Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

A network mobility management architecture for a heteregeneous network environment

Network mobility management enables mobility of personal area networks and vehicular networks across heterogeneous access networks using a Mobile Router. This dissertation presents a network mobility management architecture for minimizing the impact of handoffs on the communications of nodes in the mobile network. The architecture addresses mobility in legacy networks without infrastructure support, but can also exploit infrastructure support for improved handoff performance. Further, the proposed architecture increases the efficiency of communications of nodes in the mobile network with counter parts in the fixed network through the use of caching and route optimization. The performance and costs of the proposed architecture are evaluated through empirical and numerical analysis. The analysis shows the feasibility of the architecture in the networks of today and in those of the near future.Verkkojen liikkuuvudenhallinta mahdollistaa henkilökohtaisten ja ajoneuvoihin asennettujen verkkojen liikkuvuuden heterogeenisessä verkkoympäristössä käyttäen liikkuvaa reititintä. Tämä väitöskirja esittää uuden arkkitehtuurin verkkojen liikkuvuudenhallintaan, joka minimoi verkonvaihdon vaikutuksen päätelaitteiden yhteyksiin. Vanhoissa verkoissa, joiden infrastruktuuri ei tue verkkojen liikkuvuutta, verkonvaihdos täytyy hallita liikkuvassa reitittimessa. Standardoitu verkkojen liikkuvuudenhallintaprotokolla NEMO mahdollistaa tämän käyttäen ankkurisolmua kiinteässä verkossa pakettien toimittamiseen päätelaitteiden kommunikaatiokumppaneilta liikkuvalle reitittimelle. NEMO:ssa verkonvaihdos aiheuttaa käynnissä olevien yhteyksien keskeytymisen yli sekunnin mittaiseksi ajaksi, aiheuttaen merkittävää häiriötä viestintäsovelluksille. Esitetyssä arkkitehtuurissa verkonvaihdon vaikutus minimoidaan varustamalla liikkuva reititin kahdella radiolla. Käyttäen kahta radiota liikkuva reititin pystyy suorittamaan verkonvaihdon keskeyttämättä päätelaitteiden yhteyksiä, mikäli verkonvaihtoon on riittävästi aikaa. Käytettävissa oleva aika riippuu liikkuvan reitittimen nopeudesta ja radioverkon rakenteesta. Arkkitehtuuri osaa myös hyödyntää infrastruktuurin tukea saumattomaan verkonvaihtoon. Verkkoinfrastruktuurin tuki nopeuttaa verkonvaihdosprosessia, kasvattaenmaksimaalista verkonvaihdos tahtia. Tällöin liikkuva reitin voi käyttää lyhyen kantaman radioverkkoja, joiden solun säde on yli 80m, ajonopeuksilla 90m/s asti ilman, että verkonvaihdos keskeyttää päätelaitteiden yhteyksiä. Lisäksi ehdotettu arkkitehtuuri tehostaa kommunikaatiota käyttäen cache-palvelimia liikkuvassa ja kiinteässä verkossa ja optimoitua reititystä liikkuvien päätelaitteiden ja kiinteässä verkossa olevien kommunikaatiosolmujen välillä. Cache-palvelinarkkitehtuuri hyödyntää vapaita radioresursseja liikkuvan verkon cache-palvelimen välimuistin päivittämiseen. Heterogeenisessä verkkoympäristossä cache-palvelimen päivitys suoritetaan lyhyen kantaman laajakaistaisia radioverkkoja käyttäen. Liikkuvan reitittimen siirtyessä laajakaistaisen radioverkon peitealueen ulkopuolelle päätelaitteille palvellaan sisältöä, kuten www sivuja tai videota cache-palvelimelta, säästäen laajemman kantaman radioverkon rajoitetumpia resursseja. Arkkitehtuurissa käytetään optimoitua reititystä päätelaitteiden ja niiden kommunikaatiokumppaneiden välillä. Optimoitu reititysmekanismi vähentää liikkuvuudenhallintaan käytettyjen protokollien langattoman verkon resurssien kulutusta. Lisäksi optimoitu reititysmekanismi tehostaa pakettien reititystä käyttäen suorinta reittiä kommunikaatiosolmujen välillä. Esitetyn arkkitehtuurin suorituskyky arvioidaan empiirisen ja numeerisen analyysin avulla. Analyysi arvioi arkkitehtuurin suorituskykyä ja vertaa sitä aikaisemmin ehdotettuihin ratkaisuihin ja osoittaa arkkitehtuurin soveltuvan nykyisiin ja lähitulevaisuuden langattomiin verkkoihin.reviewe

Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks

The IMT 2020 requirements of 20 Gbps peak data rate and 1 millisecond latency present significant engineering challenges for the design of 5G cellular systems. Use of the millimeter wave (mmWave) bands above 10 GHz --- where vast quantities of spectrum are available --- is a promising 5G candidate that may be able to rise to the occasion. However, while the mmWave bands can support massive peak data rates, delivering these data rates on end-to-end service while maintaining reliability and ultra-low latency performance will require rethinking all layers of the protocol stack. This papers surveys some of the challenges and possible solutions for delivering end-to-end, reliable, ultra-low latency services in mmWave cellular systems in terms of the Medium Access Control (MAC) layer, congestion control and core network architecture

Learning-based Decision Making in Wireless Communications

Fueled by emerging applications and exponential increase in data traffic, wireless networks have recently grown significantly and become more complex. In such large-scale complex wireless networks, it is challenging and, oftentimes, infeasible for conventional optimization methods to quickly solve critical decision-making problems. With this motivation, in this thesis, machine learning methods are developed and utilized for obtaining optimal/near-optimal solutions for timely decision making in wireless networks. Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. In this context, we in the first part of the thesis study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. Initially, we develop a learning-based caching policy for a single base station aiming at maximizing the long-term cache hit rate. Then, we extend this study to a wireless communication network with multiple edge nodes. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. Next, with the purpose of making efficient use of limited spectral resources, we develop a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework\u27s tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. Following the analysis of the proposed learning-based dynamic multichannel access policy, we consider adversarial attacks on it. In particular, we propose two adversarial policies, one based on feed-forward neural networks and the other based on deep reinforcement learning policies. Both attack strategies aim at minimizing the accuracy of a deep reinforcement learning based dynamic channel access agent, and we demonstrate and compare their performances. Next, anomaly detection as an active hypothesis test problem is studied. Specifically, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. Separately, we also regard the detection of threshold crossing as an anomaly detection problem, and analyze it via hierarchical generative adversarial networks (GANs). In the final part of the thesis, to address state estimation and detection problems in the presence of noisy sensor observations and probing costs, we develop a soft actor-critic deep reinforcement learning framework. Moreover, considering Byzantine attacks, we design a GAN-based framework to identify the Byzantine sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches