Signal strength based scheme for following mobile IoT devices in dynamic environments

Λάγκας, Θωμάς; Eleftherakis, George; Dimopoulos, Konstantinos; Zhang, Jie

doi:10.1016/j.pmcj.2020.101165

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…This system features instantaneous data transmission, location tracking of soldiers, and monitoring of physiological data such as heart rate, body temperature and environmental oxygen levels. Thomas Lagkas et al [36] developed a new algorithm named Hot-Cold to locate mobile monitored targets or individuals concerning the IoT system in dynamic environments with ill-defined or ill-designed infrastructure. This technique can guarantee the proximity maintenance formed from the power of the output RF signal sent by the individuals or targets to transmit its extracted data from sensors indicating their location.…”

Section: Inference System and Iot/lpwan Networkmentioning

confidence: 99%

No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design

et al. 2020

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There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.

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Section: Inference System and Iot/lpwan Networkmentioning

confidence: 99%