Study and analysis of mobility, security, and caching issues in CCN

Existing architecture of Internet is IP-centric, having capability to cope with the needs of the Internet users. Due to the recent advancements and emerging technologies, a need to have ubiquitous connectivity has become the primary focus. Increasing demands for location-independent content raised the requirement of a new architecture and hence it became a research challenge. Content Centric Networking (CCN) paradigm emerges as an alternative to IP-centric model and is based on name-based forwarding and in-network data caching. It is likely to address certain challenges that have not been solved by IP-based protocols in wireless networks. Three important factors that require significant research related to CCN are mobility, security, and caching. While a number of studies have been conducted on CCN and its proposed technologies, none of the studies target all three significant research directions in a single article, to the best of our knowledge. This paper is an attempt to discuss the three factors together within context of each other. In this paper, we discuss and analyze basics of CCN principles with distributed properties of caching, mobility, and secure access control. Different comparisons are made to examine the strengths and weaknesses of each aforementioned aspect in detail. The final discussion aims to identify the open research challenges and some future trends for CCN deployment on a large scale

Coping with Episodic Connectivity in Heterogeneous Networks

International audienceIn this paper, we present an efficient message delivery mechanism that enables distribution/dissemination of messages in an internet connecting heterogeneous networks and prone to disruptions in connectivity. We call our protocol MeDeHa (pronounced “medea”) for Message Delivery in Heterogeneous, Disruptionprone Networks. MeDeHa is complementary to the IRTF's Bundle Architecture: while the Bundle Architecture provides storage above the transport layer in order to enable interoperability among networks that support different types of transport layers, MeDeHa stores data at the link layer addressing heterogeneity at lower layers (e.g., when intermediate nodes do not support higher-layer protocols). MeDeHa also takes advantage of network heterogeneity (e.g., nodes supporting more than one network) to improve message delivery. For example, in the case of IEEE 802.11 networks, participating nodes may use both infrastructure- and ad hoc modes to deliver data to otherwise unavailable destinations. Another important feature of MeDeHa is that there is no need to deploy special-purpose nodes such as message ferries, data mules, or throwboxes in order to relay data to intended destinations, or to connect to the backbone network wherever infrastructure is available. The network is able to store data destined to temporarily unavailable nodes for some time depending upon existing storage as well as quality-of-service issues such as delivery delay bounds imposed by the application. We evaluate MeDeHa via simulations using indoor scenarios (e.g. convention centers, exposition halls, museums etc.) and show significant improvement in delivery ratio in the face of episodic connectivity. We also showcase MeDeHa's support for different levels of quality-of-service through traffic differentiation and message prioritization

MeDeHa - Efficient Message Delivery in Heterogeneous Networks with Intermittent Connectivity

In this report, we present an efficient message delivery mechanism that enables distribution/dissemination of messages in an internet connecting heterogeneous networks and prone to disruptions in connectivity. We call our protocol MeDeHa (pronounced ``medea'') for Message Delivery in Heterogeneous, Disruption-prone Networks. MeDeHa is complementary to the IRTF's Bundle Architecture: while the Bundle Architecture provides storage above the transport layer in order to enable interoperability among networks that support different types of transport protocols, MeDeHa is able to store data at any layer of the network stack, addressing heterogeneity even at lower layers (e.g., when intermediate nodes do not support higher-layer protocols). MeDeHa also takes advantage of network heterogeneity (e.g., nodes supporting more than one network and nodes having diverse resources) to improve message delivery. For example, in the case of IEEE 802.11 networks, participating nodes may use both infrastructure- and ad hoc modes to deliver data to otherwise unavailable destinations. Another important feature of MeDeHa is that it does not rely on special-purpose nodes such as message ferries, data mules, or throwboxes in order to relay data to intended destinations, and/or to connect to the backbone network wherever infrastructure is available. The network is able to store data destined to temporarily unavailable nodes for some time depending upon current storage availability as well as quality-of-service needs (e.g., delivery delay bounds) imposed by the application. We showcase MeDeHa's ability to operate in environments consisting of a diverse set of interconnected networks and evaluate its performance via extensive simulations using a variety of synthetic-- as well as more realistic scenarios. Our results show significant improvement in average delivery ratio and significant decrease in average delivery delay in the face of episodic connectivity. We also demonstrate MeDeHa's support for different levels of quality-of-service through traffic differentiation and message prioritization

