Routing in mobile Ad Hoc Networks

A Mobile Ad Hoc Network (MANET) is built on the fly where a number of wireless mobile nodes work in cooperation without the engagement of any centralized access point or any fixed infrastructure. Two nodes in such a network can communicate in a bidirectional manner if and only if the distance between them is at most the minimum of their transmission ranges. When a node wants to communicate with a node outside its transmission range, a multihop routing strategy is used which involves some intermediate nodes. Because of the movements of nodes, there is a constant possibility of topology change in MANET. Considering this unique aspect of MANET, a number of routing protocols have been proposed so far. This chapter gives an overview of the past, current, and future research areas for routing in MANET. In this chapter we will learn about the following things: - The preliminaries of mobile ad hoc network - The challenges for routing in MANET - Expected properties of a MANET routing protocol - Categories of routing protocols for MANET - Major routing protocols for MANET - Criteria for performance comparison of the routing protocols for MANET - Achievements and future research directions - Expectations and realit

Vehicular ad hoc networking based on the incorporation of geographical information in the IPv6 header

Several approaches can be identified in the domain of vehicular ad hoc networks (VANET). Internet Protocol version 6 (IPv6) networking and non-IP geographical networking can each fulfill a subset of the application requirements. In general, a combination of both techniques is proposed to meet all of the application requirements. In this case, packets of one VANET routing protocol are encapsulated inside packets of another. This tunneling, together with the position service required for non-IP geographical unicasting, makes such a combined solution rather complex, and hence more challenging to implement, debug, and maintain. In this article, a new VANET approach is presented that relies on the key assumptions that geo-anycast functionality is not required by the applications, and that geographic unicasting is not needed when IP-based unicasting is provided. This enables the adoption of an IPv6-only VANET solution, removing the need for tunneling and position services. New techniques are required to support IPv6-based geo-broadcasting. In this article, it is described how addresses should be assigned, how geographical data can be incorporated in the IPv6 address, how the other IPv6 header fields can be used to contain additional VANET information, and how routing should be handled to guarantee that no modifications are required to the application units. The implementation of the proposed techniques is described, and the correct functionality of the solutions is experimentally demonstrated. Finally, to prove the added value compared to current state-of-the-art propositions, the presented solution is stacked up against the recently released ETSI standards TS 102 636-4-1 (geographical addressing and forwarding) and TS 102 636-6-1 (transmission of IPv6 packets over GeoNetworking protocols)

A Theoretical Review of Topological Organization for Wireless Sensor Network

The recent decades have seen the growth in the fields of wireless communication technologies, which has made it possible to produce components with a rational cost of a few cubic millimeters of volume, called sensors. The collaboration of many of these wireless sensors with a basic base station gives birth to a network of wireless sensors. The latter faces numerous problems related to application requirements and the inadequate abilities of sensor nodes, particularly in terms of energy. In order to integrate the different models describing the characteristics of the nodes of a WSN, this paper presents the topological organization strategies to structure its communication. For large networks, partitioning into sub-networks (clusters) is a technique used to reduce consumption, improve network stability and facilitate scalability

A Theoretical Review of Topological Organization for Wireless Sensor Network

The recent decades have seen the growth in the fields of wireless communication technologies, which has made it possible to produce components with a rational cost of a few cubic millimeters of volume, called sensors. The collaboration of many of these wireless sensors with a basic base station gives birth to a network of wireless sensors. The latter faces numerous problems related to application requirements and the inadequate abilities of sensor nodes, particularly in terms of energy. In order to integrate the different models describing the characteristics of the nodes of a WSN, this paper presents the topological organization strategies to structure its communication. For large networks, partitioning into sub-networks (clusters) is a technique used to reduce consumption, improve network stability and facilitate scalability

Efficient network management and security in 5G enabled internet of things using deep learning algorithms

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model

An Architecture for Coexistence with Multiple Users in Frequency Hopping Cognitive Radio Networks

The radio frequency (RF) spectrum is a limited resource. Spectrum allotment disputes stem from this scarcity as many radio devices are con confined to a fixed frequency or frequency sequence. One alternative is to incorporate cognition within a configurable radio platform, therefore enabling the radio to adapt to dynamic RF spectrum environments. In this way, the radio is able to actively observe the RF spectrum, orient itself to the current RF environment, decide on a mode of operation, and act accordingly, thereby sharing the spectrum and operating in more flexible manner. This research presents a novel framework for incorporating several techniques for the purpose of adapting radio operation to the current RF spectrum environment. Specifically, this research makes six contributions to the field of cognitive radio: (1) the framework for a new hybrid hardware/software middleware architecture, (2) a framework for testing and evaluating clustering algorithms in the context of cognitive radio networks, (3) a new RF spectrum map representation technique, (4) a new RF spectrum map merging technique, (5) a new method for generating a random key-based adaptive frequency-hopping waveform, and (6) initial integration testing toward implementing the proposed system on a field-programmable gate array (FPGA)

Performance Optimization in Wireless Local Area Networks

Wireless Local Area Networks (WLAN) are becoming more and more important for providing wireless broadband access. Applications and networking scenarios evolve continuously and in an unpredictable way, attracting the attention of academic institutions, research centers and industry. For designing an e cient WLAN is necessary to carefully plan coverage and to optimize the network design parameters, such as AP locations, channel assignment, power allocation, MAC protocol, routing algorithm, etc... In this thesis we approach performance optimization in WLAN at di erent layer of the OSI model. Our rst approach is at Network layer. Starting from a Hybrid System modeling the ow of tra c in the network, we propose a Hybrid Linear Varying Parameter algorithm for identifying the link quality that could be used as metric in routing algorithms. Go down to Data Link, it is well known that CSMA (Carrier Sense Multiple Access) protocols exhibit very poor performance in case of multi-hop transmissions, because of inter-link interference due to imperfect carrier sensing. We propose two novel algorithms, that are combining Time Division Multiple Access for grouping contending nodes in non-interfering sets with Carrier Sense Multiple Access for managing the channel access behind a set. In the rst solution, a game theoretical study of intra slot contention is introduced, in the second solution we apply an optimization algorithm to nd the optimal degree between contention and scheduling. Both the presented solutions improve the network performance with respect to CSMA and TDMA algorithms. Finally we analyze the network performance at Physical Layer. In case of WLAN, we can only use three orthogonal channels in an unlicensed spectrum, so the frequency assignments should be subject to frequent adjustments, according to the time-varying amount of interference which is not under the control of the provider. This problem make necessary the introduction of an automatic network planning solution, since a network administrator cannot continuously monitor and correct the interference conditions su ered in the network. We propose a novel protocol based on a distributed machine learning mechanism in which the nodes choose, automatically and autonomously in each time slot, the optimal channel for transmitting through a weighted combination of protocols

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE