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The vehicular network is a collection of vehicles and other components facilitated with versatile sensors to communicate, which is backboned by authentication. Present authentication protocols focus on either the lightweight feature or security. On the other hand, privacy during authentication is essential. Moreover, existing schemes are dependent on the trusted authority for checking the legitimacy of a communicating node, which suffers from the infrastructure-less scenario. Alternatively, existing protocols are dependent on a secure communication channel during the registration phase. To address these concerns, an elliptic curve cryptography-based authentication protocol has been designed. Moreover, a secure key establishment is shown in this paper. Pseudo-id-based authentication is used to provide the privacy-preservation on the scheme. On the other hand, symmetric-key cryptography with a session key is used for message encryption during communication. A security analysis on the proposed scheme using the BAN logic and AVISPA shows the protocol’s resilience capabilities on various attacks. Alternatively, the mathematical proof of the protocol represents the proof of correctness. Moreover, extensive performance analysis with the existing authentication schemes shows that the proposed algorithm is better in computation cost, communication cost, and energy cost. Lastly, the NS3 simulation shows the packet data transfer among the nodes in the network.

A substantial number of authentication protocols are designed to safeguard vehicular ad-hoc network (VANET) communication from potential attacks; however, they experienced an inability to provide a balance between lightweight and security. Existing security and privacy-preserving based authentication protocols in vehicular networks mostly rely on the trusted authority and signatures to validate the communication on road. Accomplishing the quick validation and correspondence is difficult in such methodologies and further, they endure execution obliges from coming about overhead. To overcome these issues, we have developed an enhanced lightweight and secure authentication protocol (ELSAP) for V2V Communication in VANETs. Moreover, the scheme withheld with self-authentication prior to communication that enhances the network feasibilities, which in return needs less message transfer during authentication as well as communication, indicates lightweight features. Furthermore, two or more vehicles can securely perform mutual authentication, proven by Burrow–Abadi–Needham (BAN) logic. Additionally, the competency of the proposed protocol against the current updated threats is shown via security analysis and comparisons tools such as Automated Validation of Internet Security Protocols and Applications (AVISPA). The result of the performance analysis shows that the communication cost and computational cost outperformed the earlier authentication schemes alongside the security features of the proposed protocol.

The integration of cellular networks and vehicular networks is complex and heterogeneous. Synchronization among vehicles in heterogeneous vehicular clusters plays an important role in effective data sharing and the stability of the cluster. This synchronization depends on the smooth exchange of information between vehicles and remote servers over the Internet. The remote servers predict road traffic patterns by adopting deep learning methods to help drivers on the roads. At the same time, local data processing at the vehicular cluster level may increase the capabilities of remote servers. However, global positioning system (GPS) signal interruption, especially in the urban environment, plays a big part in the detritions of synchronization among the vehicles that lead to the instability of the cluster. Instability of connections is a major hurdle in developing cost-effective solutions for deriving assistance and route planning applications. To solve this problem, a self-localization scheme within the vehicular cluster is proposed. The proposed self-localization scheme handles GPS signal interruption to the vehicle within the cluster. A unique clustering criterion and a synchronization mechanism for sharing traffic information system (TIS) data among multiple vehicles are developed. The developed scheme is simulated and compared with existing known approaches. The results show the better performance of our proposed scheme over others.

Vehicular Ad Hoc Network (VANET) emerges to ameliorate road accident, traffic congestion, and infotainment. Cognitive radio (CR) is integrated with VANET (termed as CR-VANET) to deal with the spectrum scarcity problems. For better data transmission, routing is very important in a highly dynamic CR-VANET environment. Routing in CR-VANET has several challenges due to vehicles’ high-speed movements and channel accessibility issues. This paper proposes a 2-Hop routing algorithm based on the Multi-Objective Harris Hawks Optimization (2HMO-HHO) algorithm that chooses the optimal forwarders between the source and destination vehicles. Selecting 2-hops instead of multiple hops or the entire route increases the selected route’s stability and assures successful data transmission. The simulations performed in OMNeT++ with SUMO show that the proposed algorithm achieves promising results on throughput, delay, packet delivery ratio, packet loss rate and communication overhead.

Vehicle monitoring and positioning become an essential factor in road management to secure and safeguard the vehicular network, which influences the coupling of reliability on the advanced automobile technologies. Furthermore, to predict the exact location of a car in a given time is challenging, because it depends on a myriad number of elements. Moreover, knowing the position of a vehicle helps passengers as well as increase vehicle network security. In this paper, we propose a mathematical model to predict the position of a car from a prepopulated dataset using spline interpolation. More interestingly, the prediction point of a mobile vehicle will be presented without any help from real-time monitoring devices. Simulation of vehicle positioning is done using bus trajectory data in a university environment in the University of Malaya to verify the feasibility and benefit of the proposed approach. Accordingly, a process of evaluation has been performed based on a plethora of components and existing works to show the effectiveness of the proposed method.

The strength of Vehicular Ad hoc Networks (VANETs) and the rapid deployment capability, can be used in many situations where the network should be arranged in a short time and there is a need to collect sensitive information. We consider cluster-based attack detection in data compilation wherever the neighbor nodes give the important information to the cluster head. Moreover, evidence is obtainable in the cluster head may possibly be accumulated by some vehicular nodes and executes numerous responsibilities such as decision making about delivering information. The existence of malicious nodes threatens determination making through transmitting malevolent information, which is not appropriate to the VANET categorized data and might send a substantial number of packets to the vehicles or Road Side Unit (RSU). To overcome this issue, we have proposed a Stream Position Performance Analysis (SPPA) approach. This approach monitors the position of any field station in sending the information to perform a Distributed Denial of Service (DDoS) attack. The method computes various factors like Conflict field, Conflict data and Attack signature sample rate (CCA). Using all these factors, the method identifies the trustworthiness of the packet and includes it in decision making. The proposed approach increases the performance of a Distributed Denial of Service (DDoS) attack detection in a VANET environment.

Advancement of technology has enabled access to innovative applications for connected devices. To handle growing computation requirements, the backend cloud data centers become an inefficient solution due to the caused network overhead. This is generally alleviated using edge locations deployed to meet the increasing computing demands. This article proposes a micro-level fog unit deployment to facilitate delay-sensitive applications. To manage imbalanced workloads due to the traffic density, the framework established a fog federation acting as a consortium where underutilized resources are shared to provide service quality. Moreover, we implement a price-based workload balancing algorithm to limit offloading among fog units relative to other consortium members. The experimental results show a balanced offload rate compared to traditional algorithms. Moreover, other measures, such as queue length, end-to-end delay, and workload balancing, demonstrate performance gain under the federation. Overall, 72% energy reduction is achieved through the proposed technique in comparison with the traditional nonfederated model.

With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.

Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC.

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model., Comment: 17 Pages, 12 Tables, 17 Figures

Vehicular ad hoc networks (VANETs) are a group of nodes that remain dynamically and randomly situated. VANETs are considered as one of the most prominent technologies for improving the efficiency and safety of modern transportation systems. However, the VANET is also subjected to attacks that will weaken the performance of vehicular communication. To enable communication inside the VANET system, a routing protocol helps to determine directions among nodes. VANET network nodes move very quickly from one place to another, and in that time DDoS attacks will occur in the VANET network. Therefore, it is important to implement DDoS attack detection-based communication level on the entire VANET system. The source node will send data or information to the destination using intermediate nodes, whenever a DDoS attack happens in the node. In this paper, an approach is proposed for detection of a DDoS attack in a VANET network by using the adapted stream region scheme. Once the attack occurs in the VANET network, all the data will be damaged or hacked by another attacker. To minimize the DDoS attack and optimize the assigned problem, the author is using packet marking based on adapted stream region (PMBASR) techniques on the network. The PMBASR techniques are used to trace back to the source node, then the node of origin used in RSU server for data request, and at the same time the data will receive a response in the network. Nevertheless, an analytical approach will use PMBASR to detect the DDoS attack and further improve the network performance. Finally, the Ns2 simulation result proves it to be a better result-oriented approach.

Ubiquitous computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives toward the objective of developing seamless, interoperable, and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting (EH) and power transmission using computing devices. Radio frequency (RF) based energy management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RF identification sensor networks (RSN) are EH and energy transfer (ET). These techniques enable the dynamic energy level maintenance and optimization as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives.

The vehicular ad-hoc networks integrates with long-term evolution (LTE) forming a heterogeneous network, capable of providing seamless connectivity, which meets the communication requirements of intelligent transportation systems. However, heterogeneous network-based applications involve LTE resource (data and spectrum) usage cost and must be taken care while developing such a solution. One of the scenarios is the access of the information to/from remote server over the internet via LTE for road traffic management applications. Although clustering of the vehicle is significant to minimize the data and LTE network usage, however, the problem of non-cooperation of the vehicles in clustering process and within a cluster are major issues in sharing costly data acquired from the internet. Because, who and why one (vehicle) should pay the cost is the big question, proliferating the non-cooperative behavior among the cluster members. To solve these issues, strategic game-theoretic based clustering mechanism named as cooperative interest-aware clustering (CIAC) is developed. The proposed CIAC not only balance the cost of usage by controlling non-cooperative behavior among the vehicles within the cluster but at the same time motivate vehicles to participate in the clustering process to share the data and cost as well. It consists of a cluster head selection process based on the strategic game-theoretic approach and a fair-use policy. The implementation results show superiority in performance of our protocol over the existing approaches.

