A testbed evaluation of MAC layer protocols for smart home remote monitoring of the elderly mobility pattern

Jafari, Alireza; Shirali, Mohsen; Ghassemian, Hassan

doi:10.1007/978-981-10-4166-2_86

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Aftercare monitoring of patients after being discharged from hospital and observing their convalescence for recommended period by their physicians in their own space prevents either missing a critical situation that requires sending a premed at home for assistance or an unnecessary re-admission. Continuous health and activity monitoring systems can be devised by either ambient sensors located in home [14], hospital or ambulance or wearables/implantable carried by the patients as they move [15] on the roads. A number of smart environment projects with physical testbeds and trials have been implemented [18] and the resulting datasets are available for researchers to mine, including the CASAS project [19], Technology Integrated Health Management (TIHM) [21], or smart hospital process mining for process management [24].…”

Section: A Healthcarementioning

confidence: 99%

Secure Non-Public Health Enterprise Networks

Ghassemian

Smith-Creasey

Nekovee

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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Increasing demand for secure remote operation in industry and technology advancements to support delivering efficient services and tele-mentoring have opened a new market in healthcare sector and emergency services based on 5G and Tactile internet capabilities. In a connected world, hospitals would benefit from providing the on-time availability either for continuous health monitoring or critical services to the citizens in need. In this paper, we propose a secure non-public health enterprise network concept to enable an end-to-end secure and location-agnostic communication between a patient and a healthcare service provider, and other contacts with patient's consent either in case of an emergency or to be stored in the medical records. We present how applying non-public enterprise networks can address market demand in health care sector for improved end-to-end security and privacy when dealing with personal and critical information. We present the three-tier architecture model describing continuous authentication mechanisms based on biometric collection as well as the dynamic network solutions in the healthcare domain. The biometric collection can be done using ambient/IoT sensors as well as wearable/implantable devices to monitor the patient unobtrusively. Furthermore, end-to-end security solutions should adapt dynamically based on the user profile and situation awareness to address the required level of security at the network side. We discuss the related research challenges for developing the presented non-public health enterprise platform and provide suggestions for future work based on the healthcare sector requirements and opportunities.

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Section: A Healthcarementioning

confidence: 99%

Secure Non-Public Health Enterprise Networks

Ghassemian

Smith-Creasey

Nekovee

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…The round key generation process in BCC (64 bits) is illustrated in Fig. 2 and is implemented according to (2) and (3):…”

Section: A Bcc Algorithmmentioning

confidence: 99%

“…On the other hand, in their lifetime, these sensors face challenges such as limited energy and memory, and unreliable communication. Hence, in [2], studies have been conducted to find an efficient energy protocol for data collection in smart homes. In addition, an adaptive collection algorithm for data acquisition by wearables was designed to reduce energy consumption [3].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in smart buildings, a large amount of personal data is collected by a variety of heterogeneous devices. Using the collected data, there is the possibility of tracking the daily habits of individuals or care for the elderly [2], [5]. Privacy should ensure that the data (or part of the data) is only accessed by the authorized users [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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A Low Power Cryptography Solution Based on Chaos Theory in Wireless Sensor Nodes

et al. 2019

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Unobtrusive personal data collection by wearable sensors and ambient monitoring has increased concerns about user privacy. Applying cryptography solutions to resource constraint wireless sensors as one of the privacy-preserving solutions demand addressing limited memory and energy resources. In this paper, we set up testbed experiments to evaluate the existing cryptographic algorithms for sensors, such as Skipjack and RC5, which are less secure compared to block cipher based on chaotic (BCC) on existing IEEE802.15.4 based SunSPOT sensors. We have proposed modified BCC (MBCC) algorithm, which uses chaos theory characteristics to achieve higher resistance against statistical and differential attacks while maintaining resource consumption. Our comparison observations show that MBCC outperforms BCC in both energy consumption and RAM usage and that both MBCC and BCC outperform RC5 and Skipjack in terms of security measures, such as entropy and characters frequency. Our comparison analysis of MBCC vs BCC suggests 13.44% lower RAM usage for encryption and decryption as well as 6.4 and 6.6 times reduced consumed time and energy for encrypting 32-bit data, respectively. Further analysis is reported for increasing the length of MBCC key, periodical generation of master key on the base station and periodical generation of round key on the sensors to prevent the brute-force attacks. An overall comparison of cipher techniques with respect to energy, time, memory and security concludes the suitability of MBCC algorithm for resource constraint wireless sensors with security requirements.

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“…Sensors are installed in a house to capture readings when residents perform daily routines in order to gain insights on human activities, movements, and gestures which are generally known as human daily behaviours [ 7 , 9 ] to assess the functional ability of residents for living independently [ 10 , 11 ]. For instance, an elderly patient aftercare monitoring is a common application scenario for smart environments to observe their recovery in their own space, immediately after being discharged from hospital for a duration suggested by their physicians [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

Shirali

Bayo-Monton

Fernández-Llatas

et al. 2020

Sensors

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Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents’ health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident’s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

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A testbed evaluation of MAC layer protocols for smart home remote monitoring of the elderly mobility pattern

Cited by 5 publications

References 16 publications

Secure Non-Public Health Enterprise Networks

Secure Non-Public Health Enterprise Networks

A Low Power Cryptography Solution Based on Chaos Theory in Wireless Sensor Nodes

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

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