experimental testbed deployed at IIT Madras consisted of commercially available Crossbow TelosB and MicaZ nodes and a custom-built sensor node based on the DigiNet Xbee chip, with all nodes implementing the Zigbee standard. The networks were deployed in uniform grid topologies in three different deployments with up to 228 nodes. The main aim of the experiments is to analyze the throughput and packet delivery ratio observed with single and multiple sources. The experimental results show that delivery ratio reduces with increase in data rate due to collisions and help characterise the network capacity limits.

Testbed based throughput analysis in a Wireless Sensor Network

Target tracking is an important application of Wireless Sensor Networks (WSN). This paper presents the design of a WSN testbed that used a custom-built wireless sensor node, specifically designed for target tracking. The sensor node consists of a Digi Xbee device, a Passive Infrared (PIR) and MaxSonar ultrasonic ranging sensors. Appropriate multi-channel MAC protocols were designed and implemented. The experiments were conducted with up to 20 sensor nodes in indoor and outdoor environments, with a human target moving at pedestrian speeds. We found that directional sensing, inter-sensor interference, sensing and processing delays are significant in determining the target location. This work highlights some of the practical difficulties in realizing such systems in practice. The experiments show that the ultra-sonic ranging sensor does indeed provide improved target tracking information when compared to the PIR sensor.

A testbed for distributed target tracking with directional sensors

Convoy movement problem has significant practical applications. The problem has been attempted in many styles with varying results. We address it as an AI Search and Planning and Scheduling problem. The work focuses on modeling the convoy movement problem using PDDL and attempting a solution using existing planning methods and planners. Initial results indicated problems of scalability. To address this we propose a two stage planning process.

Planning for the Convoy Movement Problem.

Since the principal assumption in case-based reasoning (CBR) is that “similar problems have similar solutions”, learning a suitable similarity measure is an important aspect in CBR. However, learning case-case similarities is often a non-trivial task and involves significant amount of domain expertise. Most techniques that arrive at a pertinent similarity measure are often incomprehensible to the domain experts. These techniques also rarely enable the user to provide expert feedback which can then be utilized to develop better similarity measures. Our work attempts to bridge this knowledge gap by developing an iterative and interactive visualization framework called iCaseViz which learns the domain experts’ notion of similarity by utilizing the user feedback. This work is different from similar work in other communities in the sense that it is tailored to cater to the needs of a system built primarily based on the CBR hypothesis. The case base visualizer demonstrated in this paper is also very efficient as it has insignificant delay during real-time user interaction on large case bases. We provide preliminary results on the efficiency of the visualizer and the effectiveness of our similarity learning algorithm on UCI datasets and a real world high dimensional case base.

Anand Kumar

Papers

Target tracking in a WSN with directional sensors using electronic beam steering

Testbed based throughput analysis in a Wireless Sensor Network

A testbed for distributed target tracking with directional sensors

Planning for the Convoy Movement Problem.

iCaseViz: Learning Case Similarities through Interaction with a Case Base Visualizer