Energy Efficient and Secure Wireless Sensor Networks Design

Wireless Sensor Networks (WSNs) are emerging technologies that have the ability to sense, process, communicate, and transmit information to a destination, and they are expected to have significant impact on the efficiency of many applications in various fields. The resource constraint such as limited battery power, is the greatest challenge in WSNs design as it affects the lifetime and performance of the network. An energy efficient, secure, and trustworthy system is vital when a WSN involves highly sensitive information. Thus, it is critical to design mechanisms that are energy efficient and secure while at the same time maintaining the desired level of quality of service. Inspired by these challenges, this dissertation is dedicated to exploiting optimization and game theoretic approaches/solutions to handle several important issues in WSN communication, including energy efficiency, latency, congestion, dynamic traffic load, and security. We present several novel mechanisms to improve the security and energy efficiency of WSNs. Two new schemes are proposed for the network layer stack to achieve the following: (a) to enhance energy efficiency through optimized sleep intervals, that also considers the underlying dynamic traffic load and (b) to develop the routing protocol in order to handle wasted energy, congestion, and clustering. We also propose efficient routing and energy-efficient clustering algorithms based on optimization and game theory. Furthermore, we propose a dynamic game theoretic framework (i.e., hyper defense) to analyze the interactions between attacker and defender as a non-cooperative security game that considers the resource limitation. All the proposed schemes are validated by extensive experimental analyses, obtained by running simulations depicting various situations in WSNs in order to represent real-world scenarios as realistically as possible. The results show that the proposed schemes achieve high performance in different terms, such as network lifetime, compared with the state-of-the-art schemes

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

Fair relay selection in wireless rural networks using game theory

Access to Internet is the key to facilitate the economic growth and development of the rural communities and to bridge the digital-divide between the urban and rural population. The traditional broadband access technologies are not always suitable for the rural areas due to their difficult topography and sparsely populated communities. Specialized relay stations can be deployed to extend the coverage of a wireless rural network but they come with an inherited increase in the infrastructural cost. An alternative is to utilize the in-range users as relays to enhance the coverage range of the wireless rural network. In this thesis, the in-range ordinary users termed as primary users (PUs) are used to act as relays for the out-of-range users called the secondary users (SUs). Two relay selection solutions, the Fair Battery Power Consumption (FBPC) algorithm and the Credit based Fair Relay Selection (CF-RS) protocol have been proposed with the aim of providing fair chance to every PU to assist the SUs, thus resulting in fair utilization of battery power of all relays along with the coverage extension. The FBPC algorithm uses the concept of proportional fairness as the relay selection criterion. However, if only proportionally fair consumption of battery power is taken as the relay selection parameter, the FBPC algorithm may result in selecting relays with poor channel conditions. The rural network may also consist of selfish PUs which need to be incentivized to use their resources for the SUs. The CF-RS protocol is developed which takes into account both the achievable data rate and consumption of battery power for selection of a relay. The CF-RS protocol is formulated using Stackelberg game which employs a credit-based incentive mechanism to motivate the self-interested PUs to help the SUs by providing instantaneous as well as long term benefit to the PUs. A basic network model consisting of PUs and SUs has been simulated and the performance of the FBPC algorithm and the CF-RS protocol have been evaluated in terms of data rate and utility achievable at the SUs, dissipation of battery power of the PUs and Jain’s fairness index to determine fairness in utilization of battery power. The results obtained show that the FBPC algorithm achieves approximately 100% fairness for utilization of battery power of relays but compromises the data rate attainable by the SUs. Thus the FBPC algorithm shall be viewed as a trade-off between the fair battery power dissipation of relays and the data rate achievable by the SUs. Whereas, the CF-RS protocol provides 55% better utility and longer service time to the SUs without harming the attainable data rate and achieves 80% fairness. When the CF-RS protocol is used for relay selection, it is advantageous even for the self-interested users to participate in the relaying process to earn some benefit to utilize it when needed to buy assistance from other users

Exploiting Heterogeneity in Networks of Aerial and Ground Robotic Agents

By taking advantage of complementary communication technologies, distinct sensing functionalities and varied motion dynamics present in a heterogeneous multi-robotic network, it is possible to accomplish a main mission objective by assigning specialized sub-tasks to specific members of a robotic team. An adequate selection of the team members and an effective coordination are some of the challenges to fully exploit the unique capabilities that these types of systems can offer. Motivated by real world applications, we focus on a multi-robotic network consisting off aerial and ground agents which has the potential to provide critical support to humans in complex settings. For instance, aerial robotic relays are capable of transporting small ground mobile sensors to expand the communication range and the situational awareness of first responders in hazardous environments. In the first part of this dissertation, we extend work on manipulation of cable-suspended loads using aerial robots by solving the problem of lifting the cable-suspended load from the ground before proceeding to transport it. Since the suspended load-quadrotor system experiences switching conditions during this critical maneuver, we define a hybrid system and show that it is differentially-flat. This property facilitates the design of a nonlinear controller which tracks a waypoint-based trajectory associated with the discrete states of the hybrid system. In addition, we address the case of unknown payload mass by combining a least-squares estimation method with the designed controller. Second, we focus on the coordination of a heterogeneous team formed by a group of ground mobile sensors and a flying communication router which is deployed to sense areas of interest in a cluttered environment. Using potential field methods, we propose a controller for the coordinated mobility of the team to guarantee inter-robot and obstacle collision avoidance as well as connectivity maintenance among the ground agents while the main goal of sensing is carried out. For the case of the aerial communications relays, we combine antenna diversity with reinforcement learning to dynamically re-locate these relays so that the received signal strength is maintained above a desired threshold. Motivated by the recent interest of combining radio frequency and optical wireless communications, we envision the implementation of an optical link between micro-scale aerial and ground robots. This type of link requires maintaining a sufficient relative transmitter-receiver position for reliable communications. In the third part of this thesis, we tackle this problem. Based on the link model, we define a connectivity cone where a minimum transmission rate is guaranteed. For example, the aerial robot has to track the ground vehicle to stay inside this cone. The control must be robust to noisy measurements. Thus, we use particle filters to obtain a better estimation of the receiver position and we design a control algorithm for the flying robot to enhance the transmission rate. Also, we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid radio-frequency/optical wireless communication system. A challenge is positioning the flying robot within optical range when the sensor location is unknown. Thus, we take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the ground sensor. Once the optical-based signal strength has achieved a certain threshold, the robot hovers within optical range. Finally, we investigate the problem of building an alliance of agents with different skills in order to satisfy the requirements imposed by a given task. We find this alliance, known also as a coalition, by using a bipartite graph in which edges represent the relation between agent capabilities and required resources for task execution. Using this graph, we build a coalition whose total capability resources can satisfy the task resource requirements. Also, we study the heterogeneity of the formed coalition to analyze how it is affected for instance by the amount of capability resources present in the agents