Sensor Event Processing on Grid

Wireless sensor networks are increasingly being deployed in many important applications. For sharing huge amount of sensor data efficiently with diverse users, an information dissemination mechanism is very necessary and important component. In this paper, we have proposed an efficient architecture integrated with sensor network and Grid technology. To disseminate the sensed data to users geographically distributed, an experimental method using Data Grid on pub/sub (publish/subscription) is designed for a u-Healthcare application and its performance is evaluated for various predicate cases

Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.Comment: Accepted Article BY IEEE Transactions on Network and Service Management, DOI: 10.1109/TNSM.2021.304938

Network as a Service and its Key Challenges in Cloud Computing

With the passage of time, cloud computing is gaining importance due its usability, flexibility, efficiency, and reachability.  Virtualization is the key component in cloud computing. Through virtualization, not only software and hardware resources are efficiently used, but also a lot of money is saved. Virtual networking is also an emerging utilization, achieved through virtualization of resources. Keeping in view the importance of this area of research, this paper discusses about virtual networking and the key challenges involved in it and in virtual switch

Sensor Proxy Mobile IPv6 (SPMIPv6)—A Novel Scheme for Mobility Supported IP-WSNs

IP based Wireless Sensor Networks (IP-WSNs) are gaining importance for their broad range of applications in health-care, home automation, environmental monitoring, industrial control, vehicle telematics and agricultural monitoring. In all these applications, mobility in the sensor network with special attention to energy efficiency is a major issue to be addressed. Host-based mobility management protocols are not suitable for IP-WSNs because of their energy inefficiency, so network based mobility management protocols can be an alternative for the mobility supported IP-WSNs. In this paper we propose a network based mobility supported IP-WSN protocol called Sensor Proxy Mobile IPv6 (SPMIPv6). We present its architecture, message formats and also evaluate its performance considering signaling cost, mobility cost and energy consumption. Our analysis shows that with respect to the number of IP-WSN nodes, the proposed scheme reduces the signaling cost by 60% and 56%, as well as the mobility cost by 62% and 57%, compared to MIPv6 and PMIPv6, respectively. The simulation results also show that in terms of the number of hops, SPMIPv6 decreases the signaling cost by 56% and 53% as well as mobility cost by 60% and 67% as compared to MIPv6 and PMIPv6 respectively. It also indicates that proposed scheme reduces the level of energy consumption significantly

mCSQAM: Service Quality Assessment Model in Mobile Cloud Services Environment

Cloud computing is high technology that extends existing IT capabilities and requirements. Recently, the cloud computing paradigm is towards mobile with advances of mobile network and personal devices. As concept of mobile cloud, the number of providers rapidly increases for various mobile cloud services. Despite development of cloud computing, most service providers used their own policies to deliver their services to user. In other words, quality criteria for mobile cloud service assessment are not clearly established yet. To solve the problem, there were some researches that proposed models for service quality assessment. However, they did not consider various metrics to assess service quality. Although existing research considers various metrics, they did not consider newly generated Service Level Agreement. In this paper, to solve the problem, we proposed a mobile cloud service assessment model called mCSQAM and verify our model through few case researches. To apply the mobile cloud, proposed assessment model is transformed from ISO/IEC 9126 which is an international standard for software quality assessment. mCSQAM can provide service quality assessment and determine raking of the service. Furthermore, if Cloud Service Broker includes mCSQAM, appropriate services can be recommended for service users using user and service conditions.</jats:p

IPTV Service Framework Based on Secure Authentication and Lightweight Content Encryption for Screen-Migration in Cloud Computing

These days, the advancing of smart devices (e.g. smart phones, tablets, PC, etc.) capabilities and the increase of internet bandwidth enables IPTV service provider to extend their services to smart mobile devices. User can just receive their IPTV service using any smart devices by accessing the internet via wireless network from anywhere anytime in the world which is convenience for users. However, wireless network communication has well a known critical security threats and vulnerabilities to user smart devices and IPTV service such as user identity theft, reply attack, MIM attack, and so forth. A secure authentication for user devices and multimedia protection mechanism is necessary to protect both user devices and IPTV services. As result, we proposed framework of IPTV service based on secure authentication mechanism and lightweight content encryption method for screen-migration in Cloud computing. We used cryptographic nonce combined with user ID and password to authenticate user device in any mobile terminal they passes by. In addition we used Lightweight content encryption to protect and reduce the content decode overload at mobile terminals. Our proposed authentication mechanism reduces the computational processing by 30% comparing to other authentication mechanism and our lightweight content encryption reduces encryption delay to 0.259 second

Content-aware QoE Optimization in MEC-assisted Mobile Video Streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm

A Novel Data Classification and Scheduling Scheme in the Virtualization of Wireless Sensor Networks

Most of the nodes in a wireless sensor network (WSN) remain idle for the maximum period of their lifetime resulting in underutilization of their resources. There are many ongoing research studies to utilize the resources of sensor nodes in an efficient way. Virtualization of sensor network (VSN) is one of the novel approaches to utilize the physical infrastructure of a WSN. VSN can be simply defined as the virtual version of a WSN over the physical sensor infrastructure. By allowing sensor nodes to coexist on a shared physical substrate, VSN may provide flexibility, cost effectiveness, and manageability. This paper proposes a QoS-aware data classification and scheduling framework for VSN in the health care sector. We develop a tiny virtual machine called VSNware for health care applications, which facilitates QoS-aware forwarding of data packets, maintaining the reliability, delay guarantee, and speed. The simulation results also show that the proposed scheme outperforms the conventional WSN approaches. </jats:p

An asymmetric key-based security architecture for wireless sensor networks

In spite of previous common assumptions about the incompatibility of public key cryptography (PKC) schemes with wireless sensor networks (WSNs), recent works have shown that they can be utilized for such networks in some manner. The major challenge of employing a PKC-based scheme in a wireless sensor network is posed by the resource limitations of the tiny sensors. Considering this sensor feature, in this paper we propose an efficient PKC-based security architecture with relatively lower resource requirements than those of previously proposed PKC schemes for WSN. In addition, our scheme aims to provide robust security in the network. Our security architecture comprises two basic components; a key handshaking scheme based on simple, linear operations and the derivation of a decryption key by a receiver node. Our architecture enables node-to-base-station and node-to-node secure communications. Analysis and simulation results show that our proposed architecture ensures a good level of security for network communications, and can be effectively implemented with the limited computational, memory, and energy budgets of current-generation sensor nodes