The authors have addressed the issue of energy saving in packet retransmission by reducing the packet loss in the first attempt. They have formulated the problem as Markov decision process and mapped the system into various states based on system dynamics like channel gain, buffer space, battery energy and harvested energy. In wireless sensor network, adaptive policy management is the best approach to work out with the variation of system states in a dynamically changing environment. The policies are defined regarding various controllable parameters like transmission power, modulation scheme, and transmission rate. The solution is sought regarding best policy selection as per the individual state of the system. They have evaluated the performance of the proposed optimum transreceiver scheme by extensive simulations with existing schemes like optimum energy allocation, adaptive modulation and adaptive power management for the various performance metrics like net bit rate and energy consumption in retransmission of packets for different system states. ReTrans energy consumption in packet retransmission P S probability of symbol error η solar to DC conversion efficiency l size of packet IET Wirel. Sens. Syst.
Summary
In many applications (i.e., navigation of vehicles in traffic, museum guide, guidance to firefighters in poor visibility, and elderly care assistance systems), location‐based services (LBS) rely on the global positioning system (GPS) for accurate positioning. LBS can be challenging to employ in an indoor environment because of the lack of a line‐of‐sight (LOS) component and other signal propagation issues. Over the last decade, researchers worldwide have been motivated to solve the problem of continuous indoor positioning in situations when GPS is ineffective. We thoroughly reviewed research conducted by many scholars and compiled this survey article, which provides a comprehensive overview of indoor pedestrian navigation systems, prevalent step length estimation models, and the current state of the art. There are two key strategies in which substantial work on indoor localization is carried out. One is a dead reckoning in which the present location of the pedestrian is calculated based on details about previous location, and the other is an inertial navigation system in which the forward acceleration from an inertial sensor strapped down to the user's body is integrated twice to establish the current location. The step length is a crucial component of the inertial sensor‐based navigation in both these strategies. For different users, step length is variable and also changes at different walking speeds for each user. As a result, the focus of this survey is on step length estimation models. The survey describes and compares various approaches to estimating step length from various perspectives, including the research method used, the length of the test path, various walking speeds, the location of the sensor device on the user's body, and the accuracy achieved in estimating step length. Because inertial sensors are embedded in smartphones, it is vital to identify the smartphone mode (i.e., phoning, texting, or pocket) during each step taken while using smartphone‐based indoor navigation. The report concludes with a quantitative evaluation of the performance of smartphone mode recognition modules. At the end, we list out challenges still to be addressed for scholars working on indoor navigation.
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