MAC protokol adaptivnog faktora ispune zasnovan na predviđanju u bežičnim senzorskim mrežama sa prikupljanjem solarne energije

Harvesting ambient energy has enabled the development of energy-harvesting wireless sensor networks (EH-WSNs). However, in these networks, the uncertainty in harvesting rate due to dynamic weather conditions raises new challenges. Therefore, this drives the development of energy harvesting-aware solutions. Formerly, many MAC protocols have been developed for EH-WSNs, which offer various features based on available harvested energy to support different applications. Nevertheless, optimizing MAC performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Therefore, this thesis presents a machine learning prediction based adaptive duty cycle medium access control (MAC) protocol for solar energy harvesting wireless sensor networks WSNs. The developed protocol incorporates information about the current and future harvested energy using mathematical formulations to improve network performance. By doing so, the proposed MAC protocol effectively addresses the primary goals of solar energy harvesting WSNs: ensuring long-term network sustainability and efficient utilization of harvested energy to enhance the application performance under dynamically changing energy harvesting conditions.Сакупљање амбијенталне енергије омогућило је развој бежичних сензорских мрежа (EH-WSN) за прикупљање енергије. Међутим, у овим мрежама, неизвесност у стопи жетве услед динамичних временских услова поставља нове изазове. Стога, ово покреће развој решења која су свесна прикупљања енергије. Раније су развијени многи MAC протоколи за EH-WSN, који нуде различите карактеристике засноване на доступној прикупљеној енергији за подршку различитим апликацијама. Ипак, оптимизација перформанси MAC-а укључивањем предвиђеног будућег уноса енергије је релативно нова у EH-WSN-овима. Стога, ова теза представља протокол адаптивног радног циклуса за контролу приступа медијуму (MAC) заснован на предвиђању заснованом на машинском учењу за бежичне WSN мреже за прикупљање соларне енергије. Развијени протокол укључује информације о тренутној и будућој прикупљеној енергији користећи математичке формулације за побољшање перформанси мреже. На тај начин, предложени MAC протокол ефикасно се бави примарним циљевима WSN-а за прикупљање соларне енергије: обезбеђивање дугорочне одрживости мреже и ефикасно коришћење прикупљене енергије за побољшање перформанси апликације под динамички променљивим условима прикупљања енергије.Sakupljanje ambijentalne energije omogućilo je razvoj bežičnih senzorskih mreža (EH-WSN) za prikupljanje energije. Međutim, u ovim mrežama, neizvesnost u stopi žetve usled dinamičnih vremenskih uslova postavlja nove izazove. Stoga, ovo pokreće razvoj rešenja koja su svesna prikupljanja energije. Ranije su razvijeni mnogi MAC protokoli za EH-WSN, koji nude različite karakteristike zasnovane na dostupnoj prikupljenoj energiji za podršku različitim aplikacijama. Ipak, optimizacija performansi MAC-a uključivanjem predviđenog budućeg unosa energije je relativno nova u EH-WSN-ovima. Stoga, ova teza predstavlja protokol adaptivnog radnog ciklusa za kontrolu pristupa medijumu (MAC) zasnovan na predviđanju zasnovanom na mašinskom učenju za bežične WSN mreže za prikupljanje solarne energije. Razvijeni protokol uključuje informacije o trenutnoj i budućoj prikupljenoj energiji koristeći matematičke formulacije za poboljšanje performansi mreže. Na taj način, predloženi MAC protokol efikasno se bavi primarnim ciljevima WSN-a za prikupljanje solarne energije: obezbeđivanje dugoročne održivosti mreže i efikasno korišćenje prikupljene energije za poboljšanje performansi aplikacije pod dinamički promenljivim uslovima prikupljanja energije

MAC protokol adaptivnog faktora ispune zasnovan na predviđanju u bežičnim senzorskim mrežama sa prikupljanjem solarne energije

Harvesting ambient energy has enabled the development of energy-harvesting wireless sensor networks (EH-WSNs). However, in these networks, the uncertainty in harvesting rate due to dynamic weather conditions raises new challenges. Therefore, this drives the development of energy harvesting-aware solutions. Formerly, many MAC protocols have been developed for EH-WSNs, which offer various features based on available harvested energy to support different applications. Nevertheless, optimizing MAC performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Therefore, this thesis presents a machine learning prediction based adaptive duty cycle medium access control (MAC) protocol for solar energy harvesting wireless sensor networks WSNs. The developed protocol incorporates information about the current and future harvested energy using mathematical formulations to improve network performance. By doing so, the proposed MAC protocol effectively addresses the primary goals of solar energy harvesting WSNs: ensuring long-term network sustainability and efficient utilization of harvested energy to enhance the application performance under dynamically changing energy harvesting conditions.Сакупљање амбијенталне енергије омогућило је развој бежичних сензорских мрежа (EH-WSN) за прикупљање енергије. Међутим, у овим мрежама, неизвесност у стопи жетве услед динамичних временских услова поставља нове изазове. Стога, ово покреће развој решења која су свесна прикупљања енергије. Раније су развијени многи MAC протоколи за EH-WSN, који нуде различите карактеристике засноване на доступној прикупљеној енергији за подршку различитим апликацијама. Ипак, оптимизација перформанси MAC-а укључивањем предвиђеног будућег уноса енергије је релативно нова у EH-WSN-овима. Стога, ова теза представља протокол адаптивног радног циклуса за контролу приступа медијуму (MAC) заснован на предвиђању заснованом на машинском учењу за бежичне WSN мреже за прикупљање соларне енергије. Развијени протокол укључује информације о тренутној и будућој прикупљеној енергији користећи математичке формулације за побољшање перформанси мреже. На тај начин, предложени MAC протокол ефикасно се бави примарним циљевима WSN-а за прикупљање соларне енергије: обезбеђивање дугорочне одрживости мреже и ефикасно коришћење прикупљене енергије за побољшање перформанси апликације под динамички променљивим условима прикупљања енергије.Sakupljanje ambijentalne energije omogućilo je razvoj bežičnih senzorskih mreža (EH-WSN) za prikupljanje energije. Međutim, u ovim mrežama, neizvesnost u stopi žetve usled dinamičnih vremenskih uslova postavlja nove izazove. Stoga, ovo pokreće razvoj rešenja koja su svesna prikupljanja energije. Ranije su razvijeni mnogi MAC protokoli za EH-WSN, koji nude različite karakteristike zasnovane na dostupnoj prikupljenoj energiji za podršku različitim aplikacijama. Ipak, optimizacija performansi MAC-a uključivanjem predviđenog budućeg unosa energije je relativno nova u EH-WSN-ovima. Stoga, ova teza predstavlja protokol adaptivnog radnog ciklusa za kontrolu pristupa medijumu (MAC) zasnovan na predviđanju zasnovanom na mašinskom učenju za bežične WSN mreže za prikupljanje solarne energije. Razvijeni protokol uključuje informacije o trenutnoj i budućoj prikupljenoj energiji koristeći matematičke formulacije za poboljšanje performansi mreže. Na taj način, predloženi MAC protokol efikasno se bavi primarnim ciljevima WSN-a za prikupljanje solarne energije: obezbeđivanje dugoročne održivosti mreže i efikasno korišćenje prikupljene energije za poboljšanje performansi aplikacije pod dinamički promenljivim uslovima prikupljanja energije

A roadmap to developing energy-efficient MAC protocol in wireless sensor networks: a case of ADP-MAC development and implementation

Over the past two decades, hundreds of protocols have been developed for diversified applications of WSN corresponding to different layers in the communication stack. Among these, Media Access Control (MAC) layer protocols are of great interest due to providing possibility of optimizing performance parameters. Despite availability of a large number of survey articles, there remains a gap for a tutorial that offers guidelines about the development process of MAC protocol. In this paper, we present a detailed tutorial for developing a MAC protocol starting from the stage of research gap identification and ending at the performance evaluation. We described the journey of development and implementation of a novel asynchronous MAC protocol ADP-MAC (Adaptive and Dynamic Polling MAC) as a case study. ADP-MAC was developed by deploying a novel concept of channel polling interval distributions, and was compared against Synchronized Channel Polling- MAC (SCP-MAC) and lightweight Traffic Auto-Adaptation based MAC (T-AAD). Finally, we proposed major milestones of protocol development along with recommendations about publishing the research

