Mobile Edge Computing for IoT applications [2]

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Privacy Preserving Data Aggregation Scheme for
Mobile Edge Computing Assisted IoT Applications

Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications

users can rent the resources of cloud service provider and do not need to build their own infrastructure. Cloud computing is widely accepted due to the features of flexibility, easy to use, high scalability, location independence and reliability. Meanwhile, with the development of sensing technology and microelectronics technology, the Internet of Things (IoT) [3, 4] allows any device to access the Internet. In the IoT vision, the object can be identified by radio-frequency identification (RFID) technique [5], and the environment parameters can be sensed by wireless sensor networks (WSN) [6]. Then, the various smart applications, such as smart grid, smart home, smart city, and intelligent agriculture, can be built by analyzing and utilizing the sensory data. Take intelligent agriculture as an example, the crop growth environment information can be obtained by different types of sensors (environmen-tal temperature and humidity, soil moisture, carbon dioxide, images, etc.) deployed at the agricultural production sites. Then the sensory data collected and analyzed by the control center, and the corresponding operations such as irrigation, cooling, fertilization and spraying can be done according to the feedback of all kinds of collected information. To take complementary advantages of the IoT and cloud, researcher have proposed the concept of cloud assisted IoT [7–10]. In the new paradigm, IoT is no longer restricted by the storage, communication and processing capacities, which are compensated by the could. On the contrary, the cloud can deal with more real life applications in a more distributed and dynamic way by combine with the IoT. Different organizations have predicted that billions of smart devices will be connected to the Internet in near future [11], and will generate huge amount of data, which should be analyzed and processed in a security and effective way. Besides, the widespread applications of IoT require the smart devices to have low latency, high data rate, fast data access for real-time data processing/analysis and decision making [12]. However, the traditional cloud computing cannot satisfy these requirements, and the concept of mobile edge computing (MEC) [13–15] was proposed by researchers. A typical architecture of mobile edge computing assisted IoT applications is shown in Figure
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Mobile-Edge Computing

Mobile-Edge Computing

• Hosting an application on a network edge platform may provide certain advantages such as low latency for services like video streaming. Offering a type of "specialized service" that ensures sufficient quality of service for such applications to function, is appropriate and can be done consistently within the evolving principles of network neutrality. Additional technical analysis would be appropriate to determine the criteria to assess which applications and services would benefit from specialized treatment. Analysis could also examine how to configure networks to ensure sufficient capacity to accommodate demand for specialized services while maintaining suitable network conditions to support a robust user experience for non-specialized services. Transparency and non-discrimination within the specialized services category, and among all non-specialized traffic, could be observed as part of a net neutrality framework that would allow end users or the owner of the content, application, or service to pay for specialized treatment subject to reasonable consumer protections important to network neutrality.
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Joint computation and communication design for UAV-assisted mobile edge computing in IoT

Joint computation and communication design for UAV-assisted mobile edge computing in IoT

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT Tiankui Zhang, Yu Xu, Jonathan Loo, Dingcheng Yang, Lin Xiao Abstract—Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV’s flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is non- convex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.
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LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications

LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications

IV. Performance Analysis The proposed technique is compared with traditional cloud based processing. The parameters taken for calculation are as under, the range of Edge device is 500 meters, the physical layer protocol is BLE for device to edge communication, the transmission power is same for all devices that are 24 dBm and the bandwidth is 10MHz for device to edge communication. The adoption and deployment of cloud computing is critical to evaluate the performance of cloud environments. Modeling and simulation technologies are suitable for evaluating performance and security issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. There are several cloud simulators that have been specifically developed for performance analysis of cloud computing environments and CloudSim is a one of them Cloud simulation application. CloudSim enables seamless modeling, simulation, and experimentation of cloud computing and application services. Now a days there are various versions of CloudSim such as CloudAnalyst, GreenCloud, Network CloudSim, EMUSIM and MDCSim. Most of them are open source and are based on java language and some are based on C++ also, the simulation type is either event based or packet level.
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Blockchain-based mobile edge computing framework for secure therapy applications

Blockchain-based mobile edge computing framework for secure therapy applications

The web and client servers are implemented with Laravel and Angular JS, respectively. The Ethereum and Hyperledger client communicates with the node.js for acquiring the IoT data. As for the IoT data, we use three gesture-tracking sensors, namely, Kinect2, Leap Motion, and Myo sensors. As shown in Figure 6, different data types are harvested and synchronized before being sent to the blockchain. A private Tor is set up with four nodes, including two onion routers. The third one acts as onion router and authority and HTTP server. The fourth node acts as an onion client that sends the blocks by using the Tor. As for edge and cloudlet solutions, we port an extended version of the open-source cloudlet-based edge computing solution, Elijah [48]. Figure 11 shows the overall delay in capturing different types of therapeutic multimedia data, adding them to a block, and saving them to the distributed repository or at the edge network repository.
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Computing at the Mobile Edge: Designing Elastic Android Applications for Computation Offloading

