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1. A Fog Computing Framework for Cognitive Portable Ground Penetrating Radars

   https://doi.org/10.1109/ICC.2019.8761107  

   Wu, Dalei; Omwenga, Maxwell M.; Liang, Yu; Yang, Li; Huston, Dryver; Xia, Tian (May 2019, IEEE International Conference on Communications (ICC))

   null (Ed.)

   With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing. 

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2. Whispering: Joint Service Offloading and Computation Reuse in Cloud-Edge Networks

   https://doi.org/10.1109/icc42927.2021.9500457  

   Nour, Boubakr; Mastorakis, Spyridon; Mtibaa, Abderrahmen (June 2021, ICC 2021 - IEEE International Conference on Communications)

   null (Ed.)

   Due to the proliferation of Internet of Things (IoT) and application/user demands that challenge communication and computation, edge computing has emerged as the paradigm to bring computing resources closer to users. In this paper, we present Whispering, an analytical model for the migration of services (service offloading) from the cloud to the edge, in order to minimize the completion time of computational tasks offloaded by user devices and improve the utilization of resources. We also empirically investigate the impact of reusing the results of previously executed tasks for the execution of newly received tasks (computation reuse) and propose an adaptive task offloading scheme between edge and cloud. Our evaluation results show that Whispering achieves up to 35% and 97% (when coupled with computation reuse) lower task completion times than cases where tasks are executed exclusively at the edge or the cloud. 

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3. An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

   https://doi.org/10.1016/j.future.2020.08.001  

   Alanezi, K.; Mishra, S. (January 2021, Future generation computer systems)

   In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment. 

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4. Data Sharing-Aware Task Allocation in Edge Computing Systems

   https://doi.org/10.1109/EDGE53862.2021.00018  

   Rabinia, Sanaz; Mehryar, Haydar; Brocanelli, Marco; Grosu, Daniel (September 2021, 2021 IEEE International Conference on Edge Computing (EDGE))

   Edge computing allows end-user devices to offload heavy computation to nearby edge servers for reduced latency, maximized profit, and/or minimized energy consumption. Data-dependent tasks that analyze locally-acquired sensing data are one of the most common candidates for task offloading in edge computing. As a result, the total latency and network load are affected by the total amount of data transferred from end-user devices to the selected edge servers. Most existing solutions for task allocation in edge computing do not take into consideration that some user tasks may actually operate on the same data items. Making the task allocation algorithm aware of the existing data sharing characteristics of tasks can help reduce network load at a negligible profit loss by allocating more tasks sharing data on the same server. In this paper, we formulate the data sharing-aware task allocation problem that make decisions on task allocation for maximized profit and minimized network load by taking into account the data-sharing characteristics of tasks. In addition, because the problem is NP-hard, we design the DSTA algorithm, which finds a solution to the problem in polynomial time. We analyze the performance of the proposed algorithm against a state-of-the-art baseline that only maximizes profit. Our extensive analysis shows that DSTA leads to about 8 times lower data load on the network while being within 1.03 times of the total profit on average compared to the state-of-the-art. 

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5. Private and rateless adaptive coded matrix-vector multiplication

   https://doi.org/10.1186/s13638-020-01887-y  

   Bitar, Rawad; Xing, Yuxuan; Keshtkarjahromi, Yasaman; Dasari, Venkat; El Rouayheb, Salim; Seferoglu, Hulya (December 2021, EURASIP Journal on Wireless Communications and Networking)

   null (Ed.)

   Abstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones. 

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