Conditional Handover (CHO) by the 3rd Generation Partnership Project (3GPP) enables efficient user mobility between Base Stations (BSs) by preselecting and preparing Target BSs (T-BSs). However, CHO relies on signal strength for T-BS selection, leading to resource blocking on multiple T-BSs due to signal fluctuations. Existing state-of-the-art methods use deep learning to narrow the list of T-BSs but still lack an effective method for resource reservation timing. This paper presents in-time CHO (iCHO) which exploits historical mobility data to estimate user dwell time at the current BS to reduce resource reservation duration. The proposed iCHO employs a Multivariate Multi-output Single-step Prediction (MMSP) model that leverages a multi-task learning approach to simultaneously predict the minimal list of required T-BSs together with the user dwell time. The model demonstrates remarkable performance across two mobility datasets of different scales, achieving T-BS prediction accuracies of 98% and 95%. It also ensures a 100% handover success rate with a minimum of three and four predicted T-BSs for both datasets, respectively, significantly limiting the list of T-BSs. Moreover, the MMSP model achieves a Mean Absolute Error (MAE) of 19 s and 45 s when predicting the user’s dwell time at the current BS. By utilizing these predictions, iCHO reserves resources at the minimum number of T-BSs immediately before handover. Thus, iCHO can save up to 99% of resources from blockage as compared to the CHO, enabling operators to increase revenue by serving up to eighteen more users with the saved resources.

The mobile network traffic patterns in urban areas significantly diverge depending on commercial and residential establishments. These regional traffic patterns provide crucial clues for predicting traffic patterns precisely. Previous studies have employed a combination of time-series and convolutional Deep Learning (DL) models to effectively capture the correlation of the regional features and traffic patterns. Despite promising results, these approaches are limited in identifying pattern similarities among sparsely located regions and can be further improved. To this end, this study proposes a GEospatial clustering and residual COnvolutional temporal long Short-term memory (GECOS) framework consisting of clustering and DL components. The proposed Urbanflow Peak Clustering (UPC) component exploits the peak traffic times of daily mobile data to obtain the groups of cells with similar traffic patterns apart from their geographical diversity. The UPC improves the scalability of existing algorithms and enables DL components to improve their accuracy by recognizing unique regional patterns and localizing the training targets. The proposed Residual Convolutional TCN-LSTM (RCTL) serves as the DL component of GECOS that improves TCN-LSTM structure through layer-wise feature transfer and enhances long-term dependency learnability. The RCTL ensures more accurate capturing of extensive spatiotemporal features through structural enhancements. The experiments conducted on real-world mobile traffic data showcase 43% improvement by GECOS compared to state-of-the-art models, enabling precise traffic engineering policies by operators.

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

Data aggregation is an important approach in IoT sensor networks since it reduces data transfer while also preserving energy and bandwidth. This research investigates the challenge of time-efficient data aggregation in wireless sensor networks, which is critical in military, civilian, and industrial applications. Effective data aggregation algorithm design and optimization are required for quick and interference-free data collection. Machine learning has received attention for outperforming classical heuristic techniques. The research presents the first Graph Neural Network (GNN) model for data aggregation in IoT sensor networks, which incorporates Graph Attention Networks (GATs) and fully connected layers. The GNN-based model learns network topology and node attributes, creating node embeddings and correcting sensor node transmitting time slots. With a centralized training procedure and adapted execution for network size change, the proposed approach achieves satisfactory performance compared to the heuristic algorithm.

Fast data aggregation is crucial for facilitating critical Internet of Things services as it enables the collection of sensory data within strict volume and time constraints. Over the past decades, the data aggregation scheduling problem for minimum latency has garnered significant research attention. Existing approaches to this problem typically schedule all data transmissions based on an aggregation tree, which is constructed without secondary interference. However, such interference can introduce delays when scheduling a transmission from a node to its parent in the tree. To this end, this study proposes an approach called Active Neighbor EXploitation (ANEX) that enables sensor nodes to switch their parents by identifying active neighbors for potential connectivity, irrespective of the receivers established in the tree. Additionally, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors, thereby allowing for more concurrent transmissions. ANEX is evaluated through theoretical analysis and extensive simulations under various scenarios. The results demonstrate that ANEX achieves up to 86% faster aggregation compared to the state-of-the-art approach while maintaining an equivalent time complexity.

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme.

