Thanks to the capabilities of the built-in sensors of smart devices, mobile crowd-sensing (MCS) has become a promising technique for massive data collection. In this paradigm, the service provider recruits workers (i.e., common people with smart devices) to perform sensing tasks requested by the consumers. To efficiently handle workers' recruitment and task allocation, several factors have to be considered such as the quality of the sensed data that the workers can deliver and the different tasks locations. This allocation becomes even more challenging when the MCS tries to efficiently allocate multiple tasks under limited budget, time constraints, and the uncertainty that selected workers will not be able to perform the tasks. In this paper, we propose a service computing framework for time constrained-task allocation in location based crowd-sensing systems. This framework relies on (1) a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, (2) queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, (3) a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and (4) a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The platform goal is to efficiently determine the most appropriate set of workers to assign to each incoming task so that high quality results are returned within the requested response time. Simulations are conducted using real datasets from Foursquare ¹ and Enron email social network. ² Simulation results show that the proposed framework maximizes the aggregated quality of information, reduces the budget and response time to perform a task and increases the average recommenders' reputation and their payment.

Femtocells (FCs) are introduced to enhance the indoor coverage and system capacity of traditional cellular networks. However, the network performance could be significantly deteriorated due to the increase in cochannel interference in dense deployment. In the literature, three spectrum usage schemes that deal with the cross-tier interference have been proposed, i.e., orthogonal assignment, underlay, and controlled underlay using mainly closed-access FCs. This paper targets the optimization of resource allocation, together with the selection of base stations (BSs) in two-tier networks assuming hybrid access FCs, to reduce the cross-tier interference. Moreover, this model aims to achieve effective spatial reuse between the macrocell (MC) and FCs while guaranteeing quality-of-service (QoS) transmissions by means of joint power control in both tiers. Simulations are conducted to show a comparison with the other three approaches.

In this paper, we consider the problem of resource allocation in two-tier networks taking into consideration nondense femtocell deployments. The following limitations can be remarked from the prior work in the field of resource allocation: (1) resources are underutilized due to the equal power distribution in macrocell, (2) access to public users in femtocells is restricted to avoid depriving own subscribers transmissions, and (3) degradation of signal-to-noise ratio due to noise effects has not been evaluated. To overcome these limitations, we propose a joint power and bandwidth allocation among two tiers together with base station selection using genetic algorithm. Our solution is able to: (1) maximize the overall system throughput, (2) find an appropriate serving base station for each user, and (3) bandwidth and power assigned to each user. Simulations were conducted and a comparison with a Weighted Water Filling algorithm is carried out.

In this paper, we propose a base station selection and resource allocation model for OFDMA macro-femtocell networks. Dense deployment of femtocells can cause severe interference for femto and macro users alike. Our framework assumes hybrid-access femtocells to grant access to public user in their vicinity and to reduce interference perceived by femto users. Full spectrum sharing is investigated for the purpose of increasing system capacity. The proposed model aims to maximize the network throughput for a given interference threshold. To do so, the model determines the best serving base station based on link conditions. Genetic Algorithm is used to solve the resource optimization problem by finding the appropriate bandwidth and power assignments for each user. Simulations were conducted and a comparison with a modified version of the Weighted Water Filling algorithm is presented.