With the interconnection of roads spanning not only cities but also multiple countries, transportation capabilities have significantly improved. This has resulted in vehicles being able to reach far-off destinations within shorter timeframes. However, this progress presents challenges in identifying and tracking vehicles, particularly high-security or pursuit vehicles involved in crimes like theft, as they move across various security operational centers. Traditionally, centralized data access through traffic zones has been used for monitoring, surveillance, and tracking purposes. Nevertheless, this approach is susceptible to manipulation and compromises privacy due to unauthorized access by limited stakeholders. To address these concerns, a decentralized alternative is proposed, utilizing a blockchain network that ensures integrity, confidentiality, and access control for vehicle surveillance. Furthermore, computer vision algorithms can be employed for automated surveillance, enabling discreet re-identification or tracking of vehicles. This paper proposes an alternate framework that diverges from the centralized solution, instead encompassing multiple surveillance zones connected through a blockchain-based design with controlled access. To achieve this, we consider traditional approaches to vehicle re-identification using image similarity matching as opposed to resource-intensive unsupervised models performed on a federated consortium blockchain, the Hyperledger fabric. The framework is evaluated based on latency, resource consumption, and the effectiveness of image processing algorithms for feature sharing through blockchain. Compared to existing frameworks with similar applications, our proposed framework demonstrates higher accuracy and shorter inference times while adhering to the specified constraints. It leverages the concept of Blockchain in multi-surveillance zones to enhance privacy and security in automated vehicle surveillance. • A framework for multi-zonal surveillance using a federated consortium blockchain using hyperledger fabric. • A comparison of vehicle make and model classification techniques enabled on the blockchain. • A comparison of multi-zonal vs single zone setup.

Abstract The detection of Hardware Trojans (HTs) in electronic components is critical to ensuring the security and integrity of electronic systems, as these malicious modifications can leak sensitive information, alter device functionality, or completely disable the device. This study introduces a novel detection method combining deep learning techniques with dual-channel image transformations to identify HTs via Side-Channel Analysis (SCA). Specifically, our approach converts side-channel time series signals, such as power consumption, electromagnetic emissions, and timing data, into dual-channel, image-like representations using Gramian Angular Field (GAF) and reshaping techniques. These transformations allow convolutional neural networks (CNNs), known for their effectiveness in image analysis, to capture subtle and complex anomalous patterns indicative of HTs. We rigorously evaluated our dual-channel methodology using publicly available Advanced Encryption Standard (AES) datasets for HTs provided by TrustHub and IEEE Dataport. Experimental results demonstrate that the proposed approach achieves superior accuracy compared to existing methods, particularly in challenging datasets. Additionally, we assessed the robustness of our model by introducing varying noise levels to simulate real-world operational perturbations such as process variations, aging, and voltage fluctuations. The proposed method maintains high detection accuracy and demonstrates enhanced resilience under noisy conditions, underscoring its practical applicability and effectiveness in detecting sophisticated HT threats.

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft.

Federated Learning (FL) for household-level Short-Term Load Forecasting (STLF) has emerged as a solution to privacy concerns in smart grids, enabling clients to collaboratively train models without sharing their consumption data with a central server. However, sharing model updates can still introduce privacy risks. A common solution to mitigate this risk is the use of differential privacy during the federation process. However, there is a lack of empirical privacy analysis of these techniques in smart grid scenarios. This paper proposes a property inference attack utilizing a single update per client to evaluate privacy risks in federated learning within smart grid contexts. We assess the privacy risk associated with the standard FedAvg algorithm and a differentially private noise-before-aggregation (NBA) FL scheme. Furthermore, we investigate the trade-off between privacy and utility in the NBA-FL scheme. Our empirical findings reveal significant information leakage with standard FedAvg scheme. An adversary with access to a single model update can identify global data properties of the FedAvg client local dataset with an Area Under the Curve (AUC) of 72%. This privacy leakage can be reduced using NBA-FL, which reduces the AUC to 60%. However, the addition of noise to the model updates results in a utility loss of up to 70% in the model’s predictive power. This significant degradation in model performance outweighs the advantages of the scheme.

With the development of low-cost computer chips and wireless networks, any object or thing can become a component of the Internet of Things (IoT). As a result, these disparate things will gain digital intelligence, enabling them to connect with real-time data and be used for a wide range of valuable and practical IoT applications. However, due to the broadcast nature of wireless communication and the presence of eavesdroppers, the IoT systems pose serious threats to privacy and message integrity, particularly with mobile sensors. Recently, physical-layer security (PLS) has been proposed as a cost-effective solution that mitigates the impact of growing security threats. In this article, we propose a PLS-based hybrid nonorthogonal multiple access (NOMA)/power division multiplexing (PDM) IoT system that provides a high probability of secured transmission, a low computational complexity, and a low-power consumption. 3-D stochastic geometry tools have been used to introduce and evaluate our proposed scheme in a variety of practical scenarios where users are randomly located in a 3-D space. Furthermore, we derive the new scheme’s key agreement ratio (KAR) expression, the probability expressions of using NOMA and PDM modes. Moreover, we derive the associated secrecy outage probability (SOP), and the average ergodic capacity (AEC) expressions for the proposed scheme as well as for the pure NOMA and pure PDM schemes. In addition, we introduce and investigate the security power efficiency (SPE) metric, that serves as a new key agreement optimization metric. Numerical results are performed to validate the obtained analytical expressions and to assess the proposed scheme performances in terms of KAR, SOP, and AEC, when compared to pure NOMA and pure PDM schemes.

With the advent of Internet of Things (IoT) systems, privacy and integrity of messages are becoming critical issues and are threatened, especially with mobile sensors. The broadcast nature of wireless communications increase information leakage in the presence of eavesdroppers. This paper proposes a hybrid Non-Orthogonal Multiple Access (NOMA)/Orthogonal Multiple Access (OMA)- based IoT systems to improve the data transmission security of moving sensors. We derive the Key Agreement Probability (KAP) expression of the proposed scheme, and we investigate the corresponding Secrecy Outage Probability (SOP) and the Average Bit Rate (ABR), when compared to pure NOMA and pure OMA transmission schemes. Simulation results are used to validate the derived expression and to evaluate the performance of the proposed scheme in terms of KAP, SOP, and ABR.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s$\rho $and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

Internet of Things (IoT) systems are becoming core building blocks for different services and applications supporting every day’s life. The heterogeneous nature of IoT devices and the complex use scenarios make it hard to build secure and private IoT systems. Physical-layer security (PLS) can lead to efficient solutions reducing the impact of the increasing security threats. In this work, we propose a new PLS-based IoT transmission scheme that offers a highly secured transmission probability, low-computational complexity, and reduced power consumption. We utilize 3-D stochastic geometry to model a more realistic IoT system and test our proposed scheme in different practical scenarios, where sensors, access points (APs), and eavesdroppers are randomly located in 3-D space. We focus on the system performance, in terms of secrecy outage probability (SOP) and secured successful transmission probability (SSTP), using tight closed-form expressions. An optimization problem is developed to deduce the optimal sensors’ transmit power, the APs’ density, and the maximum number of transmission tentative, when maximizing the SSTP. The proposed scheme outperforms the baseline retransmission scheme, in terms of SOP and SSTP based on analytical and simulation results.

Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

The affordability and deployment-flexibility of Unmanned Air Vehicles (UAVs) have ignited the development of many smart applications, including surveillance, disaster management, and smart farming. Drone's energy consumption is a critical issue and it can be controlled through different factors, depending on the application. One approach is to minimize energy consumption by defining a minimal number of strategic target-coverage locations that the drone needs to traverse and efficiently plan the drone's route through these locations. In this paper, we provide solutions that efficiently allow UAVs to cover multiple targets using their cameras. These solutions identify a minimum set of strategic locations that cover the targets and plan the drone's routes across these locations. We address the problem with the objective of minimizing the total energy consumed by the drone during its mission. We model the problem as mixed-integer programming problem and provide a set of heuristics; with and without target clustering. We evaluate the system using simulations. The results indicate the significance of clustering in minimizing the number of strategic locations and saving the drone's energy. Moreover, flexibility in selecting cluster centers provides further reduction in the strategic locations and energy consumption.