Computing at the Mobile Edge: Designing Elastic Android Applications for Computation Offloading

The Network Model: Mitigating the effects of intermit- tent connectivity can either be seen as a networking or as a software engineering problem. Even if there is a current trend to virtualize networks and their services, providing edge computing as a generic network service would require large standardization efforts, but standards are currently not available and early standardization efforts are just emerging [5]. Moreover, employing MEC and its transparent execution of networked applications is almost impossible without proper kernel and networking layer support and access to it, but network carriers and mobile telecom providers do not allow to access this information. Furthermore, the TCP protocol is not designed to support seamless handovers between intermittently connected devices at the edge of the network. To foster the practical relevance of CloudAware, our approach is based on the end-to-end architecture between mobile devices and surrogates and is independent from the internal architecture of cellular networks. Therefore, the assumed network model consists of a primary mobile device and a set of further mobile as well as fixed nodes that compose a temporary network by relying on different connections/protocols like LTE, Wifi or Bluetooth.
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Partitioning and Offloading for IoT and Video Streaming Applications that Utilize Computing Resources at the Network Edge

Partitioning and Offloading for IoT and Video Streaming Applications that Utilize Computing Resources at the Network Edge

Mobile devices have limited resources including short battery life, storage capacity and pro- cessor performance. This limits the applications that can run on it. A mobile application can be partitioned so that some parts of the application runs on a cloud. This works well for applica- tions with relatively little data to be transferred and that do not have a high level of interaction with the user. High latency is a challenge with applications that have large amounts of data to be transferred with a high level interactiveness. A cloudlet is a resource-rich computer or cluster of computers that is connected to the Internet and is available for use by nearby mobile devices. A mobile application can be partitioned so that part of it runs on the cloudlet. This work presents the MC-Skynet framework which introduces fine-grained o ffl oading approach and support for runtime and dynamic partitioning of a mobile application. This is di ff erent from previous approaches, in that MC-Skynet does not only provides dynamic partitioning and o ffl oading, but is also adaptive to the changes of the state of a cloudlet. It does this by in- troducing a cloudlet mesh network and self learning decision making module to estimate the o ffl oading cost.
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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

I N recent years, deep learning becomes an important methodology in many informatics fields such as vision recognition, natural language processing, and bioinformatics [1] [2]. Deep learning is also a strong analytic tool for huge volumes of data. In Internet of Things (IoT), it is still an open problem to reliably mine real-world IoT data from a noisy and complex environment which confuses conventional machine learning techniques. Deep learning is considered as the most promising approach to solving this problem [3]. Deep learning has been introduced into many tasks related IoT and mobile applications, with encouraging early results. For example, deep learning can precisely predict the home electricity power consumption with the data collected by smart meters, which can improve the electricity supply of the smart grid [4]. Because of its high efficiency on studying complex data, deep learning will play a very important role in the future IoT services.
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Provisioning  of  Edge  Computing  Resources  for  Heterogeneous  IoT Workload

Provisioning of Edge Computing Resources for Heterogeneous IoT Workload

1.4.2 Heterogeneous workload assignment in an MEC-IoT environment for uRLLC Along with the dramatic increase in the number of IoT devices, different IoT services with het- erogeneous QoS requirements are starting to see the light with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infras- tructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of uRLLC promised by 5G. MEC has emerged as an enabling technology to help with the realization of such services by bringing the remote cloud computing and storage capabili- ties closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this chapter the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we de- compose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decom- position approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our pro- posed approach under different parameters.
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Towards Adaptive Actors for Scalable IoT Applications at the Edge

Towards Adaptive Actors for Scalable IoT Applications at the Edge

B Computer Science Division, UC Berkeley, 387 Soda Hall, Berkeley, USA, kaifei@berkeley.edu A BSTRACT Traditional device-cloud architectures are not scalable to the size of future IoT deployments. While edge and fog-computing principles seem like a tangible solution, they increase the programming effort of IoT systems, do not provide the same elasticity guarantees as the cloud and are of much greater hardware heterogeneity. Future IoT applications will be highly distributed and place their computational tasks on any combination of end-devices (sensor nodes, smartphones, drones), edge and cloud resources in order to achieve their application goals. These complex distributed systems require a programming model that allows developers to implement their applications in a simple way (i.e., focus on the application logic) and an execution framework that runs these applications resiliently with a high resource efficiency, while maximizing application utility. Towards such distributed execution runtime, we propose Nandu, an actor based system that adapts and migrates tasks dynamically using developer provided hints as seed information. Nandu allows developers to focus on sequential application logic and transforms their application into distributed, adaptive actors. The resulting actors support fine-grained entry points for the execution environment. These entry points allow local schedulers to adapt actors seamlessly to the current context, while optimizing the overall application utility according to developer provided requirements.
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An energy  and cost aware computation offloading method for workflow applications in mobile edge computing

