Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

Browse

Filters

2019 - 20203

Settings

Has files: NoSubject: search.filters.subject.IoT Applications

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Design and Simulation of Multi-channel V-TDMA for IoT-Based Healthcare Systems
    (Springer, 2020-07) Ramachandran, Anita
    Internet of things (IoT)-based geriatric healthcare monitoring system monitors physiological and biological parameters of members in an elderly care home. This system consists of multiple network elements—wearable nodes for monitoring parameters, master nodes to process the collected information and raise alarms on observing any anomalies and intermediate nodes to relay information between the sensor nodes and master nodes. Maximum reliability, energy efficiency, and minimal latency during the data communication are the major requirements of such a system. Media access control (MAC) layer plays a significant role in achieving the above-mentioned requirements. In this paper, we propose a multi-channel variable time division multiple access (multi-channel V-TDMA) MAC protocol which includes the strengths of both standard time division multiple access (TDMA) and frequency division multiple access (FDMA) protocols. The proposed protocol efficiently provides a solution to the problem of continuous and reliable data transmission by the wearable nodes, along with the freedom of mobility.
  • No Thumbnail Available
    Item
    A Survey on Recent Advances in Wearable Fall Detection Systems
    (Hindawi Publishing Corporation, 2020-01) Ramachandran, Anita
    With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.
  • No Thumbnail Available
    Item
    Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications
    (IGI Global, 2019) K., Pradheep Kumar
    In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively