White space: Definitional perspectives and their role in exploiting spectrum opportunities

Akhtar, Fayaz; Rehmani, Mubashir Husain; Reisslein, Martin

doi:10.1016/j.telpol.2016.01.003

Cited by 108 publications

(51 citation statements)

References 92 publications

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“…As for the perspectives of our work, we intend to embed our approach into a real UAV platform, such as Parrot drones [39]. In addition, as shown in [40], a promising research direction would be the integration of cognitive radio [41] technology with UAV systems. Another future enhancement would be incorporating an optimization study to determine the optimal number of drones needed as function of the application requirements and the explored region.…”

Section: Resultsmentioning

confidence: 99%

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Messous¹,

Sedjelmaci²,

Senouci³

2017

Pervasive and Mobile Computing

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To cite this version:Mohamed-Ayoub Messous, Hichem Sedjelmaci, Sidi-Mohammed Senouci. Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system. Pervasive and Mobile Computing, Elsevier, 2017, <10.1016/j.pmcj.2017 Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system In this paper, we design and implement a novel generic mobility model, named Alphabased, for a fleet of small interconnected UAVs (Unmanned Aerial Vehicles) that collaborate to explore a geographic area (battle field, research and rescue missions, surveillance applications, etc.). In fact, due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the UAV's attributes. Hence, we propose to use a combination of energy level, coverage-area and network connectivity for mobility decision-making, in contrast to the literature where only network connectivity and area coverage are investigated. On the one hand, these two metrics are very important, especially for applications where achieving the best area-coverage and maintaining network connectivity represent an essential requirement. On the other hand, energy is another equally noteworthy constraint that should be taken into account. In fact, being a crucial resource for all mobile devices and especially for UAVs, the energy becomes vital to ensure the network lifetime and mission success. As far as we know, Alphabased mobility model is the first to ever consider a combination of these three metrics within the same decision-making criterion. A distributed scheme is adopted, where each UAV determines locally its future movement based on the information it receives from its neighbors. Moreover, a novel fuzzy inference system is implemented in order to compute the values of a followship weighting parameter, named Alpha. This latter is used to choose the most suitable neighboring UAV to follow. To validate the proposed mobility model, rigorous testing has been accomplished, through simulation work. Compared to Randombased and Forces-based mobility models, the Alpha-based mobility model achieves good coverage rate while maintaining connectivity.

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Section: Resultsmentioning

confidence: 99%

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Messous¹,

Sedjelmaci²,

Senouci³

2017

Pervasive and Mobile Computing

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“…This paper defines F as the set of all feasible f. Given a clustering configuration f, the corresponding fitness function Fit(f ) is used to denote clustering effect which reflects both the homogeneity within the same cluster and the heterogeneity among different clusters. This paper aims to maximize Fit(f ) by choosing a proper clustering configuration f. Fit(f ) can be described as (2) where N(f ) denotes the number of clusters with the clustering configuration f, and R f (i) denotes the minimized interdistance-to-intradistance ratio of the ith cluster. The interdistance-to-intradistance ratio is used to measure the relationship between distance within a cluster and that among different clusters.…”

Section: Problem Formulationmentioning

confidence: 99%

“…To increase the utilization of the wireless spectrum, cognitive radio technology has recently emerged. When licensed users (also called primary users, PUs) do not utilize the licensed wireless spectrum, cognitive radio allows unlicensed users (also called secondary users, SUs) to access the idle licensed wireless spectrum opportunistically [2]. Therefore, wireless spectrum sensing is the premise for opportunistic access of unlicensed users.…”

Section: Introductionmentioning

confidence: 99%

User Classification in Crowdsourcing-Based Cooperative Spectrum Sensing

Zhai

Wang

2017

Symmetry

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This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious). There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.

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“…Moreover, the multi-service systems can enable loose time synchronization scheme and may save signaling overhead (e.g., time advance (TA) in Long Term Evolution (LTE)), since all service signals are well-separated in the frequency domain. The spectrum allocation flexibility of the multi-service system can also be combined with other techniques such as cognitive radio networks [5] [6] [7], where the fragmented spectrum can be dynamically occupied by various type of services and keep the services from significant inter-service-band-interference (ISvcBI). It can be verified from mathematical analysis that combining different numerologies in one frequency band will destroy the orthogonality of multi-carrier systems, resulting in ISvcBI.…”

Section: Introductionmentioning

confidence: 99%