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Anomaly detection is essential to guarantee the correctness of sensory data collected from Internet of Things. The latest detection approaches only operate under one of the two general forms of short-term or long-term anomaly. In this article, we propose monotone split and conquer (MSC) scheme to tackle both anomaly forms. The proposed scheme exploits the spatial–temporal correlation between neighboring sensors to detect abnormal sensory data. MSC splits the collected data into monotonic subtrends in the training phase to establish the trend-based normal profiles for the online detection phase. To eradicate the overfitting phenomenon, we further develop a general formulation to estimate the square prediction error (SPE) control limit. Both monotone split and general formulation contribute to the advancement of MSC in terms of accuracy (ACC) and false positive rate (FPR) in the online detection phase. To evaluate the performance of MSC, we design a general anomaly model to generate artificial short-term and long-term anomalies. Numerical experiments with the Intel Berkeley Research Lab (IBRL) data set demonstrate that MSC obtains about 8% higher ACC and 5% lower FPR on average when compared to existing schemes. Remarkably, MSC requires only a few observations for anomaly detection as it is applicable to real-time systems.

Internet-of-Things (IoT) systems and their applications rely on sensory data for operating context-aware functions. To ensure reliability, these data must arrive at the base station in a timely manner. Because IoT sensors are battery powered and expected to last for years, energy consumption has always been a dominant concern. The duty cycling mechanism efficiently extends the lifespan of sensor nodes; however, it increases data aggregation time. This article studies the minimum time aggregation scheduling problem in duty-cycled sensor networks. The existing solutions route the sensory data through a predefined structure, but such structures are constructed without full awareness of collisions in the wireless medium. Therefore, in this study, the collision-resistant dynamic (CORD) scheduling approach has been proposed with an aim to provide fresh data for emerging IoT applications. The proposed approach is applicable to any initial routing structure and it dynamically changes the receiver of a transmission whenever this change can reduce aggregation time. The receiver-changing decision is made on the fly, resisting all collisions between the determined transmissions. We evaluate CORD in various simulation scenarios, and the results demonstrate that the proposed approach yields a notable improvement of up to 62% from the viewpoint of aggregation time while having comparable time complexity compared to state-of-the-art methods.

Software-defined networking provides a programmable and flexible way to manage the network by separating and centralizing the control plane. The data plane entities like software-defined switches and routers use flow entries in flow tables for forwarding the packets. However, the limited switch memory restricts the number of flow entries in the flow tables. This leads to flow table overflow and flow entry reinstallation problems, which severely degrade the network performance. This requires a comprehensive policy for timely eviction of inactive flow entries to avoid overflows and optimally maintain flow tables usage. To this end, many studies have been proposed, but none of them have suggested detailed eviction strategy for UDP flows. This paper proposes a UDP flow eviction strategy which periodically updates the statistical information of UDP flows through reinforcement learning and utilizes it to evict inactive UDP flows. This eviction strategy is combined with the existing TCP flow eviction method to form an eviction system that takes into account the protocol-specific characteristics of the flow. Through three traffic-based experiments, we found that the proposed system reduces the number of overflow occurrences by 27% and flow entries reinstallation by 28%, compared to the random and FIFO policies, resulting in 15% reduction in control signaling overhead.

Current reactive mobility management in cellular networks becomes a bottleneck for ultra-low latency 5G services and severely degrades the QoS. To satisfy the ultra-low latency requirement of 5G services, proactive mobility management is essential where next PoA of the user is predicted with minimal error. Recent studies have used different deep learning algorithms for this purpose, but their results are unacceptable in real networks due to low accuracy. This paper exploits the distributional learning capability of Generative Adversarial Network (GAN) to propose MoGAN for the prediction of user’s next PoA. The generator in MoGAN uses Gated Recurrent Unit to learn the distribution of time-series data and generates the next PoA. Meanwhile, the discriminator evaluates the generated output against the real data to determine its correctness. The model is trained in adversary mode by using the output from the discriminator. The dataset utilized in training and evaluation is collected from one of the university campuses, and the results show 96.33% of prediction accuracy, which is 5% higher than the previous study. Furthermore, MoGAN is more robust under limited data conditions, as it achieves above 90% accuracy with only 50% of the dataset.

