Context-aware Resource Allocation Research Articles

Context-aware resource allocation for vehicle-to-vehicle communications in cellular-V2X networks

Cellular Vehicle-to-Everything (C-V2X) networks provide critical support for intelligently connected vehicles (ICVs) and intelligent transport systems (ITS). C-V2X utilizes vehicle-to-vehicle (V2V) communication technology to exchange safety–critical information among neighbors. V2V communication has stringent high-reliability and low-latency requirements. The existing solutions on resource allocation for V2V communications mainly rely on channel states to optimize resource utilization but fail to consider vehicle safety requirements, which cannot satisfy safety application performance. In this paper, we focus on application-driven channel resource allocation strategy for V2V communications. First, we propose an inter-packet reception model to represent the delay between two consecutive and successful reception packets at a receiver. We then design an application-specific utility function where the utility depends on the packet reception performance and vehicle safety context. Finally, we formulate the channel resource allocation problem as a non-cooperative game model. The game model can guide each node to cooperate and achieve the trade-off between fairness and efficiency in channel resource allocation. The simulation results show that our work can significantly improve the reliability of V2V communications and guarantee the vehicle safety application performance.

Dynamic Priority-Based Service Resource Allocation for Context-Aware Conflict Resolution in Wisdom Network with Fog Computing

With the development of wisdom network, this paper assumes that intelligent devices become more and more intelligent, which can easily collect and provide a variety of context awareness data. The research goal is to design a dynamic conflict resolution strategy for context-aware resource allocation. The limited availability of resources inevitably leads to conflicts. Considering the characteristics of wisdom network, the quality of service when solving conflicts, a mechanism is proposed to improve the quality of services and to solve the resources allocation conflicts. This paper constructs the optimal model of context-aware based on a differential game and optimizes the resource allocation of context-aware based on the priority of scenarios. Fog computing is used to provide enough computing resources for the control of resource allocation of context-aware. The Bellman dynamic programming is introduced to solve the feedback Nash equilibrium solution of the proposed differential game model, to obtain the optimal allocation of service resources and solve the effectiveness of resource allocation.

Resource Allocation Scheme Based on Rate-Requirement for Device-to-Device Downlink Communications

The rate-requirement of device-to-device (D2D) users is associated with the context information of velocity and data size of users to some extent. In this study, an efficient context-aware resource allocation scheme based on rate requirement (RARR) is proposed. This scheme consists of two allocation phases. In the rate-ensuring resource allocation phase, D2D pairs are allocated a certain amount of spectrum resource according to their rate requirement. In the allocation, the interference restricted area is limited to exclude cellular users that bring a negative capacity gain to the communication system. In the residual resource reallocation phase, surplus resources are assigned to D2D pairs according to the system fairness. Simulation results indicate that the proposed RARR scheme efficiently leads to superior performance in terms of system throughput and fairness and exhibits low complexity relative to traditional resource allocation.

Energy-Efficient Predictive HTTP Adaptive Streaming in Mobile Cellular Networks

The rapid growth of mobile video traffic puts significant pressure on energy drain at the network as well as on the end users. Exploiting predicted channel information and designing energy-efficient content delivery protocols have recently drawn attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular HyperText Transfer Protocol (HTTP) based Adaptive Streaming (HAS) (e.g., dynamic adaptive streaming over HTTP). To this end, we develop a robust mobile edge-cloud assisted stochastic Predictive HTTP Adaptive Streaming (PHAS) optimization framework that utilizes unreliable predictions of wireless data rates in a finite look-ahead window to achieve the following objectives: first, to obtain an edge-cloud assisted framework for prediction-based HAS and identify its key functional entities and their interactions; second, to model uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach that dynamically allocates the risks and optimizes system efficiency over a time horizon; and third, to propose a heuristic algorithm allocating time slot ratio for multi-users, which improves network efficiency, fairness, and overall QoE under different prediction error variances, wireless link conditions, and buffer length constraints. Simulation studies and analytical results show that the proposed solution outperforms traditional methods in terms of average QoE, fairness, and energy efficiency.

PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks.

The fifth-generation (5G) communications systems are expecting to support users with diverse quality-of-service (QoS) requirements. Beside these requirements, the task with utmost importance is to support the emergency communication services during natural or man-made disasters. Most of the conventional base stations are not properly functional during a disaster situation, so deployment of emergency base stations such as mobile personal cell (mPC) is crucial. An mPC having moving capability can move in the disaster area to provide emergency communication services. However, mPC deployment causes severe co-channel interference to the users in its vicinity. The problem in the existing resource allocation schemes is its support for static environment, that does not fit well for mPC. So, a resource allocation scheme for mPC users is desired that can dynamically allocate resources based on users’ location and its connection establishment priority. In this paper, we propose a public safety users priority-based context-aware resource allocation (PS-CARA) scheme for users sum-rate maximization in disaster environment. Simulations results demonstrate that the proposed PS-CARA scheme can increase the user average and edge rate around 10.3% and 32.8% , respectively because of context information availability and by prioritizing the public safety users. The simulation results ensure that call blocking probability is also reduced considerably under the PS-CARA scheme.

Matching With Peer Effects for Context-Aware Resource Allocation in D2D Communications

In this letter, we investigate the context-aware resource allocation for device-to-device communications accounting for the quality-of-service requirements and priorities of different applications based on users’ requests. We formulate a context-aware optimization problem and implement the matching theory to solve the problem. We propose a novel algorithm where the device-to-device user equipments and resource blocks act as two opposite sets of players and interact with each other to obtain the optimal matching. We analytically prove that the algorithm converges to a two-sided exchange stability within limited number of swap operations. We also demonstrate that the proposed algorithm significantly outperforms the context-unaware resource allocation algorithm by around 62.2%.

Context-Aware Dynamic Resource Allocation for Cellular M2M Communications

While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions and degrade the successful probability as well as delay of network connection establishment. To address this issue, current LTE spec has specified access class barring (ACB) and enhanced access barring (EAB), but both methods lack a contention detection method to decide when to activate it as well as a proper way to dynamically adjust the parameter. In this work, we first proposed a contention detection method that can work with current long-term evolution (LTE) standard and is easy to be implemented. To further improve the random access performance in dynamic world, we proposed a context-aware dynamic resource allocation (CADRA) mechanism, which is a two-phase method to resolve random access contention: Phase I for the estimation of random access attempts, and Phase II for resource allocation. By CADRA, high resource efficiency and low random access delay can be achieved without prior knowledge about random access arrival traffic, and thus this approach is competent in diverse applications and scenarios of Internet of Things (IoT). Simulation results show that the proposed contention detection method works great with ACB and EAB. Our proposed CADRA has good performance in resource efficiency and delay while satisfying success probability.

An architecture and algorithm for context-aware resource allocation for digital teaching platforms

Digital Teaching Platforms (DTPs) are aimed to support personalization of classroom education to help optimize the learning process. A trend for research and development exists regarding methods to analyze multimodal data, aiming to infer how students interact with delivered content and understanding student behavior, academic performance, and the way teachers react to student engagement. Existing DTPs can deliver several types of insights, some of which teachers can use to adjust learning activities in real-time. These technologies require a computing infrastructure capable of collecting and analyzing large volumes of data, and, for this, cloud computing is an ideal candidate solution. Nonetheless, preliminary field tests with DTPs demonstrate that applying fully remote services is prohibitive in scenarios with limited bandwidth and a constrained communication infrastructure. Therefore, we propose an architecture for DTPs and an algorithm to promote the adjustable balance between local and federated cloud resources. The solution works by deciding where tasks should be executed, based on resource availability and the quality of insights they may provide to teachers during learning sessions. In this work, we detail the system architecture, describe a proof-of-concept, and discuss the viability of the proposed approach for practical scenarios.

Towards 5G: Context Aware Resource Allocation for Energy Saving

With the objective of providing high quality of service (QoS), 5G system will need to be context-aware that uses context information in a real-time mode depends on network, devices, applications, and the environment of users'. In order to continue enjoying the benefits provided by future technologies such as 5G, we need to find solutions for reducing energy consumption. One promising solution is taking advantage of the context information available in today's networks. In this paper, we take a step towards 5G by utilizing context information in the scheduling process as conventional packet scheduling algorithms are mainly designed for increasing throughput but not for the energy saving. We investigate a Context Aware Scheduling (CAS) algorithm which considers the context information of users along with conventional metrics for scheduling. An information model of context awareness along with a context aware framework for resource management is also presented in this paper. CAS is simulated applying a system level simulator and the results obtained show that considerable amount of energy is saved by utilizing the context information compare to conventional scheduling algorithms.