The integration of simultaneous wireless information and power transfer (SWIPT) and cooperative relay (CoR) techniques has evolved as a new phenomenon for the next-generation wireless communication system. CoR is used to get energy and spectral efficient network and to solve the issues of fading, path loss, shadowing, and smaller coverage area. Relay nodes are battery-constrained or battery-less devices. They need some charging systems externally as replacing or recharging of their batteries sometimes which are not feasible and convenient. Energy harvesting (EH) is the most cost-effective, suitable, and safer solutions to power up these relays. Among various types of the EH, SWIPT is the most prominent technique as it provides spectral efficiency by delivering energy and information to the relays at the same time. This paper reviews the combination of CoR and SWIPT. From basic to advanced architectures, applications and taxonomies of CoR and SWIPT are presented, various forms of resource allocation and relay selection algorithms are covered. The usage of CoR and SWIPT in the fifth-generation wireless networks is discussed. This paper focuses on the integral aspects of the CoR and SWIPT to other next-generation wireless communication systems and techniques such as multiple-input-multiple-output, wireless sensor network, cognitive radio, vehicular ad hoc network, non-orthogonal multiple access, beamforming technique, and the Internet of Things. Some open issues and future directions and challenges are given in this paper.

Internet of Vehicles (IoV) engages all the components of vehicular environment from vehicle to infrastructure not only to enhance driving experience and safety but also to facilitate computation demands of IoV based applications. Due to the increasing computation demands of such applications it is difficult to facilitate vehicular environment with the existing infrastructure. However, the computing capabilities of underutilized vehicles can be used through task offloading. Therefore, it is crucial to incorporate such environment with trusted collaborative communication. Blockchain technology is gaining popularity in this regard but it is computationally expensive and high computation leads to more energy consumption. In other words, there is a trade-off between energy consumption and trusted communication. This article presents a brief literature review and a performance evaluation of earlier published fog node selection mechanism highlighting the importance of infrastructure and the energy consumption caused by the blockchain technology. The results demonstrate that it is more feasible to implement blockchain at the existing infrastructure avoiding the burden on the vehicle itself.

This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance, or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium, (b) separate processing units make decisions in a distributed and heterogeneous manner, and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment.

This work is based on the vehicle positioning system using the statistical approaches. Moreover, the method does not depend on the real time devices such as GPS, or networking technologies.

The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic.

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

The stakeholders’ perceptions on the impacts of Urban Heat Island (UHI) are critical for reducing exposure and influencing their response to interventions that are aimed at encouraging a behaviour change. A proper understanding of the UHI impacts on the society, economy and environment is deemed an essential motivating factor for the stakeholders to work towards UHI mitigations in the local context. This study adopted an inductive qualitative approach using Stakeholder Dialogue Sessions (SDSs) to assess the perceived impacts of UHI among various stakeholders, comprising policy makers, academicians, developers and Non-Governmental Organizations (NGO), in a tropical metropolitan city. The results revealed five themes such as deterioration of public health, acceleration of urban migration patterns and spending time in cooler areas, reduction of workers’ productivity, increased energy consumption by the households and deterioration of environmental quality and natural resources that were categorized into social, economic and environmental impacts. Although most of the stakeholders were quite unfamiliar with the term UHI, they still display a good understanding of the potential impacts of UHI due to their posteriori knowledge and ability to rationalize the physical condition of the environment in which they live. The findings provide useful insights and valuable information to the local authorities to tailor necessary actions and educational campaigns to increase UHI awareness among the stakeholders. Being among the earlier studies to use a qualitative approach to attain the aforementioned objective, the findings are crucial to determine the level of understanding of the stakeholders on the impact of UHI. Through this study, the authors have highlighted the gaps and needs for knowledge improvements aimed at behaviour change among the stakeholders.

The Internet of vehicles incorporates multiple access networks and technologies to connect vehicles on roads. These vehicles usually require the use of individual long-term evolution (LTE) connections to send/receive data to/from a remote server to make smart decisions regarding route planning and driving. An increasing number of vehicles on the roads may not only overwhelm LTE network usage but also incur added cost. Clustering helps minimize LTE usage, but the high speed of vehicles renders connections unstable and unreliable not only among vehicles but also between vehicles and the LTE network. Moreover, non-cooperative behavior among vehicles within a cluster is a bottleneck in sharing costly data acquired from the Internet. To address these issues, we propose a novel destination- and interest-aware clustering (DIAC) mechanism. DIAC primarily incorporates a strategic game-theoretic algorithm and a self-location calculation algorithm. The former allows vehicles to participate/cooperate and enforces a fair-use policy among the cluster members (CMs), whereas the latter enables CMs to calculate their location coordinates in the absence of a global positioning system under an urban topography. DIAC strives to reduce the frequency of link failures not only among vehicles but also between each vehicle and the 3G/LTE network. The mechanism also considers vehicle mobility and LTE link quality and exploits common interests among vehicles in the cluster formation phase. The performance of the DIAC mechanism is validated through extensive simulations, whose results demonstrate that the performance of the proposed mechanism is superior to that of similar and existing approaches.

Cognitive Radio (CR) based Vehicular Ad hoc Network (VANET) or CR-VANET has become a very promising research domain. VANET is used to reduce road accidents, traffic congestion, and to provide other user experiences such as uninterrupted entertainment services. CR, on the other hand, solves bandwidth scarcity issue of VANET. For the high-speed mobility of the vehicles, the cognitive process of CR faces several challenges. Machine Learning (ML) has arrived as an integral tool to handle such challenges. Q-learning algorithm, a member of Reinforcement Learning (RL), which is a type of ML, is the most suitable for CR-VANET as it does not need any prior environment model and training dataset. But the problem is that it takes a longer time for learning purposes. In this paper, a dynamic ML framework is proposed. Case-based reasoning learning, cooperative spectrum sensing, teacher-student transfer learning approach will be aligned with the Q-learning for the faster convergence regarding the spectrum sensing issues in CR-VANET. The framework will accelerate the learning of the vehicles, and that is very important for the energy-efficient and real-life VANET implementation.

Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

Digital image forgery has significantly increased due to the rapid development of several tools of image manipulation. Based on the manipulation used to produce a tampered image, image forgery techniques can be characterized into three types: copy–move forgery, image splicing, and image retouching. Image splicing is achieved by adding regions from one image into another. This technique changes the content of the target image and causes variations in image features which are used to detect the forgery regions. In this study, an image splicing forgery detection method based on low-dimensional singular value decomposition of discrete cosine transform (DCT) coefficients has been presented. The suspicious input image is divided into multi-size blocks, and each block is transformed into 2D DCT. The DCT coefficients are calculated correspondingly to each block. The features from DCT are extracted using SVD algorithm. The roughness measure is calculated for the set of singular values obtained. Lastly, four types of statistical features—mean, variance, third-order moment skewness, and fourth-order moment kurtosis—are extracted from SVD features and are then arranged in a feature vector. Feature reduction has been applied by kernel principal component analysis. Finally, support vector machine is used to distinguish between the authenticated and spliced images. The proposed method was evaluated against three standard image datasets CASIA v1, DVMM v1, and DVMM v2. The proposed method shows an average detection accuracy of 97.15, 99.30, and 96.97 for DVMM v1, CASIA v1, and DVMM v2, respectively. These results outperform several current image splicing detection methods.

Vehicular communication requires vehicles to self-organize through the exchange of periodic beacons. Recent analysis on beaconing indicates that the standards for beaconing restrict the desired performance of vehicular applications. This situation can be attributed to the quality of the available transmission medium, persistent change in the traffic situation and the inability of standards to cope with application requirements. To this end, this paper is motivated by the classifications and capability evaluations of existing adaptive beaconing approaches. To begin with, we explore the anatomy and the performance requirements of beaconing. Then, the beaconing design is analyzed to introduce a design-based beaconing taxonomy. A survey of the state-of-the-art is conducted with an emphasis on the salient features of the beaconing approaches. We also evaluate the capabilities of beaconing approaches using several key parameters. A comparison among beaconing approaches is presented, which is based on the architectural and implementation characteristics. The paper concludes by discussing open challenges in the field.

Periodic beacons in vehicular ad hoc networks are transmitted with high message frequency to achieve the higher level of awareness required for the vehicular safety applications. Currently, the existing 10-MHz control channel in the dedicated short range communication (DSRC) standard is not compliant with the communication requirements in vehicular safety applications, i.e., the stipulated amendments in the DSRC offer little relief to congestion caused by the periodic beacon transmissions on the control channel, which adversely affects the message reception. In this paper, we seek to address the problem of congestion with a transmit power adaptation approach, based on the principles of cooperative game theory. The proposed approach, Adaptive transmit power Cooperative Congestion Control (AC3), is designed to allow vehicles to select their transmit power autonomously with respect to their local channel congestion. Since the number of neighbours of each vehicle and their corresponding transmit power levels vary, AC3 requires that each vehicle reduces its transmit power fairly during congestion. Explicitly, the proposed approach introduces the notion of marginal contributions of vehicles towards congestion and determines a fair power decrease for vehicles using a shapely value system model. This model requires the vehicles with the highest marginal contributions to reduce the most transmit power and vice versa. The simulation results demonstrate the utility of the proposed approach (i.e., capability to determine fair power decrease for an effective congestion control).