The rapid evolutions in microcomputing, mini-hardware manufacturing, and machine-to-machine (M2M) communications have enabled novel Internet-of-Things (IoT) solutions to reshape many networking applications. Healthcare systems are among these applications that have been revolutionized with IoT, introducing an IoT branch known as the Internet-of-Medical Things (IoMT) systems. IoMT systems allow remote monitoring of patients with chronic diseases. Thus, it can provide timely patients' diagnostic that can save their life in case of emergencies. However, security in these critical systems is a major challenge facing their wide utilization. In this article, we present state-of-the-art techniques to secure IoMT systems' data during collection, transmission, and storage. We comprehensively overview IoMT systems' potential attacks, including physical and network attacks. Our findings reveal that most security techniques do not consider various types of attacks. Hence, we propose a security framework that combines several security techniques. The framework covers IoMT security requirements and can mitigate most of its known attacks.

In this paper, we investigate a multi-objective optimization framework for secure wireless health monitoring applications. In particular, we consider a legitimate link for the transmission of a vital EEG signal, threatened by a passive eavesdropping attack, that aims at wiretapping these measurements. We incorporate in our framework the practical secrecy metric, namely secrecy outage probability (SOP), which requires only the knowledge of side information regarding the eavesdropper (Ev), instead of completely having its instantaneous channel state information (CSI). To that end, we formulate an optimization problem in the form of maximizing the energy efficiency of the transmitter, while minimizing the distortion encountered at the signal resulting from the compression process prior to transmission, under realistic quality of service (QoS) constraints. The problem is shown to be nonconvex and NP-complete. Towards solving the problem, a branch and bound (BnB)-based algorithm is presented where a δ-suboptimal solution, from the global optimal one, is obtained. Numerical results are conducted to verify the system performance, where it is shown that our proposed approach outperforms similar systems deploying fixed compression policies (FCPs). We successfully meet QoS requirements while optimizing the system objectives, at all channel conditions, which cannot be attained by these FCP approaches. Interestingly, we also show that a target secrecy rate can be practically achieved with nonzero probability, even when the Ev has a better channel condition, on the average, than that for the legitimate receiver.

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.

In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. Convolutional Auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. Then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured.

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems. Such new technologies enable monitoring patients' medical records anytime and anywhere without limiting patients' activities. However, critical challenges have emerged with remote monitoring systems due to the enormous amount of generated data that need to be efficiently processed and wirelessly transmitted to the service providers in time. Thus, in this paper, we leverage full-duplex capabilities for fast transmission, while tackling the trade-off between Quality of Service (QoS) requirements and consequent self-interference (SI) for efficient remote monitoring healthcare systems. The proposed framework jointly considers the residual SI resulting from simultaneous transmission and reception along with the compressibility feature of medical data in order to optimize the data transmission over wireless channels, while maintaining the application's QoS constraint. Our simulation results demonstrate the efficiency of the proposed solution in terms of minimizing the transmission power, residual self-interference, and encoding distortion.

The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

Biometric recognition and analysis are among the most trusted features to be used by Implantable Medical Devices (IMDs). We aim to secure these devices by using these features in emergency scenarios. As patients can witness unpredictable lethal accidents, any implantable medical device should allow access to urgent medical interventions from legitimate parties. Any delay in providing immediate medical support can endanger the patient’s life. Hence, we propose in this work an authentication scheme that allows access to the implanted devices in emergency situations for only legitimate users. We have designed in the first place a scheme for authentication using Electrocardiogram instantaneous readings. Then, we joined the latter to a fixed biometric reading, which is fingerprint reading, to enable access to emergency medical teams. We have designed a scheme in a way to prevent attackers from accessing/hijacking the device even during emergency situations . This scheme has been assisted with elliptic curve cryptography to protect the wireless exchange of requested keys. The scheme relies on the instantaneous reading of the patient’s heartbeat and his/her fingerprint reading to create a secure key. This key will validate the authentication request of the new medical team. We have analyzed this scheme deeply to verify that they offer the necessary security for the patient’s life. We have tested if the wireless exchange of the key will expose the device’s privacy. We have also tested the accuracy of the authentication process to ensure a safe and a valid performance of the authentication process . The scheme has been designed with consideration to any hardware/software limitation that characterize any implantable medical device.