An energy and cost aware computation offloading method for workflow applications in mobile edge computing

Kai Peng 1,5 , Maosheng Zhu 1 , Yiwen Zhang 2* , Lingxia Liu 1 , Jie Zhang 3 , Victor C.M. Leung 4 and Lixin Zheng 5 Abstract Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy- and cost-aware for workflow applications in MEC.
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Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning

Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning

Abstract—Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for ML tasks. Consequently, this paper aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. In order to process data close to the source, sensors are grouped according to locations, and feature learning is performed on the close by edge node. For comparison reasons, similarity-based processing is also considered. Feature learning is carried out with deep learning: the encoder part of the trained autoencoder is placed on the edge and the decoder part is placed on the cloud.
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Computing and relaying : utilizing mobile edge computing for P2P communications

Computing and relaying : utilizing mobile edge computing for P2P communications

We set the maximum compression ratio ρ max = 5, ε = 0.5, and other energy consumptions C 1 and C 2 to 0. Fig. 5 illustrates the performance gain with different channel gains of the Hops or different CPU frequencies of the UEs. In the figure, the left y-axis represents to the gain provided by the computing and relaying model and the right y-axis represents to the optimal compression rates. Note that the compression rate is limited to [1, 5] and the transmitting power of the relay node is set to 1 W. In these figures, we find that there is always a balance point where the performance gain equals 1 and ρ = ρ 1 = ρ 2 . At this point, the MEC server doesn’t work at all, and the computing and communication resources before the relay node and after the relay node are symmetric and balanced. When the channel state or the CPU frequency varies, the balance is broken and the MEC server participates in the communication. However, the cost gain line doesn’t always turn down forward or backward from the balance point due to the maximum and minimum limits to the compression rate. Furthermore, it is observed that ρ is always between ρ 1
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Impact of Mobile Edge Computing in Real World

Impact of Mobile Edge Computing in Real World

servers, virtualization and Operating System in the cloud stack. IV. IMPACT OF MOBILE EDGE COMPUTING Mobile edge computing has brought about benefits for both consumers and operators. Several consumer oriented services like Augmented Reality, Video Streaming, Remote Desktop, Gaming and operated oriented services like Big Data analysis, IoT connectivity and Connected Vehicles have gained from MEC. It has also brought about significant improvement in terms of network performance and QoE improvement. Some of the areas impacted by mobile edge computing are given below:
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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

data in a distributed set of wireless networked sensors. The threat identification in prior research have primarily focused on identifying unidentified or authenticated nodes or devices on the network or profiling them based on characteristics that are either static or predictable. Once identified and authenticated in one of the numerous ways, nodes are entirely trusted and values from them are acted upon and propagated. However, it is entirely possible for any malicious player with physical access to or in physical proximity to the network to tamper with either the device or the conditions, making it essential to identify these data integrity issues among a set of authenticated sensor nodes. As outlined by the DHS [17], threats to Integrity are real, have a huge financial impact and remain unresolved at this point. We explore and evaluate this in the context of Precision agriculture that employs a distributed mesh sensor network due to the extensive real estate that needs to be monitored and maintained at a reasonable cost. The sensors on the network are often connected via cellular, Bluetooth, or Wi-Fi networks and rely on edge computing to make decisions at the source. The solution entails the use of spatial and temporal locality of sensors on the distributed network to detect data integrity threats as shown in Figure. 3.1.
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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

various languages exist together, one will often be perceived as superior, desirable, and necessary, whereas the other will be seen in the opposite manner. The high school participants in this study expressed concerns about being identified as non-native speakers due to their accents when speaking English and accessing their phones to study course content during lunch. These participants appreciated being able to monitor their language use before presenting in public, as this allowed them to be seen as more like other English speakers. Sometimes, their non-native use of English may visibly show their EL-ness more than they wish. Living as an international student in the US was challenging for participants because tests used to show knowledge of student performance were administered in English. With English being the only language currency that is used in tests, ELs often fail to meet the law’s annual progress requirements (Butler & Stevens, 2001). As such, ELs often feel isolated and neglected for using different languages (Menken, 2009). Participants in this study tried to narrow this gap of showing their non-native-ness or EL-ness by, for example, getting instant help from mobile phones, such as by checking their pronunciation. However, having said this, the push toward relinquishing some of their own EL-ness is troubling. The question, what Discourses around ELs are in place in English-speaking settings, specifically schools, that encourage—and even force--ELs to “hide”
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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

