Localization is one of the most important system parameters in Wireless Sensor Networks (WSNs). It consists of the determination of the geographical coordinates of nodes forming the network. Traditional localization algorithms suffer from the high error of localization, then they need to be enhanced. This paper proposes a new localization algorithm namely Centroid Localization Algorithm based on Social Spider Optimization Algorithm (CLA-SSO). The proposed algorithm uses the Social Spider Optimization metaheuristic (SSO) to improve the localization of the basic Centroide Localization Algorithm (CLA) which is a range free localization algorithm. In our method, the initial spiders are initialized by the locations obtained by the CLA and optimized using the SSO metaheuristic. Simulation results show that our proposed algorithm outperforms the basic CLA in terms of localization accuracy. These results are obtained by changing some factors such as transmission radius, ratio of anchor nodes and the number of unknown nodes which affect the localization accuracy.

Smart cities are one of the main areas where IoT showed great success, collecting and processing enormous amounts of data to facilitate different applications. Smart parking is one of the evolving smart cities applications. The rapid increase in the population also results in a high number of vehicles that may lead to traffic congestion in urban cities. This traffic congestion is increasing the problem of urban mobility. Urban mobility may have an adverse impact on the quality of life as well as the economy. This problem can be mitigated with the efficient management of the parking systems. This work aims to review the IoT-based regression techniques used in smart parking systems to overcome the problem of urban mobility. IoT-based regression techniques are used to predict parking space in the parking area so that the drivers can get parking space on time and the problem of urban mobility can be mitigated. It was identified that regression techniques efficiently predicted parking space availability. This study provides a comprehensive review of regression techniques used in IoT smart parking applications. The system architecture was also presented to get a better understanding of this work.

Telepresence surgical training based on mixed reality over the Internet is exposed to various cyber-attacks. Providing an adequate level of security against such attacks becomes an essential requirement for implementing this technology. The aim of this work is to improve the security of data transmission against several potential attacks while reducing the required execution time for encryption and decryption during real-time surgical telepresence training. In this research, a cryptosystem based on an enhanced chaotic map with Markov property using the Secure Hash Algorithm with 256-bit (SHA-256) is proposed to secure data during transmission. The enhanced chaotic map governs the diffusion process for image ciphering. The proposed scheme reduces the average processing time by 23.49%, i.e., from 83.99 ms (millisecond) to 64.33 ms, compared to the current state of the art solution, which is used as a benchmark in this work. Moreover, the Peak Signal to Noise Ratio (PSNR), which is used for measuring the encryption strength, is reduced by 17.33%, i.e., from 36.13 dB (decibel) to 29.87 dB compared to the same benchmark. The proposed solution demonstrates significant improvement in securing data against brute force attack, known-plaintext attack, chosen-plaintext attack and other statistical attacks. Also, the solution reduces the processing time required for both encryption and decryption.

The rapid growth in Internet of Things IoT applications has increased the demand for Wireless Sensor Network (WSN) as an essential supportive Ad-hoc network class in the IoT stack. However, managing the network lifetime related to power consumption and network capacity is still a significant challenge that affects WSN functionality. Wireless network capacity is primarily affected by available bandwidth, error rate, and Signal to Noise Ratio (SNR). These factors have more profound effects on WSN because of limitations in power supplies and the ad-hoc mode implemented in WSN. Hence, it is essential to maintain the network lifetime and capacity to use WSN in real-world IoT applications. Data aggregation techniques with efficiently collecting and aggregating packets will help to reduce power consumption and reduce network traffic congestions.This study aims to systematically analyze and review the data aggregation techniques used in WSN. The paper presents a comprehensive survey based on the current work, component classification, and evaluation table. Additionally, an analysis based on the data aggregation technique is conducted for improving the network capacity based on the existing technologies. Also, the study proposes an aggregation framework based on the literature study, identifying the significant components used for obtaining an enhanced solution for improving network capacity in a WSN with the help of the data aggregation technique.

Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication.

Achieving good quality and minimum distortion of the video frames is one of the most challenging requirements in the telemedicine system. Transmission process for a real-time video over the wireless network is due to various real-time restrictions, such as encoding mechanism, noise, and bandwidth fluctuations. The restrictions introduce distortions and delay, hence adversely affect the reliability and quality of the video transmission system. This study aims to propose a new system which can fine-tune the encoding process dynamically. The proposed system consists of an Enhanced Video Quality and Distortion Minimization (EVQDM) algorithm to achieve guaranteed quality, minimum distortion, and the minimum delay in the transmission of the video. This system guarantees the video quality by using the adaptive video encoding technique and minimizes the distortion by considering the truncating distortion in the enhanced distortion minimization algorithm. The results of applying the proposed EVQDM algorithm and the state-of-the-art solutions are compared, and it was shown that the proposed algorithm improved the state-of-the-art solution. The video quality has been increased from 47.2 dB to 50.13 dB, the video distortion has been minimized from 0.6802 to 0.3509 and the end-to-end delay has been reduced from 123.58 ms to 112.57 ms. The proposed solution focuses on truncating distortion, to minimize the total distortion of the video. For summarization, this solution addressed the issues of achieving minimum distortion and delay while providing the guaranteed video quality within the boundaries of the real-time constraints that are imposed on the system.

The rapidly growing number of wireless devices running applications that require high bandwidths, has resulted in increasing demands for the unlicensed frequency spectrum.Given the scarcity of allocated unlicensed frequencies, meeting such demands can become a serious concern.Cognitive Radio (CR) technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues relevant to the limited availability of unlicensed frequencies.Combining CR and Wi-Fi to form the socalled White-Fi networks, has been proposed for achieving higher spectrum utilization.This article discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical applications.It also reports the analysis of such impacts through various simulation studies.Our results demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related parameters based on QoS requirements.We also propose such a sensing strategy that can adapt to the IEEE 802.11e requirements.The goal of the proposed strategy is the enhancement of the overall QoS of the applications while maintaining efficient sensing of the spectrum.Simulation results of the scenarios that implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where common sensing methods are utilized in IEEE802.11networks.

Cognitive Radio (CR) technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues relevant to the limited availability of unlicensed frequencies.Combining CR and Wi-Fi to form the so called White-Fi networks, has been proposed for achieving higher spectrum utilization.This paper discusses the spectrum sensing in White-Fi networks and the impacts that they have on the QoS of typical applications.It also reports the analysis of such impacts through various simulation studies.We also propose such a sensing strategy that can adapt to the IEEE 802.11e requirements.The proposed strategy aims to enhance overall QoS while maintaining efficient sensing.Simulation results of the proposed mechanism demonstrate a noticeable improvement in QoS.

The upcoming communication paradigms, in particular, 5G and the Internet of things (IoT) networks, will infer an enormous number of smart objects to join the global network, mostly through wireless communications technologies. Consequentially, considerable wireless traffic density and radio spectrum scarcity are expected. Cognitive radio (CR) technology is a promising solution for improving spectrum utilization to handle the potential increasing traffic of future wireless networks. This chapter explains the concept of this new technology and its various proposed definitions. The primary CR functions are explained, and their related challenges are identified. Spectrum sensing is the most important function in CR. Among several sensing methods proposed for spectrum sensing, there is no single optimized method. In this chapter, the factors that should be considered when choosing the proper sensing method are investigated. The critical issue that hinders the wide adaptation of CR technology is the noticeable QoS degradation caused by an imperfect sensing operation. The authors of this chapter have suggested that a future CR device has to be designed to support various sensing methods so it can switch between them, based on the investigated factors, for better sensing performance and QoS provisioning.

With the massive and growing number of wireless devices, the scarcity of the available frequency spectrum is a serious concern. The QoS requirements of most contemporary networking applications exacerbate this issue. Through opportunistic utilization of the available frequency spectrum, Cognitive Radio (CR) technology can offer some appropriate solutions to this problem. Unlicensed spectrum is shared very efficiently by Wi-Fi devices. To improve the availability of frequency spectrum and their efficient use, integration of CR and Wi-Fi technologies appear to be a promising approach. This combination forms the so-called White-Fi. This paper discusses the relevant spectrum assignment and sensing issues in White-Fi implementations. We also report the results of our simulation studies on the effects of sensing variations in the QoS levels of several typical applications. These studies show the effects of an intelligent sensing strategy that is aware of the application requirements on White-Fi QoS. Based on those, we propose some sensing improvements that may mitigate such effects on QoS. Our works indicate that while such an approach can result in a more efficient use of the available spectrum, the burden of sensing and the inevitable delays may result in some QoS degradations.

