Vehicular fog computing (VFC) provides solutions to relieve overload on cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to vehicles (vehicular nodes) by exploiting the under-utilized computation resources of nearby vehicular nodes. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resources allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraint. We then propose an ECOS scheme with three phases. In the first phase, we propose an efficient incentive mechanism based on resources utilization for overload cloudlet node detection. In the second phase, we propose a computational offloading selection policy to select a task from overloaded cloudlet node for offloading, which minimizes offloading cost and reduces risk of overload. Next, we propose a heuristic approach to solve the resources allocation problem between vehicular node and selected user task for energy-latency tradeoff. Simulation scenario was built using real bus traces, and extensive simulations have been conducted to examine the performance of the ECOS scheme. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost.

Mobile Edge Computing (MEC) has emerged as a new computing paradigm with great potential to alleviate resource limitations attributed to mobile device users (MDUs) by offloading intensive computations to ubiquitous MEC server. However, most of the current offloading policies allow MDUs to transmit their tasks to the same connected small base stations (sBSs), which invariably increases latency and limits performance gain due to overload. Moreover, the security issue mitigating sensitive communication of information is not adequately addressed. Therefore, in this study, in addition to proposing a joint load balancing and computation offloading technique for MEC systems, we introduce a new security layer to circumvent potential security issues. First, a load balancing algorithm for efficient redistribution of MDUs among sBSs is proposed. In addition, a new advanced encryption standard (AES) cryptographic technique suffused with electrocardiogram (ECG) signal-based encryption and decryption key is presented as a security layer to safeguard the vulnerability of data during the transmission. Furthermore, an integrated model of load balancing, computation offloading and security is formulated as a problem whose goal is to decrease the time and energy demands of the system. Detailed experimental results prove that our model with and without the additional security layers can save about 68.2% and 72.4% of system consumption compared to the local execution.

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This paper presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, we utilize deep learning with generative adversarial networks and explore distributed SDN to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. We prove the correctness of our CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that our CIDS is efficient and effective in intrusion detection for VANETs.

With the wide application of Internet-of-Medical-Things (IoMTs) or sensor nodes which equipped with sensors. These networked sensors are used to gather enormous data from different smart healthcare applications, and this collected data process for making appropriate decisions. Edge computing is an efficient platform that provides computational resources to collect sensor data. In the meantime, intelligent and accurate resource management by Artificial Intelligence (AI) has become the center of attention, especially in healthcare systems. With the help of AI, IoMT based healthcare devices will remarkably enhance the computational speed and range. But the challenging issue in these energy-hungry, short battery life, and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Thus, this paper proposes Computation Offloading using Reinforcement Learning (CORL) scheme to minimize latency and energy consumption. We first formulate the problem as a combined latency and energy cost minimization problem, satisfying the lack of limited battery capacity and service latency deadline constraints. Moreover, proposed algorithm search optimal available resources node to offload task towards the trade-off between energy and latency. The experimental results show the benefits of the proposed scheme in terms of saving energy, minimizing latency, and maximum utilization of node resources in edge-enabled sensor networks. We are using an iFogSim simulator to validate the proposed scheme under realistic assumptions.