Minho Jo

Rethinking energy efficiency models of cellular networks with embodied energy

The continuous increase in energy consumption by cellular networks requires rethinking their energy efficiency. Current research indicates that one third of operating energy could be saved by reducing the transmission power of base stations. However, this approach requires the introduction of a range of additional equipment containing more embodied energy - consumed by all processes associated with the production of equipment. This problem is addressed first in this article. Furthermore, a new cellular network energy efficiency model with embodied energy is proposed, and optimization between the number of cells and their coverage is investigated. Contrary to previous works, we have found that embodied energy accounts for a significant proportion of total energy consumption and cannot be neglected. The simulation results confirm an important trade-off between operating and embodied energies, which can provide some practical guidelines for designing energy-efficient cellular access networks. The new model considering embodied energy is not limited to just cellular networks, but to other telecommunications, such as wireless local area networks and wired networks.

A secure and efficient SIP authentication scheme for converged VoIP networks

Session Initiation Protocol (SIP) has been widely used in current Internet protocols such as Hyper Text Transport Protocol (HTTP) and Simple Mail Transport Protocol (SMTP). SIP is a powerful signaling protocol that controls communications on the Internet for establishing, maintaining and terminating sessions. The services that are enabled by SIP are equally applicable to mobile and ubiquitous computing. This paper demonstrates that recently proposed SIP authentication schemes are insecure against attacks such as off-line password guessing attacks, Denning-Sacco attacks and stolen-verifier attacks. In order to overcome such security problems, a new secure and efficient SIP authentication scheme in a converged VoIP network based on elliptic curve cryptography (ECC) is proposed and it works to exploit the key block size, speed, and security jointly.

Selfish attacks and detection in cognitive radio Ad-Hoc networks

Cognitive radio is an opportunistic communication technology designed to help unlicensed users utilize the maximum available licensed bandwidth. Cognitive radio has recently attracted a lot of research interest. However, little research has been done regarding security in cognitive radio, while much more research has been done on spectrum sensing and allocation problems. A selfish cognitive radio node can occupy all or part of the resources of multiple channels, prohibiting other cognitive radio nodes from accessing these resources. Selfish cognitive radio attacks are a serious security problem because they significantly degrade the performance of a cognitive radio network. In this article we identify a new selfish attack type in cognitive radio ad-hoc networks and propose an easy and efficient selfish cognitive radio attack detection technique, called COOPON, with multichannel resources by cooperative neighboring cognitive radio nodes.

Itinerary Planning for Energy-Efficient Agent Communications in Wireless Sensor Networks

Compared with conventional wireless sensor networks (WSNs) operating based on the client-server computing model, mobile agent (MA)-based WSNs can facilitate agent-based data aggregation and energy-efficient data collection. In MA systems, it has been known that finding the optimal itinerary of an MA is nondeterministic polynomial-time hard (NP-hard) and is still an open area of research. In this paper, we consider the impact of both data aggregation and energy efficiency in itinerary selection. We first propose the Itinerary Energy Minimum for First-source-selection (IEMF) algorithm. Then, the itinerary energy minimum algorithm (IEMA), which is the iterative version of IEMF, is described. This paper further presents a generic framework for the multiagent itinerary planning (MIP) solution, i.e., the determination of the number of MAs, allocating a subset of source nodes to each agent and itinerary planning for each MA. Our simulation results have demonstrated that IEMF provides higher energy efficiency and lower delay, compared with existing single-agent itinerary planning (SIP) algorithms, and IEMA incrementally enhances IEMF at the cost of computational complexity. The extensive experiments also show the effectiveness of MIP algorithms when compared with SIP solutions.