Route Optimization (RO) is a function to minimize packet transmission delay through the optimal path between routers communicating with each other. Mobile IPv6 (MIPv6) also supports the RO to solve the triangle routing problem. Basic PMIPv6 does not support the RO. Thus, many schemes have been proposed to support RO in PMIPv6. However, these schemes do not consider the out-of-sequence problem, in which packets arrive out of order, which may happen between the existing path and the newly established RO path. This paper proposes a scheme to solve the out-of-sequence problem more precisely and with low cost. Our proposed scheme solves the problem by using the packet sequence number and the time when the problem occurs. In this paper, we compare PMIPv6 supported by the RO and the Out-of-sequence Time Period (OTP) scheme with our proposed scheme via simulation. Evaluation of the performance reveals PMIPv6 supported by the RO had 66 of out-of-sequence packets, and the OTP scheme has 30. However, our proposed scheme does not incur out-of sequence packets. Our proposed scheme guarantees reliable packet transmission by preventing the problem.

Redundant mobile sensors might be moved in order to cover sensing holes or replace power-exhausted sensors. Within rugged terrains, the use of hopping sensors may be more suitable than wheeled mobile sensors. Since WSN communication is data-centric, globally unique ID allocation that is used for MANETs is usually not applicable for WSNs. A recent study classifies the locally unique ID allocation scheme for WSNs into two representative types: a proactive and a reactive scheme. In the reactive scheme, energy preservation is improved because ID conflict resolution is delayed until data communication is needed. Although a typical reactive ID assignment scheme is used, local uniqueness cannot be guaranteed if hopping sensors are relocated. In order to overcome the weakness, we propose the recycled ID assignment scheme for relocation of hopping sensors. Simulation results show that the proposed recycled ID assignment scheme outperforms the typical reactive scheme for relocation of hopping sensors.

Mobile Access Gateways and Local Mobility Anchors exchange mobility related signaling messages on behalf of Mobile Nodes (MNs) to support mobility in Proxy Mobile IPv6 (PMIPv6) networks. Thus, MNs maintain their connections whilst roaming network domains without the need of a mobility protocol stack. However, with the large number of MNs in a PMIPv6 domain, the loads on the network entities grow. In particular, LMA has higher probability of reaching a severe load level, since LMA handles every packet that all the MNs in the domain send and receive. Thus, this paper proposes a load balancing scheme for LMAs in PMIPv6 networks where more than one LMA exist in the domain. The proposed scheme distributes loads of LMAs evenly, therefore enhances the overall efficiency of the network. Simulation shows the proposed scheme effectively balances the loads of LMA and reduces total packet loss rate by 3%.

This paper presents the 'Poor Man's SIMO System' (PMSS) which combines two ideas, cooperative communication and diversity combination, to reduce packet losses over links in Wireless Sensor Networks (WSN). The work is based on the IEEE 802.15.4 standard and is distinct from previous works that apply the same concepts because it foregoes the need for any changes to mote hardware. We describe a Poor Man's SIMO System protocol that governs the cooperation between receivers. Three diversity combination methods are evaluated including selection diversity, equal gain and maximal ratio combining. The latter relies on a model of the instantaneous Bit Error Rate (BER) driven by Channel State Information (CSI), i.e. Received Signal Strength Indication (RSSI) and Link Quality Indication (LQI). First, we demonstrate the PMSS on residual bit error traces in a fully reproducible manner. This is followed by an implementation of PMSS in C# on the .NET Micro Framework edition of the recently released Imote2 WSN mote platform. Both, trace based analysis and implementation demonstrate significant improvements over the single receiver baseline configuration. We deliberately verified PMSS by residual bit error traces and implementation to avoid the use of simulators that depend on abstract models of wireless channels.

When sensors in wireless sensor networks fail or become energy-exhausted, redundant mobile sensors might be moved to cover the sensing holes created by the failed sensors. Within rugged terrains where wheeled sensors are unsuitable, other types of mobile sensors, such as hopping sensors, are needed. In this paper, we address the problem of relocating hopping sensors to the sensing holes. Recent study for this problem considered moving sensors along the shortest path. The shortest path might be used repeatedly and therefore create other sensing holes. In order to overcome these weak nesses, we propose multipath-based schemes considering the balanced assignment for the relocation of hopping sensors. Simulation results show that the proposed schemes guarantee a more balanced migration distribution of efficient sensors and a higher movement success ratio of required sensors than those of the shortest path-based schemes.

