Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft k-means clustering algorithm. Then, we utilize the flexibility of the soft-k-means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.

The proliferation of Wi-Fi devices has led to the rise of privacy concerns related to MAC Address-based systems used for people tracking and localization across various applications, such as smart cities, intelligent transportation systems, and marketing. These systems have highlighted the necessity for mobile device manufacturers to implement Randomized And Changing MAC address (RCM) techniques as a countermeasure for device identification. In response to the challenges posed by diverse RCM implementations, the IEEE has taken steps to standardize RCM operations through the 802.11aq Task Group (TG). However, while RCM implementation addresses some concerns, it can disrupt services that span both Layer 2 and upper-layers, which were originally designed assuming static MAC addresses. To address these challenges, the IEEE has established the 802.11bh TG, focusing on defining new device identification methods, particularly for Layer 2 services that require pre-association identification. Simultaneously, the IETF launched the MAC Address Device Identification for Network and Application Services (MADINAS) Working Group to investigate the repercussions of RCM on upper-layer services, including the Dynamic Host Configuration Protocol (DHCP). Concurrently, derandomization techniques have emerged to counteract RCM defense mechanisms. The exploration of these techniques has suggested the need for a broader privacy enhancement framework for WLANs that goes beyond simple MAC address randomization. These findings have prompted the inception of the 802.11bi TG, which aims to compile an exhaustive list of potential privacy vulnerabilities and prerequisites for a more private IEEE 802.11 standard. In this context, this tutorial aims to provide insights into the motivations behind RCM, its implementation, and its evolution over the years. It elucidates the influence of RCM on network processes and services. Furthermore, the tutorial delves into the recent progress made within the domains of 802.11bh, 802.11bi, and MADINAS. It offers a thorough analysis of the initial work undertaken by these groups, along with an overview of the relevant research challenges. The tutorial objective is to inspire the research community to explore innovative approaches and solutions that contribute to the ongoing efforts to enhance WLAN privacy through standardization initiatives.

This paper presents two methods to optimize LoRa (Low-Power Long-Range) devices so that implementing multiplier-less pulse shaping filters is more economical. Basic chirp waveforms can be generated more efficiently using the method of chirp segmentation so that only a quarter of the samples needs to be stored in the ROM. Quantization can also be applied to the basic chirp samples in order to reduce the number of unique input values to the filter, which in turn reduces the size of the lookup table for multiplier-less filter implementation. Various tests were performed on a simulated LoRa system in order to evaluate the impact of the quantization error on the system performance. By examining the occupied bandwidth, fast Fourier transform used for symbol demodulation, and bit-error rates, it is shown that even performing a high level of quantization does not cause significant performance degradation. Therefore, the memory requirements of LoRa devices can be significantly reduced by using the methods of chirp segmentation and quantization so as to improve the feasibility of implementing multiplier-less filters in LoRa devices.

Metric Multidimensional Scaling is commonly used to solve multi-sensor location problems in 2D or 3D spaces. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, because the number of anchors is small and the ranging error asymmetrically distributed. We then propose a two-step iterative approach based on geometric resolution of angle inaccuracies. The first step reduces the effect of poor ranging exchanges. The second step reconstructs the anchor positions, starting from the distances of highest likely-accuracy. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Metric techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known location, can allow a set of fixed sensors to auto-determine their position on a floor plan.