A survey of converging solutions for heterogeneous mobile networks

In 5G systems, the current machine-tomachine communications using Wi-Fi or Bluetooth provide a good opportunity to dramatically increase overall performance. Converged mobile networks can provide M2M communications with significant performance improvements by sharing unlicensed spectrum bands in cellular networks, such as Long Term Evolution-Advanced, by using cognitive radio technology. Thus, the converged mobile network will become one of the most popular future research topics because mobile multimedia content services have been generally accepted among mobile device users. In this article, we provide an overview of converged mobile networks, investigating different types of converged mobile networks, different types of convergence, and the current problems and solutions. This survey article also proposes potential research topics in converged mobile networks.

Mobile RFID Tag Detection Influence Factors and Prediction of Tag Detectability

Radio-frequency identification (RFID) readers are powered RF devices that communicate with tags (whether mobile or fixed) and read necessary information to be processed. A mobile RFID tag is detected by an RFID antenna. In a mobile RFID where the RFID tag is attached to a mobile object such as a vehicle, a human, or an animal, information is more difficult to detect than in the case where the tag is attached to a stationary object. Currently, deployment engineers and researchers use trial-and-error approaches to decide on the best conditions of the tag detection influence factors which affect tag detectability (detection rate). As expected, these approaches are time consuming. Even though mobile RFID systems have become widely used in industry and tag detection problems are crucial at deployment, very few researches on them have been conducted so far. Thus, a quick and simple method for finding tag detectability is needed to improve the traditional time consuming trial-and-error method. In this paper, we propose a unique approach ldquothe intelligent prediction method of tag detection rate using support vector machine (SVM).rdquo The intelligent method predicts the mobile RFID tag detectability instead of the trial-and-error experimental procedures. The simulation results of the proposed method are very comparable to the trial-and-error experimental approach. The proposed intelligent method gives a very high accuracy of mobile RFID tag detectability prediction and proves to be superior to the current method in time as well cost savings. The predicted tag detectability results can be used for analyzing mobile RFID tag detection influence factors and their conditions.

Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing

The emerging heterogeneous mobile network architecture is designed for an increasing amount of traffic, quality requirements, and new mobile cloud computing demands. This article proposes a hierarchical cloud computing architecture to enhance performance by adding a mobile dynamic cloud formed by powerful mobile devices to a traditional general static cloud. A mobile dynamic cloud is based on heterogeneous wireless architecture where device-to-device communication is used for data transmission between user devices. The main advantage of the proposed architecture is an increase in overall capacity of a mobile network through improved channel utilization and traffic offloading from Long Term Evolution-Advanced to device-to-device communication links. Simulations show that the proposed architecture increases the capacity of a mobile network by up to 10 percent depending on the conditions and amount of offloaded data. The offloading probability is also evaluated by taking into consideration the number of devices in the cloudlet and the content matching values. We have gained insight into how content similarity affects offloading probability much more than the number of devices in a cloudlet.

Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

Efficient Energy Management for the Internet of Things in Smart Cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

Network Selection and Channel Allocation for Spectrum Sharing in 5G Heterogeneous Networks

The demand for spectrum resources has increased dramatically with the advent of modern wireless applications. Spectrum sharing, considered as a critical mechanism for 5G networks, is envisioned to address spectrum scarcity issue and achieve high data rate access, and guaranteed the quality of service (QoS). From the licensed networks perspective, the interference caused by all secondary users (SUs) should be minimized. From secondary networks point of view, there is a need to assign networks to SUs in such a way that overall interference is reduced, enabling the accommodation of a growing number of SUs. This paper presents a network selection and channel allocation mechanism in order to increase revenue by accommodating more SUs and catering to their preferences, while at the same time, respecting the primary network operators policies. An optimization problem is formulated in order to minimize accumulated interference incurred to licensed users and the amount that SUs have to pay for using the primary network. The aim is to provide SUs with a specific QoS at a lower price, subject to the interference constraints of each available network with idle channels. Particle swarm optimization and a modified version of the genetic algorithm are used to solve the optimization problem. Finally, this paper is supported by extensive simulation results that illustrate the effectiveness of the proposed methods in finding a near-optimal solution.