Energy Efficiency inWireless Sensor Networks: Transmission Protocols and Performance Evaluation

Doktorgradsavhandling, Fakultet for teknologi og realfag, Universitetet i Agder, 2016Energy efficiency is one of the major goals for achieving green wireless communications. The recent growth in ubiquitous wireless connections and multimedia applications demands higher energy efficiency for wireless communications. As a part of this picture, wireless sensor networks (WSNs) need to be more energy efficient since the battery capacity of nodes in such networks is limited in the absence of energy harvesting sources. In general, an energy efficient protocol should perform as few as possible operations when delivering user information successfully across the network. Energy efficient data transmission schemes could utilize network resources more effectively to lower down the energy consumption level. In this dissertation research, we focus on improving energy efficiency for data transmission and medium access control (MAC) protocols in WSNs. While energy consumption is inevitable for transmitting and receiving data in a WSN, the other typical and dominant energy consumption activities are idle listening, overhearing, and retransmissions due to unsuccessful transmission attempts. An energy efficient MAC protocol conserves energy by minimizing all these auxiliary operations in order to prolong network lifetime. On the other hand, balanced energy consumption among nodes which mitigates energy hole across a WSN also helps to extend network lifetime. In this context, we propose two cooperative transmission (CT) based energy balancingMAC protocols for the purpose of WSN lifetime prolongation. The first one is an asynchronous cooperative transmission MAC protocol, in which nodes generate their own wakeup schedules based on their level number in a WSN topology. The second one is a receiver initiated cooperative transmission MAC protocol in which the CT is initiated by a relay node. It is demonstrated that both proposed CT MAC protocols are able to achieve significantly extended network lifetime. In addition, an energy conserving sleeping mechanism for synchronous duty cycling MAC protocols is also proposed in this thesis. It is an eventtriggered sleeping (ETS) mechanism, which triggers the sleep mode of a node based on the incoming traffic pattern to that node. The ETS mechanism eliminates overhearing in a WSN and achieves higher energy efficiency. Furthermore, we apply packet aggregation at the MAC layer in WSNs for achieving more energy efficient data transmission. In aggregated packet transmission (APT), multiple packets are transmitted as a batch in a frame within a single duty cycle instead of transmitting merely one packet per cycle. Numerical results demonstrate that APT achieves higher throughput and shorter delay, in addition to higher energy efficiency. To evaluate the performance of the proposed MAC protocols and transmission schemes, we develop discrete time Markov chain (DTMC) models and verify them by comparing the results obtained from both analysis and discrete-event based simulations. The analytical and simulation results match precisely with each other, confirming the effectiveness of the proposed protocols and schemes as well as the accuracy of the developed models

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Priority-Based Pipelined-Forwarding MAC Protocol for EH-WSNs

To reduce the end-to-end delay in EH-WSNs (energy-harvesting wireless sensor networks), medium access control protocols using pipelined-forwarding have been introduced and studied. In real-life applications, there are several situations where it is difficult to harvest more energy than the energy consumed. Therefore, it is crucial to design a MAC protocol that allows nodes to efficiently relay data without exhausting the power in pipelined-forwarding multihop transmission. In this paper, we propose a PP-MAC (priority-based pipelined-forwarding MAC) protocol that determines the priority of relay nodes based on the residual power and energy-harvesting rate. The proposed protocol determines the probability of a node becoming a relay node based on the priority of the node and attempts to access the channel in a distributed manner. Furthermore, the PP-MAC protocol controls the sleep interval based on the power conditions of the nodes. It also minimizes the power exhaustion problem by controlling the sleep interval based on the priority of the nodes. The performance of the proposed PP-MAC was evaluated via computer simulation, and the results indicated that PP-MAC could improve the network lifetime by mitigating the power imbalance of nodes

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp