The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels.

Several critical infrastructures are integrating information technology into their operations, and as a result, the cyber attack surface extends over a broad range of these infrastructures. Cyber attacks have been a serious problem for industries since the early 2000s, causing significant interruptions to their ability to produce goods or offer services to their clients. The thriving cybercrime economy encompasses money laundering, black markets, and attacks on cyber-physical systems that result in service disruptions. Furthermore, extensive data breaches have compromised the personally identifiable information of millions of people. This paper aims to summarize some of the major cyber attacks that have occurred in the past 20 years against critical infrastructures. These data are gathered in order to analyze the types of cyber attacks, their consequences, vulnerabilities, as well as the victims and attackers. Cybersecurity standards and tools are tabulated in this paper in order to address this issue. This paper also provides an estimate of the number of major cyber attacks that will occur on critical infrastructure in the future. This estimate predicts a significant increase in such incidents worldwide over the next five years. Based on the study’s findings, it is estimated that over the next 5 years, 1100 major cyber attacks will occur on critical infrastructures worldwide, each causing more than USD 1 million in damages.

Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semi-supervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

In smart cities, advanced metering infrastructure (AMI) of the smart grid facilitates automated metering, control and monitoring of power distribution by employing a wireless network. Due to this wireless nature of communication, there exist potential threats to the data privacy in AMI. Decoding the energy consumption reading, injecting false data/command signals and jamming the networks are some hazardous measures against this technology. Since a smart meter possesses limited memory and computational capability, AMI demands a light, but robust security scheme. In this paper, we propose a localization-based key management system for meter data encryption. Data are encrypted by the key associated with the coordinate of the meter and a random key index. The encryption keys are managed and distributed by a trusted third party (TTP). Localization of the meter is proposed by a method based on received signal strength (RSS) using the maximum likelihood estimator (MLE). The received packets are decrypted at the control center with the key mapped with the key index and the meter’s coordinates. Additionally, we propose the k-nearest neighbors (kNN) algorithm for node/meter authentication, capitalizing further on data transmission security. Finally, we evaluate the security strength of a data packet numerically for our method.

In order to cope with the phenomenal growth of mobile data traffic, unlicensed spectrum can be utilized by the Long Term Evolution (LTE) cellular systems. However, ensuring fair coexistence with Wi-Fi is a mandatory requirement. In one approach, periodically configurable transmission gaps can be used to facilitate a coexistence between WiFi and LTE. In this paper, a Multi-Armed Bandit (MAB) based dynamic duty cycle selection method is proposed for configuration of transmission gaps ensuring a better coexistence for both technologies. Then the concept is further strengthened with downlink power control mechanism using the same algorithm leading to a high energy efficiency and interference reduction. Performance results are given for different user equipment and WiFi station densities in which it is shown that significant improvements in overall throughput and energy efficiency can be achieved.