Vehicular Ad Hoc Network (VANET) is envisaged to play an important role in the safety of drivers and passengers when moving on the roads. However, VANET still faces many challenges before it could be deployed. One such challenge is shortage of radio frequency spectrum channels. VANET has been allocated 7 channels for dedicated short range communication at 5.9GHz band. The 7 channels are likely to get congested in high vehicle densities when many vehicles are contending for the same medium. Consequently, affecting the transmission of safety and emergency messages. To alleviate the problem of scarcity channels, dynamic spectrum access (DSA) through cognitive radio (CR) technology has been proposed. One of the core functions of a CR is to identify spectrum holes in licensed frequency bands that can be accessed by unlicensed users through spectrum sensing. In VANET, spectrum sensing is challenging because of the mobility nature of vehicles, dynamic topological changes as well as other unique characteristics not found in other networks. However, these challenges have not been fully studied and how they affect spectrum sensing in cognitive vehicular network (CVN). In this paper, we discuss challenges associated with spectrum sensing in CVN. We describe the primary system activity model used by many schemes proposed in literature. Furthermore, we present an in depth analysis of state-of-art cooperative spectrum sensing techniques for CVN from 2010 to May 2016. In addition, we present some of the open issues in spectrum sensing for CVN.

Congestion pricing has been defined as the best solution to efficiently alleviate the traffic congestion in a designated area. Generally, the basis of congestion pricing zone (CPZ) systems comprised of the following three main phases: vehicle detection, data communication, and enforcement. In terms of data communication, the majority of the existing systems utilize Radio Frequency IDentification (RFID) which contains several limitations such as a low transmission range, low data rate, communication drawbacks like delay, tag and reader collisions, signal fading, and interception. In this particular context, it would be very constructive to take advantage of a Vehicular ad hoc Network (VANET) that is based on the Dedicated Short Range Communication (DSRC) method to design a pervasive-congestion pricing system. This study aims to design an optimized CPZmanagement model using VANET to solve the existing mainstream problems. The proposed approach is evaluated and validated using a simulation environment and a selection of tools including NS-2, SUMO, and TraNS. The experimental results from two different scenarios (i.e. using RFID-based RSU and DSRC-based RSU) were attained after considering the number, velocity, and occupancy of the contributed vehicles, and the traffic flow. The obtained results show that DSRC outperformed RFID in terms of occupancy, traffic flow, and velocity by an average of 8.27%, 22%, and 13.75%, respectively. Consequently, the DSRC employment under VANET infrastructure not only enhances data-communication, but also reduces traffic congestion around CPZ.

The use of Information and Communication Technology (ICT) as a copilot for the drivers has a potential to improve traffic safety and efficiency. A key challenge in integrating ICT in vehicular networks is to provide the mechanisms for the delivery of safety messages called beacons. In particular, finding the trade-off between providing sufficient coverage and controlling channel congestion remains the focus in the stipulated amendments for safety message transmissions. In this paper, we handle this trade-off by proposing a Multi-metric Power Control (MPC) approach, which uses application requirements and channel states to determine a transmit power for safety messages. The MPC gives a best-effort approach to satisfy the coverage range requirement of a message as specified by the application. Moreover, the concept distinguishes among message types to provide coverage differentiation. We show that the best-effort approach of providing coverage for different messages can control congestion and as a result improve awareness by minimizing beacon collisions. The performance analysis of MPC using discrete event simulation confirms its practicality.

Display Omitted We present a directory based framework for incentives management of mobile device resources in ad-hoc mobile cloud environment.We measure the incentives in energy consumption and energy saved not in Monterey bills.We equalized the incentives using third party settlement of energy consumptions according to the directory valuations.We present future application and business prospects utilizing the proposed framework. Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a self-organizing proximate mobile cloud network and to share their resources in the network.

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC., IoT, Edge Computing, Computational Offloading Framework, 2 Algorithms one for resource constraint client and another for edge server, and a unique and easy to setup real IoT testbed idea using of the shelf smartphones, 12 pages, 12 figures

The Internet of Things has gained substantial attention over the last few years, because of connecting daily things in a wide range of application and domains. A large number of sensors require bandwidth and network resources to give-and-take queries among a heterogeneous IoT network. Network flooding is a key questioning strategy for successful exchange of queries. However, the risk of the original flooding is prone to unwanted and redundant network queries which may lead to heavy network traffic. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. IoT devices consume more energy and high computational time. More queries leads to consumption of more bandwidth, cost, and miserable QoS. Current existing approaches focused primarily on how to speed up the basic routing for IoT devices. However, solutions for flooding are not being addressed. In this paper, we propose a cluster-based flooding (CBF) as an interoperable solution for network and sensor layer devices which is also capable minimizing the energy consumption, cost, network flooding, identifying, and eliminating of redundant flooding queries using query control mechanisms. The proposed CBF divides the network into different clusters, local queries for information are proactively maintained by the intralayer cluster (IALC), while the interlayer cluster (IELC) is responsible for reactively obtain the routing queries to the destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in term of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to the standard flooding techniques. In this research work, we used Cooja simulator to evaluate the performance of the proposed CBF. According to the simulation results the proposed technique has superiority in term of traffic delay, QoS/throughput, and energy consumption, under various performance metrics compared with traditional flooding and state of the art.

