An Energy Efficiency Approach via SelfAdaptive DutyCycle in Social Contacts Scenario With the implementation arriving of the Internet of Things, multihop transmission by shortrange communication becomes more important than ever before in DTN research areas. The energy efficiency has been received more attention by researchers due to constrained energy battery. From earlier research, the vast majority of energy consumption is used for neighbors detecting. Dutycycle strategy is an efficient way to improve power efficiency, whereas it is too simple to take advantage of contacting opportunities. Therefore, we designed a sleeping strategy for the mobile devices, and named selfadaptive dutycycle, which based on the social contacts of the devices in the urban scenario. This approach mainly focuses on the historical encounters of the devices, and then it uses the weighted moving average approach to predict the encounter situation of the mobile device, and adjust the size of the dutycycle to achieve the energysaving purpose. Our simulation results show that selfadaptive dutycycle is superior to other fixed ratio of dutycycles in energy efficiency, network lifetimes. Meanwhile, the main indicators of network performance are not affected by sleeping behavior.

A LocationAware Duty Cycle Approach toward EnergyEfficient Mobile Crowdsensing This paper aims to explore the problem of energy economy of mobile devices in the Mobile Crowdsensing (MCS) scenario. The neighbor scanning mechanism of mobile devices usually consumes most of the energy in multihop message transmission. Traditional mechanisms such as chaotic neighbor detection and continuous neighbor discovery, can easily exhaust the limited energy. Since they are actually unnecessarily considering the strong correlation between the transmission opportunity and the social characteristics. Therefore, diminishing energy consumption is of key importance toward energyefficient MCS. Sleeping strategy stands out as a promising approach to improve energy efficiency and hold the the network metrics in MCS applications, while challenges still lie in achieving effective scheduling given the changing environment. In this study, we proposed a novel selfadapt sleeping scheduling approach based on the correlation between the pedestrianu0027s own historical trajectory and geographic grid information for energy saving (ESGeo). As the gridbased method can well record the encounter characteristics of nodes, ESGeo is able to pick flexible dutycycling strategies for mobile devices in each distinguishable grid. This enables, mobile devices to avoid excessive scanning in lowprobability encounter areas. Extensive simulation results further demonstrated that the proposed approach can significantly outperform the typical routing approaches in terms of energyefficiency, without largely affecting the overall networking performance.