Existing routing schemes assume an ideal environment where any form of transmission always succeeds. However, in realistic wireless sensor networks environments, where wireless links are extremely unstable, this causes numerous retransmissions. Much research has recently focused on routing schemes that guarantee energy-efficient and reliable data transmission. In circumstances without standard routing schemes in wireless sensor networks, however, it is neither practical nor efficient to propose a new routing scheme. To guarantee reliable data transmission in general way, in this paper, we propose Delay-based Reliable Data Transmission (DRDT), a modular approach that offers expansion to existing routing schemes. DRDT offers reliability in transmission through cooperative retransmission by a helper node that overheard the data packet, when the data reception of a receiver node fails due to an unstable link. The helper node is dynamically selected based on the distributed method that uses a delay when the neighbor nodes of the receiver node overhearing the data packet. DRDT reduces the number of retransmissions by considering the link quality with the receiver node. Simulation shows that DRDT improves delivery cost by up to approximately 45% compared to existing schemes.

Mobile IP is the basic solution to providing host mobility, whereas network mobility (NEMO) refers to the concept of the collective mobility of a set of nodes. The NEMO basic support protocol has been proposed in IETF as a first solution to the problem of network mobility. The main limitation of this basic solution is that it forces triangular routing, i.e., packets are always forwarded through the home agent (HA), following a suboptimal path. This is because each sub-NEMO obtains a care of address (CoA) that belongs to the home prefix of its parent mobile router. Such a CoA is not topologically meaningful in the current location, since the parent mobile router could also be away from home, and hence, packets addressed to the CoA are forwarded through the HA of the parent NEMO. To solve this problem, various extended proposals, with differing approaches and goals, exist for route optimization (RO) in NEMO applications. Their influences on the RO performance have been evaluated by classifying the detailed operations performed within the nested NEMO network, and then each category is analyzed in detail. The modeling of the detailed RO operation is intended to quantify the tradeoffs between the different approaches in order to provide a basis for the selection decision. In particular, the proposed grouping of the different proposals, based on their address configuration strategy, clarifies their similarities and differences, and provides some useful insights into the various methods that have been developed. In conclusion, it is suggested that, when choosing a solution for deploying NEMO, the designer has to balance his choices between the different pros and cons, and the different cases of application that are derived in this paper.

In previous work [1], we proposed a real-time MPEG-4 VBR video traffic prediction algorithm that calculates the probability density function (PDF) based on the last N frames, rWin, by using Cubic-spline interpolation method, and then utilize it for the prediction of next frame. We fixed the size of rWin as five in the algorithm. Although the algorithm is capable of providing more accurate prediction than those in the research literature, it still has weakness on the adapting the traffic dynamics due to the fixed size of rWin. In this paper, we first study the experimental investigation about the number of rWin, and propose an enhanced traffic prediction algorithm with dynamic rWin.

This paper investigates the construction of a multicast tree satisfying Quality of Service (QoS) real-time group communication in a heterogeneous network comprising multiple Mobile Ad-hoc NETworks (MANETs) attached to the backbone Internet. The main objective of our work is to optimize the Delay- and delay Variation Bounded Multicast Tree (DVBMT) problem, which has been proved to be NP-complete. This problem has to satisfy the minimum delay variation and the end-to-end delay within an upper bound. The well-known algorithms solved this problem are the DVMA, the DDVCA, the Cheng's algorithm, and so on. In this paper, we propose an algorithm that outperforms other algorithms in terms of the multicast delay variation in the realistic network environment. The enhancement increases to approximately 3.7%~ 32.9% in terms of that. The time complexity of the proposed algorithm is O(mn2), which is comparable to that of DDVCA.

The development of the efficient quality of service (QoS) routing algorithms in a high speed network environment is very important and, at the same time, a very difficult task due to the need to provide divergent services with QoS requirements. The distributed adaptive routing is the typical routing algorithm that is used in the current Internet. If the parameter we concern is to measure the delay on that link, then the shortest path algorithm obtains the least delay (LD) path. Meanwhile, if the parameter is to measure the link cost, then the shortest path algorithm calculates the least cost (LC) path. The delay constrained least cost (DCLC) path problem has been shown to be NP-hard. The path cost of LD path is relatively more expensive than that of the LC path, and the path delay of LC path is relatively higher than that of the LD path in DCLC problem. In this paper, we investigate the performance of a heuristic algorithm, estimated link selection (ELS) for the DCLC problem with a new parameter which is a probabilistic combination of cost and delay. We have performed empirical evaluation that compares our proposed ELS with the DCUR in various network situations. It significantly contributes to identify the low cost and low delay unicasting path and the performance improvement is up to about 49% in terms of normalized surcharge.