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With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.

Network traffic classification provides an essential contribution in network administration functions and network management such as QoS, security, and billing. Those functions need a timely and accurate detection of specific traffics. Current network traffic classification methods offer supervised and unsupervised learning capabilities for network traffic prediction or classification. Classical machine learning classifiers that use a single classification model suffer from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. These challenges in individual-based learning models have created a need for hybrid learning. Recently, hybriddeep learning has shown a significant role in traffic forecasting and classification due to its efficiency. However, a tradeoff between the aggregate models and the classification accuracy presents a substantial challenge in network traffic classification problems. In this paper, we have suggested two hybrid models that combine the Convolutional Neural Network (CNN) along with the Recurrent Neural Network (RNN) models, inclusive of the Gated recurrent unit (GRU) and Long Short-Term Memory (LSTM), to improve traffic classification accuracy. The efficiency of the suggested models has been evaluated by comparing them with various individual-based models using real network traffic traces. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification.

In the era of secure communication and the constantly changing pattern of internet applications, traditional packet classification methods fail to achieve the accuracy needed for diverse network management functions. Recently Machine Learning (ML) techniques have been used to design viable packet classification solutions. However, due to the complexity and dynamic feature of internet traffic, efficient packet classification is still challenging for various machine learning algorithms. In this paper, we propose the adoption of feature selection methods through dimensionality reduction to enhance the classifiers' performance. We evaluated the performance of four well-known classifiers, including K-nearest neighbour (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) with and without feature selection. We used two feature selection methods, including principal component analysis and Autoencoder. Experimental analysis is performed on real network traffic datasets with binary and multi-class categories. We assessed each classifier's performance using precision, recall, f-score, accuracy, and ROC. Experimental results show that the Precision, Recall, and F-score for the Multi-class problem are improved by 4.7 %, 6%, and 9%, respectively, after adopting either PCA or Autoencoder methods. The classification accuracy is also improved by up to 13%. We can also conclude that Autoencoder performed better for the KNN and LR, while PCA achieved comparable results for both the SVM and DT classifiers.

Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits.

Future mobile services and applications are bounded by user location, data, and network. These services will suffer from poor support from wireless networks due to the huge amount of mobile traffic and user mobility. The demand for contents by these services results in constraints put on latency and quality of service (QoS). Considering these problems, researchers investigated caching contents locally and proactively at the edge of the mobile edge networks (MENs). In this work, we proposed new formulation of mobility-aware latency-efficient cache placement problem for mobile edge networks (MENs) taking into account different storage capacities, users mobility, content popularity, contact probability, and latency to download the contents to user terminals (UTs). Our formulated multi-objective optimization problem aims to maximize the cache hit rate. We apply weighted-sum decision theory approach to model the decision of placing contents at the edge of the network. Simulation results are shown to gain insight of the impact of different factors on our proposed system and and evaluated the results with three other cache placement techniques.

In this paper, we present a cache placement scheme to deal with bandwidth constraint of backhaul links in 5G Small Cell wireless network. We formulated the cache placement problem as graph matching problem and presented an optimal file-helper matching algorithm based on Gale-Shapley optimal matching criterion. We define stability criterion for the matching and found our matching solution stable in the sense that every helper finds at least one file to cache given no file exceed minimum cache size. We derived file-helper based on our framework that employs users' connectivity and their social interaction pattern. Our approach is to perform unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. We showed through simulation that our algorithm increases the number of file requests served by either a local helper or a helper within the cluster that significantly reduces bottleneck backhaul traffic.

5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-toend and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy softset (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.

Automation of modern industrial plants require real-time tracking of object locations and sensing of local and ambient parameters for variety of applications such as counting and tracking of objects in assembly line, detection and positioning of failures of machines etc. Mostly, discrete Real Time Location System (RTLS) performs object tracking in existing industrial automation without its integration with the sensing and control network, which constrains application's responsiveness. In this paper, we propose an integrated solution of Wireless Positioning Sensor Network (WPSN) that is designed for accuracy, reliability, scalability and optimal network operation. The proposed WPSN is applicable for harsh indoor industrial environments for not only monitoring and control of plant operations but also for identification, localization and tracking of assets and inventory in industrial warehouses. The indoor industrial environment poses challenging conditions for radio signal propagation that adversely affects reliability of communication of sensing and location data. We approach reliability by incorporating redundancy and making our network reconfigurable through adaptive intelligent learning process. We employ adaptive clustering technique to address the need of scalable deployment for varied industrial scenarios. We include hybrid localization scheme to provide high precision positioning but with fallback reduced precision operation to deal with long-term channel impairment. The WPSN is connected with backend cloud infrastructure for low cost monitoring and control.

