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Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric---the SHAP value---which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.4 times more reduction on average in percentage of netlist recovery).

Industrial Internet of Things and Industry 4.0 enable interconnection among various devices. An opportunistic Internet of Things network is an ad-hoc network that is formed by the nodes (e.g. smart vehicles and mobile phones) by utilizing various short radio range techniques. In this kind of network, information forwarding and dissemination among other smart devices is based upon the opportunistic contact nature mainly due to network dynamics and user mobility. Routing plays an important part in these kinds of networks since there does not exist a pre-established route tyagi2013systematic. Nodes are often selected dynamically based upon many parameters such that messages can be delivered successfully to the destination devices or sinks. However, these intermediate nodes are often selfish because routing these packets costs energy. In case of incomplete cooperation and asymmetric information, message delivery can be severely degraded, which increases the network delay affecting the overall network performance. Therefore, this work proposes an incentive design mechanism based on contract theory to reward intermediate nodes appropriately to forward the messages. Contract theory is used to model the forwarding-forwarder node interaction as a labor market with private information. First, the users are classified into a finite number of types according to their ability of forwarding the message, and the service trading between the forwarding and forwarder nodes is properly modeled. Further, the necessary and sufficient conditions are derived to provide the incentives to the nodes involved in the message forwarding. Extensive simulations show that the proposed mechanism is effective in providing incentives and outperforms other benchmark schemes in terms of delivery probability, average latency, and overhead ratio.

The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility) and widely distributed network need to be carefully addressed. This paper therefore focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new Non-linear Levy Brownian Generalized Normal Distribution Optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and inter-node distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

The proposed approach is designed to boost the IEEE 802.15.4 / ZigBee-based WSN streaming efficiency by introducing multipurpose routing. We investigate how those networks can be designed in order to achieve optimum throughput using computational models to explain intra-track and inter-track interference in multipath routing networks. One mode of action in particular – Spatial-TDMA (S-TDMA), Zig Bee dependent WSNs, will greatly reduce the amount of interference, both in one- and multi-track environments. It is also seen that two-way networks can be stronger than their one-way counterparts by means of deliberately selected implementation parameters. Finally, a greater degree of spatial isolation between the routes used indicates improved average efficiency in multi-path scenarios. The simulation is one of our results.

IoT (IoTs) is a collection of autonomous things. IoTs is a new pattern that includes the existing presence of different devices. It is one of the latest technologies that provide global connectivity, management of sensors, users and information. The connectivity helps devices to be connected in a ubiquitous manner. Therefore, it can be deployed in different domains such as smart home, health, city, retail, logistics, industries and farming. This has brought a new dimension to connect with objects. There are many issues associated with IoTs such as fading, energy consumption, data security, network security, etc. The ultimate aim of this assessment is to show the different kinds of routing methods to solve the problem of network routing attacks in IoTs. This paper categorizes the attacks in terms of sinkhole, black hole and wormhole attacks in tabular format.

IoT sensors are able to aggregate data regarding their agriculture field in real time have given rise to the concept of smart farming. The agricultural sector is of great importance to fulfill food resources need of the country. Most industries, including “smart agriculture,” have been completely redone in light of new internet-of-things (IoT) technologies that brought in new quantitative approaches. Such revolutionary changes are upending traditional agricultural practices and presenting new opportunities in the face of a variety of challenges. Agricultural applications involving wireless sensors and IoT devices are analyzed in detail. For agriculture-specific use, such as soil grounding, harvest position, irrigation, bug & insect discovery, a list of available sensors is provided. In this article, we proposed a WSN framework based on the Internet of Things for use in smart agriculture, with different design levels to choose from. The first step is having sensors on the farm which are capable of collecting relevant data and determining the appropriate cluster heads. In addition, the signal strength on the communication link is calculated by means of the signal to noise ratio (SNR) in order to attain reliable and competent information diffusion over long distances. Second, the recurrence of the linear congruential generator is used to ensure the security of information spread as of agricultural sensors to base stations (BS) by means of the linear congruential producer. Comparing the proposed framework to other solutions, the simulation results showed that it significantly improved communique routine by standard of 16.3 percent in system throughput, 36.3 percent in packet drop ratio, 12.4 percent in network latency, 18 percent in energy consumption, and 19 percent in routing overheads.

Computational resources are increasingly virtualized to enable computational tasks to be offloaded to remote facilities along the route between the source and destination. The principle that underlies traditional routing, i.e., that only networking resources need to be considered, may no longer be true in a virtualized environment. In this paper, we propose a framework for the efficient utilization of multi-resource infrastructures in which computational resources can be used via the network. Such a framework intrinsically calls for the joint consideration of networking and computational resources. In particular, we focus on unifying the controls in dynamic service chaining and multiple resource management, which are the key technologies in an integrated SDN/NFV architecture. We formulate a multi-path problem for choosing the resources to use in different services. The problem can be viewed as variational inequality using the Lagrange duality and saddle point theory. Based on this, we develop an extragradient-based algorithm that controls and splits the sending rate of each service. We prove that the algorithm converges to the optimal, minimizing the system cost while maximizing service utility. Simulations for diverse scenarios demonstrate that our algorithm achieves high QoS while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

The advancement in the communication and services demand for better QoS, but in parallel the increase in network traffic demands for an effective and significant traffic controlling model to manage high network traffic and also reduce congestion. In case of MANET circumstances it is further difficult to control and manage due to high mobilizing nodes and limited resource restrictions. Even MANET’s dynamic mobility creates high maintenance overhead for effective communication. In the past most of the designed routing protocols utilize reactive routing mechanism, which are primary source driven. In case of a link failure and data failure it has to update the source node and source node only have to decide next course of action. This will generates an additional overhead in the network and may reduce throughput of the protocols. This paper aims to propose a novel Traffic Controlling Model (TCM) to efficiently manage the routing overhead in MANET. The TCM will handle heavy traffic, which is the main cause of network congestion and overhead. It first determines the network traffic and evaluates the probable Node Link Life (NLL) to recommend the TCM process. The analysis of the results generated through the experiment evaluation shows that the data routing has improved and substantiates the effectiveness of the proposal in compared to the existing routing approaches.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This paper presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

The technology of networking allows IoT devices to connect to other cloud-based devices, applications and services. To provide safe and dependable communication across diverse devices, the Internet relies on defined protocols. Standard protocols describe rules and formats which devices employ in network building and management and in data transmission. Networks are created as a technology “stack.” At the bottom of the pile lies a technology like Bluetooth LE. While other technologies, like IPv6 (the logical device for network traffic adjustment and routing), are more up to the stack. Applications which operate above these layers, such as queuing technologies, utilize technology at the top of the stack. The IoT is supposed to overrun the planet. The Internet of Things. Efficient assistance for their modeling is needed to investigate and comprehend the emerging conduct of linked objects. At the center of IoT are flexible and adaptive link patterns between objects, which may naturally be modeled on rudimentary channel-based coordination, the connection failure likelihoods, performance and waiting time, as well as the consumption of resources. The latter is particularly significant because the power and computing budgets of the items are drastically reduced.

Routing protocol for Low Power Lossy Network (RPL) is a proactive network layer routing protocol. It is suitable for Internet of Things (IoT). It is also a source routing protocol. It works by creating a tree based network topology called Directed Acyclic Graph (DAG). Based on the position of the nodes from the root, rank value is allocated for each node. Rank is a unique value given to all the nodes in the DAG. It increases in the downward direction from the root. This value is used for prevention of loops, formation and maintenance of topology. Certain malignant nodes use this rank value and introduce a false routing by forcing the neighbors to route the packets through them. The consequences of this attack are more destructive in nature. It decreases throughput and increases delay in communication. Since the devices in IoT are resources constrained, there is an urge to address rank attack immediately. In this paper, a thorough review on the effects of rank attack and its countermeasures are presented.

Network Utility Maximization (NUM) studies the problems of allocating traffic rates to network users in order to maximize the users' total utility subject to network resource constraints. In this paper, we propose a new NUM framework, Learning-NUM, where the users' utility functions are unknown apriori and the utility function values of the traffic rates can be observed only after the corresponding traffic is delivered to the destination, which means that the utility feedback experiences \textit{queueing delay}. The goal is to design a policy that gradually learns the utility functions and makes rate allocation and network scheduling/routing decisions so as to maximize the total utility obtained over a finite time horizon $T$. In addition to unknown utility functions and stochastic constraints, a central challenge of our problem lies in the queueing delay of the observations, which may be unbounded and depends on the decisions of the policy. We first show that the expected total utility obtained by the best dynamic policy is upper bounded by the solution to a static optimization problem. Without the presence of feedback delay, we design an algorithm based on the ideas of gradient estimation and Max-Weight scheduling. To handle the feedback delay, we embed the algorithm in a parallel-instance paradigm to form a policy that achieves $\tilde{O}(T^{3/4})$-regret, i.e., the difference between the expected utility obtained by the best dynamic policy and our policy is in $\tilde{O}(T^{3/4})$. Finally, to demonstrate the practical applicability of the Learning-NUM framework, we apply it to three application scenarios including database query, job scheduling and video streaming. We further conduct simulations on the job scheduling application to evaluate the empirical performance of our policy.

This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO), Butterfly Optimization Algorithm (BOA), Spotted Hyena Optimizer (SHO), Crow search algorithm(CSA), Whale optimization algorithm(WOA), Red Deer Algorithm (RDA), Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA) and Grey wolf optimization (GWO). Here, a Quad–fold review strategy comprised of planning, shortlisting, extraction, and execution have been adhered to compile this meta-analysis. The mathematical models and working principles of these techniques have been briefly elucidated. The variants of these meta-heuristic techniques have also been explored and presented. The research trend of these metaheuristic methods has also been highlighted. The findings indicate that these methods are widely used to solve different problems viz: image segmentation, optimal power flow, air pollution forecasting, drug design, wireless sensor networks, disease diagnosis, transport, and routing. Furthermore, in the healthcare sector, the use of SI techniques in selecting optimal features for diagnosis of different diseases like Cancer, Alzheimer's, Kidney disease, Anemia, Viral infection, Skin diseases have also been highlighted. Moreover, it is observed that the education-related optimization problems have been deeply explored by these meta-heuristic techniques whereas, weather forecasting is recognized as the least explored area. The binary, chaotic, and hybrid variants of EPC, HHO, BOA, SHO, CSA, WOA RDA, ALO, DA, and GWO of these metaheuristics techniques need to be deeply explored in healthcare for skin diseases, ophthalmology, viral infection, allergy along with distinct mental disorders. Finally, for better performance, the exploitation and exploration phases of these methods need to be carefully balanced.

Wireless networks are considered the best utilized network and in particular Mobile Ad-hoc NETworks (MANETs) have found many applications for its data transmission in real time applications. The accompanying challenges restrict the current protocols for the routing being specifically used in MANETs. The design issues in this network are to restrict the usage of energy while transmitting information and provide security to the nodes. In the existing routing protocols, route comes from a source where the source node seeks a way to the target node, by transmitting messages to its adjacent nodes, and the nodes use energy to do this. Mobile Ad-hoc network faces challenges in improving the energy efficiency and providing security for the routing protocol. This research work proposes the Enhanced Energy Efficient-Secure Routing (EEE-SR) protocol as a novel security algorithm to access secure data in hostile environment. In the proposed work, to secure data transmission in the network, the nodes associates with security policy. Under this policy, both the nodes have a common enforcing policy, and then they communicate with each other. The selection of the nodes for the transmission has to prove its authentication and then selection of the routing path built on the available energy threshold at the nodes in the set-up for forwarding packets in the network. The routing protocol implements the best shortest path in network and also checks for the nodes trustworthiness by evaluating the trust policies enforcement on it. The protocol enables a node to autonomously decide whether to select the neighboring node or based on the available energy threshold and security policy enforced. The algorithm evaluates increase network lifetime and low in the energy consumption, packet loss and end-to-end delay.

The selfish routing of network flow often considers a single objective, namely travel time or travel distance, and optimisation models are often guided by the principle of user equilibrium (UE). A more challenging approach is to consider multiple objectives simultaneously, as for example distance, travel time and pollution. In this paper we present a bi-criteria problem whereby the road users selfish objective of minimising their travel time is at odds with the objective of minimising the overall fuel consumption of all road users. The approach taken is to manipulate “free” parameters, namely speed limits, in an attempt to coerce the road users into behaviour which helps the latter objective. Motivated by the work done on the Price of Anarchy (PoA) into classifying the suboptimality of equilibrium with respect to the minimum total travel time we look to classify the equilibrium solutions with respect to a weighted sum model of the minimum total travel time and overall fuel consumption. Our results show that small changes to these “free” parameters can result in solutions which Pareto dominate other solutions. Whilst our measure for the suboptimality of equilibrium solutions can highlight the inefficiency of a network configuration itself, it does not allow insight into the overall quality of the solution when compared with other network configurations.

Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method: We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model’s predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions: The comparison of the empirical data to the reliability model’s predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

Gateways in the Internet of Things (IoT) play a decisive role in routing the preprocessed filtered data to cloud platforms. While investigating gateways, the need for a comprehensive systematic literature review that can elaborate the working and functionalities of IoT gateways was felt. This paper presents a systematic literature review of IoT gateways in general and smart gateways in particular. It considered papers published over the last 10 years, i.e., from 2011 till July 2021. A methodical literature analysis technique is used in this review; out of 2347 papers, 67 articles are selected for complete analysis based on well-defined criteria. The survey starts with the broad categorization of gateways in IoT as basic and smart gateways. Further, smart gateways are sub-divided into three categories as passive gateways, semi-automated and fully-automated gateways. The survey is performed based on well-defined criteria, which include: the type of gateways, their requirements, tools/platforms, the approach adopted, evaluation and their application domain. This paper demonstrates further bifurcation of functional requirements of IoT gateways. Research gaps and open issues have been identified in the area. Future prospects have accordingly being suggested to help academicians embark on advanced research.

