OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

A Privacy-Preserving Incentive Mechanism for Data Offloading in Satellite-Terrestrial Crowdsensing

Data offloading algorithm is the foundation of urban Internet of Things, which has gained attention for its large size of user engagement, low cost, and wide range of data sources, replacing traditional crowdsensing in areas such as intelligent vehicles, spectrum sensing, and environmental surveillance. In data offloading tasks, users’ location information is usually required for optimal task assignment, while some users in remote areas are unable to access base station signals, making them incapable of performing sensing tasks, and at the same time, there are serious concerns about users’ privacy leakage about their locations. Until today, location protection for task assignment in data offloading has not been well explored. In addition, existing privacy protection algorithms and data offloading task assignment mechanisms cannot provide personalized protection for different users’ privacy protection needs. To this end, we propose an algorithm known as differential private long-term privacy-preserving auction with Lyapunov stochastic theory (DP-LAL) for data offloading based on satellite-terrestrial architecture that minimizes the total payment. This not only gives an approximate optimal total payment in polynomial time but also improves the issue of poor signal in remote areas. Meanwhile, satellite-terrestrial data offloading architecture integrates wireless sensor networks and cloud computing to provide real-time data processing. What is more, we have considered long-term privacy protection goals. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. More importantly, we use Lyapunov optimization theorem to jointly optimize queue stability and total payment. We prove that our algorithm is of high efficiency in computing and has good performance in various economic attributes. For example, our algorithms are personally rational, budget-balanced, and true to the buyer and seller. We use large-scale simulations to evaluate the proposed algorithm, and compare our algorithm with existing algorithms, our algorithm shows higher efficiency and better economic properties.

Smart Architectural Framework for Symmetrical Data Offloading in IoT

With new technologies coming to the market, the Internet of Things (IoT) is one of the technologies that has gained exponential rise by facilitating Machine to Machine (M2M) communication and bringing smart devices closer to end users. By 2025, it is expected that IoT will bring together 78.4 billion of devices, thus improving the quality of life beyond our imagination; however, there are multiple potential challenges, such as the exploitation of energy consumption and the huge data traffic being generated by smart devices causing congestion and utilizing more bandwidth. Various researchers have provided an alternative to this problem by performing offloading of data, the task and computational requirements of an application at edge and fog nodes of IoT, thus helping to overcome latency issues for critical applications. Despite the importance of an offloading approach in IoT, there is need for a systematic, symmetric, comprehensive, and detailed survey in this field. This paper provides a systematic literature review (SLR) on data offloading approaches in IoT network at edge and fog nodes in the form of a classical taxonomy in order to recognize the state-of-the art mechanism(s) associated with this important topic and provide open consideration of issues as well. All of the research on classified offloading approaches done by researchers is compared with each other according to important factors such as performance metrics, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed. Finally, an efficient smart architecture-based framework is proposed to handle the symmetric data offloading issues.

Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

In a smart city, a large number of smart sensors are operating and creating a large amount of data for a large number of applications. Collecting data from these sensors poses some challenges, such as the connectivity of the sensors to the data center through the communication network, which in turn requires expensive infrastructure. The delay-tolerant networks are of interest to connect smart sensors at a large scale with their data centers through the smart vehicles (e.g., transport fleets or taxi cabs) due to a number of virtues such as data offloading, operations, and communication on asymmetric links. In this article, we analyze the coverage and capacity of vehicular sensor networks for data dissemination between smart sensors and their data centers using delay-tolerant networks. Therein, we observed the temporal and spatial movement of vehicles in a very large coverage area (25 × 25 km2) in Beijing. Our algorithm sorts the entire city into different rectangular grids of various sizes and calculates the possible chances of contact between smart sensors and taxis. We further calculate the vehicle density, coverage, and capacity of each grid through a real-time taxi trajectory. In our proposed study, numerical and spatial mining show that even with a relatively small subset of vehicles (100 to 400) in a smart city, the potential for data dissemination is as high as several petabytes. Our proposed network can use different cell sizes and various wireless technologies to achieve significant network area coverage. When the cell size is greater than 500 m2, we observe a coverage rate of 90% every day. Our findings prove that the proposed network model is suitable for those systems that can tolerate delays and have large data dissemination networks since the performance is insensitive to the delay with high data offloading capacity.