Massimo Vecchio

A Simple Algorithm for Data Compression in Wireless Sensor Networks

Power saving is a critical issue in wireless sensor networks (WSNs) since sensor nodes are powered by batteries which cannot be generally changed or recharged. As radio communication is often the main cause of energy consumption, extension of sensor node lifetime is generally achieved by reducing transmissions/receptions of data, for instance through data compression. Exploiting the natural correlation that exists in data typically collected by WSNs and the principles of entropy compression, in this Letter we propose a simple and efficient data compression algorithm particularly suited to be used on available commercial nodes of a WSN, where energy, memory and computational resources are very limited. Some experimental results and comparisons with, to the best of our knowledge, the only lossless compression algorithm previously proposed in the literature to be embedded in sensor nodes and with two well- known compression algorithms are shown and discussed.

An Efficient Lossless Compression Algorithm for Tiny Nodes of Monitoring Wireless Sensor Networks

Energy is a primary constraint in the design and deployment of wireless sensor networks (WSNs), since sensor nodes are typically powered by batteries with a limited capacity. Energy efficiency is generally achieved by reducing radio communication, for instance, limiting transmission/reception of data. Data compression can be a valuable tool in this direction. The limited resources available in a sensor node demand, however, the development of specifically designed compression algorithms. In this paper, we propose a simple lossless entropy compression (LEC) algorithm which can be implemented in a few lines of code, requires very low computational power, compresses data on the fly and uses a very small dictionary whose size is determined by the resolution of the analog-to-digital converter. We have evaluated the effectiveness of LEC by compressing four temperature and relative humidity data sets collected by real WSNs, and solar radiation, seismic and ECG data sets. We have obtained compression ratios up to 70.81% and 62.08% for temperature and relative humidity data sets, respectively, and of the order of 70% for the other data sets. Then, we have shown that LEC outperforms two specifically designed compression algorithms for WSNs. Finally, we have compared LEC with gzip, bzip2, rar, classical Huffman and arithmetic encodings.

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.

Reducing Power Consumption in Wireless Sensor Networks Using a Novel Approach to Data Aggregation

Saving energy is a very critical issue in wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with a limited capacity. Since the radio is the main cause of power consumption in a sensor node, transmission/reception of data should be limited as much as possible. To this aim, we propose a novel distributed approach to data aggregation based on fuzzy numbers and weighted average operators to reduce data communication in WSNs when we are interested in the estimation of an aggregated value such as maximum or minimum temperature measured in the network. The basic point of our approach is that each node maintains an estimate of the aggregated value. Based on this estimate, the node decides whether a new value measured by the sensor on board the node or received through a message has to be propagated along the network. We show how the lifetime of the network can be estimated through the datasheet of the sensor node and the number of received and transmitted messages. We discuss and evaluate the application of our approach to the monitoring of the maximum temperature in a 100-node simulated WSN and a 12-node real WSN. Finally, we compute the estimates of the lifetimes for both the networks.

A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks

Abstract: To know the location of nodes plays an important role in many current and envisioned wireless sensor network applications. In this framework, we consider the problem of estimating the locations of all the nodes of a network, based on noisy distance measurements for those pairs of nodes in range of each other, and on a small fraction of anchor nodes whose actual positions are known a priori. The methods proposed so far in the literature for tackling this non-convex problem do not generally provide accurate estimates. The difficulty of the localization task is exacerbated by the fact that the network is not generally uniquely localizable when its connectivity is not sufficiently high. In order to alleviate this drawback, we propose a two-objective evolutionary algorithm which takes concurrently into account during the evolutionary process both the localization accuracy and certain topological constraints induced by connectivity considerations. The proposed method is tested with different network configurations and sensor setups, and compared in terms of normalized localization error with another metaheuristic approach, namely SAL, based on simulated annealing. The results show that, in all the experiments, our approach achieves considerable accuracies and significantly outperforms SAL, thus manifesting its effectiveness and stability.