Recent technological breakthroughs have allowed unmanned aerial vehicles (UAVs) to be utilized in a broad range of new operations. Among these various applications, herein, we focus on the use of UAVs for search and rescue missions in emergency and postdisaster scenarios. In this context, self-charging technologies for drones create new challenges in the routing of UAVs with charging stations. We present a variant of the vehicle routing problem (VRP) to address the integrated use of UAVs and mobile charging stations and define the VRP with synchronized networks (VRPSN), a new class of VRPs involving the routing of UAVs whose recharge platforms can travel to different locations during an operation. This leads to two networks within the VRP that must be integrated and synchronized. This research develops a mixed-integer linear program model for the VRPSN that considers the use of UAVs and mobile charging stations in a synchronized manner. To overcome the computational limits of the MILP model, this research presents a construct-and-adjust heuristic method integrated with a genetic algorithm. As a numerical example, we test the proposed model on the Corrego do Feijao Mine located in Minas Gerais, Brazil, where a dam recently collapsed, killing many workers. Numerical tests show that the new methodology is an attractive planning method for providing efficient and rapid responses in search and rescue missions.

Power routing (PR) provides a good choice to improve the modular converter reliability by equalizing the lifetime among multi-cells. The existing PR strategies just equalize the lifetime of power semiconductor devices (PSDs) by routing lighter loads for aged cells. However, the neglect of capacitor may mislead the multi-cell lifetime convergence. Besides, the light load allocation of the aging cell does not always mean the lifetime extension considering topologies and operation principles. Hence, this paper proposed a capacitor lifetime-based PR for input-series-output-parallel (ISOP) connected dual active bridge (DAB) converter. The proposed strategy can achieve the same accuracy as existing PR strategies, greatly reducing computation time and the complexity of the hardware circuit. Moreover, the non-monotonic relationship between the damage growth rate and load of each cell is revealed and a damage growth rate constraint is developed to ensure the PR feasibility. Simulation is performed to verify the validity of the proposed strategy and constraints. Finally, a scaled down prototype has been developed to verify the PR feasibility and obtain the non-monotonic relationship, verifying the necessity of proposed constraints. In addition, the effect of PR on the thermal stress and cumulative damage of capacitors is verified by a temperature test.

Wireless Sensor Network (WSN) has the characteristics limited computing, memory and energy, so a routing protocol that supports WSN network performance is needed. Routing protocols based Ant Colony Optimization (ACO) are very suitable for use in WSN for routing data packets. In this report, we will try out several kinds of parameter values that bear on the pheromone evaporation (ρ) on the ACO algorithms applied to the WSN routing protocol with grid topology on the number of nodes variables in the WSN network. Where this can increase the number of delivery packets on a busy WSN network and each node transmits a packet continuously. From the results, the value of ρ 0.75 makes WSN performance on the grid topology with the number of nodes 12 and 30 very good in terms of the delivery packet, but with the number of nodes 70 the value of ρ 0.70 makes WSN performance better than the delivery packet side.

Routing technologies have long been available in many automobiles and smart phones, but the nearly random nature of traffic on road networks has always encouraged further efforts to improve the reliability of navigation systems. Given the networks' uncertainty, an adaptive dynamic route selection model based on reinforcement learning is proposed. In the proposed method, the Markov decision process (MDP) is used to train simulated agents in a network so that they are able to make independent decisions under random conditions and, accordingly, determine the set of routes with the shortest travel time. The aim of the research was to integrate the MDP with a multi-nomial logit model (a widely used stochastic discrete-choice model) to improve finding the stochastic shortest path by computing the probability of selecting an arc from several interconnected arcs based on observations made at the arc location. The proposed model, tested with real data from part of the road network in Isfahan, Iran, and the results obtained demonstrated its good performance under 100 randomly applied stochastic scenarios.

In wireless sensor networks, sensor nodes are deployed to collect data, perform calculations, and forward information to either other nodes or sink nodes. Recently, geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very efficient. However, the local minimum phenomenon, which hinders greedy forwarding, is a major problem in geographic routing. This phenomenon is attributed to an area called a hole that lacks active sensors, which either prevents the packet from being forwarded to a destination node or produces a long detour path. In order to solve the hole problem, mechanisms to detect holes and determine landmark nodes have been proposed. Following the proposed mechanisms, landmark-based routing was developed in which the source node first sends a packet to the landmark node, and the landmark node then sends the packet to the destination. However, this approach often creates a constant node sequence, causing nodes that perform routing tasks to quickly run out of energy thus producing larger holes. In this paper, a new approach is proposed in which two virtual ellipses are created with the source, landmark, and destination nodes. Forwarding is then guided along virtual ellipses. Furthermore, a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple landmarks. Thus, the proposed approach improves both geographic routing and energy efficiency routing. Simulation experiments show that the proposed approach increases the battery life of sensor nodes, lowers the end-to-end delay, and generates a short path.

