Profiling Energy Efficiency and Data Communications for Mobile Internet of Things

Abstract

This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their onboard sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.

An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

“…The Internet of Things (IoT) era has opened up as an emerging research trend over recent years. The IoT has originated from the needs of better monitoring, control, and management in various areas, such as healthcare, education, entertainment, energy manager [7], and transportation [8]. The IoV plays as a part of the IoT, but it has distinctive characteristics.…”

“…The headers periodically upload flow value and density value information for their road segments. Traffic data estimation follows (6) and (7). Once the traffic data of the road segments have been estimated, the verification process begins, during which the traffic data of road segments under investigation and inflow neighboring road segments, including flow and density values, are collected.…”

“…(1) If (Fuzzy Based Evaluation(r c ) == 'congested') then (2) Verfication Result = NULL (3) For each r i do (4) If (Exist Outflow(lane j ,r k ) == true) then (5) Update Neighbor(r j ) to inflow neighbor table of r k (6) End if (7) End For (8) For each inflow neighbor r k of r c do (9) = Select header(r k ) (10) End for (11) While (Verfication Result = NULL) do (12) performs Update traffic(flow, density) to traffic flow table of r c (13) performs Publish(flow, density, r c ) to the control center (14) For each inflow neighbor r k of r c do (15) performs Update traffic(flow, density) to traffic flow table of r k (16) performs Publish(flow, density, r k ) to the control center (17) End For (18) do Rankine-Hugoniot Validation( , 1 , 2 , . .…”

Spectrum Gap Analysis With Practical Solutions for Future Mobile Data Traffic Growth in Malaysia

“…In the past several years, the use of mobile data services has broadly and rapidly increased, becoming an essential and necessary addition in the lives of most mobile cellular users [1], [2]. Mobile data services are widely used by users, Internet-of-Things (IoT) applications [3]- [14], and Machineto-Machine (M2M) connections [15]- [23].…”

“…In addition, the escalation of video streaming (which has become an effectively employed tool for life learning, news, entertainment and other purposes) further leads to increased mobile data demands. The rapid growth of IoT applications and M2M connections are two other effective factors that contribute to additional mobile data demands [2], [15], [26]- [30]. The IoT was titled as the upcoming industrial revolution in mobile telecommunication systems.…”