TAN: A Distributed Algorithm for Dynamic Task Assignment in WSNs

This works develops a novel hierarchical algorithm for task assignment (TA), coordination and communication of multiple UAVs engaging multiple targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAVs in the subtree of a given node to the targets. Simulation experiments show that the combination of flocking and TA algorithms gives the best performance. Clear-cut advantages of the TA algorithm are shown to exist in cases where the hit probability tends to one. An improvement of efficiency, representing the ratio between killed targets and the number of missile launches, is obtained for larger numbers of UAVs only if the engagement times are long enough, utilizing the improved coverage achieved by more UAVs.

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms.

This work develops distributed hierarchical task assignments and communication algorithms for flocks of unmanned aerial vehicles dispersed in an unknown theater while engaging multiple moving targets. The dynamical changes require distributed algorithms, without relying on a central agent, which may constitute a single point-of-failure and is exposed to communication breaks. Our methodology overcomes the typical deficiencies of a centralized solution by organizing the agents in spanning trees. Whenever possible, the spanning trees merge into a single tree that clusters the maximum number of agents. Using relaxation methods and inputs from its parent and children, every agent attempts to optimize its own solution for task assignment while communicating using an ad-hoc protocol. Simulation experiments show clear-cut advantages of using the proposed task assignment and communication algorithms.

Multiple mobile robot system under decentralized control has become more popular because of advantage of fault tolerance and extensibility. This paper focuses on a planning method for an interactive transportation task by multiple mobile robot system. In this task, task assignment is important to solve interaction among mobile robots. In this paper, we propose the self-generating task assignment algorithm in order to realize efficient sorting task. First, we considered parallelism in transportation task. Second, we defined variable of “Progress” in order to estimate value of tasks which can execute independently and in parallel. Using Progress, robots can select executable parallel task and generate task assignment based on efficiency. We developed multi-robot simulator and multi robot system in order to verify the effectiveness.

With the advent of semi-autonomous robotic assistance systems, their integration into human teams is starting to gain steam as part of the vision of human+machine hybrid activities. Unlike their fully autonomous counterparts, semi-autonomous robotic systems mainly rely on human assistance from time to time via teleoperation when human expertise is needed to accomplish a given task. As these robots will need to request human assistance via teleoperation, mapping these requests to human teleoperators stands as a difficult optimization problem. In this paper, after shedding some light on our envisioned FiWi enhanced network infrastructure and its role in realizing the emerging Tactile Internet, we formulate the problem of joint prioritized scheduling and assignment of delay-constrained teleoperation tasks to human operators with the objective to minimize the average weighted task completion time, maximum tardiness, and average operational expenditure (OPEX) per task. We then propose our context-aware prioritized scheduling and task assignment (CAPSTA) algorithm to achieve suitable trade-offs between the contradicting objectives of the problem. Further, to estimate the end-to-end packet delay of local and non-local teleoperation over FiWi enhanced networks, we develop our analytical framework, which flexibly allows for the coexistence of conventional human-to-human (H2H) and haptic human-to-machine (H2M) traffic.

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.

Pipelined execution of streaming applications enable processing of high-throughput data under performance constraint. We present an integrated approach to synthesizing pipelined software for dual-core architectures. We target streaming applications modeled as task graphs that are amenable to static analysis. We develop a versatile task assignment algorithm that considers the combined effect of workload imbalance between processors and inter-processor communication. Our technique, which runs in pseudo-linear time, provably maximizes application throughput. Furthermore, we develop an approximation algorithm for task assignment whose complexity is strictly polynomial. It provides the designer with an adjustable knob to controllably trade solution quality with algorithm runtime and memory requirement. Empirical throughput measurements using an FPGA-based dual-core system validate our theoretical results. Our exact algorithm consistently outperforms a recent competitor. Compared to exact task assignment, the approximate method runs about 3 times faster, requires about 20 times less memory, and results in only 1% to 5% throughput loss.