In this paper, we propose an interference-aware multicast routing scheme for a single-radio Wireless Mesh Networks carrying multiple multicast flows. We focus on multicast routing over WMNs with stationary nodes, such as community wireless networks. We present a new metric that is based on three components: Path Interference Cost (Ip), Broadcast Benefit Factor (BF), and Hop Count (HC). We proposed a formula to compute Link Interference Cost that is aggregated into Ip. The goal of our metric is to choose a join path to multicast tree for a new receiver node that causes lower intra-flow and inter-flow interferences. The benefit of the proposed metric is to improve flow throughput. We also proposed a route discovery scheme for discovering the join path, computing metric, and making route selection. We evaluated the performance of our scheme through simulation under variety of network conditions.

Emergency situations in a mining environment have the potential for the loss of human life. Tracking mobile assets and personnel can significantly improve the safety of all miners. Existing distributive localization algorithms are not well suited for a mobile asynchronous mine environment. We propose the Mobile Cooperative Localization Algorithm (MCLA) to increase the accuracy and number of nodes which are localized. The proposed algorithm has been validated through simulation using ns-3 and demonstrates an increase in accuracy for a mobile distributed environment.

Challenges of utilizing cloud services by mobile users arise from few inherent characteristics such as user mobility and device energy level. In this paper, we propose a scheme to monitor the energy level and communication quality as a part of a mobile user context information. We subsequently developed a resource allocation and scheduling scheme to adapt to the context for the benefit of both the cloud user and the cloud provider. The objective is to reduce the execution cost of the jobs while meeting the jobs deadline set by the users. A Deadline-Based Simulated Annealing algorithm was used for resource allocation and scheduling of resources. Simulation shows advantages to the context aware cloud system over the context free cloud system in terms of execution cost and resource utilization.

In this paper, we propose an optimal power allocation scheme that minimizes the energy per bit of worst user (i.e., the user with highest energy per bit) in a multiuser wireless communication network. The problem of determining energy efficient power allocation to improve the worst user is a constrained non-convex nonlinear fractional programming problem. We propose an iterative energy efficient power allocation algorithm that guarantees optimal solution. We use parametric equivalent formulation to get the optimal solution. Numerical solutions obtained using simulations are presented and compared with equal power allocation scheme.

Directional antennas are known for spatial reuse, extended range, less interference, and less energy consumption as compared to omnidirectional antennas. In this paper, we propose an energy-efficient scheduled directional MAC (DTRAMA) algorithm for sensor networks, which is based on TRAMA with some modifications to exploit spatial reuse of directional antennas. In TRAMA, nodes achieve energy efficiency by following traffic-adaptive communication and sleep schedules. DTRAMA introduces spatial reuse checks in TRAMA to allow an otherwise sleep node to schedule transmission. Our simulation results show that DTRAMA is effective in exploiting spatial reuse of directional antennas by improving packet delivery ratio and packet delay of TRAMA. It, however, compromises energy efficiency by reducing average sleep time of a node.

In this paper, we apply the cross entropy optimization (CEO) to the problem of joint multiple relay assignment and source/relay power allocation (JMRAPA) in green cooperative cognitive radio (GCCR) networks. We use shared-band amplify and forward relaying for cooperative communication in the JMRAPA problem. The proposed JMRAPA maximizes the total rate and minimizes the greenhouse gas emissions in GCCR networks. It is a non-convex combinatorial optimization problem and is NP-hard. We propose to use concave upper bound that makes it a convex problem. The effectiveness of the proposed CEO-based method is shown through simulation results.