Nowadays, Implantable Medical Devices (IMDs) rely mainly on wireless technology for information exchange. In spite of the many advantages wireless technology offers to patients in terms of efficiency, speed and ease; it puts the patients' health in serious danger if no proper security mechanism is deployed. The IMDs rely generally on resources that are relatively simple and sometimes require surgery to be altered. Therefore, common security mechanisms cannot be simply implemented in fear of consuming all the resources held for healthcare purposes. A certain balance between security and efficiency must be found in each IMD architecture. In this work, we try to avoid encryption algorithms to protect IMDs from Man-In-The-Middle (MITM) attacks. Encryption is generally used to protect communication confidentiality. However, this method is still a subject for replay and MITM attacks. In this work, we propose to create a signature protocol that protects IMDs from MITM attempts using less resources than common encryption/decryption algorithms. This signature algorithm is dynamic, which means that the signature output depends on a key and the same message can have different signatures if this key is different. This dynamic part will be introduced using chaotic generators.

Secure communication in medical devices is a pillar in ensuring patient's safety. However, in emergency cases, this can hinder the recovery of the patient. If an emergency team cannot give themselves access to the IMD without the user's assistance, they may be unable to offer any help. This paper introduces a security scheme for similar cases. By creating a backdoor to the IMDs, legal authentication may be performed with the IMD and gain access to it. This work presents a procedure for an emergency team to validate their actions to the IMD without the need of the patient's conscious. This is ensured using hashing function and elliptic curves for the security key generation. The seed that will be used will be the heart rhythm of the patient. The authentication process introduced will only allow access to the identified parties. An eavesdropper will be unable to interfere during emergency cases and can threaten patients' lives.

Wireless medical devices play an important role in providing safety and privacy to patients suffering from major health issues. These light-weight devices can be worn inside or outside the patient's body and provide more convenience and reliable doctor-patient communication. However, the design, development, and usage of these devices play a critical role in present network paradigm. They are vulnerable to network threats and attacks which break the confidentiality, integrity and availability protocols in networking scenarios. Thus, it is important to have identification and authentication of only the authorized peoplewho can operate the device. This paper proposes Dynamic Time Warping (DTW) algorithm for providing trusted authentication and identification of only authorized people using ECG signal. Here, DTW algorithm is used to measure the correlation between different ECG signal records. Experiments were carried out to evaluate the proposed algorithm with a large database consisting of users of al1 ages, including abnormal ECG data and long span of time intervals between ECG recordings for evaluating the reliability of the proposed algorithm. Comparative evaluation of the proposed sy stem show ed that, it is not only efficient, but also light weight in comparison to the existing systems.

Implantable medical devices are being increasingly used to treat or monitor different medical conditions. For such purposes, wireless is the most desired communication scheme to be implemented in these devices. On the other hand, the wireless scheme increases security threats on these electronic devices, and any possibility of attack on the medical device may have lethal consequences. The patients usually have their implantable medical devices configured and monitored by their doctors. But for practical purposes, most of the time they possess a remote control for daily non-critical operations. This remote control can be considered as an open gate for attackers to target those medical devices and cause major harm. Motivated by this, we analyze in this paper the communication scheme implemented in the wireless devices, having as a starting point an Implantable Insulin Pump to develop a new protocol that can be used in the remote control-implantable device communication, and that will rely on plain text messages to avoid encryption implementation. Finally, we will analyze how the novelties introduced with this protocol can secure such a wireless link.

Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.

Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.