Context-Aware Uplink Resource Allocation Using Request Header Information for ertPS Services in IEEE 802.16e Networks

In this paper, we propose a simple and effective way to improve the quality of service (QoS) of uplink resource allocation for the extended-real-time polling service (ertPS) in IEEE 802.16e broadband wireless networks. The primitive ertPS algorithm determines the amount of resources allocated to a given subscriber station only according to the requested volume in current polling cycles regardless of the knowledge of demand trend. This trend is continuously increasing or declining, which may be incomplete to truly reflect reasonable resource allocation for subscriber stations in different demand tendencies. To cope with this problem, we developed a context-aware ertPS algorithm which gathers statistics on the numbers of request headers of medium access control layer management messages among various subscriber stations, and thus uses the statistical information to enable a dynamic allocation of additional uplink resources to subscriber stations with continuously increasing bandwidth demands. The simulation results show that in comparison with the original ertPS algorithm, the proposed context-aware resource allocation scheme is effective to increase the data throughput as well as to decrease the packet loss rate and packet delay so that the user-perceived QoS can be improved.

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.

Energy-Efficient Context-Aware Matching for Resource Allocation in Ultra-Dense Small Cells

With the explosive growth of mobile data traffic and rapidly rising energy price, how to implement caching at small cells in an energy-efficient way is still an open problem and requires further research efforts. In this paper, we study the energy-efficient context-aware resource allocation problem, which falls into the category of mixed integer nonlinear programming (MINLP) and is NP-hard. To provide a tractable solution, the MINLP problem is decoupled and reformulated as a one-to-one matching problem under two-sided preferences, which are modeled as the maximum energy efficiency that can be achieved under the expected matching. An iterative algorithm is developed to establish preference profiles by employing nonlinear fractional programming and Lagrange dual decomposition. Then, we propose an energy-efficient matching algorithm based on the Gale-Shapley algorithm, and provide the detailed discussion and analysis of stability, optimality, implementation issues, and algorithmic complexity. The proposed matching algorithm is also extended to scenarios with preference, indifference, and incomplete preference lists by introducing some tie-breaking and preference deletion rules. The simulation results demonstrate that the proposed algorithm achieves significant performance and satisfaction gains compared with the conventional algorithms.

Context-aware resource allocation for cellular wireless networks

Current cellular networks are often overloaded by Smartphone traffic, while the users’ Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station’s scheduler (i) observes Context Information (CI) from the user’s environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld’s applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links’ efficiency without sacrificing the users’ QoS.

Context Aware Resource Allocation in Distributed Sensor Networks

Distributed Sensor Networks (DS'.s) have been an area for active research over the past few years, due to potentially widespread applications in creating smart computing environment. In this work, we have proposed new smart computing environment for super/hyper market. The proposed system uses Distributed Sensor Networks (DSN's) for real-time tracking of the products. While new technologies are making sensors nodes smarter, smaller, and cheaper, it is very challenging to find an optimal to allocate sensors and other network resources. Context awareness is one of the methods to improve resource utilization in Distributed Sensor Networks (DSN's). We have studied Indian retail industry as case study and we listed out the drawbacks of Indian retail industry. But proposed system can be applied to most of the retail industries across the globe to overcome similar problems. As a part of proposed system, we have derived many contexts in retail industry based on human perspective. At a basic level of design, sensors are deployed in each rack which provides the real time information about available products in each rack. This information can be used by warehouse manager, store manager for day to day activity in super/hyper market. R&D team can also use this information to understand consumer behaviour. The context information derived from sensor node is used in allocation sensor node or resources. We have tested our proposed system using Castalia simulation tool, a derivative of OMNeT++. The results obtained from our experiment shows advantages of context aware system. As DSN's are data centric, analyzing and implementing system in user context prospective will greatly contribute in terms of cost, performance. We have simulated the context aware resource allocation in distributed sensor networks to test the operation scheme in terms of performance parameters. The DSN's promise to revolutionize sensing in a wide range of application domains. This is because of their reliability, accuracy, flexibility, cost effectiveness and ease of deployment, Smart sensors can offer vigilant surveillance and can detect and collect data.