The power allocation in decode and forward (DF) relaying for cooperative cognitive radio network (CCRN) with an objective of maximizing energy efficiency (EE) is a constraint nonlinear non-convex fractional programming problem (CNNFPP). The optimization needs to satisfy the primary users interference constraints and secondary users outage constraints. We proposed the optimal power allocation in DF relaying by transforming the CNNFPP power allocation problem into a concave fractional program. We also present numerical results to demonstrate the effect of different network parameters on the EE of CCRN.

Energy consumption is a key issue in wireless ad hoc and sensor networks. Cooperative communication represents a potential candidate to improve the energy efficiency of wireless networks and this efficiency can be further improved by considering the variable packet size. In this paper, we address the question of the optimal packet size for data that improves the efficiency of energy consumption in energy constrained wireless cooperative ad hoc networks. Energy efficiency is used as an optimization metric and the problem is formulated as a nonlinear optimization problem. The accurate solution of optimal problems is crucial and existing methods proposed for such formulation can still converge to a local solution. In this paper, a novel global optimization approach based on a branch and bound (BB) framework and convexification techniques that address the solution to global optimality known as αBB is introduced. The numerical results show that, this algorithm offers mathematical guarantees for convergence to a point arbitrarily close to the global minimum in reasonable computational time.

In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive radio with objective of maximizing energy efficiency is a constraint nonlinear nonconvex fractional programming (CNNFP) problem. We present the optimal power allocation in DF relaying by transforming the CNNFP power allocation problem into a concave fractional program by using Charnes-Cooper transformation. We also present an iterative e-optimal solution for the CNNFP problem using Dinkelbach algorithm. The convergence of the iterative algorithm is proved and numerical solutions obtained using simulations for DF cooperative communications are presented.

In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio network is investigated as a constrained fractional programming problem (CFPP). The fractional objective is defined in terms of bits per Joule per Hz. The proposed CFPP is a nonlinear non-convex optimization problem. We propose an iterative power algorithm (IPA) that guarantees e-optimal solution. The convergence of IPA is proved and numerical solutions obtained using simulations are presented.

Cooperative relaying techniques have been shown to provide spatial diversity in fading wireless environment. As a result, they increase link reliability, provide higher capacity, reduce transmit power, and extend transmission range as opposed to non-cooperative transmission (direct transmission). Although the use of cooperative relaying has been proven to achieve those gains in the absence of interference (single flow), it is not clear how much gain we can expect from using cooperative relaying in multi-flow scenario specifically the total network capacity. The network capacity in multi-hop multi-flow settings is severely affected by interference between links and, this effect increases when the cooperative relaying is imposed. In this paper, we use a clique based capacity analysis to investigate the performance gain (loss) on network capacity for wireless ad hoc networks by using cooperative relaying. It is observed that the throughput drops significantly when cooperative links are imposed in the network.

In Multicast, since almost simultaneous communication take place along several branches of a Multicast distribution tree, these branches tend to mutually block each other due to interference. We utilized a model of scheduling in automobile production line called GORBY to divide the nodes in the Multicast distribution tree into three groups (GORby-Group or GG). We calculate the SINR of the node based on interferences caused by all the nodes within that GG, assuming when the given node is in the transmit state then all the nodes within its GG will also be in the transmit state. Finally we use the computed SINRs in our join Multicast group algorithm to achieve the most SINR, which we call it GORby Interference-Aware (GIA) metric in case of a node, and Path GIA (PGIA) in case of a path.

Cooperative diversity techniques have recently received a lot of attention due to their ability to provide spatial diversity in fading wireless environment. It increases link reliability, provides higher capacity, reduces transmit power, and extends transmission range for the same level of performance and modulation rate. This paper studies how to select a path from source to destination that minimizes the end-to-end latency in wireless ad hoc networks by using cooperative transmission at the physical layer. We propose an Edge Node based Greedy Cooperative Routing (ENBGCR) algorithm, where we modify the Geographic Routing (GR) algorithm to incorporate the cooperative transmission and extend the coverage range of the nodes. The numerical analysis are presented to validate the performance of the proposed routing protocol. We found that the ENBGCR algorithm is energy efficient, results in lower end-to-end delay, and involves less number of hops or transmissions for the same QoS requirement as compared to a standard implementation of next hop selection algorithm.

