The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories.Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values.It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead.Existing State of the art schemes suffer from computational overhead due to deterministic or random treebased tags generation which further increases as the file size grows.This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with keyvalue pair for each block of the file.Further, an algorithm for hash tablebased duplicate block identification and storage (HDBIS) is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index.Hash tables normally have a consistent time complexity for lookup, generating, and deleting stored data regardless of the input size.The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.

One of the main causes of energy consumption in Internet of Vehicles (IoV) networks is an ill-designed network congestion control protocol, which results in numerous packet drops, lower throughput, and increased packet retransmissions. In IoV network, the objective to increase network throughput can be achieved by minimizing packets re- transmission and optimizing bandwidth utilization. It has been observed that the congestion control mechanism (i.e., the congestion window) can plays a vital role in mitigating the aforementioned challenges. Thus, this paper present a cross-layer technique to controlling congestion in an IoV network based on throughput and buffer use. In the proposed approach, the receiver appends two bits in the acknowledgment (ACK) packet that describes the status of the buffer space and link utilization. The sender then uses this information to monitor congestion and limit the transmission of packets from the sender. The proposed model has been experimented extensively and the results demonstrate a significantly higher network performance percentage in terms of buffer utilization, link utilization, throughput, and packet loss.

As the growth of the Internet of Things (IoT) persists, it becomes imperative to deliberate on strategies for protecting the security and privacy inside the confines of resource constrained devices and their data, while also preserving optimal performance. This research paper offers an innovative solution at the intersection of IoT middleware and Blockchain technology, specifically the Hyperledger fabric. Through the use of a distributed decentralized ledger, we overcome many of the limitations of current IoT networks. This paper outlines a robust layered IoT model that could be applied to any use case, providing security and privacy at the edge of IoT devices. We conducted an implementation setup to test the model and validate the security measures embedded through Blockchain design. Additionally, we improved IoT devices interoperability through the use of semantic ontologies. Overall, this research contributes to the ongoing effort to create a secure and efficient IoT ecosystem.

Mobile ad hoc cloud (MAC) is one of the key enabling technologies for realizing mobile cyber-physical–social systems (MCPSSs). A MAC is a distributed computing infrastructure that enables mobile devices to share computing resources in an ad hoc environment. Resource allocation is one of the key components of MAC and plays a vital role in system and application performance. Existing resource allocation schemes are designed to utilize single wireless communication technology (WCT) or rely on an eclectic system that exclusively selects single communication technology. Moreover, these schemes do not consider link lifetime, which significantly affects application performance. Consequently, these schemes cannot satisfy low latency and high data rate requirements of emerging resource-intensive MCPSS applications, such as merged reality-based multiplayer games. Thus, this work proposes a new resource allocation scheme that simultaneously uses multiple WCTs and considers link lifetime during the resource allocation process. This study also proposes a Markov chain-based link lifetime prediction mechanism. In comparison with existing mechanisms, the proposed link lifetime prediction mechanism considers the history of a user's visited locations and time spent at each location. The performance of the proposed scheme is evaluated in a wide range of network and application scenarios using Network Simulator 3.

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs.

The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

The drastic increase in road accidents has motivated transport community to safeguard passengers from serious injuries and casualties. Intelligent Transportation System (ITS) introduced smart vehicles that can wirelessly communicate with each other to contribute to the enhancement of road safety by forming a network on ad-hoc basis called Vehicular Ad-hoc Networks (VANETs). In VANETs, vehicles periodically broadcast beacon messages to get better and timely awareness of road's condition. However, these unencrypted beacon messages bring a serious concern on the people's privacy if an adversary overhears them. The research community has proposed to use a pseudonym instead of real identity; however, vehicle's location traces can still be built with ease. Numerous location privacy protection techniques have been proposed; however, they work on turning the vehicle's radio transmitter off which consequently can affect safety applications. Protecting the drivers’ location at the risk of sacrificing their safety makes the key purpose of VANETs questionable. In this paper, we aim to develop a holistic safety-aware location preserving scheme called Coupling Privacy with Safety (CPS) which ensures to provide drivers’ privacy along with their safety. CPS contributes to providing protection against syntactic linking attack, semantic linking attack, misleading attack/false alarm, Sybil attack and impersonation attack. It also avoids congestion and communication overhead. It also maintains QoS along with location protection and provides revocation facility.

