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

Wireless sensor network as a distribute database

Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources. This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted

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 and encryption for ECG biomedical signal in healthcare system

The ECG data needs large memory storage device due to continuous heart rate logs and vital parameter storage. Thus, efficient compression schemes are applied to it before sending it to the telemedicine center for monitoring and analysis. Proper compression mechanisms can not only improve the storage efficiency but also help in faster porting of data from one device to another due to its compact size. Also, the collected ECG signals are processed through various filtering techniques to remove unnecessary noise and then compressed. In our scheme, we propose use of buffer blocks, which is quite novel in this field. Usage of highly efficient methods for peak detection, noise removal, compression and encryption enable seamless and secure transmission of ECG signal from sensor to the monitor. This work further makes use of AES 256 CBC mode, which is barely used in embedded devices, proves to be very strong and efficient in ciphering of the information. The PRD outcome of proposed work comes as 0.41% and CR as 0.35%, which is quite better than existing schemes. Experimental results prove the efficiency of proposed schemes on five distinct signal records from MIT-BIH arrhythmia datasets

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

A Suggested Lightweight Lossless Compression Approach for Internet of Everything Devices

محدودية مساحة التخزين، وارتفاع حركة مرور نقل بيانات المتحسس وكفاءة نقل الطاقة هي عينات من المشاكل الصعبة في تطوير تطبيقات إنترنت كل شيء. وقد تم في هذا البحث معالجه هذه المشاكل من خلال اقتراح طريقة ضغط بدون فقدان للبيانات، والتي تقوم على عمليات خفيفة بالتعقيد. الطريقة المقترحة تعمل بكفاءة حتى مع الاجهزة ذات الأداء المنخفض. وعلاوة على ذلك، تحسن  أجهزة المتحسس بشكل فعال في إنترنت الكل شيء بواسطة التقليل من استهلاك الطاقة والموارد المستخدمة. وبالتالي، توفير الطاقة واطالة عمر أجهزة إنترنت الكل شيء. تم اختبار الطريقة المقترحة، على مجموعتين من البيانات كمعيار اساسي حسب نسبة الضغط المحسوبة على الرسائل بين نمط شخص الى شخص. بالإضافة إلى ذلك، نسبة ضغط على المتحسسات الطبية (دقات القلب ودرجة حرارة الجسم) بين نمط آلة والى شخص من إنترنت الكل شيء. في الاختبارين، الطريقة قد حصلت على مقياس ضغط كبير.Limit storage space, high traffic sensor data transfer and power efficient transmission are samples of the challenging issues in the development of Internet of Everything (IoE) apps. This paper tackles these issues by presenting a suggested lossless compression approach according to lightweight operations. The suggested approach is working efficiently even with a low-performance equipment. Furthermore, enhancing the sensor node effectively of IoE by minimizing energy exhaustion and resource utilizing. Hence, provision power and expanding the age of IoE devices. The suggested approach is evaluated by using two datasets as a benchmark by calculating compression ratio firstly, on messages between person to person and secondly, on healthcare sensors (HeartRate and Body Temperature) between machine to person pattern of IoE.  In two tests, the suggested approach may obtain a significant compression ratio

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

Integrating secure mobile P2P systems and Wireless Sensor Networks

Aquesta tesi tracta de les diferents limitacions trobades a WSN per a habilitar-ne el desplegament en nous escenaris i facilitar la difusió de la informació obtinguda. A un nivell baix, ens centrem en el consum d'energia, mentre que, a un nivell més alt, ens focalitzem en la difusió i la seguretat de la informació. Reduïm el consum d'una mote individual en xarxes amb patrons de trànsit dinàmic mitjançant la definició d'una funció de planificació basada en el conegut controlador PID i allarguem la vida d'una WSN globalment distribuint equitativament el consum energètic de totes les motes, disminuint el nombre d'intervencions necessàries per a canviar bateries i el cost associat. Per tal d'afavorir la difusió de la informació provinent d'una WSN, hem proposat jxSensor, una capa d'integració entre les WSN i el conegut sistema P2P JXTA. Com que tractem informació sensible, hem proposat una capa d'anonimat a JXTA i un mecanisme d'autenticació lleuger per a la seva versió mòbil.Esta tesis trata las diferentes limitaciones encontradas en WSN para habilitar su despliegue en nuevos escenarios, así como facilitar la diseminación de la información obtenida. A bajo nivel, nos centramos en el consumo de energía, mientras que, a un nivel más alto, nos focalizamos en la diseminación y seguridad de la información. Reducimos el consumo de una mota individual en redes con patrones de tráfico dinámico mediante la definición de una función de planificación basada en el conocido controlador PID y alargamos la vida de una WSN globalmente distribuyendo equitativamente el consumo energético de todas las motas, disminuyendo el número de intervenciones requeridas para cambiar baterías y su coste asociado. Para favorecer la diseminación de la información procedente de una WSN hemos propuesto jxSensor, una capa de integración entre las WSN y el conocido sistema P2P JXTA. Como estamos tratando con información sensible, hemos propuesto una capa de anonimato en JXTA y un mecanismo de autenticación ligero para su versión móvil.This thesis addresses different limitations found in WSNs in order to enable their deployment in new scenarios as well as to make it easier to disseminate the gathered information. At a lower level, we concentrate on energy consumption while, at a higher level, we focus on the dissemination and security of information. The consumption of an individual mote in networks with dynamic traffic patterns is reduced by defining a scheduling function based on the well-known PID controller. Additionally, the life of a WSN is extended by equally distributing the consumption of all the motes, which reduces the number of interventions required to replace batteries as well as the associated cost. To help the dissemination of information coming from a WSN we have proposed jxSensor, which is an integration layer between WSNs and the well-known JXTA P2P system. As we are dealing with sensitive information, we have proposed an anonymity layer in JXTA and a light authentication method in its mobile version

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