Vehicular cloud (VC) is an emerging technology where multiple vehicles form a cloud to share their abundant resources and carry out a heavy job in a cooperative manner. By using VC, each vehicle can perform various VC applications requiring heavy resources. In this regards, it is not a trivial issue to choose appropriate vehicles to complete the given task according to vehicular mobility and each VC service type. In this paper, we suggest a reliable vehicle selection algorithm (RVSA) to minimize the cost of completing the requested task for solving mixed integer nonlinear programming (MINLP) vehicle selection problem. Evaluation result demonstrates that RVSA can achieve to reduce significantly the task completion cost over a wide range of vehicular mobility compared with other conventional algorithms.

Vehicular ad hoc networks (VANETs) are taking more attention from both the academia and the automotive industry due to a rapid development of wireless communication technologies. And with this development, vehicles called connected cars are increasingly being equipped with more sensors, processors, storages, and communication devices as they start to provide both infotainment and safety services through V2X communication. Such increase of vehicles is also related to the rise of security attacks and potential security threats. In a vehicular environment, security is one of the most important issues and it must be addressed before VANETs can be widely deployed. Conventional VANETs have some unique characteristics such as high mobility, dynamic topology, and a short connection time. Since an attacker can launch any unexpected attacks, it is difficult to predict these attacks in advance. To handle this problem, we propose collaborative security attack detection mechanism in a software-defined vehicular networks that uses multi-class support vector machine (SVM) to detect various types of attacks dynamically. We compare our security mechanism to existing distributed approach and present simulation results. The results demonstrate that the proposed security mechanism can effectively identify the types of attacks and achieve a good performance regarding high precision, recall, and accuracy.

In this paper, we consider the problem of optimal dynamic service function (SF) placement and flow routing in a SF chaining (SFC) enabled network. We formulate a multi-objective optimization problem to maximize the acceptable flow rate and to minimize the energy cost for multiple service chains. We transform the multi-objective optimization problem into a single-objective mixed integer linear programming (MILP) problem, and prove that the problem is NP-hard. We propose a polynomial time algorithm based on linear relaxation and rounding to approximate the optimal solution of the MILP. Extensive simulations are conducted to evaluate the effects of the energy budget, the network topology, and the amount of server resources on the acceptable flow rate. The results demonstrate that the proposed algorithm can achieve near-optimal performance and can significantly increase the acceptable flow rate and the service capacity compared to other algorithms under an energy cost budget.

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

Current evolved packet core (EPC) systems in LTE/LTE-A networks suffer from the exponentially increased mobile traffic and thus research on new EPC architectures is ongoing. In this paper, we introduce a virtualized EPC (vEPC) system where middlebox functions of the existing EPC systems are implemented in virtualized software modules and the virtualized software modules operate over selected physical service nodes. To minimize the impact of the increased transmission cost in vEPC, the optimal placement of middlebox functions is also investigated. Numerical results demonstrate that the proposed scheme can achieve lower packet transmission cost than the conventional one.

Mobile collaborative community (MCC) is an emerging technology where multiple mobile nodes (MNs) conduct a job (e.g., large content download) in a cooperative manner. In this paper, we consider a scenario in which multiple MNs form MCC for content download through wireless wide area network (WWAN) and share of the downloaded content through wireless local area network (WLAN). In the collaborative content download for MCC, the content chunk size assigned to an MN and the sharing order of the received chunk should be carefully determined to reduce the content download time. Therefore, we formulate an optimization problem that jointly considers the chunk size and the sharing order to minimize the content download time. Specifically, the optimization problem is formulated as a mixed integer non-linear programming (MINLP) problem that is known as NP-hard. The original optimization problem is relaxed into a linear programming (LP) problem and a heuristic algorithm minimizing the content download time and operating in a polynomial time is proposed based on the 2-opt algorithm. Simulation results demonstrate that the proposed algorithm can achieve near-optimal performance to the MINLP optimal solution and can reduce the content download time compared with other algorithms by choosing proper chunk size and sharing order.

In wireless data access applications, reduction of both the access latency and the wireless traffic volume is essential. In this paper, we propose a fast wireless data access scheme (FW-DAS) for wireless data access applications in which data objects are frequently updated and fast access to data objects is indispensable. In FW-DAS, different operation modes are defined depending on the data object popularity, and only popular data objects are proactively pushed to the access point/base station to minimize the access latency while mitigating the traffic load over the wireless link. An analytical model for the access latency is developed and an operation mode selection algorithm is introduced to reduce the access latency. Extensive simulation results show the effects of access-to-update ratio, data popularity, cache size, data object size, and wireless bandwidth. Analytical and simulation results demonstrate that FW-DAS can reduce the access latency with reasonable traffic load compared with poll-each-read (PER)/callback (CB) and their combinations.

In this paper, we analyze the energy efficiency of IEEE 802.11 power save mode in multi-rate environments. We consider two transmission schedules: random and rate-aware schedules. Numerical results demonstrate that the rate-aware schedule can reduce the expected idle time by 36%.