Received Signal Strength Index Research Articles

Environmental-Adaptive RSSI-Based Indoor Localization

This paper addresses a novel method for localizing a stationary object in an indoor office environment. The proposed method utilizes the received-signal-strength index (RSSI) of radio signals radiating from fixed reference nodes and reference tags placed at known positions to generate a precise signal propagation model. Signal attenuation parameters are updated online according to environmental variation; thus, the proposed method has environmental-adaptation capabilities. Subsequent experiments were conducted to demonstrate the superiority of the proposed technique over a commercial location-based service (LBS) chipset.

A Handover Prediction Model and its Application to Link Layer Triggers for Fast Handover

Cross layer handover schemes are expected to provide seamless services to users on the move. To achieve this goal, a link layer must timely trigger handover protocols of upper layers so that they can finish necessary handover procedures before current wireless link terminates. Therefore, a mobile node needs an appropriate model to predict impending handover to generate link layer triggers in time. In this paper, we propose a mobility model that does not assume any radio propagation environments and movement patterns of a mobile node. Through statistical analysis of the received signal strength index (RSSI) datasets measured in various radio propagation environments, we validate that an adaptive autoregressive process can be used as a handover prediction model. With the proposed model, we also propose a link layer triggering scheme. Since the prediction process inevitably introduces errors, we devise a statistical compensation method to initiate link layer triggers in a timely manner considering the given handover signaling delay bound. Simulation results show that the error compensation method can significantly decrease the rate of late link layer triggers with the reasonable increase in the false alarm rate.

Planning system for indoor wireless network

A novel wireless LAN prediction tool using the genetic algorithm and neural network has been proposed. We establish a site survey tool system to predict the received signal strength index (RSSI) in an indoor environment. The system includes six items. (1) The fading function: it corrects the functional characteristics of the RSSI for different types of wireless LAN cards in free space. (2) The setting of the attributes of obstacles in the indoor environment: the idea of a "single attribute of local area" is proposed. If there are the same obstacles in one area, we set the area as one attribute. (3) The genetic algorithm: where we use reproduction, crossover and mutation to obtain the propagation loss through the different obstacles (Li). (4) The neural network: we use neural network concept to correct the prediction error arising from the multipath effect in the indoor environment. (5) The auxiliary judgment for the sampling points: the method is helpful to users in establishing the best sampling points. (6) The calibration of prediction results: we use calibration to correct the prediction error arisen from Li.