The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

The Mobile Edge Computing (MEC) mitigates the bandwidth limitation between the edge server and the remote cloud by directly processing the large amount of data locally generated by the network of the internet of things (IoT) at the edge. To reduce redundant data transmission, this paper proposes a data collection scheme that only gathers the necessary data from IoT devices along a trajectory. Instead of using and transmitting location information (to preserve location anonymity), a virtual coordinate system called "distance vector of hops to anchors" (DV-Hop) is used. The proposed trajectory encoding algorithm uses ellipse and hyperbola constraints to encode the position of interest (POI) and the trajectory route to the POI. Sensors make routing decisions only based on the geometric constraints and the DV-Hop information, both of which are stored in their memory. The proposed DV-Hop updating algorithm enables the users to collect data in an IoT network with mobile nodes. The experiments show that in heterogeneous IoT networks, the proposed data collection scheme outperforms two other state-of-the-art topology-based routing protocols, called ring routing, and nested ring. The results also show that the proposed scheme has better latency, reliability, coverage, energy usage, and provide location privacy compared to state-of-the-art-schemes.

This article addresses the dynamic vehicle routing problem (DVRP). DVRP is a challenging variation of the classic vehicle routing problem in which some customers are not known in advance. The objective is to incorporate new customers into the schedule as they become known while still attempting to minimize the cost of serving all customers without violating the problem constraints. This work proposes an effective population-based approach that integrates various algorithmic components to address DVRP. The approach combines a local search algorithm with various evolutionary operators (crossover and mutation) in an adaptive manner. To promote diversity, the proposed approach utilizes a population of solutions and uses a quality-and-diversity strategy to retain only promising solutions. The well-known 21 DVRP benchmark instances are utilized to test the performance of the proposed approach. An experimental comparison is carried out to assess the contribution of the integrated components. Results demonstrate that the integrated components significantly improve search performance. It is also shown that the proposed approach produces new best results for several instances when compared with the best methods reported in the literature.

Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing did not take into account how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.

We introduce and study a class of optimization problems we call replenishment problems with fixed turnover times: a very natural model that has received little attention in the literature. Clients with capacity for storing a certain commodity are located at various places; at each client the commodity depletes within a certain time, the turnover time, which is constant but can vary between locations. Clients should never run empty. The natural feature that makes this problem interesting is that we may schedule a replenishment (well) before a client becomes empty, but then the next replenishment will be due earlier also. This added workload needs to be balanced against the cost of routing vehicles to do the replenishments. In this paper, we focus on the aspect of minimizing routing costs. However, the framework of recurring tasks, in which the next job of a task must be done within a fixed amount of time after the previous one is much more general and gives an adequate model for many practical situations. Note that our problem has an infinite time horizon. However, it can be fully characterized by a compact input, containing only the location of each client and a turnover time. This makes determining its computational complexity highly challenging and indeed it remains essentially unresolved. We study the problem for two objectives: min–avg minimizes the average tour cost and min–max minimizes the maximum tour cost over all days. For min–max we derive a logarithmic factor approximation for the problem on general metrics and a 6-approximation for the problem on trees, for which we have a proof of NP-hardness. For min–avg we present a logarithmic factor approximation on general metrics, a 2-approximation for trees, and a pseudopolynomial time algorithm for the line. Many intriguing problems remain open.

Real-time data transmission is one of the objectives of MANET (mobile ad-hoc network) to handle emergencies like a forest fire, flood, and earthquake. In this scenario, quick delivery of data is itself a challenging task for MANET and there is a possibility of load imbalance due to packet transmission in between source-destination pairs via the shortest path due to congestion or control overhead. In this paper, a novel routing protocol, called ACOLBR (ACO based load balancing routing), is design to control the congestion and balancing the load among the multiple paths in between source to destination. The similarity between the environment of Ant Colony and the MANET inspires the authors to apply ACO (Ant colony optimization) technique during routing in MANET to control congestion and balance the load in the network. In our proposal, two colonies of ants (red/blue) carry their packets based on the network condition. A decision variable is designed to select red/blue ant for transferring packets based on different network parameters such as bandwidth, energy, mobility, and distance. The selection of red ant means the route where the concentration of red pheromone is maximum and reverse is true for blue ant. This protocol is also concerns about the link failure during packet transmission in a route. Simulation results using OMNET++ show that ACOLBR outperforms ARA, ANTHOCNET, FACO, AODV, DSDV, DSR, CA-ARTT, and MOAODV in terms of load balancing efficiently in the route for data transmission in between source to destination.

