الفهرس 
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Abstract
The popularity of mobile devices with sensors and their advanced sensing capabilities is captivating the attention of researchers to modern techniques, such as the Internet of Things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. The proper recruiting of smartphone users and task assignment are critical to the success of MCS in general. Some of the MCS research challenges are to maximize data and task quality, minimize sensing cost, guarantee privacy, energy limitation of mobile node resources, and task assignment. This thesis discusses the task assignment problem in MCS, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The scenario assigns sensing tasks to an appropriate number of participants in a specific area with minimal sensing time to maximize the quality of sensed data. Our goals are minimizing aggregate sensing time for mobile users, which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality. This thesis introduces a task assignment framework called location time-based algorithm (LTBA). LTBA is a framework that enhances task assignment in MCS and focuses on sensing time intervals and the location of tasks and mobile users’ paths to minimize aggregate sensing time and maximize total task quality. LTBA is a hybrid framework that combines of two algorithms: (1) the greedy online allocation algorithm and (2) the bio-inspired travel-distance-balance-based algorithm (B-DBA). The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user. B-DBA was location-based and worked on maximizing total task quality. LTBA is a two-layers framework. The first layer tries to find the overlap in time intervals between the new arrival tasks and tasks in the mobile users’ pool. The second layer assigns the nearest task to the mobile user’s path. The process of assigning the nearest task to the mobile user’s path depends on Euclidean distance and one of two bio-inspired search algorithms: the ant colony optimization algorithm (ACO) or the particle swarm optimization algorithm (PSO). We introduce two models: LTBA_ACO and LTBA_PSO. LTBA_ACO model applies ACO, whereas LTBA_PSO applies PSO. The results demonstrate that the average task quality is 80.88 % for both LTBA_ACO and LTBA_PSO, 69.28%, and 77.33% for B-DBA, and greedy, respectively. The sensing time was reduced to 630.500, 622, 1573, and 881-time units for LTBA_ACO, LTBA_PSO, B-DBA, and greedy, respectively. Combining the algorithms (the greedy online allocation algorithm and B-DBA) improved MCS task assignment for total task quality and sensing time. The results demonstrated that combining the two algorithms in LTBA with the two models achieved the best performance for total task quality and total sensing time. Then greedy algorithm came in second place, followed by B-DBA.