Vehicle to vehicle (V2V) communication has gained importance in recent times because of the increasing number of traffic accidents and advancements in information sharing. A secure and reliable data transfer has become important to ensure the safety and trust of vehicular network users. Because of scalability of V2V communication, proposed solutions must have low computational complexities and free from latency issues. In this paper, we utilize the channel characteristics of wireless networks in V2V communication, which are used to generate link fingerprints. By using blockchain technology, data authentication among vehicles can be achieved in real time. The proposed algorithms are used to address the time complexity and delay issues in the Internet of Vehicles (IoV), which are lightweight and provide real time adversary detection within the network. Blockchain technology is used to generate blocks in which each hash is generated and shared with corresponding vehicles. The hash itself is not generated if an adversary affects the communication among vehicles. The Pearson Correlation Coefficient is calculated for each link and it is calculated as 0.9749 when there is no adversary and 0.1282 when an adversary is introduced into the network. The time complexity is computed as low as O(1) for the network.

Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy efficiency poses critical concerns. Under imperfect channel state information (CSI), this paper investigates joint optimization of user selection, power allocation and the number of activated Base Station (BS) antennas of multiple IoT devices considering the transmit power and different quality of service (QoS) requirements in combinatorial mode to maximize energy efficiency. The optimization problem formulated is a non-convex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into tractable convex optimization problem and separated into inner and outer loop subproblems. The paper proposed joint energy efficient iterative algorithm, which utilizes successive convex approximation technique and Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. Simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy efficiency maximization.

Due to limited resources and scalability, security protocols for Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of secured data transfer. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is O(1). The energy dissipation is calculated for IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52 mJ to 53 mJ for each IoT node and 313.626 mJ for the entire network. RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.

The protection of Smart Meters (SMs) from cyber attacks are of utmost importance because SMs in Advanced Metering Infrastructure (AMI) are physically unprotected and produce large amount of sensitive data. Due to scalability, SMs are small-sized and low cost devices having low computational capabilities. The algorithms designed to complete the security requirements of SMs should be light-weight. To address this issue, this paper proposes a light-weight security solution to address man-in-the-middle attack, data tempering and blockchain based data provenance. Received Signal Strength Indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient (ρ) of 0.9102 is achieved when no adversarial node is present in between connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of ρ are computed when adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain based data provenance, all the hash values of packet header are 100% matched with the hash functions present at the Data Concentrator Unit (DCU) which shows no adversary’s involvement in AMI. For cases when adversary is in the AMI, hash functions show no match with the hash values present at DCU.

With the rapid growth of the Internet of Things (IoT) applications in Maritime Transportation Systems (MTS), cyber-attacks and challenges in data safety have also increased extensively. Meanwhile, the IoT devices are resource-constrained and cannot implement the existing security systems, making them susceptible to various types of debilitating cyber-attacks. The dynamics in the attack processes in IoT-enabled MTS networks keep changing, which makes a traditional offline or batch ML-based attack detection systems intractable to apply. This paper provides a novel approach of using an adaptive incremental passive-aggressive machine learning (AI-PAML) method to create a network attack detection system (NADS) to protect the IoT devices in an MTS environment. In this paper, we propose an NADS that utilizes a multi-access edge computing (MEC) platform to provide computational resources to execute the proposed model at a network end. Since online learning models face data saturation problems, we present an improved approximate linear dependence and a modified hybrid forgetting mechanism to filter the inefficient data and keep the detection model up-to-date. The proposed data filtering ensures that the model does not experience a rapid increase in unwarranted data, which affects the model's attack detection rate. A Markov transition probability is applied to control the MEC selection and data offloading process by the IoT devices. The performance of the NADS is verified using selected benchmark datasets and a realistic IoT environment. Experimental results demonstrate that AI-PAML achieves remarkable performance in the NADS design for an MTS environment.