Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

Telemedicine refers to the use of information and communication technology to provide and support health care mainly for the purpose of providing consultation. It is also a way to provide medical procedures or examinations to remote locations. It has the potential to improve both the quality and the access to health care services delivery while lowering costs even in the scarcity of resources. Understanding the potentiality of telemedicine, many developing countries are implementing telemedicine to provide health care facility to remote area where health care facilities are deficient. Bangladesh is not an exception to this either. In this paper we mention the reasons why Bangladesh has to move for telemedicine. We also present the past and on-going telemedicine activities and projects in Bangladesh. Analyzing these projects we have found out some factors which should be assessed carefully for successful implementation of telemedicine application. Finally we propose a prototype telemedicine network for Bangladesh that can improve health facilities through telemedicine by making a connection between rural health facility providers and special hospitals.

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A wireless body area network (WBAN) allows the integration of low power, invasive or non-invasive miniaturized sensors around a human body. Each intelligent sensor has enough capability to analyze and process the physiological parameters and to forward all the information to a central intelligent node for disease management, diagnosis and prescription. The data transmission rate of various biosensors is heterogeneous. Furthermore, the limited energy resources and computational power of these sensors have urged the development of low power energy efficient medium access control (MAC) protocol. This paper studies the performance of Preamble-Based time division multiple access (PB-TDMA) protocol for a heterogeneous non-invasive WBAN. Simulation results show that the performance of PB-TDMA protocol outperforms S-MAC and IEEE 802.11 DCF in terms of throughput and power consumption.

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<p>Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.</p>

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