Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors’ performance, tasks’ priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.

This paper presents a set of algorithms that are developed for real-time dynamic switching between collaboration levels in a human-robot target recognition system. The algorithms were developed for a closed-loop controller to maximize system performance, despite deviations in the parameter values. These developments enable smooth real-time adaptation of the combined human-robot system to many possible changes of the environment, human operator, and robot performance. System performance was analyzed in simulations for a variety of target probability distributions. Two hundred independent simulations for each target probability distribution were conducted to calculate algorithm performance for a variety of conditions. Values for human operations were taken from a target recognition experiment dealing with detecting melons for a robotic melon harvester. The numerical analysis results indicated that the developed dynamic switching algorithms resulted in improved system performance that, in some cases, was increased by more than 90%.

This research proposes a multi-sensor task allocation framework for security of supply networks aimed to maximise the number of correctly detected and reported security events (defined as tasks). The framework includes a double layer system consisting of a process layer and a monitoring layer. The process layer allocates sensors to tasks using an ant colony algorithm. The monitoring layer applies four task administration protocols (TAPs) specially developed and implemented to deal with high time-consuming tasks, conflicts in task priorities and sensor failure, defined in this research as overloading, deception and tampering of sensors, respectively. A system objective function for sensor to task allocation was developed to allow computation of the expected value of system performance given the sensor and the task parameters. Sensory limitations evaluated including reliability, distance coverage and the limited number of sensors are addressed in the decision-making process. The framework enables detection of tasks as soon as they occur in every location along the supply network, based on the sensor network distribution. The dual layer system analyses reveal that TAPs increase the systems performance in the scenarios of deception, tampering and overloading by more than 64% with respect to the number of unallocated tasks in comparison to a single layer system. Overall availability was analysed using Monte Carlo simulation and the fault tolerant system yielded significantly increased number of treated tasks (by 11%, p = 0.02).

In this paper, we propose a Modified Distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. Tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.