4,238 research outputs found
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study
The accuracy of indoor wireless localization systems can be substantially
enhanced by map-awareness, i.e., by the knowledge of the map of the environment
in which localization signals are acquired. In fact, this knowledge can be
exploited to cancel out, at least to some extent, the signal degradation due to
propagation through physical obstructions, i.e., to the so called
non-line-of-sight bias. This result can be achieved by developing novel
localization techniques that rely on proper map-aware statistical modelling of
the measurements they process. In this manuscript a unified statistical model
for the measurements acquired in map-aware localization systems based on
time-of-arrival and received signal strength techniques is developed and its
experimental validation is illustrated. Finally, the accuracy of the proposed
map-aware model is assessed and compared with that offered by its map-unaware
counterparts. Our numerical results show that, when the quality of acquired
measurements is poor, map-aware modelling can enhance localization accuracy by
up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless
Communications, 201
A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm
Due to the familiarity of smart devices and the advancements of mobile Internet, there is a significant need to design an effective indoor localization system. Indoor localization is one of the recent technologies of location-based services (LBS), plays a vital role in commercial and civilian industries. It finds useful in public security, disaster management, and positioning navigation. Several research works have concentrated on the design of accurate 2D indoor localization techniques. Since the 3D indoor localization techniques offer numerous benefits, this paper presents a Novel 3D Indoor Node Localization Technique using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO-WLSE algorithm aims to improvise the localization accuracy with reduced computational time. Here, the OBSO algorithm is employed for estimating the initial locations of the target that results in the elimination of NLOS error. With respect to the initial location by OBSO technique, the WLSE technique performs iterated computations rapidly to determine the precise final location of the target. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. A number of simulations were run to test the model's accuracy, and the results were analyzed using a variety of metrics
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm
Due to the familiarity of smart devices and the advancements of mobile Internet, there is a significant need to design an effective indoor localization system. Indoor localization is one of the recent technologies of location-based services (LBS), plays a vital role in commercial and civilian industries. It finds useful in public security, disaster management, and positioning navigation. Several research works have concentrated on the design of accurate 2D indoor localization techniques. Since the 3D indoor localization techniques offer numerous benefits, this paper presents a Novel 3D Indoor Node Localization Technique using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO-WLSE algorithm aims to improvise the localization accuracy with reduced computational time. Here, the OBSO algorithm is employed for estimating the initial locations of the target that results in the elimination of NLOS error. With respect to the initial location by OBSO technique, the WLSE technique performs iterated computations rapidly to determine the precise final location of the target. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. A number of simulations were run to test the model's accuracy, and the results were analyzed using a variety of metrics
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