Saving bandwidth and energy of mobile and IoT devices with link predictions

Abstract

Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised. Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android. We evaluated our approach with real sensor data and showed how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

“…Not surprisingly, in the context of monitoring, various applications have been identified, e.g., health [ 60 , 89 , 115 , 158 , 159 ], smart buildings [ 90 , 116 , 160 ], agriculture [ 161 , 162 ], stress [ 61 , 117 , 136 , 163 ], transportation [ 91 ], military defense [ 164 ], etc. Other challenges comparatively highly studied in the included papers are QoS [ 92 , 93 , 118 , 128 , 137 , 138 , 139 , 152 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 ] with , and energy saving [ 62 , 63 , 94 , 95 , 96 , 114 , 119 , 142 , 143 , 144 , 175 ] with .…”

“…Another motivation is related to management which refers to the configuration, maintenance, and monitoring ( ), such as monitoring the elderly [ 134 , 135 ] or detection of health-related problems [ 58 , 69 , 80 , 115 , 158 ], detection of abnormal driving behavior [ 64 ], etc. QoS is also an important motivation with , as optimization of resource-constrained IoT devices [ 137 , 169 , 173 ], or forecast the connectivity and bandwidth of mobile devices [ 139 ]. In addition, data privacy and security are another significant motivation, as to address the data privacy concern in healthcare applications [ 92 , 165 ], or in autonomous driving [ 166 ], or to improve access control techniques of smart devices [ 118 ].…”