Data offloading in IoT environments: modeling, analysis, and verification

Abstract

Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

Internet of Things of Electronic Seal Base on GPRS Short Message Service and Thingspeak

“…The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches [4][5][6].…”

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

“…The key barriers to offloading computation in MEC are the network bandwidth and latency [40]. If too many simultaneous offloading requests are made through the wireless access to the cloud, then severe interference may be created, resulting in a reduced data rates for data transmission [41,42]. In the said perspective, overlapping coverage of the services can be exploited to process the user requests efficiently.…”

EFFORT: Energy efficient framework for offload communication in mobile cloud computing

“…To overcome the resource scarcity of smart devices, offloading computations is the demand in need 3 . MCC is a combination of CC and mobile computing that is generally a feasible solution to offload computations.…”

“…However, the key barriers to offloading computation in MCC are the network bandwidth and latency 4 . If too many simultaneous offloading requests are made through the wireless access to the cloud, then severe interference may be created, resulting in a reduced data rates for data transmission 3,5 . Besides latency, since mobile devices contain enormous amount of personal data, there are concerns related to the data offloaded to the mobile cloud, such as trust, authentication, secure communication, privacy, and malicious attacks (like DoS) 6 .…”