This dissertation, THE INTERSECTION OF YOUNG CHILDREN’S PLAY ACTIVITIES AND MULTIMODAL PRACTICES FOR SOCIAL PURPOSES, by REBECCA I.. CLOUGH, was prepared under the direction of the cand[r]

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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

walked numerous times, but I always reminded myself their motives and feelings were a lot different during the 1960s. Reminding myself of the historical context during the Atlanta 9 research was my way of following Milner's advice of self-reflection. Through this process of self-awareness, I realized that my own racial, cultural, and professional positionality meant I had to work twice as hard to prove my research was valid, credible, and worthy. Likewise, I must acknowledge that my positionality also privileged me in ways that others may not have been privileged. For instance, even though a couple of the narrators seemed hesitant initially to indulge me with their stories, I believe they were eventually persuaded because they saw me as one of them, a Black student in higher education pursuing a doctoral degree. Only 2% of Americans have earned doctoral degrees and 6.6% of those doctoral degrees are earned by Blacks. 32 They likely empathized with my efforts and remembered what breaking through barriers felt like. It also may have helped that I was not an outsider looking for a sensationalized news story to report, nor was I a struggling biographer seeking financial gain. Perhaps they saw, in me, a piece of their own stories. I was possibly the very reason they had decided to integrate the schools in the first place. Whatever the reason, I was conscious of my positionality and biases throughout the process. And, I soon discovered that the more research I gathered, the more I did not know about my own race and culture.
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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

Two-level random intercept logistic models were used to assess the level of autocorrelation for the datasets for years one, three, and five of teaching. 𝜂 𝑖𝑗 = 𝛽 0 + 𝛽 1 × 𝐺𝑟𝑜𝑢𝑝 𝑖𝑗 + 𝛽 2 × 𝐺𝑒𝑛𝑑𝑒𝑟 𝑖𝑗 + 𝛽 3 × 𝐴𝑔𝑒 𝑖𝑗 + 𝛽 4 × 𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑖𝑗 + 𝛽 5 × 𝐹𝑅𝐿 𝑗 + 𝛽 6 × 𝑀𝑖𝑛𝑜𝑟𝑖𝑡𝑦 𝑗 + 𝛽 7 × 𝐶𝐶𝑅𝑃𝐼 𝑗 + 𝛽 8 × 𝐶𝑙𝑖𝑚𝑎𝑡𝑒 𝑗 + 𝛽 9 × 𝑅𝑎𝑐𝑒 𝑗 + 𝑣 0𝑖 + 𝑒 𝑖𝑗 The models initially contained the teacher-level predictors of gender, race/ethnicity, age, and subject taught and the school-level variables of grade level, percentage of economically disadvantaged students, school ethnic diversity, CCRPI score, and School Climate score. The variables were used because they have been cited in the literature to be associated with teacher attrition and are believed to be either outcome predictors or confounders. Leite (2017) defines confounders as covariates with direct effects on the probability of treatment assignment and outcome. Using the HLM SuperMix program, the ICC for years one and three was determined to be 0.139 and 0.138, indicating that much of the variation is attributable to the teachers, rather than to the schools. While there is not a clear cutoff value of ICC, there is a consensus in the literature towards adjusting for the effects of clustering when the ICC is greater than zero (Guo
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Collaborative Edge Computing in Mobile Internet of Things

Collaborative Edge Computing in Mobile Internet of Things

in our study, fold-changes in gene expression between treated and untreated cells are shown without normalization to an endogenous control as described in Material and Methods (51). Inhibition of CtBP binding with PxDLS-containing partners using NSC95397 also caused decreases in hexon mRNA in both KE37 cells (2- to 20-fold) and Jurkat cells (5- to 10- fold) (Figure 2-6C & 2-6D). CtBP inhibition, however, has a noticeably different effect on E1A expression in both of these T cell lines where E1A is upregulated by 1.5- to 4-fold. The expression of E3 was minimally impacted in these cells. These data suggest that CtBP binding with PxDLS- containing partners may be repressing transcription of E1A in T cells and that inhibiting this binding allows for expression. In contrast, CtBP may paradoxically be necessary for expression of the viral late gene hexon in lymphocytes, since it was maximally downregulated by NSC95397 treatment in both the B and T cell lines.
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