As interconnects dominate circuit performance in modern FPGAs, placement becomes a crucial stage for timing closure. Traditional FPGA placers seldom consider the timing constraints, and thus may lead to illegal routing solutions. In this paper, we present an incremental timing-driven placement flow for advanced FPGAs. First, a timing-based global placement strategy is designed to guide heterogeneous blocks to desired locations with satisfied timing constraints. Then, a timing-aware packing algorithm is developed to mitigate the design complexity while improving the timing results. Finally, we propose a critical path-based optimization method to generate optimized layout without timing violations. We evaluate our algorithm based on industrial circuits using an advanced FPGA device. The experimental results show that our placer achieves an 5.1% improvement in worst slack, and produces placements that require 16.7% less time to route when compared with the leading commercial tool Xilinx Vivado.

The multiplicative weight update (MWU) schema is a powerful tool to design online routing algorithms. However, even the latest research still employs or incorporates the inefficient online single-path routing (OSR) algorithm. In this article, we design an online multipath routing (OMR) algorithm via the MWU schema in order to exploit the network path diversity. Compared to OSR and comparative multipath solutions, OMR has the same worst-case performance guarantee in theory and also has significant performance advantage in practice.

Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art deep learning based approaches. The learned policies are more effective than the traditional hand-crafted ones, and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions and even real-world dataset., Comment: 10 pages, 4 figures

Wireless Sensor Networks (WSNs) emerged as major data gathering paradigm due to its wide variety of applications. Achieving energy efficient reliable data delivery is an important concern in WSNs. In a network, packet loss reduces the reliable data transmission rate. Retransmissions are required to increase the reliable data transmission rate. However, number of retransmissions also increase the network energy consumptions and packet delivery delay. Packet loss rate can be reduced by selecting a best successor node in a routing path. In this work, a Learning Automata based Routing mechanism is proposed for wireless sensor networks to achieve energy efficiency and reliable data delivery. In this paper, learning automata has been adopted to calculate next node’s selection probability in a routing path using node’s score, quality of link and previous selection probability. Further, an energy efficient reliable routing mechanism is proposed using combination of learning automata and A-star search algorithm. The proposed mechanism determines energy efficient and reliable routing paths in WSNs by favoring highest residual energy, good quality of link , more free buffer space and less distance. Finally, simulation results indicate that the proposed algorithm reduces the energy consumptions, data delivery delay, data transmissions and increases the network lifetime.

Providing high-speed Internet services using satellite networks have attracted researchers from both academia and industry mainly due to the characteristics of Low Earth Orbit (LEO) satellite networks such as global coverage, scalability, and lower transmission delays. Implementation of Software-Defined Networking (SDN) in Non-Terrestrial Networks (NTNs) can help to achieve the goals set for 5G and beyond networks. Since satellite networks have a specific architecture, some of the legacy protocols no longer remain useful. Therefore, to satisfy the diverse Quality of Service (QoS) requirements for a variety of applications, we propose a novel and centralized QoS-aware routing algorithm, called fybrrLink in which the global view of the network in SDN is utilized. We implement a modified Bresenham’s algorithm and Dijkstra’s algorithm to find the optimal path in a significantly reduced computation time. Also, taking advantage of the deterministic satellite constellation, we propose a flow rule transfer algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated with multiple NS3 simulations, and results confirm that our approach outperforms other state-of-the-art algorithms.

The design of large devices suggests fundamental and efficient innovation in Field Programmable Gate Array (FPGA) interconnect structure to improve power consumption, density and performance. Mesh-based FPGA architectures are generally designed to maximize logic utilization and to ensure layout scalability whereas Tree-based FPGA architectures are designed to increase interconnect utilization. In this paper, we propose a new Mesh of Trees (MoTs) FPGA architecture where we try to benefit from Mesh of Clusters (MoCs) FPGA layout scalability and Tree-based FPGA efficient intra-cluster interconnect utilization thanks to Butterfly Fat Tree (BFT) topology. In order to attain the efficient use of the proposed routing interconnect resources, we develop Computer-Aided Design (CAD) tools that respond to the target MoTs FPGA features. In addition, we define the power, area and delay metric models according to the proposed architecture criteria. Experimentation results show that MoTs architecture with a cluster arity 32 offers the best trade-off between power, area and delay. Furthermore, we conduct a comparison between MoTs and MoCs FPGAs in general and the best MoTs and MoCs solutions which ensure the best trade-off between power, area and delay in particular. Results show that the best MoTs architecture reduces power, area, delay and energy by an average of 29%, 6%, 40% and 47%, respectively in comparison with the best MoCs architecture.

Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to find the best routes with minimum cost for a number of vehicles serving a number of scattered customers under some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routing problem, metaheuristic optimization algorithms are widely used for tackling this type of challenge. Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm that mimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitated vehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to preserve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, two sets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMP data set which has 27 instances with different models. Moreover, the results achieved by modified CHIO are compared against the results of other 13 well-regarded algorithms. For the first data set, the modified CHIO is able to gain the same results as the other comparative methods in two out of ten instances and acceptable results in the rest. For the second and the more complicated data sets, the modified CHIO is able to achieve very competitive results and ranked the first for 8 instances out of 27. In a nutshell, the modified CHIO is able to efficiently solve the capacitated vehicle routing problem and can be utilized for other routing problems in the future such as multiple travelling salesman problem.

