Although SPECTRUM is a conceptual framework, the factors upon which it is established have been strongly recommended as key attributes influencing the choice of incentives in mobile crowdsensing [
47,
108]. For instance, Talasila et al. [
47] recommend that the designers of mobile crowdsensing systems consider key factors such as the desired spatio-temporal properties of the data (i.e. accounting for area coverage and timing requirements), the level of data reliability required (i.e. accounting for quality and quantity requirements), the monetary cost, the user privacy, the user effort and the resource consumption on mobile devices. They added that going forward, all these factors need to be considered by designers when building systems and choosing an appropriate incentive for a particular crowdsensing situation [
47]. Similarly, Zaman et al. [
108] recommend that when determining the reward for participants in mobile crowdsensing, several factors should be considered including, spatio-temporal characteristic of the event (i.e. again accounting for area coverage and timing requirements), privacy valuation, fairness (i.e. accounting for users’ effort in terms of adequacy of reward), purpose of sensed data (i.e. addressing usefulness) etc. The concept of SPECTRUM addresses all of the aforementioned factors. It is hoped that by proposing the concept of SPECTRUM and highlighting the state of knowledge in relation to the outlined factors, the groundwork is laid for empirical investigation into how various factors may influence the type of incentive mechanism that is considered most appropriate for any given crowdsensing task.
In terms of practical implication, the outlined conceptual framework, i.e., SPECTRUM, can enable urban computing professionals and researchers consider a broader range of factors and consequently adopt the most appropriate incentive mechanism to attain large scale participation and citizen engagement in mobile crowdsensing. This implies that cities can get smarter and better equipped to develop new intelligence in monitoring, understanding and responding to a wide range of urban problems.
Research opportunities and challenges
With the introduction of the outlined conceptual framework come new research opportunities and challenges that need to be further investigated in order to fully explore the potential of SPECTRUM in improving citizen participation in mobile crowdsensing. While the adoption of the most appropriate type of incentive as facilitated by SPECTRUM is a crucial step towards improving citizen participation in mobile crowdsensing, actual engagement of participants will also depend strongly on whether the right incentive has been properly designed and implemented. Hence, in highlighting research challenges and areas where further studies are needed to improve citizen participation in mobile crowdsensing, it is important to consider design and implementation issues relevant to the different factors outlined in the concept of SPECTRUM.
For a start, the concept of human grouping which has emerged as a way to simplify the process of understanding and utilising socioeconomic factors in selecting an appropriate incentive is yet to be fully explored in the context of mobile crowdsensing [
1]. The idea behind human grouping, for purpose of designing effective incentive mechanisms, is that participants in each group will share similar social and economic goals, different from other groups and hence would be motivated differently. The importance of incorporating human grouping into mobile crowdsensing, a process described by Lane [
122] as
community-
aware sensing, has been emphasised in several studies, including [
123,
124].
GroupMe, proposed in [
124] is a giant stride in this direction to help facilitate the discovery of groups within mobile crowdsensing systems. However, several research challenges still need to be adequately addressed in order to fully maximise the potential of using human grouping to improve the outcomes of decision making related to appropriateness of incentive mechanism. A typical example is how to accurately design robust solutions to automatically identify and characterize mobile users into virtual communities and social networks in a way that is both dynamic and privacy-friendly.
Privacy is a reoccurring term in mobile crowdsensing research and also the second factor in the concept of SPECTRUM. As earlier highlighted in this study, a key priority requirement that must be considered when designing incentives for mobile crowdsensing is the need for privacy-preserving mechanisms that ensure personal information cannot be gleaned from mined patterns [
1]. Unfortunately, the design of privacy-preserving mechanisms is hampered by the fact that there is a trade-off between privacy guarantees and sensing fidelity [
53]. The enforcement of privacy-preserving measures often degrades the quality of sensed data, thus also potentially decreasing its utility [
30]. Worse still, individual perception of privacy and data sensitivity varies and strongly depends on socio-cultural and contextual differences ‐ factors that are difficult to accurately measure in urban scale [
53]. Furthermore, privacy of mobile crowdsensing systems is still in its infancy and requirements may vary slightly depending on the area of application [
24]. For example, in designing crowdsensing applications for use in emergency conditions, there may be additional requirement to allow the specified privacy settings to be overridden when necessary (e.g. by paramedics or doctors). These factors combine to make the design of privacy-preserving mechanisms difficult. Further studies that demonstrate how privacy-preserving mechanisms can be incorporated into the design of different types of mobile crowdsensing incentives are therefore required.