The technological advancements in the field of internet of things (IoT) have paved way for the evolution of internet of vehicles (IoV), where the things are replaced with vehicles through enabling technologies such as vehicular adhoc networks (VANETs) and intelligent transportation systems (ITS).The technology of simultaneous wireless information and power transfer (SWIPT) provides potential opportunity for increasing the lifetime of the energy-autonomous nodes in vehicular networks. Moreover, the integration of SWIPT with cognitive radio sensor networks (CRSN) has been proved as a vital technique for increasing throughput and effective utilization of the spectrum.In this paper, we consider a cooperative CRSN with near field SWIPT technique for autonomous vehicles (AUV) to monitor the air quality, where the secondary user (SU) forwards and sends the information to the destination through the energy harvested from the radio frequency (RF) signal, thereby focusing towards achieving maximum throughput and also address the trade-off between performance and energy consumption. The problem is formulated using Energy Harvesting and Information Transfer(EHIT) technique and the proof-of-concept is presented based upon a small-scale hardware model to test the performance of the sensors. The results indicate that the proposed SWIPT method for energy autonomous sensors provides optimal solution and has higher performance in comparison to the state-of-art solutions.

[EN] Vehicular ad hoc network (VANET) is an emerging and promising technology, which allows vehicles while moving on the road to communicate and share resources. These resources are aimed at improving traffic safety and providing comfort to drivers and passengers. The resources use applications that have to meet high reliability and delay constraints. However, to implement these applications, VANET relies on medium access control (MAC) protocol. Many approaches have been proposed in the literature using time division multiple access (TDMA) scheme to enhance the efficiency of MAC protocol. Nevertheless, this technique has encountered some challenges including access and merging collisions due to inefficient time slot allocation strategy and hidden terminal problem. Despite several attempts to study this class of protocol, issues such as channel access and time slot scheduling strategy have not been given much attention. In this paper, we have relatively examined the most prominent TDMA MAC protocols which were proposed in the literature from 2010 to 2018. These protocols were classified based on scheduling strategy and the technique adopted. Also, we have comparatively analyzed them based on different parameters and performance metrics used. Finally, some open issues are presented for future deployment.

A vehicular ad hoc network (VANET) is an emerging and promising wireless technology aimed to improve traffic safety and provide comfort to road users. However, the high mobility of vehicles and frequent topology changes pose a considerable challenge to the reliable delivery of safety applications. Clustering is one of the control techniques used in VANET to make the frequent topology changes less dynamic. Nevertheless, research has shown that most of the existing clustering algorithms focus on cluster head (CH) election with very few addressing other critical issues such as cluster formation and maintenance. This has led to unstable clusters which could affect the timely delivery of safety applications. In this study, enhanced weight-based clustering algorithm (EWCA) was developed to address these challenges. We considered any vehicle moving on the same road segment with the same road ID and within the transmission range of its neighbour to be suitable for the cluster formation process. This was attributed to the fact that all safety messages are expected to be shared among the vehicles within the vicinity irrespective of their relative speedto avoid any hazardous situation. To elect a CH, we identified some metrics on the basis of the vehicle mobility information. Each vehicle was associated with a predefined weight value based on its relevance. A vehicle with the highest weight value was elected as the primary cluster head (PCH). We also introduced a secondary cluster head (SeCH) as a backup to the PCH to improve the cluster stability. SeCH took over the leadership whenever the PCH was not suitable for continuing with the leadership. The simulation results of the proposed approach showed a better performance with an increase of approximately40%- 45% in the cluster stability when compared with the existing approaches. Similarly, cluster formation messages were significantly minimized, hence reducing the communication overhead to the system and improving the reliable delivery of the safety applications.

