Academic literature on the topic 'SENCER NETWORKS'

This paper investigates a cognitive radio network where a secondary sender assists a primarytransmitter in relaying primary information to a primary receiver and also transmits its own information toa secondary recipient. This sender is capable of jamming to protect secondary and/or primary informationagainst an eavesdropper and self-powering by harvesting radio frequency energy of primary signals.Security capability of both secondary and primary networks are analyzed in terms of secrecy outageprobability. Numerous results corroborate the proposed analysis which serves as a design guidelineto quickly assess and optimize security performance. More importantly, security capability trade-offbetween secondary and primary networks can be totally controlled with appropriate selection of systemparameters.

Temporary connection failures and route changes happen in a Mobile Ad Hoc Network (MANET). MANET enjoys extensive variety of applications like in tactical networks, Sensor networks. Much battery backup is required while tuning a node that is far from the sender node While compared to the node which is near in respect to sender, In this paper we are proposing an approach of optimized utilization of battery backup in MANET Battery Backup is a main constraint in mobile ad hoc networks Most of the battery is wasted in tuning to the networks repeatedly there by making the mobile node vulnerable to Jail. This paper emphasize on proper utilization of battery backup by varying the signal strength according the distance of the nodes. Modified FireFly Algorithm (MFFA) is greatly utilized in this research for boosting up of battery backup. The cluster head should maintain a table for battery backup and decision of task distribution will be based on this table.

Voice over Internet Protocol (VoIP) has grown quickly in the world of telecommunication. Wireless Local Area Networks (WLANs) are the most performance assuring technology for wireless networks, and WLANs have facilitated high-rate voice services at low cost and good flexibility. In a voice conversation, each client works as a sender or a receiver depending on the direction of traffic flow over the network. A VoIP application requires high throughput, low packet loss, and a high fairness index over the network. The packets of VoIP streaming may experience drops because of the competition among the different kinds of traffic flow over the network. A VoIP application is also sensitive to delay and requires the voice packets to arrive on time from the sender to the receiver side without any delay over WLAN. The scheduling system model for VoIP traffic is an unresolved problem. The objectives of this paper are to identify scheduler issues. This comprehensive structure of Novel Voice Priority Queue (VPQ) scheduling system model for VoIP over WLAN discusses the essential background of the VPQ schedulers and algorithms. This paper also identifies the importance of the scheduling techniques over WLANs.

Standard core of communications’ networks is represent by active elements, which carries out the processing of transmitted data units. Based on the results of the processing the data are transmitted from sender to recipient. The hardest challenge of the active elements present to determine what the data processing unit and what time of the system to match the processing priority assigned to individual data units. Based on the analysis of the architecture and function of active network components and algorithms, artificial neural networks can be assumed to be effectively useable to manage network elements. This article focuses on the design and use of the selected type of artificial neural network (Hopfield neural network) for the optimal management of network switch.

Social networks or social media is structure social that builds from node to node which generally is individual or organization. That will bundle with one or more type relation specific like score, vision, idea, friends and posterity and others. Analysis social network look at social connection like node or tie This time is so many social networks that already operation but all of them still using based on text for send message, comment, and email so it will the problem that mostly happen by smart phone user is they have difficulties for typing and for PC user occasionally having mistake because miss understanding meaning from sender.