Power networks are an important infrastructure that requires close monitoring and recurring assessment during and following a catastrophic event. The drone’s unique aerial and unmanned nature has made it an efficient and powerful tool for damage assessment of the power networks. In this article, we propose a drone-routing framework to enable the systematic and automatic assessment of power networks considering wireless charging of the drone during the scanning. This article incorporates the resilience-oriented line priority index to periodically prioritize the power lines based on their specifications and conditions, and by doing so, this improves the efficiency of the assessment. A multiobjective mixed-integer linear programming model is developed to dynamically determine the drone’s optimal routing and speed in order to assure that the drone gathers sufficient data while completing the assessment mission in the shortest period of time. Since the proposed optimization algorithm needs to be solved multiple times for different sections of the power network, we propose a set of solution algorithms to reduce the computational burden and allow the proposed algorithm to reach the optimal solution quickly. Simulation results on a power network consisting of 77 nodes and 73 lines illustrate the effectiveness of the proposed approach in performing network-wide dynamic assessment using a drone.

The net orders in detailed routing are crucial to routing closure, especially in most modern routers following the sequential routing manner with the rip-up and reroute scheme. In advanced technology nodes, detailed routing has to deal with complicated design rules and large problem sizes, making its performance more sensitive to the order of nets to be routed. In literature, the net orders are mostly determined by simple heuristic rules tuned for specific benchmarks. In this work, we propose an asynchronous reinforcement learning (RL) framework to automatically search for optimal ordering strategies and a transfer learning (TL) algorithm to improve performance. By asynchronous querying, the router, pre-training the RL agents, and finetuning with the TL algorithm, we can generate high-performance routing sequences to achieve a 26% reduction in the DRC violations and a 1.2% reduction in the total costs compared with the state-of-the-art detailed router.

In this paper we study time-dependent scheduling problems where activities consume a resource with limited availability. Activity durations as well as resource consumptions are assumed to be time-dependent and the resource can be replenished between activities. Because of the interaction of time-dependent activity durations and resource consumptions, scheduling policies based on starting all activities as early as possible may fail due to unnecessarily high resource consumptions. We propose a dynamic discretization discovery algorithm that generates a partially time-expanded network during the search. We propose preloading techniques allowing to significantly reduce the computational effort if the approach is embedded in an iterative solution procedure that frequently evaluates activity sequences that start with the same activities. We evaluate our approaches on a case of routing a fleet of electric vehicles in which vehicles can recharge batteries during the route., 40 pages

This study introduces "shortcut routing," a novel routing mechanism in capsule networks that addresses computational inefficiencies by directly activating global capsules from local capsules, eliminating intermediate layers. An attention-based approach with fuzzy coefficients is also explored for improved efficiency. Experimental results on Mnist, smallnorb, and affNist datasets show comparable classification performance, achieving accuracies of 99.52%, 93.91%, and 89.02% respectively. The proposed fuzzy-based and attention-based routing methods significantly reduce the number of calculations by 1.42 and 2.5 times compared to EM routing, highlighting their computational advantages in capsule networks. These findings contribute to the advancement of efficient and accurate hierarchical pattern representation models., 8 pages, published at IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E104.A(8)

Modern Internet of Things (IoT) applications are heavily data driven and often require reliable data streams to achieve high-quality data mining. The concept of edge computing is introduced to reduce data latency and communication bandwidth between the cloud server and IoT edge devices. However, inefficient routing that may cause transmission failure or unnecessary data (re)transmission is still a key obstacle to obtain good and reliable data mining results. In this paper, network coding combined with opportunistic routing is used to improve energy efficiency in wireless IoT infrastructure, considering the existence of link correlation. Studies have shown that packet receptions on wireless links are correlated, which is completely contrary to the assumption of link independence used in existing routing mechanisms. This assumption causes estimation errors in the calculation of expected number of transmissions for forwarders, which further affects the selection of forwarder set, and ultimately affects the performance of the protocol. We propose an intra-session network coding mechanism based on the mining of link correlation. A novel smart routing method is proposed to accurately estimate the number of transmissions required by forwarders, together with an algorithm for selecting a forwarder set with more optimal number of transmissions. Simulation results demonstrate that the proposed mechanism can achieve fewer transmissions and offer more energy efficient communications for wireless edge IoT applications.

