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Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular Computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi–Sugeno–Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes—road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature to well compute the flow tables. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed.

Edge computing is a research hotspot that extends cloud computing to the edge of the network. Due to the recent developments in computation, storage and network technology for end devices, edge networks have become more powerful, making it possible to integrate locator/identity separation protocol (LISP) into these networks. Accordingly, in this paper, we introduce LISP into edge routers at the edge network, focusing primarily on the delay problem of mapping resolution and cache updating in LISP with the help of edge computing. To solve this delay problem, we first analyze the communication process of the locator/identity separation network and consider using the prediction method to underpin this research. In order to achieve a good prediction result, we propose and implement a Fixed Identity Mapping Prediction Algorithm (FIMPA) based on collaborative filtering, and further verify the effectiveness of the proposed algorithm through experiments on real-world data.

© 2013 IEEE. Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming more challenging, concerning the network dynamics and distributed task offloading. In this paper, we propose 'FairEdge', a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-and-bins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that, FairEdge outperforms conventional task offloading methods, and it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%.

The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning platforms such as the IP Multimedia Subsystem (IMS). The NFV concept is evolving towards a lightweight solution based on containers that, by contrast to classic virtual machines, do not carry a whole operating system and result in more efficient and scalable deployments. On the other hand, IMS has become an integral part of the 5G core network, for instance, to provide advanced services like Voice over LTE (VoLTE). In this paper we combine these virtualization and service provisioning concepts, deriving a containerized IMS infrastructure, dubbed cIMS, providing its assessment through statistical characterization and experimental measurements. Specifically, we: $\textit {i)}$ model cIMS through the queueing networks methodology to characterize the utilization of virtual resources under constrained conditions; $\textit {ii)}$ draw an extended version of the Pollaczek-Khinchin formula, which is useful to deal with bulk arrivals; $\textit {iii)}$ afford an optimization problem focused at maximizing the whole cIMS performance in the presence of capacity constraints, thus providing new means for the service provider to manage service level agreements (SLAs); $\textit {iv)}$ evaluate a range of cIMS scenarios, considering different queuing disciplines including also multiple job classes. An experimental testbed based on the open source platform Clearwater has been deployed to derive some realistic values of key parameters (e.g., arrival and service times).

Internet of Things (IoT) are increasingly common in our society, and can be found in civilian settings as well as sensitive applications, such as battlefields and national security. Given the potential of these devices to be targeted by attackers, they are a valuable source in digital forensic investigations. In addition, incriminating evidence may be stored on an IoT device (e.g., Amazon Echo in a home environment and Fitbit worn by the victim or an accused person). In comparison to IoT security and privacy literature, IoT forensics is relatively under-studied. IoT forensics is also challenging in practice, particularly due to the complexity, diversity, and heterogeneity of IoT devices and ecosystems. In this paper, we present an IoT-based forensic model that supports the identification, acquisition, analysis, and presentation of potential artifacts of forensic interest from IoT devices and the underpinning infrastructure. Specifically, we use the popular Amazon Echo as a use case to demonstrate how our proposed model can be used to guide forensics analysis of IoT devices.

The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.

In RPL routing protocol, the destination advertisement object (DAO) control messages are announced by the child nodes to their parents to build downward routes. A malicious insider node can exploit this feature to send fake DAOs to its parents periodically, triggering those parents, in turn, to forward the fake messages upward to the root node. In this letter, we show how this behavior can have a detrimental side effect on the performance of the network, increasing power consumption, latency, and reducing reliability. To address this problem, a new scheme is introduced to mitigate significantly the effect of the DAO attack on network performance.

The Rivest Cipher 4 (RC4) has been one of the most popular stream ciphers for providing symmetric key encryption, and is now proposed as an efficient cipher within light-weight cryptography. As an algorithm it has been considered to be one of the fastest stream ciphers and one of the easiest to implement. Unfortunately, despite its simplicity of usage, a number of attacks on it have been found. Therefore, various improvements of this algorithm exist in cryptography, but none of them use proper randomness. This paper outlines modified version of RC4 and which has the desirable features of an efficient stream cipher algorithm, and which integrates the Symmetric Random Function Generator (SRFG) method. Though RC4 uses pseudorandom features with an initialisation vector and a seed value, the use of true randomness in RC4 is novel in this domain. Therefore, this paper proposes a modified RC4 as MRC4, and which then evaluates the statistical features of MRC4 based upon parameters such as non-linearity, resiliency, balancedness, propagation and immunity. Further, we have compared the security features and confusion-diffusion attributes with some recent variants of RC4 and have found that MRC4 is efficient in withstanding against attacks. The experimental results show that MRC4 supports a 60% better confusion property and 50% better diffusion as compared to the original RC4 method.

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge , a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Simultaneous localization and environment mapping (SLAM) is the core to robotic mapping and navigation as it constructs simultaneously the unknown environment and localizes the agent within. However, in millimeter wave (mmWave) research, SLAM is still at its infancy. This paper consists a first of its kind in mapping an indoor environment based on the RSS, Time-Difference-of-Arrival, and Angle-of-Arrival measurements. We introduce MOSAIC as a new approach for SLAM in indoor environment by exploiting the map-based channel model. More precisely, we perform localization and environment inference through obstacle detection and dimensioning. The concept of virtual anchor nodes (VANs), known in literature as the mirrors of the real anchors with respect to the obstacles in the environment, is explored. Then, based on these VANs, the obstacles positions and dimensions are estimated by detecting the zone of paths obstruction, points of reflection, and obstacle vertices. Then, extended Kalman filter is adapted to the studied environment to improve the estimation of the points of reflection hence the mapping accuracy. Cramer-Rao lower bounds are also derived to find the optimal number of anchor nodes. Simulation results have shown high localization accuracy and obstacle detection using mmWave technology.

Intelligence mining is one of the most promising technologies for effectively extracting intelligence (and knowledge) to enhance the quality of decision making. In Taiwan, the government curtails underground economic activities and facilitates tax management via ubiquitous e-invoice information processing and intelligence mining for B2C transactions with management realized via privacy-preserved and robust consumer carriers. In this paper, we study the concept of carriers, a medium that facilitates the transfer of an e-invoice from a business to a consumer in a B2C transaction. Implementations of carriers not only depend on the underlying hardware, software, and network infrastructures that support their services, but also on consumers willingness to use them. In this paper, we review the Taiwan's Second Generation E-invoicing System, which is designed to promote the use of e-invoices in the consumer sector, and identify four problems that require further attention. These problems are: (1) no e-invoice data for immediate review, (2) limited readability of carriers by POS (Point of Sales), (3) lack of seamless integration into purchase behaviors, and (4) carrier traceability. We then discuss possible solutions to overcome these concerns, in hope of offering some insight into future mobile commerce based on e-invoice carriers in the cloud computing era. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.