Sensor networks have been recognised as one of the most advanced technologies of the 21st century with vast practical applications. The life of a sensor network is mainly determined by its energy consumption. Commercially available sensor nodes are battery driven devices. As most sensor nodes are deployed widely scattered and in isolated areas, replacing battery is not an option. This dissertation focuses on extending the lifespan of sensor networks by reducing energy consumption in design and operation of sensor nodes. The study goes in depth to analyse the state of art technology to achieve energy efficiency in sensor nodes and identify scope for further research in this field. In the architecture of sensor nodes, multipliers are the main blocks for designing an energy efficient processor. Vedic Mathematics provides principles of high speed multiplication. The main reason for power dissipation in multiplier circuit is due to power dissipation of full adder circuit. Low power multipliers have been designed by using low power adders. Motivated by this, a high speed Vedic multiplier has been designed using multiplexer based adder. When compared with existing Vedic multipliers, proposed designs showed significant improvement in reduction of delay and energy consumption. Sensor nodes consume maximum power during data communication. So processing data locally at each node in a sensor network is important for minimizing power consumption. High processing speed and low area designs are in ever growing demand. In order to predict outcomes, based on previous inputs, ALU can be designed with neurons. Processing speed of ALU can be improved by replacing conventional multipliers with Vedic multipliers. This research work suggests implementation of high speed ALU using Vedic neurons. The analysis of the results shows that the proposed design leads to x reduction in the delay and reduction in LUT count (an indicator of area) of the ALU. Use of energy efficient power amplifiers is an essential requirement for sensor nodes, as power amplifiers are responsible for the main power consumption in the transceivers of sensor nodes. Again, wider band width is another important requirement for power amplifiers used in sensor transceivers especially in wireless visual sensor networks and wireless multimedia sensor networks. Reliability of a power amplifier can be increased by designing it at smaller supply voltage. This thesis suggests improvements in design of power amplifier in class E configuration, for transceivers in wireless sensor nodes. In order to achieve wider band width, cascade of common drain followed by common source in class E configuration has been designed; and for more reliable operation with higher efficiency, class E in double cascoded has been implemented. The proposed designs, when simulated in SPICE, higher efficiencies and band widths have been achieved. This research also explored to design a robust solar energy harvesting system to enhance life time of sensor nodes. Proposed solar energy supply system mainly consists of a solar panel, rechargeable battery and a control circuit. To obtain sufficient voltage to charge battery, electrical energy generated through panel is boosted by boost converter. Different sensor nodes are supplied with energy from this system. An inverter is also designed for AC applications. Experimental results show that this compact, self-sufficient system enables outdoor based wireless sensor network nodes to operate successfully for longer periods.

Network programming is one of the most important applications in Wireless Sensor Networks as It provides an efficient way to update program Images running on sensor nodes without physical access to them. Securing these updates, however, remains a challenging and important issue, given the open deployment environment of sensor nodes. Though several security schemes have been proposed to impose the authenticity and Integrity protection on network programming applications, they are either energy Inefficient as they tend to use digital signature or lacks the data confidentiality. In addition, due to the absence of secure memory management in the current sensor hardware, the attacker could inject malicious code into the program flash by exploiting buffer overflow In the memory despite the secure code dissemination. The contribution of this thesis Is to provide two software-based security protocols and one hardware-based remote attestation protocol for network programming application. Our first protocol deploys multiple one-way key chains for a multi-hop sensor network. The scheme Is shown to be lower In computational, power consumption and communication costs yet still able to secure multi??hop propagation of program images. Our second protocol utilizes an Iterative hash structure to the data packets in network programming application, ensuring the data confidentiality and authenticity. In addition, we Integrated confidentiality and DoS-attack-resistance in a multi??hop code dissemination protocol. Our final solution is a hardware-based remote attestation protocol for verification of running codes on sensor nodes. An additional piece of tamper-proof hardware, Trusted Platform Module (TPM), is imposed into the sensor nodes. It secures the sensitive information (e.g., the session key) from attackers and monitors any platform environment changes with the Internal registers. With these features of TPM, the code Injection attack could be detected and removed when the contaminated nodes are challenged in our remote attestation protocol. We implement the first two software-based protocols with Deluge as the reference network programming protocol in TinyOS, evaluate them with the extensive simulation using TOSSIM and validate the simulation results with experiments using Tmote. We implement the remote attestation protocol on Fleck, a sensor platform developed by CSIRO that Integrates an Atmel TPM chip.

Wireless sensor networks (WSNs) have a wide variety of applications in civilian, medical and military applications. However, the nodes in such a network are limited to one type of action: sensing the environment. With increasing requirements for intelligent interaction with the environment, there is a need to not only perceive but also control the monitored environment. This has led to the emergence of a new class of networks, referred to as wireless sensor and actor networks (WSANs), capable of performing both sensing and acting tasks on the environment. The evolution from WSNs, which can be thought of as performing only read operations, to WSANs, which can perform both read and write operations, introduces unique and new challenges that need to be addressed. In this research, the fundamental challenges required for effective operation of WSANs are analyzed from the following three different perspectives: (i) operation correctness, (ii) resource optimality, and (iii) operation performance. The solutions proposed to address the challenges are evaluated with the optimal solution and other competing approaches through analytical and simulation results. The feasibility of the proposed solutions is demonstrated through a testbed implementation.

