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Assigning input ports to output ports and allowing flits to pass through the switch without colliding is accomplished by Switch Allocation (SA), a crucial pipeline stage in the Network-on-Chip (NoC) router. Earlier research improved the efficiency of matching to enhance SA processes. The studies in question, however, failed to account for the importance of information sharing between the various phases of the router’s pipeline. To improve NoC routers’ allocation, this article introduces the Allocation Service (AS) concept. By adding a new pipeline stage called Request Finalization (RF) before the SA stage, the AS can be optimized. The RF stage’s objective is to complete the uniform request and Endpoint Congestion Causing (EPC) requests for SA before sending them on their way. These results inform the proposal of a new design, the CUE-Router, to increase AS by the integration of an RF stage and inter-pipeline communication between routers. Two new types of router architecture form the basis for CUE-Router. The first thing we do in this work is introduce URR-Router, a router that only allocates EPC requests if there are no uniform requests in the SA stage, effectively giving uniform requests a priority. The second example is the EPR-Router, which alleviates endpoint congestion by favoring EPC requests over uniform ones. In this work, we take a fresh look at the SA process optimization that underpins the low-latency CUE-Router architecture by cycling through a series of prioritization changes between uniform requests and EPC requests. CUE-Router greatly improves SA efficiency and network performance by facilitating communication between the various stages of the pipeline. Based on the results of the analysis, our approach has the potential to significantly enhance performance with minimal additional effort.

In this paper, the reconfiguration of swarms of unmanned aerial vehicles after simultaneous failures of multiple nodes is considered. The objectives of the post-failure reconfiguration are to provide collision avoidance and smooth energy-efficient movement. To incorporate such a mechanism, three different failure recovery algorithms are proposed namely thin plate spline, distance- and time-optimal algorithms. These methods are tested on six swarms, with two variations on failing nodes for each swarm. Simulation results of reconfiguration show that the execution of such algorithms maintains the desired formations with respect to avoiding collisions at run-time. Also, the results show the effectiveness concerning the distance travelled, kinetic energy, and energy efficiency. As expected, the distance-optimal algorithm gives the shortest movements, and the time-optimal algorithm gives the most energy-efficient movements. The thin plate spline is also found to be energy-efficient and has less computational cost than the other two proposed methods. Despite the suggested heuristics, these are combinatorial in nature and might be hard to use in practice. Furthermore, the use of the regularization parameter $\lambda $ in thin plate spline is also investigated, and it is found that too large values on $\lambda $ can lead to incorrect locations, including multiple nodes on the same location. In fact, it is found that using $\lambda = 0$ worked well in all cases.

In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that have been calculated based on the location of the detected object. Two separate fuzzy controllers are proposed to control the UAV’s motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base. These rules will be utilized to adjust the control parameters during the mission and landing period in runtime. To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, show that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.

Switch Allocation (SA) holds a critical stage in Network-on-Chip (NoC) routers, its performance gets affected adversely due to Head-of-Line (HoL) blocking. In traditionally used Input-Queued Routers (IQR), packets are arranged in a particular order in each Virtual Channel (VC). This implementation is vulnerable to HoL blocking, as the switch allocator can allocate only those packets which are available at the head in a VC. In this paper, Swapped Buffer (SB) Router architecture is proposed to schedule packets in input buffers by using SB registers. The VCs are designed as SBs, this allows the packets stored in SB registers along with the head packet of VC to participate in SA. The concept of the SB register minimizes the conflicts in SA and thus reduces HoL blocking, therefore improves the performance of NoC. This paper proposes a priority mechanism to prioritize the non-head packets as compared to head packets in case of conflict between them. Two methods have been proposed in this paper, to enhance the performance of the NoC router. First, a VC allocation technique is proposed to optimize the order of packets in the input buffer. Next, SB-Router is combined with the Fill VC allocation technique to further enhance the performance of NoC routers. The performance of the proposed router is evaluated and the experimental results indicate that our design achieves latency improvement of 68.75% over (Time-Series) TS-Router for uniform traffic at the injection rate of 0.42 flits/cycle for a 64 node mesh network with moderate power consumption and area usage. The performance improvement in packet latency for traces from Princeton Application Repository for Shared-Memory Computers (PARSEC) has also been evaluated. With the achieved reduction in latency, the proposed method has the potential to serve high-speed operations while mapping different applications on multiple core architectures.

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.

Nonintrusive Appliance Load Monitoring (NIALM) is used to analyze individual’s house energy consumption by distinguishing variations in voltage and current of appliances in a household. The method identifies load consumption of each appliance from the aggregated home energy consumption. NIALM will also provide information of load consumptions of each appliance by indirectly detecting the abnormal changes of appliance usage. The proposed NIALM approach is based on features extraction from load consumptions measurements of electrical power signals in order to classify appliance’s state of operation. In this work, we have improved the identification accuracy and the detection of appliances based on their operational state by employing Machine Learning (ML) technique; namely k-nearest neighbor (k-NN) classification algorithm. The dataset used to perform this process is from the publicly available (PLAID) of power, voltage and current signals of appliances from several houses. This is used as benchmark data set. The PLAID dataset is collected and processed for each appliance and our classification results based on k-NN algorithm achieved high accuracy and is able to gain cost-effective solution. In addition, the result shows that k-NN classifier is a proven as an efficient method for NIALM techniques when compared with other proposed different ML options. Based on the used dataset, the average F-score measure obtained using the k-NN classifier is 90%. Possible reasons behind these findings are discussed and areas for further exploration are proposed.

A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. As a solution for this, a multi-layered and distributed control system is proposed for the swarm of drones in the formation of hierarchical levels based on the leader-follower approach. The complexity of developing a large system can be reduced in this way. To ensure the tracking performance and response time of the ensemble system, nonlinear and linear control designs are presented; (a) Sliding Mode Control connected with Proportional-Derivative controller and (b) Linear Quadratic Regular with integral action respectively. The safe travel distance strategy for collision avoidance is introduced and integrated into the control designs for maintaining the hierarchical states in the formation. Both designs provide a rapid adoption with respect to their settling time without introducing oscillations for the dynamic flight movement of vehicles in the cases of (a) nominal, (b) plant-model mismatch, and (c) external disturbance inputs. Also, the nominal settling time of the swarm is improved by 44% on average when using the nonlinear method as compared to the linear method. Furthermore, the proposed methods are fully distributed so that each UAV autonomously performs the feedback laws in order to achieve better modularity and scalability.

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs.

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.

The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods.

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents.

Monitoring and diagnostic systems are required in modern Network-on-Chip implementations to assure high performance and reliability. A dynamically clustered NoC monitoring structure for traffic and fault monitoring is presented. It is a distributed monitoring approach which does not require any centralized control. Essential issues concerning status data diffusion, processing, and format are simulated and analyzed. The monitor communication and placement are also discussed. The results show that the presented monitoring structure can be used to improve the performance of an NoC. Even a small adjustment of parameters, for example, considering monitoring data format or monitoring placing, can have significant influence to the overall performance of the NoC. The analysis shows that the monitoring system should be carefully designed in terms of data diffusion and routing and monitoring algorithms to obtain the potential performance improvement.