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

To eliminate the phenomenon of the digital divide in the area lacking Internet infrastructure support and accomplish the vision of “The Internet is for everyone” envisaged by Vint Cerf, our group pioneered a dual-structural edge networking paradigm, being utilized to construct a DSN (dual-structural network) for multimedia content delivery. The aim of this paper is to study multimedia content delivery capability for the edge networking paradigm. First of all, for further understanding the delivery capability, we formalize DSN in terms of network architecture and logical entity interaction and propose a hierarchical model and triple B model sequentially. Then, we propose a multi-dimension analysis methodology of multimedia content delivery for networking paradigm from a high-level perspective, in which network performance and user evaluation are taken into account. Further, according to the methodology, we design a specific comparative analysis model of multimedia content delivery capability for DSN, consisting of sub-model based on transmission performance and sub-model based on user utility. Lastly, we conduct direct and indirect comparative analysis studies by utilizing the model, and numerical results shed light on that DSN outperforms TCP/IP, NDN (named data networking) and BSN (broadcast-storage network) in terms of the delivery capability. Given this, we conclude that DSN is a more promising paradigm for multimedia content delivery.

Chikungunya is a mosquito instinctive disease that spreads hurriedly in various parts of the country. For the awareness and prevention measure of this disease a new paradigm in Smart Health (S-Health) required to be devised. The auspicious prospective of evolving Internet of Things (IoT) technologies for interconnected heterogeneous devices and objects has played a vital role in the next generation health care systems for eminent patient care to protect the citizens from these types of diseases. Still there is a need for real time health monitoring to analyze the patients for early preventive measures and precautions for healthy life. S-Health care IoT has substantial impending for the cognizance of analogues monitoring. It includes the interconnected apps, objects (devices and people), communication technologies, tracking system, and patients' knowledge base. This paper presents an IoT-enabled model where data collected from the sensors, objects, and people will be gathered at the cloud to take the preventive actions by healthcare professionals. Precautionary measures will be taken by collecting the information about causes of growth of mosquitoes. The suitability of the approach is validated at the base layer of the IoT and data is transmitted to the cloud with the help of edge nodes. From simulations, it is endorsed that the proposed approach is better over ME-CBCCP protocol.

The network connectivity in dynamic networks depends on a small number of highly mobile nodes. Identifying the influential nodes is one of the most engaging challenges for mobile applications, such as data offloading or worm propagation control. Reachability is an important metric to uncover node influence. Both TRGs (temporal reachability graphs) and CJEGs (critical journey evolving graphs) provide approaches to calculate reachability. Nevertheless, these approaches are only for epidemic scenarios. In practice, due to node privacy or limited battery life, message transmission is, to a certain extent, a probabilistic dissemination. Accordingly, reachability is difficult to exactly determine. As a structure of tight‐knit nodes, a community is born of a coarse‐grained estimation of reachability under a probabilistic propagation scenario. Based on an existing overlapping community detection framework, ie, AFOCS (an unsupervised machine learning algorithm), we propose an evolving overlapping community detection algorithm, ie, EFOCS, and further developed a metric, ie, OR_CEN, to estimate the reachability under the probabilistic propagation scenario. A content delivery experiment showed that OR_CEN accurately reveals the influence of nodes in dynamic networks. Based on OR_CEN, we also propose several target set selection algorithms and discuss their application in mobile data offloading. Analysis and simulation experiments indicated that CBS_OR, the target set selection algorithm based on OR_CEN centrality, has more advantages in scalability and distributed computing than CBS_AFOCS (an algorithm based on an aggregated AFOCS community), TRG_GREEDY (a greedy algorithm based on TRGs) and RANDOM (an algorithm based on a random selection strategy). Moreover, CBS_OR exhibited a significant better offloading effect than the algorithms mentioned earlier.

Resource‐intensive real‐time IoT and 5G network applications require low latency, high data rates, and considerable computing and storage resources. Mobile cloud computing systems cannot fulfill the requirements of these applications due to high communication latency. Consequently, recently proposed edge computing implementations require adding new infrastructure or updating existing infrastructure. In addition, edge computing does not exploit the capabilities of end devices, which have become more powerful than supercomputers in the last decade. To overcome the drawbacks of mobile cloud and edge computing systems, we have proposed a private mobile edge cloud in which multiple mobile and stationary devices such as sensors, robots, home appliances, and smartphones, interconnected through infrastructure‐less and infrastructure‐based wireless local area networks are combined to create a small‐scale cloud data center at a local physical area such as a home. We have also discussed opportunities and research challenges associated with development of proposed system and a high‐level architecture based on a cross layer design for an efficient management of resources.