This paper explores how an all pair shortest path can be obtained in a wireless sensor network when sensors fail. Sensors are randomly deployed in a predefined geographical area, simulating the deployment of sensors from an airplane, and finding shortest path between all the sensors deployed based on distance. A major problem to address in wireless sensor networks is the impact of sensor failures on existing shortest paths in the network. An application is developed to simulate a network and find shortest paths affected by a sensor failure and find alternative shortest path. When a sensor fails, all the shortest paths and all the remaining sensors in the network are checked to see if the sensor failure has any impact on the network. Alternative shortest path is calculated for those paths affected by sensor failures.

This thesis is concerned with the design and analysis of new Bayesian network based mobility algorithms for mobile Wireless Sensor Networks (WSNs). The hypothesis for the work presented herein is that incorporating Artificial Intelligence (Al) at the level of the sensor nodes will improve their performance (coverage, connectivity and lifetime) and result in fault tolerance capabilities, in the face of uncertainty associated with incomplete information regarding the network. Two types of mobility strategy are presented and investigated. Firstly, a new gazing mobility strategy is presented which is biologically inspired from herbivores grazing pastures. As part of the latter strategy, and instead of deploying a large number of static sensor nodes to cover a region of interest, a smaller number of mobile nodes are deployed which migrate around the region to achieve coverage over time. To enable the performance evaluation of this strategy a new coverage measure called Coverage Against Time was created. A new decentralised Bayesian network based grazing mobility algorithm called BNGRAZ is presented which uses evidence derived from neighbouring nodes to predict the probability of performance (coverage and connectivity) changes associated with moving in a particular direction. Evidence is also obtained from a new Coverage Approximation (CA) algorithm which enables each sensor node to approximate the WSN coverage in order to determine areas in need of servicing. The performance of BNGRAZ is compared to a fixed path mobility technique, Random Waypoint (RWP) mobility model, and a new Grazing Reference Point Group Mobility (GRPGM) algorithm developed as part of this work. Secondly, a self-healing strategy which physically relocates sensor nodes to repair coverage holes, due to the failure of sensor nodes, is presented. A new decentralised Bayesian network based mobility algorithm called BayesMob, which uses local neighbour information, was created to coordinate the self-healing motion. The algorithm enables sensor nodes to predict the probability of an increase in coverage given a move in a particular direction to repair coverage holes. In addition, the thesis outlines the development of a WSN simulator. The latter provides a tool for evaluating the performance of mobile WSNs. All mobility strategies and algorithms discussed herein were simulated using the new WSN simulator.

