Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network

Tao, Shuai; Kudo, Mineichi; Pei, Bingnan; Nonaka, Hirokazu; Tanida, Jun

doi:10.1109/thms.2014.2365466

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…The total latency L C (i, q) taken to find the accurate location using the cloud server is demonstrated by (15). It depends on: a) propagation time between the user device and the MEC server L C,d1 as shown in (16), b) propagation time between the MEC server and the cloud server L C,d2 as defined in (17) where ω M,C is the distance between MEC and cloud servers, c) propagation time between the cloud server and the user device L C,d3 as presented in (18) where ω l,C is the distance between the cloud server and the user device, d) transmission time to send the localization request L C,s as found in (19) where Γ M,C is the transmission rate between the MEC server and cloud server, e) transmission time to receive the localization response L C,r as shown in (20) where Γ l,C is the transmission rate between the cloud server and the user device, f) processing time of the localization request on the MEC and the cloud server L C,p as defined in ( 21) where ρ C is the number of CPU cycles that are needed to process one bit on the cloud server and f C is the computational capability of the cloud server, g) processing time of the localization result on the user device L l,p (i), h) time for localization process on the cloud server L C,z (q) as presented in (22).…”

Section: ) Mec Offloadingmentioning

confidence: 99%

“…This leads to poor localization accuracy due to additional signal attenuation resulting from transmission through walls and large obstacles [7]. These indoor localization techniques can be applied at different transmission technologies such as WiFi [15], Bluetooth Low Energy (BLE) [16], RFID [17], Ultra-wideband (UWB) [18], IEEE 802.15.4 [19], Infrared [20], Ultrasonic [21], and Zigbee [22]. In this paper, three types of transmission technologies are examined.…”

Section: Introductionmentioning

confidence: 99%

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A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

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Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

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Section: ) Mec Offloadingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

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“…To overcome this drawback, Tao et al [20] attached binary infrared sensors to the ceiling of an office. Weak evidence such as people location, moving direction, and personal desks was synthesized to achieve soft tracking.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Luo

Guan

Tan

et al. 2017

Sensors

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Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.

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“…International Workshop of Advanced Manufacturing and Automation (IWAMA 2016) between infrared sensor module and camera devices are shown in Table I. In order to overcome the effects of obstacles, light condition, tackle with complex data and preserve individual privacy, [14] use a binary ceiling infrared sensor network for multiperson locating and soft tracking. Soft tracking means little physical or psychological disturbance with users.…”

Section: Related Workmentioning

confidence: 99%

Development of Person Localization and Activities Recognition System

Gao¹,

Tao²,

Ji³

et al. 2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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This paper first presents the trends and related works of localization and activity recognition of human body. Then we introduced the discussion of multiple studies for person localization and activities recognition. And finally we can see that the ceiling binary infrared sensor network for locating and recognition showed growing advantages which were not limited by light condition, obstacles, fast computing, and most of all, this system can preserve individual privacy to some extent. Moreover, this system is with high precision, stable performance, high reliability, low cost and low power consumption. So it solves some current existing problems in human behavior recognition system.

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Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network

Cited by 25 publications

References 22 publications

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Development of Person Localization and Activities Recognition System

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