The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this paper we propose a novel genetic programming method that removes phenotypic duplicates from the source individuals to initialise the target individuals. Furthermore, assuming that the transferred knowledge used in initialisation already includes all the important knowledge explored for the source problem, it is more effective to explore new regions that have not been explored for the source problem. Therefore, we propose novel genetic operators that prohibit the search from revisiting the source individuals when solving the target problem. To speed up the revisit check, we propose to adapt a powerful hashing method for routing policies that greatly improves the efficiency of the genetic operators. Our experimental results show that the proposed method significantly outperforms the existing genetic programming approaches with knowledge transfer in terms of both initial and final solution quality.

It is necessary for cost consideration to minimize the number of the used layers in a PCB design. Traditionally, the number of the used layers outside components in bus assignment depends on the locations of the bus pins inside components in escape routing in a PCB design. In this paper, the concept of introducing the flexible escape directions in escape routing is considered in the bus assignment outside components in a PCB design. Clearly, the flexible consideration of the escape directions inside components can lead to the reduction on the number of the used layers in the bus assignment outside components. Given a set of buses on a set of components in a PCB design, based on the introduction of the flexible escape directions in escape routing and the construction of the possible bus connections in bus assignment, the two upper bounds of the layer numbers inside and outside components can be firstly computed. By eliminating the redundant bus connections for the given buses, an integrated algorithm can be further proposed to minimize the number of the used layers in a PCB design. Based on the assignment constraints from the intersection relations inside components, the physical connections of the given buses can be assigned onto a minimal set of used layers. Compared with the two-phase algorithm using Yan’s routing algorithm in direction-constrained rectangle escape routing and Yan’s assignment algorithm in bus assignment, the experimental results show that our proposed integrated algorithm use reasonable CPU time to reduce 32.1% of the layer number for 10 tested examples in a PCB design on the average.

We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm., arXiv admin note: text overlap with arXiv:1810.01443

Summarization: This paper presents a Cumulative Unmanned Aerial Vehicle Routing Problem (CUAVRP) approach to optimize Humanitarian Coverage Path Planning (HCPP). Coverage path planning consists of finding the route which covers every point of a certain area of interest. This paper considers a Search & Rescue mission, using a homogeneous fleet of Unmanned Aerial Vehicles (UAVs). In this scenario, the objective is to minimize the sum of arrival times at all points of the area of interest, thus, completing the search with minimum latency. The HCPP problem is transformed into a Vehicle Routing Problem by using an approximate cellular decomposition technique to discretize the area into a grid, where the rectangles represent the UAV sensor’s field of view. The center points of the formed rectangles, become the nodes used for a UAV routing problem. This approach uses the objective of minimizing the sum of arrival times at customers, found in the Cumulative Capacitated Vehicle Routing Problem (CCVRP), adjusted for the Search & Rescue Coverage Path Planning using UAVs. The Min-max objective is also implemented and tested. Three versions of a Parallel Weighted Greedy Randomized Adaptive Search Procedure - Variable Neighborhood Decent (GRASP-VND) algorithm is implemented to solve the Cumulative UAV Routing Problem for Humanitarian Coverage Path Planning. Presented on: European Journal of Operational Research

The rake movements in rail freight transportation are executed by dynamically allocating empty rakes to observed demands. This work proposes an alternate cycle planning strategy based on the average demand data. The system is modeled as the Pickup and Delivery Problem with customer specified origin and destination and multiple full truckload demands by each customer. Initially, an Exact Formulation of the problem that gives optimal cycles is presented. A modified Order First Split Second Heuristic (OFSS) is proposed to solve the deterministic version of this problem where a giant tour is constructed by assigning a value to each node based on the greedy path from that location. The splitting algorithm forms multiple cycles satisfying maximum cycle time constraint from the Giant Tour. A heuristic solution obtained by splitting optimal giant tour is also determined to study the performance of proposed heuristic. Considering the possibility of deviation of actual demands from average, an adaptive algorithm that modifies the planned cycles to accommodate the changes in demand is presented. Computations show that the heuristic gives near-optimal solution in terms of net revenue and the number of vehicles required. Testing this algorithm on dynamic demands show that the initial cycle plan can be followed with minor modification if the Degree of Dynamism of the system is small. The proposed algorithm is tested on Indian freight rail data and the improvements in rake utilization are noted.

Scheduling the recharging of electric vehicle fleets under different scenarios is an important but open problem. One important scenario is that vehicles travel at different speeds in different periods since traffic congestion is common in urban areas nowadays. Therefore, in this article, a novel time-dependent electric vehicle routing problem with congestion tolls is proposed. If a vehicle enters a peak period, a fixed congestion toll needs to be paid in this problem. A mixed integer linear programming model is established and an adaptive large neighborhood search (ALNS) heuristic is designed to solve the model. The model and solving method are validated and evaluated extensively with benchmark instances. Results indicate that a certain level of congestion tolls could prevent vehicles from entering peak periods and relieve road congestions significantly. Furthermore, the ALNS heuristic could provide much better solutions for the problem than typical optimization software, such as Gurobi, in much shorter running time.