Multi-objective routing optimization using evolutionary algorithms

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

Transformation to advanced mechatronics systems within new industrial revolution: A novel framework in Automation of Everything (AoE)

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

On Designing a Machine Learning Based Wireless Link Quality Classifier

Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environment for radio signals significantly varies. In such environments, intelligent algorithms that can provide robust, resilient and adaptive links are being investigated to complement traditional algorithms in maintaining a reliable communication. In this respect, the data-driven link quality estimation (LQE) using machine learning (ML) algorithms is one of the most promising approaches. In this paper, we provide a quantitative evaluation of design decisions taken at each step involved in developing a ML based wireless LQE on a selected, publicly available dataset. Our study shows that, re-sampling to achieve training class balance and feature engineering have a larger impact on the final performance of the LQE than the selection of the ML method on the selected data.Comment: accepted in PIMRC 2020. arXiv admin note: text overlap with arXiv:1812.0885

Cross-layer network lifetime optimization considering transmit and signal processing power in WSNs

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 maximizing the network lifetime (NL). In this paper, we optimize 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 characterize 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

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, 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 criteri

Analysis and optimisation of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform

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 manuscript 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 platform 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

Quality of service aware cross-layer network lifetime maximization in battery-constrained wireless sensor networks

In wireless sensor networks (WSNs), network lifetime (NL) maximization plays a significant role in striking a compelling compromise between maximizing the overall throughput and minimizing the energy dissipation, while extending the duration of adequate communications without battery-replacement, when the sensor nodes rely on limited energy supply. Hence, this thesis focuses on the NL maximization of battery-constrained WSNs, which is vitally important in industrial applications, where thousands of sensors may be deployed within a specific target area and the energy dissipation of each sensor node has to be minimized in order to reduce the overall cost of the applications to the industry. However, maintaining stringent quality of service (QoS) requirements under the above-mentioned NL constraints can be challenging and requires careful consideration of several conflicting design tradeoffs. Naturally, the above-mentioned energy dissipation characteristics are dependent on the entire seven-layer OSI protocol stack, where each layer contributes to the dissipated energy. Therefore, NL maximization necessitates a cross-layer operation across all these layers, where each layer has to minimize its energy dissipation without deteriorating the QoS. Hence, our objective is to maximize the NL using cross-layer design techniques in the interest of maintaining certain QoS requirements and to provide the system designer with well-informed decisions prior to embarking on hardware implementations. Hence, our approach is to investigate and to model progressively more realistic WSNs.We commence with a broad overview of the WSNs, of the design objectives and of the NL maximization techniques that have been investigated in the literature. We then provide a concise introduction to convexity, convex optimization, to the Lagrangian dual problem and to the Karush-Kuhn-Tucker (KKT) optimality conditions, which will be extensively used in our studies. Having presented the fundamentals, we formulate an initial study of the NL maximization problem based on a simple string topology in order to form a basic framework for the NL maximization of more realistic large scale networks. In this particular study, we maximize the NL in an interference-limited WSN considering a beneficial rate and power allocation scheme under both additive white Gaussian noise (AWGN) and fading channel characteristics, where we employ the KKT optimality conditions for obtaining the optimal solution to the NL maximization problem using closed-form expressions. Therefore, we were able to derive analytical expressions of the globally optimal NL for a string network operating in an interference-limited scenario, while communicating either over an AWGN or over fading channels for a given link schedule. Furthermore, the maximum NL, the energy dissipation per node, the average transmission power per link and the lifetime of all nodes in the network are obtained. We quantify how the maximum NL is reduced as a function of the fading statistics due to the poor channel conditions. Furthermore, we demonstrate that given a certain network-sum-rate, the simultaneous scheduling of weakly interfering links benefits from the associated spatial reuse by allowing each node to transmit at a lower rate, which requires a reduced transmission power and hence results in an increased NL. We also conclude that the choice of the particular scheduling scheme depends on the application, since a lower source rate favors infrequent transmissions requiring a low transmit power, while avoiding the detrimental effects of interference, when aiming for extending the NL. However, we observe that for higher source rates, a higher NL can be achieved by aggressive spatial reuse. An interesting observation is that increasing the distance between the consecutive nodes substantially reduces the NL, especially for lower source rates. However, quite surprisingly, increasing the distance between the consecutive nodes results in an improved NL for higher source rates. This is due to the reduced impact of the interferers located at a higher distance. More explicitly, even though the transmit power required has to be increased to satisfy the rate constraint, at the same time the interferers are moved a bit further away. In this particular study, the NL and source rate are considered as the QoS measure as a function of both the transmit rate and the power, where an adaptive scheme is assumed. Finally, our proposed algorithm achieved reduced complexity NL maximization compared to other techniques found in the literature.We then extend our NL maximization problem to a realistic scenario, where the parameters are selected from the practical data sheet of a National Instruments device, which is based on the IEEE 802.15.4 Standard and the energy dissipation of the signal processing operations, i.e. the energy dissipation of the transceiver circuits, is considered. Since achieving a reasonable NL at the cost of a tolerable end-to-end bit error rate (BER) for a fixed-rate system using various modulation and coding schemes (MCSs) is an important objective for the system designer considering the QoS, we strike a trade-off between the BER and the NL, which is crucial for network designers at an early design stage. Therefore, we aim for maximizing the NL for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values, which guarantees maintaining the predefined QoS of each link operating over either an AWGN channel or a Rayleigh block-fading channel, while considering or disregarding the signal processing power (SPP). We observed that especially for low target SINRs, the SPP has a dominant impact on the NL. However, for higher target SINRs the achievable NL only considering the transmit power whilst disregarding the SPP forms a benchmark for the achievable NL of the particular scenario, when the SPP is jointly considered along with the transmit power.As a further advance, a more realistic network is considered, where the same National Instruments device, which is based on the IEEE 802.15.4 Standard is used as a reference. For this realistic network, we also had to reconsider our NL definition, where we maximize the NL of a WSN relying on randomly and uniformly distributed fully connected nodes. This fully connected WSN imposes an exponentially increasing routing complexity upon increasing the number of nodes. More particularly, we focus our attention on the crosslayer optimization of the power allocation, scheduling and routing operations for the sake of NL maximization for predetermined per-link target SINR values. We use the so-called exhaustive search algorithm (ESA) as our benchmarker and conceive a near-optimal single objective genetic algorithm (SOGA) imposing a substantially reduced complexity in fully connected WSNs. We show that our NL maximization approach is powerful in terms of prolonging the NL, while striking a trade-off between the NL and the QoS requirements. Finally, we consider a multiobjective NL maximization problem, where the end-to-end delay and the energy dissipation are considered as our conflicting design objectives. More explicitly, we proposed a novel NL optimization design in order to reflect the effect of the end-to-end delay on the NL along with the aggregate energy dissipation of the same route. The distinctive aspect of this study is the simultaneous optimization of both the aggregate energy dissipation and of the end-to-end delay as a multi-objective optimization problem in order to provide the system designer with a trade-off between Pareto-optimal energyand delay-solutions. We employ multi-objective evolutionary algorithms (MOEAs), namely the so-called non-dominated sorting based genetic algorithm-II (NSGA-II) and the multiobjective differential evolution (MODE) algorithm for obtaining the set of Pareto optimal NL-aware routes striking a trade-off between the aggregate energy dissipation and the end-to-end delay. Moreover, we characterize both the complexity and the convergence of both algorithms compared to the ESA