We empirically evaluate the current state of the computational offloading categories.We identified research gap in computational offloading with the introduction of ART.We propose an offloading framework for native and ART based mobile application.We provide the proof of concept experiment which validates the proposed framework. Mobile cloud computing is a promising approach to augment the computational capabilities of mobile devices for emerging resource-hungry mobile applications. Android-based smartphones have opened real-world venues for mobile cloud applications mainly because of the open source nature of Android. Computational offloading mechanism enables the augmentation of smartphone capabilities. The problem is majority of existing computational offloading solutions for Android-based smartphones heavily depends on Dalvik VM (an application-level VM). Apart from being a discontinued product, Dalvik VM consumes extra time and energy because of the just-in-time (JIT) compilation of bytecode into machine instructions. With regard to this problem, Google has introduced Android Runtime (ART) featuring ahead-of-time (AHOT) compilation to native instructions in place of Dalvik VM. However, current state-of-the-art offloading solutions do not consider AHOT compilations to native binaries in the ART environment. To address the issue in offloading ART-based mobile applications, we propose a computational offloading framework. The proposed framework requires infrastructural support from cloud data centers to provide offloading as a service for heterogeneous mobile devices. Numerical results from proof-of-concept implementation revealed that the proposed framework improves the execution time of the experimental application by 76% and reduces its energy consumption by 70%.

Cloud computing has emerged as a popular computing model to process data and execute computationally intensive applications in a pay-as-you-go manner. Due to the ever-increasing demand for cloud-based applications, it is becoming difficult to efficiently allocate resources according to user requests while satisfying the service-level agreement between service providers and consumers. Furthermore, cloud resource heterogeneity, the unpredictable nature of workload, and the diversified objectives of cloud actors further complicate resource allocation in the cloud computing environment. Consequently, both the industry and academia have commenced substantial research efforts to efficiently handle the aforementioned multifaceted challenges with cloud resource allocation. The lack of a comprehensive review covering the resource allocation aspects of optimization objectives, design approaches, optimization methods, target resources, and instance types has motivated a review of existing cloud resource allocation schemes. In this paper, current state-of-the-art cloud resource allocation schemes are extensively reviewed to highlight their strengths and weaknesses. Moreover, a thematic taxonomy is presented based on resource allocation optimization objectives to classify the existing literature. The cloud resource allocation schemes are analyzed based on the thematic taxonomy to highlight the commonalities and deviations among them. Finally, several opportunities are suggested for the design of optimal resource allocation schemes.

Mobile ad hoc networks (MANETs) are defined as a collection of wireless mobile nodes that are characterized as independent, dynamic, self-adaptive and infrastructure less. This mobility and dynamic in nature of MANET urge for new set of networking strategies to be implemented so as to provide efficient communication during natural disaster. Mobile crowd management is yet an emerging new exploration for search and rescue (SAR) operations. The phase of catastrophic management has communication to play a vital role. In MANETs, the end nodes communicate through the TCP/IP protocol. The layers of TCP/IP model change frequently due to mobility and resource overhead. This makes routing in MANETs one of the challenging research area. This review provides an overview of MANET based routing protocols employed for communication during SAR operations. The main purpose of this survey is to provide taxonomy on communication strategies of MANETs during disaster and emergency situation. Furthermore, this paper examines the existing MANET based SAR techniques and the significant contribution made by researches in the past few years based upon routing protocol, communication techniques, type of infrastructure and phase of disaster management.

Next generation Vehicular Ad Hoc Network (VANET) needs seamless connectivity with high level of quality of services (QoS) and high data rates services. The complexity of VANET raises concern about the QoS and the reliability of the communication handover which refer to the connectivity and operation of conveying an ongoing call or data session between Vehicles to Vehicles (V2V) and Vehicles to Infrastructure (V2I) in VANET communication mode. This communication faces a huge challenge. The vehicles are moving so fast and highly dynamic that may cause frequent handover which may cause signal delay and communication loss. This paper provides an up-to-date literature of handover schemes for VANET over LTE-A wireless network. A discussion on thumbnail of the key decision parameters to be used in the handover algorithm and comparative summary of selected scheme for VANET over LTE-A concludes this work. This study, thus contributes to the understanding of the available options and gaps for further studies and standardization activities to this area of research.

Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.

This paper describes a parameterized approach to the Ad Hoc On-Demand Distance Vector (AODV) routing protocol using a network-simulator 2 (ns2). By utilizing two AODV’s protocol functions, which are HELLO messages and local route repair, we explore the more flexible approach on these two important functions, rather than a fixed setting within the default AODV protocol. HELLO message is used to detect the broken link, while the local route repair in AODV is used to fix and discover alternative routes in the event of route failure. In this paper, two functions to optimize AODV performance have been utilized. The first is link break detection time (Llb), using the HELLO message to detect link failure, and the second is link break position parameter (Lbp) for AODV’s local route repair. The results show that the default AODV setting does not yield the best results for most defined network scenarios. In some cases, improvement compared to the default setting can be as high as 38%, for local route repair strategies. This paper presents a potential flexible and parameterized approach for dealing with link breaks and route repairing strategies for AODV protocol.