Wireless Sensor Networks (WSNs) have been widely studied in the last decade because of the large set of applications that can be potentially enabled by this novel network paradigm, as environmental monitoring, home automation, localization and tracking of mobile users, seamless and ubiquitous data exchange. However, the severe constraints in terms of energy, processing, memory and reliability of the low--cost sensor devices left a number of unresolved problems, open to research. This thesis tackles two of the most interesting problems concerning WSNs, namely localization and traffic management. Accurate sensor localization is crucial for a wide variety of WSN applications and protocols, including monitoring, routing, scheduling, data fusion and so on. Typically, localization algorithms are based on an infrastructure of nodes which are aware of their geographical positions, called beacons. These nodes broadcast their coordinates in order to let the other nodes in the network, referred to as stray nodes, infer their own position by means of some estimation technique. The topic has been widely investigated, both in simulation and, more recently, in experimental testbeds. Nonetheless, the performance obtained by most of the proposed algorithms is still unsatisfactory, in particular in indoor environments. Therefore, further research is needed. In this thesis, the problem has been tackled from different perspectives, in order to gain a deep and clear understanding of the several facets that characterize this interesting domain. As a consequence, we collected a rather wide set of results that apparently may not seem to be strongly correlated, though they all fit within the same research project. As a first step, we compare well known localization algorithms in indoor environments for static networks, based on a Received Signal Strength Indicator (RSSI) ranging estimation technique. Results shed light on the actual potentialities and limits of common localization algorithms in indoor environments in a real testbed. In particular, we observed the strong impact of the shadowing random attenuation of the power of the received signal on the performance of the localization algorithms. Therefore, research has been addressed to the reduction of the shadowing term in the RSSI measures. To this end, we investigated the effect of the carrier frequency and of the antenna anisotropy on the randomness of the shadowing component, again using real measurements collected in different testbeds. Successively, our attention turned to the problem of beacon positioning that we observed may strongly affect the localization performance. We analyzed the optimal beacons placement both using an exhaustive but very complex approach and a heuristic scheme that provides close to optimal solutions while maintaining a linear complexity with the number of beacons. Looking at the localization performance separating the different effects of localization algorithms, channel parameters and beacon positioning, has been important to understand the contribution of each one of these aspects to the localization error and how much is it possible to improve the localization accuracy by means of a single technique, that usually is chosen depending on the particular scenario and available resources. Then nodes mobility has been included into the framework. We first considered an Autonomous Mobile Robot (AMR) that can interact with sensors, but is also capable to self--localize using onboard odometry. Leveraging on the complementarities of WSN and AMR, we considered the Simultaneous Localization and Mapping (SLAM) problem that consists in creating the map of an area without any a priori knowledge of the environment, while localizing the nodes in the WSN by combining the information provided by the WSN and by the AMR odometry. Second, we considered a completely mobile wireless networks in which heterogeneous nodes with different self--localization capabilities can interact one another on an opportunistic basis, exchanging localization information with other nodes that occasionally happen to be in geographical proximity. The performance of this scheme has been analyzed through a mathematical framework. We considered a Maximum Likelihood (ML) approach, a Linear Matrix Inequality (LMI) system and a simple heuristic strategy to define opportunistic localization algorithms. The validity of the mathematical model has been confirmed through experimental measures. We considered two main settings, one in which a node can rely on a single opportunistic interaction and one in which multiple contacts can be set while the node remains in the same position. We analyzed the different techniques, finding that in the first scenario, if the channel is not very noisy and the self--localization of the cooperative node quite accurate, the heuristic algorithm performs very well, in some case slightly better than ML, while the ML approach is very robust and improves localization accuracy even in difficult scenarios. If multiple interactions are available, then the heuristic technique is quite poor and it is better to rely on the LMI technique. Moreover LMI is quite flexible, because it does not require an initial guess of the position by the stray node and it can be used both with and without ranging information. The static scenario analysis was very useful to focus on the mobile localization. The design of the proposed algorithms and scenarios, the simulation parameters and limits, are strictly related to the knowledge of the channel behavior and localization performance gained from the previous studies. The opportunistic idea raises from two main considerations: the limited accuracy of the beacon based RSSI localization in a real scenario and the quite good ranging accuracy of RSSI when limiting the distance. Alongside the main research line concerning localization in WSN, during the Ph.D. we also investigated other research topics, namely traffic management and underwater sensor network, which are not directly related to the previous ones but still of great scientific and educational interest. The most significant achievements obtained in these areas have also been collected in the thesis for two reasons. First, these topics still belong to the context of wireless sensor networks, sharing some basic characteristics such as the assumptions of simplicity and energy constraints. Second, the excursion on different but correlated fields may potentially open new perspectives to well known problems, thus contributing to the innovation and the progress of the research. As mentioned, the second problem addressed in this thesis regards the traffic management in WSN. Usually all the nodes in a WSN send packets to a common node, called sink. This traffic pattern, under a certain load, can lead to congestion problems, causing packet losses, high delays and waste of energy. The proposed solutions in literature usually aim at detecting the occurence of congestion by involving in this task many nodes, sometimes the entire network. We propose a different protocol, called Efficient Packet Converge Casting (EPC$^2$), that mitigates the congestion at the sink involving only a fixed number of nodes, namely the sink's neighbors. Another scenario we look into was that of underwater wireless sensor networks (UWSNs), which enable a number of applications as for radio WSNs, fostering interest in this research field. Similarly to radio networks, the energy efficiency remains a main issue. Nodes are powered by battery and it is very important to extend the network lifetime as much as possible. The different characteristics of the environment in which nodes are deployed raise new research challenges that require novel protocol design. We addressed the energy efficiency problem in UWSN with two different approaches. First, we investigated the effect of duty--cycle and node density on the energy consumption of the network, assuming that nodes can use different power levels to transmit. Second, we proposed a channel management scheme to optimize the energy consumption, considering the strong relationship between distance, frequency and channel attenuation. Both solutions are very simple and suitable for the low--complexity underwater sensor devices and do not need any central unit to coordinate, but they work asyncronously and distributely.