Scarcity of the radio frequency spectrum available for use by the vast number of wireless devices is already a challenge faced by ubiquitous communication networks, including the Internet of Things. The Cognitive Radio (CR) technology provides promising solutions to address part of such challenges. CR is based on allowing the so-called holes, the spectrums that remain unoccupied by their licensed users, to be utilized by others. To detect these holes, several spectrum sensing methods have been proposed by researchers. Each of these methods has its advantages and shortcomings for a given CR operation scenario. In particular, sensing methods can greatly affect the QoS levels of applications for various users of CR networks. In this paper, the effects of various spectrum sensing parameters and functions on the QoS levels of applications running on CR devices are reported. Based on those, the authors propose a QoS-aware fuzzy scheme for selecting the proper sensing method, from the catalog of available ones. We show that the proposed scheme mitigates the degrading impacts of the sensing operations on the QoS levels of a diverse range of CR-based applications.

The rapidly growing number of wireless communication devices has led to massive increases in radio traffic density, resulting in a noticeable shortage of available spectrum.To address this shortage, the Cognitive Radio (CR) technology offers promising solutions that aim to improve the spectrum utilization.The operation of CR relies on detecting the so-called spectrum holes, the frequency bands that remain unoccupied by their licensed operators.The unlicensed users are then allowed to communicate using these spectrum holes.As such, the performance of CR is highly dependent on the employed spectrum sensing methods.Several sensing methods are already available.However, no individual method can accommodate all potential CR operation scenarios.Hence, it is fair to ascertain that the performance of a CR device can be improved if it is capable of supporting several sensing methods.It should obviously also be able to select the most suitable method.In this paper, several spectrum sensing methods are compared and analyzed, aiming to identify their advantages and shortcomings in different CR operating conditions.Furthermore, it identifies the features that need to be considered while selecting a suitable sensing method from the catalog of available methods.

There is a growing interest in cloud computing due to its various benefits such as the efficient utilization of computing resources.However, privacy and security concerns are among the main obstacles facing the widespread adoption of this new technology.For instance, it is more desirable for many potential organizations and users that privacy protections and access authorizations on their data stored in the cloud remain under their control and only authorized entities can have access to the data even for the cloud server.In this paper, we propose a method that enables cloud clients more control of data security requirements on their data stored in the cloud.The data is protected by a client before it is sent to the cloud in a secure manner that only authorized users can access it.To provide a complete protection from unauthorized access, even the cloud provider is prevented from revealing the data content and access control policies.The client or data owner has complete control on what methods to use to protect the data and on who can have access on the data.The proposed method is based on a combination of cryptography techniques, including the Chines Remainder Theorem, symmetric and asymmetric encryptions.The proposed method combines access control and key sharing in one mechanism.In addition, the proposed method allows a client to use a unique key to encrypt the data and attaches it securely to its encrypted data.Only authorized users can have access to the key in order to decrypt the encrypted data.The data has all the security requirements independently attached to it including the integrity proof.The proposed method is efficient and has its computational overheard minimized.With all the security requirements and metadata stored with the data itself, the proposed method is also flexible and suitable for protecting clients' data in the cloud computing environment.

Cloud computing offers many benefits for efficient utilization of computing resources. However, for many potential users and organizations security concerns, and in particular confidentiality of data and access control issues, outweigh such benefits. Some of these concerns relate to traditional network security issues, like confidentiality of data in transit to the cloud server. Some other cases are more relevant to the nature of cloud itself. An example of such a case is when the user desires that data access authorizations remain under their control, without revealing them to the cloud server. In both situations, the challenge is to provide the required solutions with minimal computational and storage overheads. This paper takes up that challenge to provide some solutions that have such characteristics. Our reported solutions are based on the Chines Remainder Theorem and allow for tailored enforcement and updating of access control parameters by the data owner with minimal overheads. In our approach, the data owner can provide each authorized user with the unique symmetric keys, used to encrypt a particular set of data to be shared by them. Furthermore, the security parameters are attached to the data and remain hidden. Consequently, they are not revealed to the cloud provider and unauthorized users at any time.