An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs

Adaptive Decision Feedback Equalization Under Parallel Adaptation

In this paper, we first propose an alternative decision feedback equalizer (DFE) scheme to the DFE scheme of Labat, Macchi and Laot. Both schemes are broken into two distinct operation modes which feature a linear equalizer during acquisition and a DFE after the initial convergence has been achieved. While avoiding some potential problems with the latter DFE, our scheme achieves similar overall performance in terms of convergence speed and steady state error. A second contribution is the development of a strategy that simultaneously adapts the linear equalizer and the DFE in a parallel fashion such that the switch-over between modes is no longer necessary. Consequently, smoother and usually faster convergence is achieved as switching disruptions are reduced. This scheme is especially advantageous under noisy and severe conditions as supported by our simulations

Reliability Based Soft Transition Technique for Dual-Mode Blind Equalizers

In this paper, we propose a new technique that facilitates soft transition between a startup algorithm and a decision directed (DD) algorithm in blind adaptive equalizers. The algorithm-pair combined using a reliability measure that is proportional to a

Adaptive channel estimation using least mean squares algorithm for cyclic prefix OFDM systems

Orthogonal frequency division multiplexing (OFDM) delivers high data transmission rate and forms the basis of Beyond 3G. The channel estimation is imperative for the implementation of OFDM. Cyclic Prefix (CP) based block Recursive Least Squares (RLS) channel estimation algorithm has been proposed for OFDM systems but it increases computational complexity. In this paper, we propose a block LMS (Least Mean Squares) channel estimation algorithm which promises less computation but delivers comparable and promising results

An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs

Propagation Measurement of a Pedestrian Tunnel at 24 GHz for 5G Communications

In this paper, we report the results of a field measurement campaign carried out inside a pedestrian tunnel at 24 GHz in two conditions, namely, empty tunnel scenario and busy tunnel scenario with pedestrian movement. The experiment measures the fading effects of various groups of pedestrian crowds using directional antennas at the transmitter and receiver for millimeter-wave radio communications. Having presented and analyzed the measurement data in several diverse scenarios, we have further investigated human scattering effects in the crowded pedestrian tunnel and performed ray-tracing simulation for an empty pedestrian tunnel condition. Because tunnel is an enveloped scenario that is not bound by any geographic areas, the results of this study can be applied to a wider scenario like other pedestrian tunnels across the globe. Above all, these findings contribute towards ensuring wireless connectivity for everyone even in a remote scenario like underground passages