Load-Aware Cell Switching in Ultra-Dense Networks: An Artificial Neural Network Approach

Most online cell switching solutions are sub-optimal because they are computationally demanding, and thus adapt slowly to a dynamically changing network environments, leading to quality-of-service (QoS) degradation. This makes such solutions impractical for ultra-dense networks (UDN) where the number of base stations (BS) deployed is very large. In this paper, an artificial neural network (ANN) based cell switching solution is developed to learn the optimal switching strategy of BSs in order to minimize the total power consumption of a UDN. The proposed model is first trained offline, after which the trained model is plugged into the network for real-time decision making. Simulation results reveal that the performance of the proposed solution is very close to the optimal solution in terms of trade-off between the power consumption and QoS

Employing industrial quality management systems for quality assurance in outcome-based engineering education: a review

With the world becoming flat with fluid boundaries, engineers have to be global in their outlook and their pedigree. Due to the need for international acceptance of engineering qualification, the incorporation of Outcome-Based Education (OBE) has become common and global accreditation treaties such as the Washington Accord have been ratified. Further, it becomes important, especially for an engineering university with a global outlook preparing its students for global markets, to ensure that its graduates attain the planned outcomes. Additionally, the higher education institutions need to make sure that all the stakeholders, including students, parents, employers, and community at large, are getting a quality educational service, where quality is categorized as (1) product-based ensuring that the graduate attained the planned outcomes and skills, and (2) process-based keeping an eye on whether the process is simple, integrated, and efficient. The development of quality movements, such as Total Quality Movement (TQM), Six Sigma, etc., along with quality standards such as ISO 9001 has been instrumental in improving the quality and efficiency in the fields of management and services. Critical to the successful deployment of a quality culture is the institutionalization of an integrated Quality Management System (QMS) in which formally documented processes work according to the Vision and Mission of an institute. At the same time, commitment to Continuous Quality Improvement (CQI) to close the loop through effective feedback, would ensure that the planned outcomes are attained to the satisfaction of all the stakeholders, and that the process overall is improving consistently and continuously. The successful adoption of quality culture requires buy-in from all the stakeholders (and in particular, the senior leadership) and a rigorous training program. In this paper, we provide a review of how a QMS may work for the provision of quality higher education in a 21st-century university

Energy optimization in ultra-dense radio access networks via traffic-aware cell switching

We propose a reinforcement learning based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex

Reinforcement Learning Driven Energy Efficient Mobile Communication and Applications

Smart city planning is envisaged as advance technology based independent and autonomous environment enabled by optimal utilisation of resources to meet the short and long run needs of its citizens. It is therefore, preeminent area of research to improve the energy consumption as a potential solution in multi-tier 5G Heterogeneous Networks (HetNets). This article predominantly focuses on energy consumption coupled with CO 2 emissions in cellular networks in the context of smart cities. We use Reinforcement Learning (RL) vertical traffic offloading algorithm to optimize energy consumption in Base Stations (BSs) and to reduce carbon footprint by applying widely accepted strategy of cell switching and traffic offloading. The algorithm relies on a macro cell and multiple small cells traffic load information to determine the cell offloading strategy in most energy efficient way while maintaining quality of service demands and fulfilling users applications. Spatio-temporal simulations are performed to determine a cell switch on/off operation and offload strategy using varying traffic conditions in control data separated architecture. The simulation results of the proposed scheme prove to achieve reasonable percentage of energy and CO 2 reduction

Mobility management-based autonomous energy-aware framework using machine learning approach in dense mobile networks

A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%