Opportunistic spectrum access is a propitious technique to overcome the under-utilization of spectrum bands. In this work, we design an experimental test-bed for evaluating an un-slotted spectrum access scheme under real indoor environment conditions. To this end, we use the USRP software defined radio platform along with the GNURadio software that incorporates the PHY and MAC functions and modules. Our contribution is multi-fold. First, we design a MAC protocol to integrate the packet based transmission of the coexisting PU/SU network, while compensating for spectrum sensing imperfection as well as collision detection faults. Second, we evaluate the USRP-induced latency (delay) and show that it has random behavior. We work around it to obtain a fixed packet transmission time which is crucial for the channel access scheme realization and evaluation. Third, we perform helping experiments to quantify the spectrum sensing imperfection in terms of false alarm and detection probabilities. We also quantify the imperfection in collision detection. Finally, we evaluate the performance of the whole channel access scheme and compare its results to the classical sense-transmit scheme. We show that 28.5% increase in SU throughput can be achieved for the same PU packet collision rate.

Implantable Medical Devices (IMDs) are a growing industry regarding personal health care and monitoring. In addition, they provide patients with efficient treatments. In general, these devices use wireless communication technologies that may require synchronization with the medical team. Even though wireless technology offers satisfaction to the patient's daily life, it is still prone to security threats. Many malicious attacks on these devices can directly affect the patient's health in a lethal way. Using insecure wireless channels for these devices offers adversaries easy ways to steal the patient's private data and hijack these systems. This can cause damage to patients and render their devices unusable. In the aim of protecting these devices, we explore in this paper a new way to create symmetric encryption keys to encrypt the wireless communication held by the IMDs. This key generation will rely on chaotic systems to obtain synchronized Pseudo-Random keys that will be generated separately in the system. This generation is in a way that the communication channel will avoid a wireless key exchange, protecting the patient from key theft. Moreover, we will explore the performance of this generator from a cryptographic point of view, ensuring that these keys are safe to use for communication encryption.

Smart Grid (SG)-aware cellular networks are expected to decrease their energy consumption and consequently decrease the global carbon emissions. At the same time, cellular operators are required to meet the end-user requirements in terms of throughput. In this paper we propose a novel strategy to pave the way for the cellular operators to integrate with the SG. Our strategy is based on Dynamic Spectrum Assignment (DSA) approach. We formulate the trade-off situation of the operators as a reward function. The objective is to maximize the reward while decreasing the energy consumption. We study homogeneous, spatial-heterogeneous and spatio-temporal heterogeneous types of traffic. We study the performance of the proposed strategy in a dynamic electricity pricing context. We show that by adapting the spectrum utilization properly, the cellular operator can achieve higher rewards while using less energy compared to an operator deploying classical reuse, for low and intermediate traffic loads. We show also that the proposed DSA-based strategy is capable of adapting to the system dynamics; electricity pricing as well as end-users traffic.

Many spectrum sensing techniques have been proposed to allow a secondary user (SU) to utilize a primary user's (PU) spectrum through opportunistic access. However, few of them have considered the tradeoff between accuracy and energy consumption by taking into account the selfishness of the (SUs) in a distributed network. In this work, we consider spectrum sensing as a game where the payoff is the throughput of each SU/player. Each SU chooses between two actions, parallel individual sensing and sequential cooperative sensing techniques. Using those techniques, each SU will distributively decide the existence of the PU. Due to the repetitive nature of our game, we model it using evolutionary game (EG) theory which provides a suitable model that describes the behavioral evolution of the actions taken by the SUs. We address our problem in two cases, when the players are homogeneous and heterogeneous respectively. For the sake of stability, we find the equilibria that lead to evolutionary stable strategies (ESS) by proving that our system is evolutionary asymptotically stable, in both cases, under certain conditions on the sensing time and the false alarm probability.

Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high bandwidth inter-vehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases introduce additional challenges in this highly mobile environment, where the CR enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations. As the rules allow local spectrum sensing only under the assurance of high accuracy, there is an associated tradeoff in obtaining assuredly correct spectrum updates from the database at a finite cost, compared to locally obtained sensing results that may have a finite error probability. This paper aims to answer the question of when to undertake local spectrum sensing and when to rely on database updates through a novel method of exploiting the correlation between 2G spectrum bands and TV whitespace. We describe experimental studies that validate our approach and quantify the cost savings made possible by intermittent database queries.