The recent advances in antenna technology have made smart beamforming antennas attractive candidates to be deployed in multi-hop wireless networks. Beamforming antennas can increase the spatial reuse of the channel and consequently the network capacity. Due to practical and economical considerations, deploying beamforming antennas in the whole network is sometimes infeasible. In this paper, we focus on multi-hop wireless networks with heterogeneous antenna capabilities. We study the problem of optimal antenna placement to maximize the total network throughput. We first introduce an antenna-aware conflict graph using the notion of virtual links. With the objective of minimizing the size of the maximum clique, we formulate our problem as a mixed-integer linear programming problem. Moreover, we propose a beamforming antenna placement heuristic algorithm for the partial deployment of beamforming antennas to upgrade existing wireless networks originally equipped with traditional omni-directional antennas. Our numerical results show that the proposed algorithm produces near-optimal results in a small fraction of time.

Cooperative diversity techniques have recently received a lot of attention due to their ability to provide spatial diversity in fading wireless environment, thus increases link reliability, provides higher capacity and reduces transmit power for the same level of performance. In this paper we study a joint problem of relay selection, power allocation and routing in multi-hop wireless ad hoc networks based on cooperative transmission. In particular, an optimal routing strategy is proposed to minimize the end-to-end total transmission power subject to end-to-end target rate. An ad-hoc routing strategy is proposed to find an optimal route (in terms of total transmit power minimizing) for decode-and forward strategy based on the well known Dijikstra algorithm which can be easily implemented in distributed way. Simulation results show that the proposed strategy has great improvement in terms of power saving compared with traditional non-cooperative shortest path algorithms, by more than 70% in some simulation scenario.

Recently, the use of directional antennas in multi-hop wireless networks has received increasing attention in the research community due to their potential benefits and numerous advantages over omni-directional antennas. However, the majority of existing work has focused on homogeneous wireless networks where all the nodes are equipped with the same beamforming antennas. In this work, we focus on multi-hop wireless networks with heterogeneous antennas capabilities, a scenario that is expected to prevail in next generation wireless networks. We address the fundamental question: Given a specific network topology with heterogeneous antennas, what are the theoretical capacity bounds of such network? For that purpose, we formulate the problem as an optimization problem and compute the upper and lower bounds. Our results show that the capacity of heterogeneous multi-hop wireless networks can be significantly increased even with small fraction of directional nodes.

The demand for QoS has made its support an essential component in today's wireless networks. The use of directional antennas in multi-hop wireless networks is known to provide substantial increase in the utilization of the wireless medium. However its effectiveness in providing QoS assured routing is unexplored. In this paper, we study the problem of bandwidth guaranteed routing in contention-based wireless mesh networks with directional antennas. We present a detailed analysis for the interference pattern of such networks taking into account the challenges imposed by the use of directional antennas such as deafness. Based upon this analysis, we formulate the QoS routing problem as an optimization problem. We then present a routing and admission control heuristic to solve the problem. Our simulation results demonstrate the effectiveness of our routing scheme and the necessity of admission control for QoS support.

Directional antennas in multihop wireless networks offer a potential increase in the spatial reuse of the wireless channel. However, existing directional MAC protocols are not capable of fully exploiting this essential benefit. In this paper, we propose OPDMAC which is an opportunistic directional MAC protocol that is motivated by the possibility of achieving higher spatial reuse. The proposed protocol is novel in its backoff mechanism with directional antennas. If a node is forced to backoff due to a missing CTS or acknowledgement in one direction, OPDMAC explores transmission opportunity in other directions. This minimizes the idle wait time of the wireless channel and increase the channel utilization. In addition, OPDMAC is found to decrease the effect of deafness without additional control overhead. Our simulation results show that OPDMAC outperforms existing directional MAC protocols in terms of throughput, delay and fairness.

