Transformation to advanced mechatronics systems within new industrial revolution: A novel framework in Automation of Everything (AoE)
IEEE Access, 2019
The recent advances in cyber-physical domains, cloud, cloudlet and edge platforms along
with the ... more The recent advances in cyber-physical domains, cloud, cloudlet and edge platforms along with the evolving Artificial Intelligence (AI) techniques, big data analytics and cutting-edge wireless communication technologies within the Industry 4.0 (4IR) are urging mechatronics designers, practitioners and educators to further review the ways in which mechatronics systems are perceived, designed, manufactured and advanced. Within this scope, we introduce the service-oriented cyber-physical advanced mechatronics systems (AMSs) along with current and future challenges. The objective in AMSs is to create remarkable intelligent autonomous products by 1) forging effective sensing, self-learning, Wisdom as a Service (WaaS), Information as a Service (InaaS), precise decision making and actuation using effective location-independent monitoring, control and management techniques with products, and 2) maintaining a competitive edge through better product performances via immediate and continuous learning, while the products are being used by customers and are being produced in factories within the cycle of Automation of Everything (AoE). With the advanced wireless communication techniques and improved battery technologies, AMSs are capable of getting independent and working with other massive AMSs to construct robust, customisable, energy-efficient, autonomous, intelligent and immersive platforms. In this regard, rather than providing technological details, this paper implements philosophical insights into 1) how mechatronics systems are being transformed into AMSs, 2) how robust AMSs can be developed by both exploiting the wisdom created within cyber-physical smart domains in the edge and cloud platforms, and incorporating all the stakeholders with diverse objectives into all phases of the product life-cycle, and 3) what essential common features AMSs should acquire to increase the efficacy of products and prolong their product life. Against this background, an AMS development framework is proposed in order to contextualize all the necessary phases of AMS development and direct all stakeholders to rivet high quality products and services within AoE.
Deploying Unmanned Aerial Vehicle (UAV) swarms in delivery systems is still in its infancy
with r... more Deploying Unmanned Aerial Vehicle (UAV) swarms in delivery systems is still in its infancy with regards to the technology, safety, aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects- e.g., increasing efficacy, reducing the air traffic resulting in safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This paper analyses several delivery schemes within a platform, such as delivery with and without using air highways, and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium and FIFO) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space” (dMAiMD) and several other new techniques along with Hungarian and Cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method and constraints. Several scenarios are tested on the simulator using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs. Additionally, private industries, logistics operators, municipalities are expected to benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical implementation of UAV delivery systems.
ABSTRACT Maintaining high energy efficiency is essential for increasing the lifetime ot wireless ... more ABSTRACT Maintaining high energy efficiency is essential for increasing the lifetime ot wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximising the network lifetime (NL). In this paper, we optimise the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal processing power (SPP) P sp on the NL. We characterise the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. For a per-link target BER requirement (PLBR) of 10−3, our results demonstrate that a ‘continuous-time’ NL in the range of 0.58 – 4.99 years is achieved depending on the MCSs, channel configurations, and SPP.
In wireless sensor networks (WSNs), the network lifetime (NL) is a crucial metric since the senso... more In wireless sensor networks (WSNs), the network lifetime (NL) is a crucial metric since the sensor nodes usually rely on limited energy supply. In this paper, we consider the joint optimal design of the physical, medium access control (MAC), and network layers to maximize the NL of the energy-constrained WSN. The problem of NL maximization can be formulated as a nonlinear optimization problem encompassing the routing flow, link scheduling, transmission rate, and power allocation operations for all active time slots (TSs). The resultant nonconvex rate constraint is relaxed by employing an approximation of the signal-to-interference-plus-noise ratio (SINR), which transforms the problem to a convex one. Hence, the resultant dual problem may be solved to obtain the optimal solution to the relaxed problem with a zero duality gap. Therefore, the problem is formulated in its Lagrangian form, and the Karush–Kuhn–Tucker (KKT) optimality conditions are employed for deriving analytical expressions of the globally optimal transmission rate and power allocation variables for the network topology considered. The nonlinear Gauss–Seidel algorithm is adopted for iteratively updating the rate and power allocation variables using these expressions until convergence is attained. Furthermore, the gradient method is applied for updating the dual variables in each iteration. Using this approach, the maximum NL, the energy dissipation per node, the average transmission power per link, and the lifetime of all nodes in the network are evaluated for a given source rate and fixed link schedule under different channel conditions.
Wireless ad hoc networks suffer from several limitations, such as routing failures, potentially e... more Wireless ad hoc networks suffer from several limitations, such as routing failures, potentially excessive bandwidth requirements, computational constraints and limited storage capability. Their routing strategy plays a significant role in determining the overall performance of the multi-hop network. However, in conventional network design only one of the desired routing-related objectives is optimized, while other objectives are typically assumed to be the constraints imposed on the problem. In this paper, we invoke the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) and the MultiObjective Differential Evolution (MODE) algorithm for finding optimal routes from a given source to a given destination in the face of conflicting design objectives, such as the dissipated energy and the end-to-end delay in a fully-connected arbitrary multi-hop network. Our simulation results show that both the NSGA-II and MODE algorithms are efficient in solving these routing problems and are capable of finding the Pareto-optimal solutions at lower complexity than the 'brute-force' exhaustive search, when the number of nodes is higher than or equal to 10. Additionally, we demonstrate that at the same complexity, the MODE algorithm is capable of finding solutions closer to the Pareto front and typically, converges faster than the NSGA-II algorithm.
Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor ne... more Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximising the network lifetime (NL). In this study, the authors optimise the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal processing power (SPP) P sp on the NL. They characterise the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. The NL maximisation is formulated as a non-linear optimisation problem taking into account the lower-bounded SINR, the energy consumption constraint, the maximum transmit power per link and again, periodic transmit-TS scheduling for all active TSs. The non-linear energy consumption constraint encountered is relaxed by employing a change of variables, which converts the problem into a linear form. Hence they can obtain the globally optimal solution of the original problem by solving a linear programming problem with the aid of the interior point method. The author's results demonstrate that for a per-link target BER requirement (PLBR) of 10 −3 , a 'continuous-time' NL of 4.99 years (yr) is achieved by 1/2-rate convolutional coded soft-decoded quadrature phase-shift keying for an additive white Gaussian noise channel, when ignoring the SPP, which is reduced to 0.89 yr because of the SPP. By contrast, the best NL that is achieved by 1/2-rate serially concatenated coding after ten iterations at a PLBR of 10 −3 in a Rayleigh fading channel, which is reduced from 1.55 to 0.58 yr because of the SPP.
Emerging technologies, such as the Internet of things, smart applications, smart grids and machin... more Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.
Network lifetime (NL) maximization techniques have attracted a lot of research attention owing to... more Network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance for extending the duration of the operations in the battery-constrained wireless sensor networks (WSNs). In this paper, we consider a two-stage NL maximization technique conceived for a fully-connected WSN, where the NL is strictly dependent on the source node's (SN) battery level, since we can transmit information generated at the SN to the destination node (DN) via alternative routes, each having a specific route lifetime (RL) value. During the first stage, the RL of the alternative routes spanning from the SN to the DN is evaluated, where the RL is defined as the earliest time, at which a sensor node lying in the route fully drains its battery charge. The second stage involves the summation of these RL values, until the SN's battery is fully depleted, which constitutes the lifetime of the WSN considered. Each alternative route is evaluated using cross-layer optimization of the power allocation, scheduling and routing operations for the sake of NL maximization for a predetermined per-link target signal-to-interference-plus-noise ratio values. Therefore, we propose the optimal but excessive-complexity algorithm, namely, the exhaustive search algorithm (ESA) and a near-optimal single objective genetic algorithm (SOGA) exhibiting a reduced complexity in a fully connected WSN. We demonstrate that in a high-complexity WSN, the SOGA is capable of approaching the ESA's NL within a tiny margin of 3.02% at a 2.56 times reduced complexity. We also show that our NL maximization approach is powerful in terms of prolonging the NL while striking a tradeoff between the NL and the quality of service requirements.
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Papers by Halil Yetgin
with the evolving Artificial Intelligence (AI) techniques, big data analytics and cutting-edge wireless
communication technologies within the Industry 4.0 (4IR) are urging mechatronics designers, practitioners
and educators to further review the ways in which mechatronics systems are perceived, designed, manufactured and advanced. Within this scope, we introduce the service-oriented cyber-physical advanced
mechatronics systems (AMSs) along with current and future challenges. The objective in AMSs is to
create remarkable intelligent autonomous products by 1) forging effective sensing, self-learning, Wisdom
as a Service (WaaS), Information as a Service (InaaS), precise decision making and actuation using
effective location-independent monitoring, control and management techniques with products, and 2)
maintaining a competitive edge through better product performances via immediate and continuous learning,
while the products are being used by customers and are being produced in factories within the cycle of
Automation of Everything (AoE). With the advanced wireless communication techniques and improved
battery technologies, AMSs are capable of getting independent and working with other massive AMSs to
construct robust, customisable, energy-efficient, autonomous, intelligent and immersive platforms. In this
regard, rather than providing technological details, this paper implements philosophical insights into 1) how
mechatronics systems are being transformed into AMSs, 2) how robust AMSs can be developed by both
exploiting the wisdom created within cyber-physical smart domains in the edge and cloud platforms, and
incorporating all the stakeholders with diverse objectives into all phases of the product life-cycle, and 3)
what essential common features AMSs should acquire to increase the efficacy of products and prolong their
product life. Against this background, an AMS development framework is proposed in order to contextualize
all the necessary phases of AMS development and direct all stakeholders to rivet high quality products and
services within AoE.
with regards to the technology, safety, aviation rules and regulations. Optimal use of UAVs in dynamic
environments is important in many aspects- e.g., increasing efficacy, reducing the air traffic resulting in
safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs
and constraints. This paper analyses several delivery schemes within a platform, such as delivery with
and without using air highways, and delivery using a hybrid scheme along with several delivery methods
(i.e., optimal, premium and FIFO) to explore the use of UAV swarms as part of the logistics operations.
In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional
space” (dMAiMD) and several other new techniques along with Hungarian and Cross-entropy Monte Carlo
techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach
is performed in such a way that UAV swarms in several warehouses are deployed optimally given the
delivery scheme, method and constraints. Several scenarios are tested on the simulator using small and big
data sets. The results show that the distribution and the characteristics of data sets and constraints affect
the decision on choosing the optimal delivery scheme and method. The findings are expected to guide the
aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient
and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated
results into account for their functional design of UAVs along with other companies that aim to deliver their
products using UAVs. Additionally, private industries, logistics operators, municipalities are expected to
benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical
implementation of UAV delivery systems.