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With the ongoing development of Indoor Location-Based Services, accurate location information of users in indoor environments has been a challenging issue in recent years. Due to the widespread use of WiFi networks, WiFi fingerprinting has become one of the most practical methods of locating mobile users. In addition to localization accuracy, some other critical factors such as cost, latency, and users' privacy should be considered in indoor localization systems. In this study, we propose a lightweight Convolutional Neural Network (CNN)-based method for edge devices (such as smartphones) to overcome the above issues by eliminating the need for a cloud/server in the localization system. To enable the use of the proposed model on resource-constraint edge devices, post-training optimization techniques including quantization, pruning and clustering are used to compress the network model. The proposed method is evaluated for three different open datasets, i.e., UJIIndoorLoc, Tampere and UTSIndoorLoc, as well as for our collected dataset named SBUK-D to verify its scalability. The results demonstrate the superiority of the proposed method compared to state-of-the-art studies. We also evaluate performance efficiency of our localization method on an android smartphone to demonstrate its applicability to edge devices. For UJIIndoorLoc dataset, our model with post-training optimizations obtains approximately 99% building accuracy, over 98% floor accuracy, and 4 m positioning mean error with the model size and inference time of 60 KB and 270 us, respectively, which demonstrate high accuracy as well as amenability to the resource-constrained edge devices.

Approximate computing is an emerging paradigm to improve power and performance efficiency for error-resilient application. Recent approximate adders have significantly extended the design space of accuracy-power configurable approximate adders, and find optimal designs by exploring the design space. In this paper, a new energy-efficient heterogeneous block-based approximate adder (HBBA) is proposed; which is a generic/configurable model that can be transformed to a particular adder by defining some configurations. An HBBA, in general, is composed of heterogeneous sub-adders, where each sub-adder can have a different configuration. A set of configurations of all the sub-adders in an HBBA defines its configuration. The block-based adders are approximated through inexact logic configuration and truncated carry chains. HBBA increases design space providing additional design points that fall on the Pareto-front and offer better power-accuracy trade-off compared to other configurations. Furthermore, to avoid Mont-Carlo simulations, we propose an analytical modelling technique to evaluate the probability of error and Probability Mass Function (PMF) of error value. Moreover, the estimation method estimates delay, area and power of heterogeneous block-based approximate adders. Thus, based on the analytical model and estimation method, the optimal configuration under a given error constraint can be selected from the whole design space of the proposed adder model by exhaustive search. The simulation results show that our HBBA provides improved accuracy in terms of error metrics compared to some state-of-the-art approximate adders. HBBA with 32 bits length serves about 15% reduction in area and up to 17% reduction in energy compared to state-of-the-art approximate adders., Comment: Submitted to the IEEE-TCAD journal, 16 pages, 16 figures

Wireless sensor networks (WSNs) can facilitate continuous monitoring of patients and increase early detection of emergency conditions and diseases. High density WSNs helps us to accurately monitor a remote environment by intelligently combining the data from the individual nodes. Due to energy capacity limitation of sensors, enhancing the lifetime and the reliability of WSNs are important factors in designing of these networks. The clustering strategies are verified as effective and practical algorithms for reducing energy consumption in WSNs and can tackle WSNs limitations. In this paper, an Energy-efficient weight-based Clustering Protocol (EWCP) is presented. Artificial retina is selected as a case study of WSNs applied in body sensors. Cluster heads' (CHs) selection is equipped with energy efficient parameters. Moreover, cluster members are selected based on their distance to the selected CHs. 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