SRAMs are aimed at multiprocessing

The periodic task set assignment problem in the context of multiple processors has been studied for decades. Different heuristic approaches have been proposed, such as the Best-Fit (BF), the First-Fit (FF), and the Worst-Fit (WF) task assignment algorithms. However, when processors are not dedicated but only periodically available to the task set, whether existing approaches still provide good performance or if there is a better task assignment approach in the new context are research problems which, to our best knowledge, have not been studied by the real-time research community. In this paper, we present the Best-Harmonically-Fit (BHF) task assignment algorithm to assign periodic tasks on multiple periodic resources. By periodic resource we mean that for every fixed time interval, i.e., the period, the resource always provides the same amount of processing capacity to a given task set. Our formal analysis indicates that if a harmonic task set is also harmonic with a resource's period, the resource capacity can be fully utilized by the task set. Based on this analysis, we present the Best-Harmonically-Fit task assignment algorithm. The experimental results show that, on average, the BHF algorithm results in $53.26$ , $42.54$ , and $27.79$ percent higher resource utilization rate than the Best-Fit Decreasing (BFD), the First-Fit Decreasing (FFD), and the Worst-Fit Decreasing (WFD) task assignment algorithms, respectively; but comparing to the optimal resource utilization rate found by exhaustive search, it is about $11.63$ percent lower.

Task assignment and scheduling algorithms for heterogeneous computing systems can be classified as iterative and non-iterative techniques, and are designed to optimize a specific cost function defined on the system. The quality of the solutions generated is controlled by the nature of this cost metric. The common metrics that are used include minimizing the overall execution time or minimizing the load on the maximum loaded processor. In this work, a new set of cost metrics have been proposed that can be used by iterative task assignment algorithms. These metrics exploit the fact that in iterative algorithms the mapping of the subtasks to the processors is known at every iteration. They reflect the actual scheduling cost of the application, thereby improving the quality of the solutions generated by the algorithm. The proposed metrics are evaluated using a learning automata based iterative algorithm. Observations are made regarding the nature of the metrics from the results obtained.

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The paper proposes a novel matrix completion algorithm for data collection in wireless sensor networks through incorporating a randomised version of Kaczmarz method. By splitting the matrix completion problem into two convex sub-problems and solving the optimal probability computing problem in the randomised Kaczmarz method approximately with the D-optimal design solution, we reduce the reconstruction error and accelerate the convergence speed of the matrix completion computation. The synthetic data experiments show that the proposed algorithm presents more accurate reconstruction accuracy and faster reconstruction speed than the state-of-the-art matrix completion algorithms. Furthermore, we verify the practicality of the proposed matrix completion algorithm in real data collection scenario of wireless sensor networks through the experiments based on the real sensed dataset.

One of the key challenges in the implementation of ZigBee networks is the development of algorithms which provide autonomous meshing of beacons with minimal or no interference. This feature is out of the scope of current ZigBee capabilities. The energy and bandwidth constraints and the potential large-scale deployment pose challenges in efficient resource allocation and sensor management. In this review paper, we will focus on the problems which hinder the reliable and low-latency operation of deeply-embedded wireless sensor networks. We will also analyze the proposed solutions which play a key role in bolstering ZigBee as the true mediator in making the current scenario of wireless sensor networks (WSN) networking more reliable and thus paving the way for meeting the challenges and requirements demanded by next-generation ZigBee networks. The Review paper has been divided into two parts. In the first part, we briefly introduce the beaconing mechanism of IEEE 802.15.4 and analyze it from the point of view of mesh networks. In the second part, we review the hidden-node problem from the perspective of IEEE 802.15.4. Finally, we conclude with some remarks on the future scope of ZigBee networking.

Fault tolerance has been identified as key challenges in the design and operations of Wireless Sensor Network (WSNs). Failures are inevitable in wireless sensor networks due to inhospitable environment and unattended deployment. Therefore, it is necessary to detect the networks for recovery from the failure to sustain it. WSNs are self-organized using clustering algorithms to conserve energy. The sensor nodes are organized in several small clusters where there are cluster heads in each cluster. These CHs (Cluster Heads) gather data from their local clusters aggregate them & send them to the base station with shortest distance as a forwarding metric using Dijkstra algorithm. The proposed scheme is supposed to be an efficient fault detection and recovery mechanism to make the network fault-tolerant and achieve reliability, latency and energy efficiency. Performance analysis and evaluation will be made using several scenarios of wireless sensor networks.