Efficient Data Compression with Error Bound Guarantee in Wireless Sensor Networks

We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and compare it with traditional methods for temporal and spatial data compression. The experimental validation reveals that our approach outperforms several existing wireless sensor network's data compression methods in terms of compression efficiency and signal reconstruction.Comment: ACM MSWiM 201

Antioxidants: nanotechnology and biotechnology fusion for medicine in overall

Antioxidant is a chemical substance that is naturally found in our food. It can prevent or reduce the oxidative stress of the physiological system. Due to the regular usage of oxygen, the body continuously produces free radicals. Excessive number of free radicals could cause cellular damage in the human body that could lead to various diseases like cancer, muscular degeneration and diabetes. The presence of antioxidants helps to counterattack the effect of these free radicals. The antioxidant can be found in abundance in plants and most of the time there are problems with the delivery. The solution is by using nanotechnology that has multitude potential for advanced medical science. Nano devices and nanoparticles have significant impact as they can interact with the subcellular level of the body with a high degree of specificity. Thus, the treatment can be in maximum efficacy with little side effect

Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks

Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs

Enabling Compression in Tiny Wireless Sensor Nodes

A Wireless Sensor Network (WSN) is a network composed of sensor nodes communicating among themselves and deployed in large scale (from tens to thousands) for applications such as environmental, habitat and structural monitoring, disaster management, equipment diagnostic, alarm detection, and target classification. In WSNs, typically, sensor nodes are randomly distributed over the area under observation with very high density. Each node is a small device able to collect information from the surrounding environment through one or more sensors, to elaborate this information locally and to communicate it to a data collection centre called sink or base station. WSNs are currently an active research area mainly due to the potential of their applications. However, the deployment of a large scale WSN still requires solutions to a number of technical challenges that stem primarily from the features of the sensor nodes such as limited computational power, reduced communication bandwidth and small storage capacity. Further, since sensor nodes are typically powered by batteries with a limited capacity, energy is a primary constraint in the design and deployment of WSNs. Datasheets of commercial sensor nodes show that data communication is very expensive in terms of energy consumption, whereas data processing consumes significantly less: the energy cost of receiving or transmitting a single bit of information is approximately the same as that required by the processing unit for executing a thousand operations. On the other hand, the energy consumption of the sensing unit depends on the specific sensor type. In several cases, however, it is negligible with respect to the energy consumed by the communication unit and sometimes also by the processing unit. Thus, to extend the lifetime of a WSN, most of the energy conservation schemes proposed in the literature aim to minimize the energy consumption of the communication unit (Croce et al., 2008). To achieve this objective, two main approaches have been followed: power saving through duty cycling and in-network processing. Duty cycling schemes define coordinated sleep/wakeup schedules among nodes in the network. A detailed description of these techniques applied to WSNs can be found in (Anastasi et al., 2009). On the other hand, in-network processing consists in reducing the amount of information to be transmitted by means of aggregation (Boulis et al., 2003) (Croce et al., 2008) (Di Bacco et al., 2004) (Fan et al., 2007)

Compression-based Data Reduction Technique for IoT Sensor Networks

في شبكات أجهزة استشعار إنترنت الأشياء ، يعد توفير الطاقة أمرًا مهمًا جدًا نظرًا لأن عقد أجهزة استشعار إنترنت الأشياء تعمل ببطاريتها المحدودة. يعد نقل البيانات مكلفًا للغاية في عقد أجهزة استشعار إنترنت الأشياء ويهدر معظم الطاقة ، في حين أن استهلاك الطاقة أقل بكثير بالنسبة لمعالجة البيانات. هناك العديد من التقنيات والمفاهيم التي تعنى بتوفير الطاقة ، وهي مخصصة في الغالب لتقليل نقل البيانات. لذلك ، يمكننا الحفاظ على كمية كبيرة من الطاقة مع تقليل عمليات نقل البيانات في شبكات مستشعر إنترنت الأشياء. في هذا البحث ، اقترحنا طريقة تقليل البيانات القائمة على الضغط (CBDR) والتي تعمل في مستوى عقد أجهزة استشعار إنترنت الأشياء. يتضمن CBDR مرحلتين للضغط ، مرحلة التكميم باستخدام طريقة SAX والتي تقلل النطاق الديناميكي لقراءات بيانات المستشعر ، بعد ذلك ضغط LZW بدون خسارة لضغط مخرجات المرحلة الاولى. يؤدي تكميم قراءات البيانات لعقد المستشعر إلى حجم ابجدية الـ SAX إلى تقليل القراءات ، مع الاستفادة من أفضل أحجام الضغط ، مما يؤدي إلى تحقيق ضغط أكبر في LZW. نقترح أيضًا تحسينًا آخر لطريقة CBDR وهو إضافة ناقل حركة ديناميكي (DT-CBDR) لتقليل إجمالي عدد البيانات المرسلة إلى البوابة والمعالجة المطلوبة. يتم استخدام محاكي OMNeT ++ جنبًا إلى جنب مع البيانات الحسية الحقيقية التي تم جمعها في Intel Lab لإظهار أداء الطريقة المقترحة. توضح تجارب المحاكاة أن تقنية CBDR المقترحة تقدم أداء أفضل من التقنيات الأخرى في الأدبياتEnergy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature

Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks

Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms

Energy efficient and latency aware adaptive compression in wireless sensor networks

Wireless sensor networks are composed of a few to several thousand sensors deployed over an area or on specific objects to sense data and report that data back to a sink either directly or through a series of hops across other sensor nodes. There are many applications for wireless sensor networks including environment monitoring, wildlife tracking, security, structural heath monitoring, troop tracking, and many others. The sensors communicate wirelessly and are typically very small in size and powered by batteries. Wireless sensor networks are thus often constrained in bandwidth, processor speed, and power. Also, many wireless sensor network applications have a very low tolerance for latency and need to transmit the data in real time. Data compression is a useful tool for minimizing the bandwidth and power required to transmit data from the sensor nodes to the sink; however, compression algorithms often add a significant amount of latency or require a great deal of additional processing. The following papers define and analyze multiple approaches for achieving effective compression while reducing latency and power consumption far below what would be required to process and transmit the data uncompressed. The algorithms target many different types of sensor applications from lossless compression on a single sensor to error tolerant, collaborative compression across an entire network of sensors to compression of XML data on sensors. Extensive analysis over many different real-life data sets and comparison of several existing compression methods show significant contribution to efficient wireless sensor communication --Abstract, page iv