## 5 Conclusions and Future Work  
Collecting delay-tolerant sensor data with OppNets has received considerable attention in recent years. This is mainly because OppNets exploit pervasive user devices such as smartphones and tablets to offer a scalable and resource efficient solution for connecting multitudes of spatially displaced sensors to major infrastructure in urban environments to provide various Smart City services. Regardless of these promising benefits, the success of this approach lies in the willingness of users to contribute with the resources on their devices. As with most OppNet routing solutions, subjecting only few popular devices to most of the forwarding duties leads to higher resource consumption on them. Due to the lack of fairness, users of popular devices may respond by withdrawing participation, which may eventually lead to degraded network performance. In this paper, we investigated the existing challenges of improving fairness in OppNets for sensed data collection, and then, suggested a set of design guidelines for fair routing techniques. We then followed our guidelines to propose the Fair Locality Aware Routing (FLARoute) technique. Tailored for sensed data collection, FLARoute addresses the issue of fairness in OppNets by exploiting concepts of spatial locality to implement a fair forwarding utility, namely PoiUtility. Unlike existing solutions, PoiUtility combines encounter-based forwarding principles with locality-aware mechanisms to allow a wider range of nodes to participate in message forwarding. Fairness is improved as PoiUtility increases the likelihood of delivering messages through less popular nodes, even under higher degrees of spatial locality. The adopted approach also improves throughput significantly by utilizing less popular nodes when popular nodes are over-burdened. FLARoute also implements two new distributed mechanisms, namely Relative Burden Detection (ReBurD) and Maximum Burden Estimation (MaxBE). Our ReBurD mechanism, unlike existing approaches, provides a non-scenario-specific representation of node burden by keeping account of the number of message transmissions. The mechanism is also able to locally compute the instantaneous relative burden on a node, which increases and decreases according to the node's participation in forwarding. MaxBE facilitates fair forwarding decisions by allowing nodes to give a local estimate of the maximum burden among their neighbours, without incurring excessive overheads from metadata transmissions. We also propose the Fair Locality Aware Forwarding (FLAFord) algorithm, which makes fair forwarding decisions with available knowledge. It forwards messages via two types of routes: one that presents a higher change of delivery, regardless the level of burden on relay nodes; and one that can make progress through less burdened nodes. The FLAFord algorithm also defines a new burden balancing mechanism to relieve relay nodes in the latter phase of excessive burden. Evaluation results in the ONE simulator show that the performance of applicable routing protocols could be significantly improved with FLARoute. Incorporating FLARoute into PRoPHET routing protocol increased fairness, energy distribution, and throughput. FLARoute also reduced PRoPHET's transmission overhead and average energy consumption. As compared with an existing fair routing solution, namely CCAF, FLARoute achieves more fairness, consumes less energy on the most popular nodes, and delivers more messages within a TTL of 1 day. To the best of our knowledge, FLARoute represents the first fair routing technique to consider the impact of spatial locality inherent to user movement in urban environments. Hence, it promises improved throughput and fairness under characteristics of sensed data collection scenarios that would compromise the performance of existing fair routing solutions.