Mobile edge computing (MEC) is emerging as a cornerstone technology to address the conflict between resource-constrained smart devices (SDs) and the ever-increasing computational demands of the mobile applications. MEC enables the SDs to offload computational-intensive tasks to the nearby edge nodes for providing better quality-of-services (QoS). The recently proposed offloading strategies, mainly consider a centralized approach for a limited number of SDs. However, with the growing popularity of the SDs, these offloading models may have the scalability issue and can be susceptible to single point failure. Although there are few distributed offloading models in the literature, they ignore the vast computational resources of the cloud, load sharing between the MEC servers, and other optimization parameters. Toward this end, we propose an efficient computation offloading scheme for a distributed load sharing MEC network in cooperation with cloud computing to enhance the capabilities of the SDs. We formulate a nonlinear multiobjective optimization problem by applying queuing theory to model the execution delay, energy consumption, and payment cost for using edge and cloud services. To solve the formulated problem, we propose a stochastic gradient descent (SGD) algorithm based solution approach to jointly optimize the offloading probability and transmission power of the SDs for finding an optimal trade-off between energy consumption, execution delay, and cost of the SDs. Finally, we perform extensive simulations to demonstrate the effectiveness of the proposed offloading scheme. Moreover, compared to the other solutions, the proposed scheme is scalable and outperforms the existing schemes.

Advancements in sensor and hardware technology have surged the growth of smart devices (SDs), including smartphones, and wearable devices. The data generated by the built-in sensors are utilized by different applications such as health-care, smart-city, and connected-vehicles. However, due to the computation and energy limitations of the SDs, they often need to offload the computation-intensive tasks for processing to the remote server. The cloud-based offloading can meet various applications’ demands, but due to high network latency, it is inefficient for real-time applications. Fog computing provides an alternative for the same, as it aggregates the fog nodes’ resources at the edge of the network to meet the computational requirements of the real-time applications. In this paper, we consider a Fog-Cloud architecture consisting of multiple SDs, fog nodes, and the cloud. We use appropriate queuing models to simulate the traffic delay at different network elements and formulate a non-linear multi-objective optimization problem to minimize the energy consumption, execution delay, and cost of remote execution. Finally, the Stochastic Gradient descent (SGD) algorithm based solution approach is proposed that jointly optimizes offloading probability and transmission power to find the optimal trade-off between the offloading objectives. Simulation results show the effectiveness and the efficiency of the proposed system validated by the results.

Smart contracts in Ethereum blockchain network are self-executable scripts used for managing tokenized assets and access rights between different entities on the blockchain network. They are significant addition to blockchain technology for achieving data transparency and reliability in maintaining the digital relationship between two or more entities. However, the smart contract is in its nascent stage and suffers from various limitations, including immutability and code secrecy. In this paper, we address the inability of smart contracts to access the on-chain data. The smart contract currently fetches the on-chain data by taking external oracle assistance, which can be compromised to influence the customers. To address the issue, we propose a decentralized mechanism for accessing the blockchain data directly from the smart contracts by indexing single or multiple parameters of a transaction in each block using the Merkle-Patricia Trie (MPT). The idea is to increase the data transparency of blockchain applications. The paper provides a sequential search methodology that can retrieve transactions from N blocks, satisfying specific conditions, in O(N). The complexity is further reduced to O(N/k) by partitioning the blockchain data into k disjoint subsets to achieve parallelism in the search procedure. We experimentally verify the effectiveness of the proposed methodology with a case study on the Ethereum blockchain data.

The recent advancement in home automation technologies and inter-connectivity of smart appliances has contributed immensely in developing intelligent home environment. The complex smart home environment consists of smart devices from different vendors having different configuration and communication standards that introduce the problem of a heterogeneous subsystem in a smart home. A need is emerging for a central management system to control all these various devices. In this paper, we propose an action-based framework that acts as a middleware between the user and smart appliances which allow the user to control these heterogeneous devices in a federated manner. The proposed framework is based on a controller, for accepting user input and sending instructions to the intended smart devices. Framework maintains the integrity of an instruction send to the device and employs the mechanism so that only authentic user can access them. We also incorporate Human System Interaction (HSI) in our framework, by developing an Android application which receives input in the form of motion gestures, to fulfill the human-centric interaction requirements of the user to communicate with the smart home appliances. We evaluate the performance of our framework by implementing the controller in a local home network and the cloud.

Advancement in smartphones has facilitated the investigation of new modalities of human-machine interaction, including communication through touch, voice, and gestures. In-depth, the researchers examined the problem of recognizing distinct gestures (surface, hand, and motion). However, the gesture recognition algorithm pitches discontinuity while the user performs the subsequent continuous gesture. The discontinuity may occur due to the selection of a delimiter to differentiate between successive motions or the employment of a complex algorithm to boost the accuracy of gesture detection, which takes significant time to recognize the gesture before a user may enter the next gesture. Further, gesture recognition based on template matching, machine learning models, and neural networks requires a lot of storage space, processing resources, or both, which are resource-intensive for smartphones. This research proposes a novel Axis-Point Continuous Motion Gesture (APCMG) recognition algorithm that uses accelerometer sensor data to recognizes continuous motion gestures in real time. The algorithm has low computational complexity and easily implemented on resource-constrained devices with minimal computing cost, memory, and energy. The prime objective of the APCMG is to find the start and end of a gesture from a continuous stream of accelerometer sensor data and recognize the gesture in real-time. To demonstrate the APCMG efficacy, the experimental simulation of the Android application for dialing a phone number is considered. The App acknowledges 12 continuous gestures corresponding to 0 to 9 number, delete, and calls termination. The experimental simulations collected 7500 gestures samples from the 25 volunteers. The algorithm efficiently recognizes isolated and continuous gestures with 95% and 94% accuracy, respectively. The proposed algorithm efficiently recognizes isolated and continuous gestures with minimal energy consumption.

In the modern era, smartphones are increasingly becoming an integral and essential part of our daily life. Although the hardware capabilities of the smartphones (i.e., processing, memory, battery, and communication) are improving every day, however, it is not enough to handle computation-intensive applications, such as image processing, data analytics, and encryption. To overcome these limitations, mobile cloud computing (MCC) is introduced, which augments the capabilities of smartphones and resources of the cloud to provide better QoS performance to the user. The idea is to save resources in the smartphones by offloading the computationally intensive tasks to the cloud. In this context, researchers have proposed several offloading frameworks, mainly addressing challenges of why-what-when and where to offload. In this paper, however, we explore another challenging issue of offloading, i.e., how-to-offload. More specifically, we analyze different networking libraries (HttpURLConnection, OkHttp, Volley, Retrofit) and study their performance on various dynamic factors such as data size, communication media, hardware and software of the smartphone. Our objective is to explore if an application can use the same networking library for all the smartphones and all purposes or there is a need to make an adaptive decision based on the local constraints. To understand this, we perform a comprehensive analysis of the networking libraries on different Andriod smart-phones in the real environment and found that there is a need of adaptive network library selection because libraries perform changes in different scenarios.