Millimeter wave (mmWave) communication has raised increasing attentions from both academia and industry due to its exceptional advantages. Compared with existing wireless communication techniques, such as WiFi and 4G, mmWave communications adopt much higher carrier frequencies and thus come with advantages including huge bandwidth, narrow beam, high transmission quality, and strong detection ability. These advantages can well address difficult situations caused by recent popular applications using wireless technologies. For example, mmWave communications can significantly alleviate the skyrocketing traffic demand of wireless communication from video streaming. Meanwhile, mmWave communications have several natural disadvantages, e.g., severe signal attenuation, easily blocked by obstacles, and small coverage, due to its short wavelengths. Hence, the major challenge is how to overcome its shortcomings while fully utilizing its advantages. In this paper, we present a taxonomy based on the layered model and give an extensive review on mmWave communications. Specially, we divide existing efforts into four categories that investigate physical layer, MAC layer, network layer, and cross layer optimization, respectively. First, we present an overview of some technical details in physical layer. Second, we summarize available literature in MAC layer that pertains to protocols and scheduling schemes. Third, we make an in-depth survey of related research work in network layer, providing brain storming and methodology for enhancing the capacity and coverage of mmWave networks. Fourth, we analyze available research work related to cross layer allocation/optimization for mmWave communications. Fifth, we make a review of mmWave applications to illustrate how mmWave technology can be employed to satisfy other services. At the end of each section described above, we point out the inadequacy of existing work and identify the future work. Sixth, we present some available resources for mmWave communications, including related books about mmWave, commonly used mmWave frequencies, existing protocols based on mmWave, and experimental platforms. Finally, we have a simple summary and point out several promising future research directions.

With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.

Unmanned aerial vehicles (UAVs) have various applications in different settings, including for example, surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This article studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to cyber-attack artificial potential field (RCA-APF) algorithm. The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using received signal strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states, and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS’s physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces, and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.