Emergency situations in mines result in loss of precious human lives. In this paper we demonstrate the viability of the architecture of a Wireless Sensor Network (WSN) in underground mines to achieve mine safety. We propose Minimum Delay Maximum Lifetime (MDML) routing in the wireless sensor network to achieve reliable low-delay routing of emergency data traffic and increased lifetime of the network by employing energy efficient routing for periodic non-emergency regular traffic. Routing metrics proposed in MDML is specifically designed considering signal propagation characteristics in underground mines. We also suggest configuring priority queue in a sensor node giving higher priority to emergency traffic. Our simulation shows that zero loss and low delay can be achieved for emergency traffic. It also shows that MDML is effective in trading slight loss of network lifetime for achieving high reliability and low-delay.

The access link contention can severely constrain the end- to-end throughput of the path between a source and destination mobile node connected through the backbone of the wireless mesh network (WMN). In this paper, we propose an integrated routing system for WMN that includes both the backbone paths and the ad-hoc paths formed as a result of direct communication among mobile nodes without going through the backbone. In our proposed routing system, an alternative ad-hoc path can be used only when the primary backbone path is severely constrained due to access links contention. We propose a scheme that allows the source mobile node to evaluate the throughput of the backbone and ad-hoc paths, and select one path for communicating with the destination. We implemented the proposed routing system in OPNET simulator, and evaluated the performance of our scheme under variety of conditions. Simulation results show that the alternative ad-hoc path is effective in delivering higher throughput when backbone path is severely constrained.

A novel approach to event boundary detection is proposed, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of the algorithm is to localize node transmission along the event boundary, since the sensory data from nodes not along the boundary is not required for event boundary detection. Simulations demonstrate that the algorithm has a linear efficiency function when related to event radius of sufficient size and further demonstrates that the boundary of an event may be successfully mapped using agents.

Although cellular networks such as GPRS (2.5G) or UMTS (3G) have achieved both circuit switching and packet switching services with wide area coverage, they still fall short of meeting the high data rate demands of mobile users. Integrating cellular network with other high-speed and low-cost wireless technologies is a key challenge for migrating to 4G. The IEEE 802.11 technology that has been well-developed and widely used in local area networks seems to be a proper choice for fulfilling users' expectations in hotspots. Its ad hoc mode of operation allows mobiles users to achieve connectivity with low infrastructure support. This paper proposes a design to achieve the internetworking between 802.11 mobile ad hoc network (MANET) and UMTS. The key approach in our design is to model inter-system handover as inter-SGSN handover, and connect gateway in the ad hoc network with the GGSN

RAON is a P2P system for MANET that performs query-forwarding decision taking into account link instability and power constraints. File download traffic causes significant drop in its query forwarding performance. We propose two modifications to RAON, called CAON, to reduce the effect of congestion on the query forwarding performance caused by the download traffic: (1) local feedback mechanism; and (2) download site selection algorithm to avoid overlapping download and unstable query traffic paths. Our simulation results show that CAON improves the success rate and delay of query search on RAON with low overhead.

Content Delivery over Internet is optimized for low user perceived latency by designing specialized Content Delivery Networks (CDN). Request Routing systems in CDNs generally achieve low user latency by balancing the server load and selecting the nearest server. Although Traffic engineering is another factor that impacts the client latency, which is not generally integrated with the Request Routing. In this paper we proposed a CDN architecture that employs a TE-Friendly Request Routing (TFRR) system, which not only distributes the server load but also achieves network efficiency by balancing the path loads. We also presented a detailed TFRR system design and an algorithm to perform the TFRR function. Our simulation results show that the proposed CDN with our TFRR algorithm achieves both server and traffic load balancing.

Recently Wireless Mesh Network (WMN) has become popular especially for its low cost deployment in the areas of poor network infrastructure and terrain of difficult deployment. Although paths in WMN backbone network are stable, the access link contention can severely constrain the end-to-end throughput. We proposed an integrated routing system for WMN that exploits both paths through the backbone network and through the ad-hoc access network of mobile nodes. The ad-hoc path is considered as an alternative path and is used only when the primary backbone path is severely constrained due to access links contention. We have shown through simulation that alternative path is effective in delivering high throughput in that situation. We also proposed a scheme for initiating the route discovery of the ad-hoc path. We investigated the use of OSPF in the backbone network and proposed solutions of dealing with multi-area OSPF configuration in the backbone and auto-configuration of OSPF wireless links.