The vision of ubiquitous computing is based on the fact that future computers will merge up with the surrounding environment in IoT domain. Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN) are two important cornerstones for pervasive computing as they combine the physical and virtual world, thereby bridging the gap between cyber space and physical world of real things. RFID enables the identification and detection of entities while WSN are used to sense the condition of the environment or object. The integration of RFID and WSN have paved way for the existence of RSN (RFID Sensor Networks), thereby providing extended capabilities, scalability, portability, lower cost and novel perspective towards a broad range of applications. This paper presents a brief introduction about the evolution of RSN, major issues in RSN and energy management with regards to Energy Harvesting (EH), energy request and transfer. It also investigates into the problems encountered for efficient energy transmission. Differently from the classic schemes in the literature that deals with scalability, security and communication protocol aspects, the proposed methodology focuses on energy management issue which is of utmost importance for wide area RSN. Furthermore, the paper provides insights into the preliminary experimental evaluation and its comparative analysis with existing schemes pertaining to performance metrics.

The deficient round robin (DRR) and stochastic fair queue (SFQ) are the active queue mechanism (AQM) techniques. These AQM techniques play important role in buffer management in order to control the congestion in the wired-cum-wireless network by dropping packets during the buffer overflow or near to overflow. This research study focus on the performance evaluation of the DRR and SFQ using different scenarios such as increasing number of node scenario, pause time scenario and mobility scenario. We evaluate the performance of DRR and SFQ based on two parameters such as average packet delay and average packet dropped. In case of increasing number of nodes, the SFQ has outperformed than DRR by having comparatively low per packet delay. DRR has higher packet dropped ratio as compare to SFQ. In mobility and pause time scenario, SFQ has less per packet delay while DRR has less packet dropped ratio These results revealed that DRR performance was affected by an increase in the number of nodes in a network. The DRR send the packet in a round-robin fashion without caring about the bandwidth of a path due to which the packet dropped ratio was high. On another hand, the SFQ has comparatively outperformed in all scenarios by having less per packet delay. SFQ become aggressive by dropping more data packets during buffer overflow. In short, SFQ will be preferred for a network where the congestion occurred more frequently.

The importance of Wireless Sensor Network (WSN) increases due to deployment for geographical, environmental and surveillance purpose in war fields. WSN facing several challenges due to its complex nature including key problems, such as routing and medium access control protocols. Several approaches were proposed for the performance evaluation of WSN on the basis of these issues due to the fact that MAC layer access protocols have a great impact on the performance of WSN. In this paper, we investigated the performance evaluation of three well known MAC Access protocols, i.e. sensor medium access control protocol (SMAC), carrier sense multiple access with collision avoidance (CSMA/CA), and time division multiple access (TDMA) over adhoc on demand distance vector (AODV) routing protocol. The number of simulation scenarios were carried out by using NS- 2, the simulation metrics used are throughput, end-to-end delay and energy consumed. Simulation results showed that SMAC out perform CSMA/CA and TDMA by consuming less energy, less end to end delay and high throughput due to contention based approach to access the medium for transmission.

Recently, vehicular ad hoc networks (VANETs) have received more attention in both academic and industry settings. One of the challenging issues in this domain is routing algorithms. VANETs become even more challenging, when they get benefit from intelligent solutions to predict the most stable nodes in the network to communicate with. There are several contributing factors which have influence on this process; including density, velocity, location, and distance. To the best of our knowledge, density and velocity have the most impact on the precision of an intelligent routing algorithm for VANET. In this paper, we investigate how density along with velocity can affect intelligent position-based routing protocols in vehicular ad hoc networks. These types of routing protocols are selected since there is no requirement to establish and save a route between source and destination during the routing process which is suitable for dynamic nature of vehicular networks. We have analyzed the impact of velocity and density at the same time on most widely used position-based routing protocols using different scenarios through NS-2. The obtained results are then illustrated based on average delay, packet delivery ratio, routing overhead, and link failure rate as routing performance indicators.

Digital watermarking, which has been proven effective for protecting digital data, has recently gained considerable research interest. This study aims to develop an enhanced technique for producing watermarked images with high invisibility. During extraction, watermarks can be successfully extracted without the need for the original image. We have developed discrete wavelet transform with a Haar filter to embed a binary watermark image in selected coefficient blocks. A probabilistic neural network is used to extract the watermark image. To evaluate the efficiency of the algorithm and the quality of the extracted watermark images, we used widely known image quality function measurements, such as peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC). Results indicate the excellent invisibility of the extracted watermark image (PSNR=68.27dB), as well as exceptional watermark extraction (NCC=0.9779). Experimental results reveal that the proposed watermarking algorithm yields watermarked images with superior imperceptibility and robustness to common attacks, such as JPEG compression, rotation, Gaussian noise, cropping, and median filter.