Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Service Delay in a scenario with two cloudlet servers. The method has a dual focus on computation and communication elements, controlling Processing Delay through virtual machine migration and improving Transmission Delay with Transmission Power Control. The foundation of the proposal is a mathematical model of the scenario, whose analysis is used on a comparison between the proposed approach and two other conventional methods; these methods have single focus and only make an effort to improve either Transmission Delay or Processing Delay, but not both. As expected, the proposal presents the lowest Service Delay in all study cases, corroborating our conclusion that a dual focus approach is the best way to tackle the Service Delay problem in Edge Cloud Computing.

Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control

In the last few years, Internet of Things, Cloud computing, Edge computing, and Fog computing have gained a lot of attention in both industry and academia. However, a clear and neat definition of these computing paradigms and their correlation is hard to find in the literature. This makes it difficult for researchers new to this area to get a concrete picture of these paradigms. This work tackles this deficiency, representing a helpful resource for those who will start next. First, we show the evolution of modern computing paradigms and related research interest. Then, we address each paradigm, neatly delineating its key points and its relation with the others. Thereafter, we extensively address Fog computing, remarking its outstanding role as the glue between IoT, Cloud, and Edge computing. In the end, we briefly present open challenges and future research directions for IoT, Cloud, Edge, and Fog computing.

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Foundations and Evolution of Modern Computing Paradigms : Cloud, IoT, Edge, and Fog

Phishing, or web spoofing, is a growing problem: the Anti-Phishing Working Group (APWG) received almost 14,000 unique phishing reports in August 2005, a 56% jump over the number of reports in December 2004 [3]. For financial institutions, phishing is a particularly insidious problem, since trust forms the foundation for customer relationships, and phishing attacks undermine confidence in an institution. Phishing attacks succeed by exploiting a user's inability to distinguish legitimate sites from spoofed sites. Most prior research focuses on assisting the user in making this distinction; however, users must make the right security decision every time. Unfortunately, humans are ill-suited for performing the security checks necessary for secure site identification, and a single mistake may result in a total compromise of the user's online account. Fundamentally, users should be authenticated using information that they cannot readily reveal to malicious parties. Placing less reliance on the user during the authentication process will enhance security and eliminate many forms of fraud. We propose using a trusted device to perform mutual authentication that eliminates reliance on perfect user behavior, thwarts Man-in-the-Middle attacks after setup, and protects a user's account even in the presence of keyloggers and most forms of spyware. We demonstrate the practicality of our system with a prototype implementation.

Phoolproof Phishing Prevention

Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

Cloudlets Activation Scheme for Scalable Mobile Edge Computing with Transmission Power Control and Virtual Machine Migration

Random data encryption algorithm (RDEA) is an advanced security model as compared to the Data Encryption Standard. It makes use of a random key generator to get the key from the cipher key database. The RDEA seems to be a great option for attaining greater security when we compare with 64-bit key of DES algorithm. Most of the random generators offered today are software-based random generators, which are not capable of generating actual random information. Because software random generators generate random information by using a fixed data set, their output can be predictable. This predictability weakens software encryption capability. Therefore, we would develop a modification to this algorithm by using pseudo-random key generation, which reduces the computation at both the encryption and decryption side without affecting the encryption capability. The cipher keys generated by means of pseudo-random methods will enhance the diffusion rate, which will attract higher security as well as throughput.

Satyabrata Swain

Papers

Efficiency Comparison and Analysis of Pseudo-random Generators in Network Security