A mobile ad hoc network provides communication and network services to internet of things and cyber physical system applications. The failure of a link in mobile ad hoc network during data transmission increases communication and energy consumption cost due to route rediscovery and reselection process. It may also result into a communication failure and therefore cyber physical system application failure. To avoid link failure during data transmission, a link lifetime prediction model is required. This paper proposes two link lifetime prediction models: LLPC and LLPH. LLPC model predicts link lifetime based on current information of nodes such as mobility and residual energy whereas LLPH uses history of link lifetime intervals to predict the link lifetime. Compared to existing models, LLPC model considers node mobility as well as residual energy whereas LLPH relies on history of links rather than user's mobility patterns or social relationships.

Transmission Communication Protocol (TCP) is responsible for reliable transferring of heavy traffic of data over the Internet. Congestion control is one of the main challenges for TCP in today's Internet. TCP Tahoe and TCP Reno are the oldest versions of TCP, proposed to solve the congestion issues. With the passage of time, different versions of TCPs are introduced to fulfill the network demands. TCP Compound, TCP CUBIC, and TCP Fusion are the default TCP versions in Microsoft Windows, Linux and Sun Solaris operating systems respectively. TCP CUBIC is designed for early, low bandwidth, short distance networks that are why it is facing fairness, TCP friendliness issues in today's long distance high bandwidth Cyber-Physical Systems (CPS). In this paper, TCP CUBIC* is proposed to enhance the performance of TCP CUBIC in long distance, high bandwidth CPS. Thus, the aim of this research is to enhance the performance of TCP CUBIC to solve the fairness and TCP friendliness issues for high bandwidth, long distance CPS. NS-2 simulator is used in all performance evaluation tests. According to results, TCP CUBIC* shows better performance results as compared to original TCP CUBIC.

A tremendous amount of content and information are exchanging in a vehicular environment between vehicles, roadside units, and the Internet. This information aims to improve the driving experience and human safety. Due to the VANET's properties and application characteristics, the security becomes an essential aspect and a more challenging task. On the contrary, named data networking has been proposed as a future Internet architecture that may improve the network performance, enhance content access and dissemination, and decrease the communication delay. NDN uses a clean design based on content names and Interest Data exchange model. In this paper, we focus on the vehicular named data networking environment, targeting the security attacks and privacy issues. We present a state of the art of existing VANET attacks and how NDN can deal with them. We classified these attacks based on the NDN perspective. Furthermore, we define various challenges and issues faced by NDN-based VANET and highlight future research directions that should be addressed by the research community.

Nowadays research is heading towards the integration of cloud computing and Internet of Things thus creating a Cloud of Things (CoT). This combination generates a new paradigm for pervasive and ubiquitous computing. However, reliable CoT-based services, particularly, highly delay-sensitive services, such as, healthcare, require energy-efficient CoT architectures. Considerable efforts have been proposed to improve the efficiency of CoT architectures. This paper analyses CoT architectures and platforms, as well as the implementation of CoT in the context of smart healthcare. Subsequently, the paper explains some related issues of CoT, including the lack of standardization. Moreover, it focuses on energy efficiency with an in depth analysis of the most relevant proposals available in the literature. An evaluation of all the energy efficiency solutions investigated in this paper shows there is still a need to improve energy efficiency, especially regarding QoS and performance.

The key goal of Internet of Things (IoT) has been the provision of value-added services based on the ubiquitously available smart devices that can offer diverse services by interacting with each other. However, the paradigm has evolved to its next phase, Social Internet of Things (SIoT), with the inception of an idea to empower these devices with consciousness. This cognizance enables these smart devices to socialize with each other based on shared context and mutual interests. The Social Internet of Vehicles (SIoV) applies SIoT concepts in the vehicular domain to revolutionize the existing ITS (Intelligent Transport System) by adding value to existing VANET (Vehicular Ad-hoc Network) technology. This paper presents a scalable SIoV architecture based on Restful web technology. Furthermore, this paper emphasizes the importance of web technology to meet the required interoperability to support the composition of numerous services. The paper also discusses the enabling technologies and protocols.