Exploiting rush hours for energy-efficient contact probing in opportunistic data collection

In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the use of mobile devices carried by people in their daily life to collect sensor data opportunistically. As the movement of these mobile nodes is, by definition, uncontrolled, contact probing becomes a challenging task, particularly for sensor nodes which need to be aggressively duty-cycled to achieve long life. It has been reported that when the duty-cycle of a sensor node is fixed, SNIP, a sensor node-initiated probing mechanism, performs much better than mobile node-initiated probing mechanisms. Considering that the intended applications are delay-tolerant, mobile nodes tend to follow some repeated mobility patterns, and contacts are distributed unevenly in temporal, SNIP-RH is proposed in this paper to further improve the performance of contact probing through exploiting Rush Hours during which contacts arrive more frequently. In SNIP-RH, SNIP is activated only when the time is within Rush Hours and there are enough data to be uploaded in the next probed contact. As for the duty-cycle, it is selected based on the mean of contact length that is learned on line. Both analysis and simulation results indicate that under a typical simulated road-side wireless sensor network scenario, SNIP-RH can significantly reduce the energy consumed for probing the contacts, that are necessary for uploading the sensed data, or significantly increase the probed contact capacity under a sensor node's energy budget for contact probing

Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks

The increasing ubiquity of smartphones coupled with the mobility of their users will allow the use of smartphones to enhance the operation of wireless sensor networks. In addition to accessing data from a wireless sensor network for personal use, and the generation of data through participatory sensing, we propose the use of smartphones to collect data from sensor nodes opportunistically. For this to be feasible, the mobility patterns of smartphone users must support opportunistic use. We analyze the dataset from the Mobile Data Challenge by Nokia, and we identify the significant patterns, including strong spatial and temporal localities. These patterns should be exploited when designing protocols and algorithms, and their existence supports the proposal for opportunistic data collection through smartphones

Exploiting rush hours for energy-efficient contact probing in opportunistic data collection

Abstract—In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the use of mobile devices carried by people in their daily life to collect sensor data opportunistically. As the movement of these mobile nodes is, by definition, uncontrolled, contact probing becomes a challenging task, particularly for sensor nodes which need to be aggressively duty-cycled to achieve long life. It has been reported that when the duty-cycle of a sensor node is fixed, SNIP, a sensor node-initiated probing mechanism, performs much better than mobile node-initiated probing mechanisms. Considering that the intended applications are delaytolerant, mobile nodes tend to follow some repeated mobility patterns, and contacts are distributed unevenly in temporal, SNIP-RH is proposed in this paper to further improve the performance of contact probing through exploiting Rush Hours during which contacts arrive more frequently. In SNIP-RH, SNIP is activated only when the time is within Rush Hours and there are enough data to be uploaded in the next probed contact. As for the duty-cycle, it is selected based on the mean of contact length that is learned online. Both analysis and simulation results indicate that under a typical simulated roadside wireless sensor network scenario, SNIP-RH can significantly reduce the energy consumed for probing the contacts, that are necessary for uploading the sensed data, or significantly increase the probed contact capacity under a sensor node’s energy budget for contact probing. I

The impact of message replication on the performance of opportunistic networks for sensed data collection

Opportunistic networks (OppNets) provide a scalable solution for collecting delay-tolerant data from sensors to their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. Hence, OppNets for sensed data collection are characterised by high node population and degrees of spatial locality inherent to user movement. We study the impact of these characteristics on the performance of existing OppNet message replication techniques. Our findings reveal that the existing replication techniques are not specifically designed to cope with these characteristics. This raises concerns regarding excessive message transmission overhead and throughput degradations due to resource constraints and technological limitations associated with portable handheld user devices. Based on concepts derived from the study, we suggest design guidelines to augment existing message replication techniques. We also follow our design guidelines to propose a message replication technique, namely Locality Aware Replication (LARep). Simulation results show that LARep achieves better network performance under high node population and degrees of spatial locality as compared with existing techniques