DEEP: Density-based proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility

This paper investigates proactive data dissemination and storage schemes for wireless sensor networks (WSNs) with mobile sinks. The focus is on schemes that do not impose any restrictions on the sinks mobility pattern. The goal is to enable the sink to collect a representative view of the networks sensed data by visiting any set of x out of n nodes, where x@?n. The question is how to obtain this while maintaining a good trade-off between the communication overhead of the scheme, the storage space requirements on the nodes, and the ratio between the number of visited nodes x and the representativeness of the gathered data. To answer this question, we propose density-based proactivE data dissEmination Protocol (DEEP), which combines a probabilistic flooding with a probabilistic storing scheme. The DEEP protocol is formally analyzed and its performance is studied under simulations using different network densities and compared with a scheme based on random walks, called RaWMS.

A Fuzzy Approach to Data Aggregation to Reduce Power Consumption in Wireless Sensor Networks

In this paper, we propose a novel distributed approach based on fuzzy numbers and weighted average operators to reduce power consumption in wireless sensor networks when we are interested in the estimation of an aggregated value such as maximum or minimum temperature measured in the network. The basic point of our approach is that each node maintains an estimate of the aggregated value. Based on this estimate, the node decides whether a new value measured by the sensor on board the node or received through a message has to be propagated along the network. We present the application of our approach to the monitoring of the maximum temperature in a two-hundred square meters flat and discuss how the lifetime of the network can be determined through the datasheet of the used mote and the number of received and sent messages

Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach ☆

Abstract Reliable wide-area monitoring with Wireless Sensor Networks (WSNs) remains a problem of interest: simply deploying more nodes to cover wider areas is generally not a viable solution, due to deployment and maintenance costs and the increase in radio interference. One possible solution gaining popularity is based on the use of a reduced number of mobile nodes with controllable trajectories in the monitored field. In this framework, we present a distributed technique for iteratively computing the trajectories of the mobile nodes in a greedy fashion. The static sensor nodes actively assist the mobile nodes in this task by means of a bidding protocol, thus participating towards the goal of maximizing the area coverage of the monitored field. The performance of the proposed technique is evaluated on various simulation scenarios with different number of mobile and static nodes in terms of achieved coverage and mean time to achieve X % coverage. Comparison with previous state-of-the-art techniques reveals the effectiveness and stability of the proposed method.

Adaptive Lossless Entropy Compressors for Tiny IoT Devices

Internet of Things (IoT) devices are typically powered by small batteries with a limited capacity. Thus, saving power as much as possible becomes crucial to extend their lifetime and therefore to allow their use in real application domains. Since radio communication is in general the main cause of power consumption, one of the most used approaches to save energy is to limit the transmission/reception of data, for instance, by means of data compression. However, the IoT devices are also characterized by limited computational resources which impose the development of specifically designed algorithms. To this aim, we propose to endow the lossless compression algorithm (LEC), previously proposed by us in the context of wireless sensor networks, with two simple adaptation schemes relying on the novel concept of appropriately rotating the prefix-free tables. We tested the proposed schemes on several datasets collected in several real sensor network deployments by monitoring four different environmental phenomena, namely, air and surface temperatures, solar radiation and relative humidity. We show that the adaptation schemes can achieve significant compression efficiencies in all the datasets. Further, we compare such results with the ones obtained by LEC and, by means of a non-parametric multiple statistical test, we show that the performance improvements introduced by the adaptation schemes are statistically significant.

A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm

The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In this paper, a two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality. Compression is measured in terms of difference in percentage between the sizes of the original and compressed images, whereas quality is computed as mean squared error between the reconstructed and the original images. We discuss the application of the proposed approach to well-known benchmark images and show how the quantization tables determined by our method improve the performance of the JPEG algorithm with respect to the default tables suggested in Annex K of the JPEG standard.