In the context of monetary incentives, several privacy-friendly solutions have emerged upon which future studies can build. For example, APISENSE is a mobile crowdsensing application based on monetary incentive that enforces user privacy by allowing participants to control access to the sensors on their mobile devices. The user chooses whether to participate or not depending on the perceived threat to privacy. In return, the system rewards the user with redeemable credit based on the quantity and quality of data contributed [
30]. The reward system is a weighted approach that allocates more credit to sensors that are more privacy-invading [
30]. The user therefore enjoys the flexibility to disable some of the sensors for privacy reasons. This solution is quite useful because it gives a fair chance to both the users that are profit-driven and those that are privacy-conscious to maximise the outcome of their participation in a way that does not appear exploitative. In a similar monetary incentive scheme based on reverse auction mechanism, users’ privacy was guaranteed by enabling them to bid and claim their reward anonymously, while at the same time ensuring high quality output is delivered [
125]. These aforementioned privacy-friendly solutions, as well as other existing privacy-aware monetary incentive systems [e.g.
126‐
128] provide real opportunities and the foundation knowledge upon which future studies can expand.
Monetary incentive and its relationship with the quality of crowdsensed data is another key area that needs to be further investigated in order to fully explore the potential of SPECTRUM in improving citizen participation in mobile crowdsensing. It is argued that once money is involved in crowdsensing, the participants are more likely to deceive or cheat the system to increase financial gains [
1]. Such cheating might involve the submission of fake data. The situation is further complicated when one considers the distributed nature of participants [
28]. Participants are likely to submit sensing data of diverse quality due to difference in their spatial–temporal contexts and personal effort levels [
9]. There is even strong evidence that monetary incentives do not affect quality of work, but rather merely affect the number of times a worker is willing to do a task [
28]. This position is in contradiction to a recent study by Talasila et al. [
47] demonstrating that by increasing the monetary reward assigned to crowdsensing tasks, the quality can also be improved. This situation calls for further studies to empirically investigate the relationship between monetary incentives and the quality of crowdsensed data.
In terms of implementation, another challenge associated with quality requirement is how to design a relevant incentive mechanism that facilitates honest and efficient contributions and also avoids unnecessary rewards to low quality crowdsensed data [
1]. In other words, the key issue is how to technically estimate the quality of sensing data without pre-existing knowledge of the specific sensing behaviour of each participating user and the corresponding ground truth information at the time of data capture to independently verify the correctness of the data [
9]. This area of research is still grossly under-investigated and only a few studies exist. For example, by extending the well-known Expectation Maximization algorithm that combines Bayesian inference and maximum likelihood estimation to determine the quality of crowdsensed data, and further applying the classical Information Theory to quantify the effectiveness of crowdsensed data, Peng et al. [
9] inferred fair and proper rewards for participants using the estimated values of quality and contribution. However, a major limitation of the abovementioned solution is the lack of a standardised approach based on which both participants and service providers can accurately estimate cost of participation.
Determining the right amount that participants expect to receive for their efforts in contributing crowdsensed data is a complex challenge. Typically, the expectation of each contributor is different and their opinion on the perceived cost of their participation varies, depending on personal judgement of resource utilisation and the unique context or current situation they are involved in at the time [
112]. Accurately estimating the appropriate amount for each participant and monitoring how that will change with time and context is an increasingly difficult task that requires deeper investigation [
129]. A common practice is to avoid this problem of estimating the expected amount for rewarding participants’ efforts by using the reverse auctions technique, where the need for the requester to set or guess a reasonable amount for users is eliminated and the participants are allowed to set the amount themselves [
125,
130]. For example, Koutsopoulos [
14] proposed an incentive mechanism based on a reverse auction model that uses a negotiation process to reduce sensing cost while ensuring the quality of sensed data. However, an inherent limitation of this approach is that time-delays from the underlying negotiation process may degrade the purpose for which the data is collected, particularly if the data is meant to serve a real-time solution such as weather forecasting, urban parking, etc. [
93]. Users might also find the negotiation process too cumbersome [
93]. Further research is required to thoroughly address these challenges.
