Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Introduction to Geometry. By H. S. M. Coxeter. Pp. xiv, 443. 1961. (John Wiley)

Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the nontrusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, roadside units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block.” Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint proof-of-work (PoW) and proof-of-stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (PoW). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

Blockchain-Based Decentralized Trust Management in Vehicular Networks

Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Vehicle to everything (V2X) is a new generation of information and communication technologies that connect vehicles to everything. It not only creates a more comfortable and safer transportation environment, but also has much significance for improving traffic efficiency, and reducing pollution and accident rates. At present, the technology is still in the exploratory stage, and the problems of traffic safety and information security brought about by V2X applications have not yet been fully evaluated. Prior to marketization, we must ensure the reliability and maturity of the technology, which must be rigorously tested and verified. Therefore, testing is an important part of V2X technology. This article focuses on the V2X application requirements and its challenges, the need of testing. Then we also investigate and summarize the testing methods for V2X in the communication process and describe them in detail from the architectural perspective. In addition, we have proposed an end-to-end testing system combining virtual and real environments which can undertake the test task of the full protocol stack.

A Survey of Vehicle to Everything (V2X) Testing.

Advances on localization techniques for wireless sensor networks

The age of automation is upon us. Few decades earlier, nearly all the flying vehicles were human-controlled. Nowadays, almost every air vehicle is partially automated or getting closer to full automation. This race towards full automation has led to the introduction of features like autopilot. Unmanned aerial vehicles (UAVs) are the tiniest version of all types of air vehicles. The widespread usage of autonomous UAVs has spawned the need for safe landing zone (SLZ) detection techniques for UAV landing. A SLZ detection becomes an important face of a mission when the UAV needs emergency landing due to the technical difficulties or adverse weather conditions on the way of its operation. Before directly proceeding for landing, a UAV has to decide whether the landing zones are safe or not. On-board visual sensors provide potential information of the ground surface in the form of image or signal. Different image processing and safe landing area detection (SLAD) algorithms are then used to identify the best possible landing sites from the input data. In this survey, we discuss indoor and outdoor landing zone detection techniques. We further classify outdoor landing zones as either static or dynamic and discuss existing literature in the specific categories. We critique the shortcomings of existing SLZ detection techniques while also acknowledging their contributions. Further, we point to potential areas of improvement and future directions of the safe landing zone detection algorithms we surveyed. This survey paper may be a useful tutorial for understanding the types of landing zones and landing zone detection techniques for the UAVs, the strengths of zone detection algorithms, and the open areas for future improvement and research.

A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs)

Vehicular Ad Hoc Networks (VANETs) enhance road safety by frequently exchanging real-time data about road conditions through wireless sensor nodes. This exchange allows vehicles to detect potential hazards by sharing information about their location and speed. As vehicles communicate, there is need for a mechanism to verify the trustworthiness of messages that are sent in the network. This paper proposes a reputation model that aids vehicles in a road network to evaluate the reliability of their peers. In this scheme, each receiving vehicle requests other vehicles within its communication range to give their opinion about the trustworthiness of the sending vehicle. Alternatively, the receiving vehicle gets opinion about the sending vehicle from the Road Side Unit (RSU). The scheme applies conditional probability to identify malevolent peers. We present an algorithm for the proposed model and perform simulations to validate our work. Simulation results demonstrate that on average, the proposed scheme achieves a 90% accuracy in detecting malicious messages. The scheme is also scalable.

A distributed reputation scheme for situation awareness in Vehicular Ad Hoc Networks (VANETs)

The establishment of convenient and reliable cryptographic keys is not always feasible, especially in low-powered wireless devices where a complex key management infrastructure is unaffordable. Physical layer-based key generation approaches, on the other hand, allow two legitimate users to establish a common secret key by exploiting the parameters of the wireless channel, such as the underlying impulse response. In this paper, a fundamental bound on the maximum achievable key generation rate (KGR) over wireless fading channels with asymmetric additive white Gaussian noise (AWGN) is derived. A Nakagami- m fading channel is considered, and the effect of non-reciprocity on the forward and reverse channels correlation is demonstrated. We validate the theoretical platform through simulations using a key generation protocol based on the level crossing rate (LCR) of the fading process. The proposed LCR protocol models the Nakagami channel by utilizing the sum-of-sinusoids approach, where Doppler shift, fading severity, and number-of-paths are taken into account. The proposed protocol also incorporates a two-level quantizer to extract keys, where the channel estimates between a wireless transmitter and receiver can be used as the basis. Besides providing an upper bound on the KGR, the analytical and simulation results intertwine the effect of channel reciprocity with the key generation process.

An Upper Bound on PHY-Layer Key Generation for Secure Communications Over a Nakagami-M Fading Channel With Asymmetric Additive Noise

Traditional cost-benefit analysis CBA quantifies the value of information security safeguards in terms of their expenses compared to their savings before and after their implementation. This paper considers CBA from the attacker's viewpoint, adding another type of measurement, the willingness to endure consequences. The authors propose a new set of equations and examine their implications vis-i-vis two typical network attacks, identity theft and intellectual property theft.

Jared Oluoch

Papers

Advances on localization techniques for wireless sensor networks

A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs)

A distributed reputation scheme for situation awareness in Vehicular Ad Hoc Networks (VANETs)

An Upper Bound on PHY-Layer Key Generation for Secure Communications Over a Nakagami-M Fading Channel With Asymmetric Additive Noise

Knowing the Enemy at the Gates: Measuring Attacker Motivation