Negli ultimi anni, le reti wireless di sensori (WSN) sono state molto studiate a causa delle numerose applicazioni in cui possono essere usate, come il monitoraggio ambientale, la domotica, la localizzazione e il tracking di utenti mobili. Le forti limitazioni dei nodi sensori in termini di energia, processamento, memoria e affidabilita', lasciano ancora aperti molti problemi per la ricerca. Questa tesi affronta due problemi molto importanti relativi alle reti wireless di sensori: la localizzazione e la gestione del traffico. Un'accurata localizzazione dei sensori e' importante per molte applicazioni per WSN, come monitoraggio, routing, scheduling, data fusion e molte altre. Tipicamente, gli algoritmi di localizzazione si basano su una infrastruttra di nodi, detti nodi ancora che conoscono la loro posizione geografica. Questi nodi trasmettono in broadcast le loro coordinate agli altri nodi della rete, che da queste informazioni ricavano la loro posizione tramite tecniche di stima. L'argomento e' stato largamente studiato, sia con simulazioni sia, piu' recentemente, con testbed sperimentali. Ciononostante, l'accuratezza ottenuta dalla maggior parte degli algoritmi proposti e' ancora insufficiente, soprattutto in ambienti interni. E' quindi necessario cercare nuove metodologie e nuovi approcci. In questa tesi, il problema e' stato affrontato da diversi punti di vista, in modo da capire in maniera piu' chiara e accurata i diversi aspetti che lo caratterizzano. Come conseguenza, abbiamo raccolto una vasta quantita' di dati che potrebbero apparire come non molto legati uno all'altro, ma che in realta' rientrano tutti nello stesso progetto di ricerca. Come primo passo, abbiamo confrontato algoritmi di localizzazione proposti in letteratura in uno scenario indoor e con nodi statici, stimando la distanza tra i nodi utilizzando la potenza del segnale ricevuto (RSSI). I risultati ci hanno permesso di capire le potenzialita' e i limiti dei piu' diffusi algoritmi di localizzazione in ambiente indoor e in un testbed reale. In particolare, abbiamo osservato il grande impatto che ha sulle prestazioni di localizzazione l'aleatorieta', data dal termine di shadowing, della misura di potenza ricevuta. Abbiamo quindi cercato delle strategie per ridurre la varianza di questo termine aleatorio. A questo scopo, abbiamo studiato l'effetto della frequenza della portante, utilizzando una stima della potenza ricevuta multi--canale, e l'impatto dell'anisotropia dell'antenna sulle oscillazioni dei valori di potenza ricevuta. Entrambi glil studi sono stati fatti con misure reali raccolte in diversi testbed. Successivamente, abbiamo analizzato il problema del posizionamento dei nodi ancora, dopo aver osservato l'incidenza che questo ha sull'accuratezza della localizzazione. Abbiamo confrontato il posizionamento ottimo dei nodi ancora usando sia una tecnica esaustiva, ma computazionalmente molto complessa, sia uno schema euristico che raggiunge prestazioni molto vicine all'ottimo pur mantenendo una complessita' lineare con il numero di ancore. Guardare alle prestazioni di localizzazione separando i diversi effetti degli algoritmi, dei parametri di canale e del posizionamento dei nodi ancora e' stato importante per capire il contributo dei diversi aspetti all'interno dell'errore di localizzazione e quanto sia possibile migliorare la precisione della localizzazione ottimizzando uno di questi aspetti, che solitamente viene scelto in base allo scenario e alle risorse disponibili. Quindi, abbiamo incluso nel nostro scenario anche nodi mobili. All'inizio abbiamo considerato un robot mobile (AMR) che poteva interagire con i sensori, ma anche capace di localizzarsi grazie all'odometria. Facendo leva sulla complementarieta' della rete di sensori e del robot mobile, abbiamo studiato e implementato un algoritmo di localizzazione e mappatura simultanea (SLAM), problema che consiste nel creare la mappa di un'area senza nessuna conoscenza a priori dell'ambiente e in contemporanea localizzare i nodi sensore confrontando le informazioni provenienti dai sensori e quelle ricavate dall'odometria del robot. Poi abbiamo considerato uno scenario piu' generale composto da nodi mobili ed eterogenei, con diverse capacita' di autolocalizzazione, che possono interagire uno con l'altro in modo opportunistico, scambiandosi informazioni di localizzazione con altri nodi che occasionalmente si trovano in prossimita'. Le prestazioni di questo schema sono state analizzate in un modello matematico. Abbiamo studiato un approccio a Massima Verosimiglianza (ML), uno basato su Linear Matrix Inequalities (LMI) e una semplice strategia euristica per definire gli algoritmi di localizzazione opportunistica. La validita' del modello matematico e' stata confermata attraverso misure sperimentali. Abbiamo considerato due scenari principali, uno in cui un nodo puo' contare su una sola interazione opportunistica e uno dove possono essere fatti contatti multipli mentre il nodo resta nella stessa posizione. Abbiamo analizzato le diverse tecniche, trovando che nel primo caso, se le informazioni di autolocalizzazione del nodo cooperatore e di ranging sono buone, l'algoritmo euristico ha buone prestazioni, a volte addirittura meglio della Massima Verosimiglianza, che invece e' estremamente robusto e riesce a migliorare la stima di localizzazione anche in scenari molto difficili. Se invece sono disponibili numerose interazioni, allora l'algoritmo euristico porta prestazioni scarse ed e' meglio utilizzare la tecnica LMI, specialmente utilizzando l'informazione di ranging. Inoltre l'LMI non richiede una conoscenza della posizione iniziale del nodo incognito. Lo scenario statico e' stato molto utile per studiare in maniera efficace la localizzazione mobile. La scelta degli algoritmi proposti e dello scenario, i parametri di simulazione e i limiti, sono strettamente legati a quello che abbiamo studiato riguardo al canale wireless e alle prestazioni di localizzazione nei lavori precedenti. L'idea dello scenario opportunistico infatti e' venuta a partire da due considerazioni: la limitata precisione della localizzazione con ancore basata su RSSI in uno scenario reale e la buona precisione nella stima di distanza con RSSI quando la distanza e' limitata. Affianco al principale filone di ricerca riguardante la localizzazione nelle WSN, durante il dottorato di ricerca abbiamo approfondito anche altri argomenti, come la gestione del traffico e le reti di sensori sottomarine, che non sono direttamente collegate con il tema principale, ma sono comunque di grande interesse scientifico. I risultati piu' significativi ottenuti in questi temi sono stati inseriti all'interno della tesi per due motivi. Innanzitutto, questi argomenti appartengono al contesto delle reti di sensori wireless, condividendo alcune caratteristiche di base quali l'assunzione di semplicita' e le limitazioni energetiche. Inoltre, il trattare campi diversi ma correlati, puo' aprire nuove prospettive a problemi noti, contribuendo cosi' all'innovazione della ricerca. Il secondo problema affrontato in questa tesi e' stato la gestione del traffico in reti di sensori wireless. Spesso, i nodi di una rete di sensori mandano i pacchetti ad un nodo comune, chiamato sink. Questo modello di traffico, quando il carico cresce, puo' portare a problemi di congestione, causando perdita di pacchetti, ritardi e spreco di energia. Le soluzioni proposte in letteratura solitamente cercando di individuare l'inizio di una congestione, utilizzando in questo compito molti nodi, talvolta l'intera rete. Il protocollo proposto, chiamato Efficient Packet Converge Casting (EPC$^2$), mitiga la congestione al sink, ma coinvolgnedo solo un numero fissato di nodi, i vicini del sink. Un altro scenario che abbiamo analizzato in questa tesi, sono state le reti sottomarine di sensori che, come nel caso delle reti radio, possono essere utilizzato per molteplici applicazioni e quindi hanno ricevuto molta attenzione dal mondo della ricerca. Similmente alle reti radio, l'efficienza energetica e' un problema molto sentito. I nodi sono alimentati a batteria ed e' molto importante incrementare la vita della rete il piu' possibile. La profonda diversita' dell'ambiente in cui i nodi sono disposti crea nuove sfide per la ricerca che richiedono la progettazione di nuovi protocolli. Abbiamo affrontato il problema dell'efficienza energetica in reti sottomarine con due diversi approcci. Abbiamo studiato l'effetto del duty--cycle and della densita' dei nodi sul consumo energetico della rete, assumendo che i nodi potessero usare diversi livelli di potenza in trasmissione. Quindi abbiamo proposto uno schema di utilizzazione della banda disponibile per ottimizzare il consumo energetico, facendo leva sulla forte relazione tra distanza, frequenza e attenuazione del canale. Entrambe le soluzioni sono molto semplici e adatte ai dispositivi sottomarini che hanno forti limitazioni. Inoltre non richiedono una unita' centrale per essere coordinate, ma operano in modo asincrono e distribuito.

In general, sensor networks have two competing objectives: (i) maximization of network performance with respect to the probability of successful search with a specified false alarm rate for a given coverage area, and (ii) maximization of the network’s operational life. In this context, battery-powered sensing systems are operable as long as they can communicate sensed data to the processing nodes. Since both operations of sensing and communication consume energy, judicious use of these operations could effectively improve the sensor network’s lifetime. From these perspectives, the paper presents an adaptive energy management policy that will optimally allocate the available energy between sensing and communication operations at each node to maximize the network performance under specified constraints. With the assumption of fixed total energy for a sensor network operating over a time period, the problem is reduced to identification of a network topology that maximizes the probability of successful detection of targets over a surveillance region. In a two-stage optimization, a genetic algorithm-based meta-heuristic search is first used to efficiently explore the global design space, and then a local pattern search algorithm is used for convergence to an optimal solution. The results of performance evaluation are presented to validate the proposed concept.

MTConnect is an open and extensible protocol designed for the exchange of data between shop floor devices and software applications. MTConnect allows manufacturers to facilitate retrieval of information and data from factory devices, such as machine tools, sensors, and controllers. Currently, MTConnect users read data from sensors through proprietary sensor interfaces using adaptors. The suite of Institute of Electrical and Electronics Engineers (IEEE) 1451 standards defines a set of open, common communication interfaces for sensor networks, including both sensor interfaces and network interfaces. This paper proposes an integration architecture of MTConnect with IEEE 1451 standard-based sensor networks. In the architecture, MTConnect plays a network interface role in the IEEE 1451 standard-based sensor networks via an MTConnect Agent. An adaptor is used to provide the mapping between the MTConnect Agent and the IEEE 1451 sensor network. A prototype system integrating MTConnect with IEEE 1451.2-based sensor network has been developed. Two case studies are provided to illustrate the integration.

Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present.

Abstract Taking into account modern trends, the analysis of the construction principles of telemadic systems, networks and complexes is presented in the work. The general structure of providing telemedicine services is developed. Structures of realization of portable and mobile telemedicine complexes are offered. Infocommunication systems and networks for implementation in mobile telemedicine complexes are analyzed. The advantages of using the sensor mobile body area network of the IEEE 802.15.6 WBAN standard in mobile telemedicine complexes are justified. Keywords: telemedicine, telemedicine complex, medical services, device structure, microcontroller, sensor network, wireless sensor body area network.

This work presents and evaluates a fuzzy-probabilistic strategy to save energy in Wireless Sensor Networks (WSNs). The energy savings are obtained with the sensor nodes, no longer sensing and transmitting measurements. In this simple strategy, in each epoch each sensor node transmits its measurement with probability p, and does not transmit with probability (1 􀀀 p), does not correlate with that of any other sensor node. The task at the sink node, which is to estimate the sensor field at non-sensed points, is solved using fuzzy inference to impute the non-transmitted data followed by regression or interpolation of the sensed scalar field. In this, Nadaraya-Watson regression, regression with Fuzzy Inference and Radial Base Functions Interpolation are compared. The compromise curve between the value of p and the accuracy of the sensor field estimation measured by root mean square error (RMSE) is investigated. When compared to a published linear prediction strategy of the literature, the results show a small loss of performance versus the great simplification of the procedure in the sensor node, making it advantageous in applications that require extremely simple network nodes.

An embedded sensor network is a network of sensor nodes deployed in the physical world that interacts with the environment. Each sensor node is a physically small and relatively inexpensive computer that has one or more sensors. These sensor nodes are often networked, allowing them to communicate and cooperate with each other to monitor the environment. Typically, an embedded sensor network is controlled by its own applications that can access the sensor nodes within the network. On the other hand, the sensor nodes cannot be easily accessed by applications outside of the network. Moreover, even within the same network, different applications might encounter a race condition when they are trying to access a sensor node simultaneously. The issue is related to system management. However, not much research has been done with a focus on the management of sensor nodes. In the past few years, Cloud computing has emerged as a new computing paradigm to provide reliable resources, software, and data on demand. As for resources, essentially, Cloud computing services provide users with virtual servers. Users can utilize virtual servers without concerning about their locations and specifications. With such an inspiration, this paper proposes a system, Sensor Agent Cloud, where users can access the sensor nodes without worrying about their locations and detailed specifications. Sensor Agent Cloud virtualizes a physical sensor node as a virtual “sensor agent”. Users can use and control sensor agents with standard functions. Each sensor agent operates on behalf of its user. The mandatory coordination of these sensor agents is related to the system management. Therefore, Sensor Agent Cloud must be an autonomic system that manages itself with minimum human interference. In addition, Sensor Agent Cloud supports international standard technologies regarding programming and agent communication (C and IEEE FIPA standard). Thus, it is expected that the proposed Sensor Agent Cloud can enhance the applicability and usability of embedded sensor networks in many application areas.

This paper depicts the modeling of hardware and software integration of a smart solar-data logger system (SSDLS). It is principally fabricated for Photo Voltaic (PV) module designers, who can gather essential data from these devices and store data in a cloud network. Solar energy is a dependable renewable energy source that is both ecologically efficacious and capable of alleviating the power shortage. For the prototype designing, a 20-watt PV array is used to convert the sunlight to DC. Arduino UNO is used as a controller of this device. The voltage sensor senses the DC voltage, and the temperature sensor senses the temperature of the PV array. Collected essential data is displayed in LCD as well as stored in the memory unit. The real-time clock is used for showing the exact date and time. These essential data can also find in cloud networks. An external DC power supply is added for biasing the Arduino circuit.

Wireless Body Area Networks, also known as the Wireless Body Sensor Networks, provides the monitoring of the health parameters in remote areas and where the medical facility is not available. Wireless Body Sensor Networks contains the body or placement of the sensors on body for measuring the medical and non-medical parameters. These networks share the wireless medium for the transmission of the data from one place to another. So the design of Medium Access Control is a challenging task for the WBSNs due to wireless media for less energy consumption and mobility. Various MAC protocols are designed to provide less energy consumption and improve the network lifetime. This paper presents the study of these existing MAC layer protocols based on different QoS parameters that define the network quality.

Software-Defined Networking has been used to leverage security solutions for wireless sensor networks. However, this paradigm turns networks vulnerable to distributed denial of service attacks. IDIT-SDN is a tool for Software-defined Wireless Sensor Networks devised for DoS and DDoS attacks simulation and detection. This tool provides a framework for anomaly detection and a communication protocol to share security wise information from the sensor network to the controller. We demonstrate its use by showing a cooperative DDoS attack detection and attacker identification application based on distributed (every node) and centralized (controller) anomaly detection.

Based on our previous work on Mobile Actuator and Sensor Network, Applied Fractional Calculus, Sensor Networks and BUMMPEE (Bottom-Up Modeling of Mass Pedestrian flows implications for the Effective Egress of individuals with disabilities), a general framework is proposed for modeling and managing Mass Pedestrian Evacuations (MPE) in this paper. A distinctive feature compared with previous work is the incorporation of Individuals with Disabilities (IwDs) in understanding modeling and control of mass pedestrians evacuations. Networked Segway Supported Responders (NSSR) have been firstly employed in the research of modeling and control/managing problem of crowd pedestrians as mobile sensors and mobile actuators. Future simulation and experimental results will be referenced for public policy professionals and planners for better evacuation policy making and route planning.

With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.