Over the last few years, several serious data breaches have raised significant public awareness of data privacy and have motivated researchers to envisage novel data sharing and management schemes. Meanwhile, personal information is becoming a valuable commodity; however, the business model used by service providers rarely involves sharing the revenue made by selling personal data, with the individuals - the data owners. This paper proposes an alternative storage and data-sharing method leveraging Blockchain technology, Smart Contracts, and Attributed-based Encryption. Blockchain’s core features such as transparency and immutability of transactions are utilised to create a decentralised system that allows individuals to maintain control over their data and directly benefit from it. Smart contracts, integrated with Attribute-based encryption schemes, are leveraged to allow users to securely share their data in an automated process, enabling them to receive a fair share of revenue for allowing service providers to use their data.

Blockchain technology has emerged as a transformative force in various sectors, including finance, healthcare, supply chains, and intellectual property management. Beyond Bitcoin’s role as a decentralized payment system, Ethereum represents a notable application of blockchain, featuring Smart Contract functionality that enables the development and execution of decentralized applications (DApps). A key feature of Ethereum , and public blockchains in general, is pseudonymity, typically achieved by using public keys as pseudonyms for users. Despite implementing several privacy-preserving techniques, the public recording of user activities on the blockchain allows various deanonymization methods that can profile users, reveal sensitive information , and potentially re-identify them. Most blockchains, such as Bitcoin , Litecoin , and Cardano, employ the Unspent Transaction Output (UTXO) model for accounting, which focuses on individual transactions and is susceptible to various deanonymization techniques. In contrast, Ethereum uses an account-based transaction model, integrating the concepts of accounts and wallets at the protocol level. This makes most UTXO-based deanonymization techniques ineffective for Ethereum. However, alternative methods with the potential to deanonymize Ethereum users have been proposed and developed. Privacy preservation techniques have been used to counteract deanonymization attempts; however, the challenges related to these techniques, their effectiveness and efficiency, and the trade-off between usability and protection levels remain areas for further exploration. This survey presents a comprehensive analysis of state-of-the-art privacy preservation along with deanonymization techniques in the blockchain and Ethereum ecosystems. This survey examines the intrinsic mechanisms supporting pseudonymity in Ethereum, providing a detailed assessment of the advantages and disadvantages of privacy preservation techniques, and suggests potential countermeasures against those deanonymization methods. It also discusses the implications arising from the intersection of DApps and data protection legislation , which is vital for ensuring the coexistence and advancement of groundbreaking blockchain capabilities and protecting user data.

This paper proposes an offloading strategy for communication resources and computing resources. Based on the communication background of NOMA, this strategy combines D2D communication and cellular mobile communication to consider the use of resources in the communication process. According to the performance of communication resources, the power of cellular network users and D2D users is controlled to make the task unloading decision between users and small MEC server base stations. The purpose is to find the minimum sum of the total cost of the user group within the coverage of the entire base station. The cost is obtained from the task completion time and the base station usage payment. The experimental results show that this method can find the optimal solution of the user group cost, and it also has a good performance in user fairness. In reality, taking D2D communication into consideration for coordinated distribution in a cellular network can greatly reduce the cost of the two.

In order to solve the problem of task offloading with dependent subtasks in mobile edge computing (MEC), we propose a graph mapping offloading model based on deep reinforcement learning (DRL). We model the user's computing task as directed acyclic graph (DAG), called DAG task. Then the DAG task is converted into a topological sequence composed of task vectors according to the custom priority. And the model we proposed can map the topological sequence to offloading decisions. The offloading problem is formulated as a Markov decision process (MDP) to minimize the trade-off between latency and energy consumption. The evaluation results demonstrate that our DRL-based graph mapping offloading model has better decision-making ability, which proves the availability and effectiveness of the model.

Sparse Mobile Crowdsensing (SparseMCS) has become an efficient method for collecting and inferring sensor data at the urban scale, attracting many researchers' attention. However, there is a risk of location privacy being leaked when the participants complete the task. The existing SparseMCS location privacy-preserving mechanisms focus on protecting location information from the spatial dimension while ignoring the time dimension's location privacy. To solve this problem, we combine spatiotemporal activity privacy with location differential privacy and propose a novel location privacy-preserving mechanism. More specifically, location differential privacy is used to limit the adversary's information gain in the space dimension, and spatiotemporal activity privacy is used to determine the adversary's information gain in the time dimension. The combination of the two can protect the user's spatiotemporal activity patterns over a period. To achieve efficient sensing data collection, we train the privacy-preserving model when the server is offline. Since the location privacy-preserving mechanism will affect the accuracy of the sensing data, in order to reduce the data loss, we design a privacy-preserving model consisting of three parts: First, to fit the original location sensing data to the confusion location, we learned a data adjustment function. Secondly, we solve the optimal position confusion function through nonlinear programming that aims to minimize data loss. Third, we use an inference algorithm that is sensitive to data loss to improve the accuracy of the final inferred data. Our work is within the acceptable error range of 10-3~10-2, which can achieve more comprehensive and stronger location privacy.

Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary. Such DApps open a marketplace in which services are delivered to end-users by contributors which are then incentivised by cryptocurrencies in an automated, peer-to-peer, and trustless fashion. However, blockchain, consolidated by smart contracts, only ensures on-chain data security, autonomy and integrity of the business logic execution defined in smart contracts. It cannot guarantee the quality of service of DApps, which entirely depends on the services’ performance. Thus, there is a critical need for a trust system to reduce the risk of dealing with fraudulent counterparts in a blockchain network. These reasons motivate us to develop a fully decentralised trust framework deployed on top of a blockchain platform, operating along with DApps in the marketplace to demoralise deceptive entities while encouraging trustworthy ones. The trust system works as an underlying decentralised service providing a feedback mechanism for end-users and maintaining trust relationships among them in the ecosystem accordingly. We believe this research fortifies the DApps ecosystem by introducing an universal trust middleware for DApps as well as shedding light on the implementation of a decentralised trust system.

Convergence of critical infrastructure and data, including government and enterprise, to the dynamic Internet of Things (IoT) environment and future digital ecosystems exhibit significant challenges for privacy and identity in these interconnected domains. There are an increasing variety of devices and technologies being introduced, rendering existing security tools inadequate to deal with the dynamic scale and varying actors. The IoT is increasingly data driven with user sovereignty being essential - and actors in varying scenarios including user/customer, device, manufacturer, third party processor, etc. Therefore, flexible frameworks and diverse security requirements for such sensitive environments are needed to secure identities and authenticate IoT devices and their data, protecting privacy and integrity. In this paper we present a review of the principles, techniques and algorithms that can be adapted from other distributed computing paradigms. Said review will be used in application to the development of a collaborative decision-making framework for heterogeneous entities in a distributed domain, whilst simultaneously highlighting privacy preserving issues in the IoT. In addition, we present our trust-based privacy preserving schema using Dempster-Shafer theory of evidence. While still in its infancy, this application could help maintain a level of privacy and nonrepudiation in collaborative environments such as the IoT.

With the widespread of Internet-of-Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders, such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this article proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their data set to the data service provider as a noncooperative game model. Based on the Nash equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.

The General Data Protection Regulation (GDPR) gives control of personal data back to the owners by appointing higher requirements and obligations on service providers who manage and process personal data. As the verification of GDPR-compliance, handled by a supervisory authority, is irregularly conducted; it is challenging to be certified that a service provider has been continuously adhering to the GDPR. Furthermore, it is beyond the data owner's capability to perceive whether a service provider complies with the GDPR and effectively protects her personal data. This motivates us to envision a design concept for developing a GDPR-compliant personal data management platform leveraging the emerging blockchain and smart contract technologies. The goals of the platform are to provide decentralised mechanisms to both service providers and data owners for processing personal data; meanwhile, empower data provenance and transparency by leveraging advanced features of the blockchain technology. The platform enables data owners to impose data usage consent, ensures only designated parties can process personal data, and logs all data activities in an immutable distributed ledger using smart contract and cryptography techniques. By honestly participating in the platform, a service provider can be endorsed by the blockchain network that it is fully GDPR-compliant; otherwise, any violation is immutably recorded and is easily figured out by associated parties. We then demonstrate the feasibility and efficiency of the proposed design concept by developing a profile management platform implemented on top of the Hyperledger Fabric permissioned blockchain framework, following by valuable analysis and discussion.

The emerging Blockchain (BC) and Distributed Ledger technologies have come to impact a variety of domains, from capital market sectors to digital asset management in the Internet of Things (IoT). As a result, more and more BC-based decentralized applications for numerous cross-domain services have been developed. These applications implement specialized decentralized computer programs called Smart Contracts (SCs) which are deployed into BC frameworks. Although these SCs are open ato public, it is challenging to discover and utilize such SCs for a wide range of usages from both systems and end-users because such SCs are already compiled in form of byte-codes without any associated meta-data. This motivates us to propose a solution called Semantic SC (SSC) which integrates RESTful semantic web technologies in SCs, deployed on the Ethereum Blockchain platform, for indexing, browsing and annotating such SCs. The solution also exposes the relevant distributed ledgers as Linked Data for enhancing the discovery capability. To achieve this goal, the OWL-S service ontology is extended by incorporating some domain specific terminologies, which are used in the development of the proposed SSCs. As a result, SSC can be utilized to enrich queries for a domain-specific terms across multiple distributed ledgers, which greatly increases the discovery capability of decentralized IoT applications and services. Contribution in standardization is also discussed. We believe that our research work takes the first steps towards connecting BC-based decentralized services with semantic web services in order to provide better IoT ecosystems.

The major objective of evolution towards 5G networks is to support increasing number of end devices with stringent latency and high bandwidth requirements. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are paving the way in supporting such requirements of 5G networks. In this paper, we propose a seamless mobility management for the users when they move from a SDN controller coverage to another in 5G heterogeneous networks. Our proposed solution utilizes the key-value Distributed Hash Table (DHT) to catch users' mobility in a distributed SDN controller to address scalability and seamlessness, where mobile device may join and leave between different associated SDN controllers. The proposed solution allows to select an appropriate AP with cooperation of mobile devices and controllers, so that network performance can be maximized and users' demand can be met in a dynamically changing of network condition. The performance evaluated using OMNeT++ simulator imparts that the solution introduced in this paper can successfully reduce handover latency around 50% compared to the conventional mobility management solutions.

Mobile crowd-sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing a better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this paper, a novel trust model called experience-reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in an MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called experience and reputation are calculated accordingly. By incorporating the experience and reputation trust indicators (TIs), trust relationships between the users are established, evaluated, and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in an MCS testbed, which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers a better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.

Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.

In recent years, Blockchain has been expected to create a secure mechanism for exchanging not only for cryptocurrency but also for other types of assets without the need for a powerful and trusted third-party. This could enable a new era of the Internet usage called the Internet of Value (IoV) in which any types of assets such as intellectual and digital properties, equity and wealth can be digitized and transferred in an automated, secure, and convenient manner. In the IoV, Blockchain is used to guarantee security of transactions that the transactions are nearly impossible to be altered; thus it is impractical to retract once a transaction is confirmed. Therefore, to strengthen the IoV, before making any transactions it is crucial to evaluate trust between participants for reducing the risk of dealing with malicious peers. In this article, we clarify the concept of IoV and propose a trust-based IoV model including a system architecture, components and features. Then, we present a trust platform in the IoV considering two concepts, Experience and Reputation, originated from Social Networks for evaluating trust between two any peers in the IoV. The Experience and Reputation are characterized and calculated using mathematical models with analysis and simulation in the IoV environment. We believe this paper consolidates the understandings about IoV technologies and demonstrates how trust is evaluated and used to strengthen the IoV. It also opens important research directions on both IoV and trust in the future.

Trust has been exploring in the era of Internet of Things (IoT) as an extension of the traditional triad of security, privacy and reliability for offering secure, reliable and seamless communications and services. It plays a crucial role in supporting IoT entities to reduce possible risks before making decisions. However, despite a large amount of trust-related research in IoT, a prevailing trust evaluation model has been still debatable and under development. In this article, we clarify the concept of trust in the Social Internet of Things (SIoT) ecosystems and propose a comprehensive trust model called REK that incorporates third-party opinions, experience and direct observation as the three Trust Indicators. As the convergence of the IoT and social network, the SIoT enables any types of entities (physical devices, smart agents and services) to establish their own social networks based on their owners' relationships. We leverage this characteristic for inaugurating Experience and Reputation, which are originally two concepts from social networks, as the two paramount indicators for trust. The Experience and Reputation are characterized and modeled using mathematical analysis along with simulation experiments and analytical results. We believe our contributions offer better understandings of trust models and evaluation mechanisms in the SIoT environment, particularly the two Experience and Reputation models. This paper also opens important trust-related research directions in near future.

Nowadays a cellular network suffers from a data traffic load in a metropolitan area due to the enormous number of mobile devices connectivity. Therefore, the users experience many issues because of a congestion and overload at an access network such as low throughput, long latencies and network outages. Current network operator's solutions, such as capping data usage and throttling a connection speed, have a negative effect on the user satisfaction. Therefore, alternative solutions are needed such as Access Point (AP)-based complementary network. In this paper, we use WiFi as a complementary network to Long-Term Evolution (LTE). We propose a seamless network architecture between LTE and WiFi networks, by utilizing the packet gateway (P-GW) as an IP flow anchor between LTE and WiFi to maintain a seamless connectivity. The proposed architecture has two new components, Access Network Query Protocol-Data Server (ANQP-DS) and Access Zone Control (AZC), to WiFi core network for managing UE authentication and balancing the load of UEs between APs. Finally, we demonstrate and validate the effectiveness of our proposed idea over other prior approaches based on comparison with a current handover and Extensible Authentication Protocol-Authentication and Key Agreement (EAP-AKA) mechanisms in the literature through simulations.

The recent advancements in cellular communication domain have resulted in the emergence of Machine-to-Machine applications, in support of the wide range and coverage provision, low costs, and high mobility. 5G network standards represent a promising technology to support the future of Machine-to-Machine data traffic. In recent years, Human-Type-Communication traffic has seen exponential growth over cellular networks, which resulted in increasing the capacity and higher data rates. These networks are expected to face challenges such as explosion of the data traffic due to the future of smart devices data traffic with various Quality of Service requirements. This paper proposes a novel data traffic aggregation model and algorithm along with a new 5G network slicing based on classification and measuring the data traffic to satisfy Quality of Service for smart systems in a smart city environment. In our proposal, 5G radio resources are efficiently utilized as the smallest unit of a physical resource block in a relay node by aggregating the data traffic of several Machine-to-Machine devices as separate slices based on Quality of Service for each application. OPNET is used to assess the performance of the proposed model. The simulated 5G data traffic classes include file transfer protocol, voice over IP, and video users.

In the Internet of Thing, data is almost collected, aggregated and analyzed without human intervention by machine-to-machine communications resulting in raising serious challenges on access control. Particularly in Smart City ecosystems in which multi-modal data comes from heterogeneous sources, data owners cannot imagine how their data is used to extract sensitive information. Thus, there is a critical need for novel access control methods that minimize privacy risks while increase ability of personalized access control. Our solution is to build a trust-based usage control mechanism called TUCON that enables stakeholders to set access control policies based on their trust relationships with data consumers. In this study, we introduce two novel paradigms integrated in the Smart City shared platform: a Trust Service Platform and a Data Usage Control, then bring them together to establish the new mechanism. The conceptual model, the architecture, the formalization, and the practical development of TUCON is described in detail. We also show the roles and the interactions of TUCON components in the Smart City platform. Our contributions lie in a new trust model with a trust computation procedure based on semantic web technologies, a novel trust-based usage control conceptual model including a formalization, a practical expression and an architecture for Smart City systems. We believe this study provides better understanding on both trust and usage control in the Internet of Things and opens several important research directions in the future.

The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could affect the acceptance of sharing data in smart cities. In this study, we focus on data usage transparency and accountability and propose a trust model for data sharing in smart cities, including system architecture for trust-based data sharing, data semantic and abstraction models, and a mechanism to enhance transparency and accountability for data usage. We apply semantic technology and defeasible reasoning with trust data usage policies. We built a prototype based on an air pollution monitoring use case and utilized it to evaluate the performance of our solution.

Understanding the propagation dynamics of information/an epidemic on complex networks is very important for discovering and controlling a terrorist attack, and even for predicting a disease outbreak. As an effective method, with analyzing the structure of a propagation network, a large number of previous studies have analyzed the propagation dynamics. Most of these studies are based on a special network structure to make such analysis. However, a propagation network has dynamically changed structure during the propagation. How to track, recognize and model such dynamic change is a big challenge. Along with the popularity of smart devices and the rapid development of the Internet of Things (IoT), massive mobile data is automatically collected. In this article, as a typical use case, we investigate the impact of network structure on epidemic propagation dynamics by analyzing the massive mobile data collected from smart devices carried by the volunteers of Ebola outbreak areas. From this investigation, we obtain two observations. Based on these observations and the analytical ability of Apache Spark on streaming data and graphs, we propose a simple model to track and recognize the dynamic structure of a network. Moreover, we introduce and discuss open issues and future work for developing this proposed recognition model.

In this paper, we propose a novel energy efficient solution for TDM Passive Optical Networks (TDM-PONs), which is a widely used access network technology, to facilitate local customer internetworking. Here, our prime objectives are to ensure traffic latency among different customers connected through TDM-PON and maximize energy saving performance of Optical Network Units (ONUs), which are TDM-PON equipment situated at customer premises. To do so, we devise a novel sleep management protocols for ONUs. In our proposal, when some ONUs need to exchange traffic, they maintain sleep mode as a group in order to reduce traffic latency among the customers attached with those ONUs. To date, considering traffic forwarding between access and core network entities, researchers have come up with several solutions to manage sleep mode in ONUs in order to improve PON energy saving. However, to the best of our knowledge, how ONUs can manage sleep mode and at the same time satisfy latency requirements while managing data flows among them has been paid little attention. The effectiveness of our solution is validated and compared with other alternative approach by simulation. Simulation results demonstrate that proposed solution reduces inter-ONU traffic delay noticeably compared to other solution while providing satisfactory energy saving performance of ONUs.

Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios. We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture.

The devices profile for Web services (DPWS) standard enables the use of Web services for resource-constrained devices, main components of the Internet of Things (IoT). DPWS can power the next generation of IoT applications by connecting millions of networked devices and services on the Web. This paper presents a simulator, called DPWSim, to support the use of this technology. DPWSim featuring secure messaging, dynamic discovery, service description, service invocation, and publish-subscribe eventing can be used to prototype, develop, and test products in terms of DPWS communication protocols. It can also support the collaboration among manufacturers, developers, and designers during the new product development process.

Service-oriented architecture (SOA) is realized by independent, standardized, and self-describing units known as services. This architecture has been widely used and verified for automatic, dynamic, and self-configuring distributed systems such as in building automation. This paper presents a building automation system adopting SOA paradigm with devices implemented by device profile for web service (DPWS) in which context information is collected, processed, and sent to a composition engine to coordinate appropriate devices/services based on the context, composition plan, and predefined policy rules. A six-phased composition process is proposed to carry out the task. In addition, two other components are designed to support the composition process: building ontology as a schema for representing semantic data and composition plan description language to describe context-based composite services in form of composition plans. A prototype consisting of a DPWSim simulator and SamBAS is developed to illustrate and test the proposed idea. Comparison analysis and experimental results imply the feasibility and scalability of the system.

The concept of connectedness, as inspired by Social Networking Service, is a key factor which participates in changing the way people interact with each other over the Internet. On the other hand, connected world as envisioned by the Internet of Things aims to expand the idea of connectivity to include everything in the physical world to a big network called the Internet. In order to realize the integration between the world of connected people and the world of connected devices, intelligence including semantics and recommendation acts as a key factor to expand the basic communication functionalities to include search, discovery, mashup of new services and filtering. We propose a framework to facilitate the next generation of communication between people and devices, and a preliminary prototype including three modules DPWSim, ThingsGate, ThingsChat along with a use case discussion.

During off-peak hours of a day, when incoming and outgoing traffic arrival rate used to be low, a Mobile Terminal (MT) can stay in idle mode for long time. For tracking the idle MTs, a wireless access network invokes them to conduct location update whenever they enter into a new location. This allows the network to route a call successfully. To provide any IP packet based service, Mobile IP (MIP) is very important protocol undeniably. However, Proxy Mobile IP was developed by IETF without considering idle mode condition of MTs. Consequently, an idle MT needs to conduct MIP binding (Layer 3 location update) whenever it moves to a new area of a Mobile Access Gateway (MAG) although it does not have any incoming or outgoing packets during idle periods. This phenomenon unnecessarily increases location update signaling cost. In our previous work, we proposed a mechanism using which only Layer 2 location update is needed when an MT is in idle mode and the Layer 3 location update is conducted after a call arrives for an idle MT in Optical and Wireless Converged Access Networks (OWCAN). This allows saving energy in the mobility management nodes (e.g. MAG, Base Stations) significantly. Based on our previous work, in this paper we propose a novel algorithm for the Optical Network Units (ONUs) of an OWCAN. This algorithm decides when to interrupt modules of a sleeping ONU to wake up and which frames should be forwarded for uplink transmission. We have found that adopting this algorithm an ONU of an OWCAN can save energy significantly. Therefore, our solutions mainly contribute in two-fold to make a green OWCAN. First, it minimizes energy consumption in the mobility management nodes. Second, our solution also reduces energy consumption and improves bandwidth utilization in the optical backhaul.

Nowadays Passive Optical Network (PON) requires that Optical Network Units (ONUs) wake up periodically to check if the Optical Line Terminal (OLT) has any message directed to them. This implies that ONUs change from sleeping mode in which they just consume 1 W to active mode in which the consumption goes up to 10 W. In many cases, the OLT does not have any packets for the ONU and it goes to sleep again, what supposes a waste of energy. In this paper, we propose a novel Hybrid ONU that relies on a low-cost and low-energy technology, IEEE 802.15.4, to wake up those ONUs that are going to receive a packet. Our first estimations demonstrates that our solution would save around 25000$ per year and OLT.

To manage multicast group in wired or wireless domain, the hosts need to send internet group management protocol (IGMP) report message when they are queried by the multicast routers. In fact, the bandwidth of wireless domain and mobile nodes' battery power are scarce resources; therefore, it can be big burden for the receiver-driven layered video multicasting (RLVM) receiving mobile hosts to follow the conventional report sending phenomenon, where a host needs to send a report message for subscribing each of the video layers. In this paper, we propose a mechanism for reducing the number of IGMP report messages for RLVM receiving hosts. The protocol overhead and efficiency are evaluated, and compared with conventional IGMPs. Our results show that applying proposed idea in IGMP the number of report messages can be minimized significantly for RLVM services.

In this paper, we introduce the personalized Internet protocol television (IPTV) services for new business opportunities in next generation networks (NGN) and present the direction and strategies for services deployment in network operators' perspective. Personalized IPTV will be key solutions for value added services over Internet and emergence of Web and NGN will provide open platform and new business model for IPTV services in the future.

Internet protocol television (IPTV), one of the most emerging services, offers multimedia streaming services with security, reliability, and relevant quality of service (QoS)/quality of experience (QoE). It provides added values to all the involving players including customers and also brings technical and business challenges to those players. For IPTV services, we expect to adopt the next generation network (NGN) environment for high quality and the Web technologies for personalization to meet the customer's necessity. Web can provide an open, flexible, and agile platform. Therefore, in this paper, we propose personalized IPTV services based on Web-based open platform in NGN environment and present functional architecture for Web-based personalized IPTV services. Technical issues for deploying the proposed IPTV services using Web are also provided. The objective of this paper is to analyze the critical architectural issues for developing viable and feasible networking platform models for personalized IPTV service.

In this paper, we consider a multihoming mobile router to be connected via dual interfaces such as WLAN(Wireless LAN) and WiBro(Mobile WiMAX). In order to provide redundancy and fault tolerance, we propose load balancing scheme using policy routing in a mobile router. This scheme can improve network reliability and guarantee the continuous connectivity with desirable Quality of Service(QoS). To efficiently support multihoming and network mobility in mobile router, we provide the implementation method of our proposed scheme using netfilters tool of Linux and show the performance results in our experimental environment.

We propose a new optical hybrid switching system that takes advantage of both optical burst switching (OBS) and optical circuit switching (OCS) technologies. This system classifies incoming IP traffic flows into short-lived and long-lived flows. We model the system as a single server queue in a Markovian environment. The burst generation process is assumed to follow a two-state Markov modulated Poisson process (MMPP), and the service rate fluctuates based on the number of concurrent OCS sessions. Results for the mean delay and queue size are derived.