Another issue associated with rewarding the effort of participants is that which may occur if the ease of the sensing task renders the participation cost low and as a result, the reward amount is also fixed low. It has been demonstrated that the number of users participating in crowdsensing initiatives reduces when tasks are split into subtasks of lower rewards [
131,
132]. In this case, even though the reward amount might be deemed appropriate based on the participation cost, motivation to participate will still be weak if the number of sensing tasks to which a participant is engaged is not large enough to amount to a significant sum [
93]. Future research in monetary incentives needs to provide for this kind of scenario when considering the effort of participants.
Furthermore, it is important to consider that in certain situations the reward amount attached to a crowdsensing task cannot sufficiently motivate some participants, particularly if they are driven by intrinsic motivations and not necessarily financial gains. Anawar and Yahya [
133] noted that the interpretation of incentives in crowdsensing literature is still loose, mostly focusing on monetary incentives and poorly addressing situations in which participants are mostly driven by intrinsic motivation, which is also known as the “third drive”. In such situations where reward amount is not a factor, the use of monetary incentives may destroy pre-existing intrinsic motivations in a process known as “crowding out” [
28]. Crowding out can also occur when the situation is reversed i.e. when some users expect monetary reward in a scheme that only promotes social rewards. A conventional approach to minimising the problem of crowding out is to use hybrid incentive mechanisms that combine both monetary and non-monetary reward models. For example, Jaimes et al. [
31] recommend a combination of monetary rewards and other types of incentives such as intrinsic and social-based incentives, etc. in order to increase user participation.
CityZen is a crowdsensing platform for citizen engagement in smart city management that incentivises participants with monetary rewards, civic recognitions and discounted tickets to zoos, museums, etc. [
57]. Similarly, QuaCentive is a quality-aware incentive framework for mobile crowdsensing, implemented by appropriately integrating one monetary (i.e. reverse auction) and two non-monetary (i.e. reputation and gamification) incentive mechanisms [
28]. The
NAIST Photo participatory sensing system proposed by Ueyama et al. [
134] also provides option for a combination of monetary and gamification incentives. To sum it up, D’Hondt et al. [
135] express their conviction that large-scale crowdsensing can be achieved through an incentive scheme that carefully balances altruism with a form of direct or indirect remuneration, not necessarily of monetary nature.
With the complexities and implementation overhead associated with monetary incentives, there are possibilities that some of today’s mobile crowdsensing systems running on monetary incentives will be translated or complemented with non-monetary incentives in the future, e.g., increased social recognition and other task-related awards issued by hosting communities [
10]. The robustness and sustainable use of such socio-technical systems driven by hybrid incentive mechanisms will depend on the ability to understand and resolve potential issues bothering on ethics and fairness [
136]. Hence, there is need for a deeper investigation into the implications of allowing both monetary and non-monetary incentive mechanisms to co-exist and be used concurrently to reward participants in the same mobile crowdsensing system.
Mobile crowdsensing initiatives that have strict area coverage requirements can also pose significant challenges when the adopted incentive must be designed in such a way as to steer participants contribution towards meeting the specific requirements [
31]. A few studies (e.g. [
31,
93]) have proposed incentive mechanisms that aim to address the problem of poor data capture in some areas and data redundancies in others. Prominent amongst these solutions is SPREAD, a monetary incentive designed to select the lowest cost participants that are best distributed spatially to cover the area of interest within a defined budget [
31]. This implementation is based on a combination of the Greedy Set Cover algorithm, the Weighted Variance Maximization algorithm and the well-known Reverse Auction Dynamic Price with Recruitment (RADP-VPC-RC) mechanism [
31]. Similarly, the concept of “steered crowdsensing” has been proposed as a viable solution to address the issue of poor area coverage. In this approach, different redeemable credit points are assigned to various locations and users motivated by these incentives can choose to make it to the locations of interest to capture data samples [
93]. While the aforementioned solutions have made significant research contributions, further studies are required to address the problem of poor area coverage along various directions, such as the use of mobility profiles as one of the selection criteria when recruiting participants [
65]; recruiting participants with high demographic diversity [
66]; increasing the coverage area by understanding the mobility patterns of different groups [
67]; involving participants with broad and diverse social interaction patterns [
68]; and the use of density maps to estimate the number of participants in a given area [
25]. It is also important to investigate the problem of poor area coverage from the perspective of unequal representation of various community stakeholders and interest groups in the participatory sensing process and how that influences the fairness and reliability of the crowdsensed data for urban decision making.