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The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning platforms such as the IP Multimedia Subsystem (IMS). The NFV concept is evolving towards a lightweight solution based on containers that, by contrast to classic virtual machines, do not carry a whole operating system and result in more efficient and scalable deployments. On the other hand, IMS has become an integral part of the 5G core network, for instance, to provide advanced services like Voice over LTE (VoLTE). In this paper we combine these virtualization and service provisioning concepts, deriving a containerized IMS infrastructure, dubbed cIMS, providing its assessment through statistical characterization and experimental measurements. Specifically, we: $\textit {i)}$ model cIMS through the queueing networks methodology to characterize the utilization of virtual resources under constrained conditions; $\textit {ii)}$ draw an extended version of the Pollaczek-Khinchin formula, which is useful to deal with bulk arrivals; $\textit {iii)}$ afford an optimization problem focused at maximizing the whole cIMS performance in the presence of capacity constraints, thus providing new means for the service provider to manage service level agreements (SLAs); $\textit {iv)}$ evaluate a range of cIMS scenarios, considering different queuing disciplines including also multiple job classes. An experimental testbed based on the open source platform Clearwater has been deployed to derive some realistic values of key parameters (e.g., arrival and service times).

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining desired performance due to the complicated and changeable environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the flexibility of routing protocols. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without the interruption of network services. Through extensive performance evaluation studies and simulations, the results demonstrated that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade.

Putting trust in the world of the Internet of Things, where served and serving entities are often unknown, is very hard especially when personal and business information is often being exchanged for providing and consuming services. Moreover, the issues of interoperability and scalability of billions of heterogeneous things in the IoT systems require more attention. A user-centric model of complex interconnected things must be designed in a way that not only makes things trustworthy for common people but it also provides the solution for interoperability and scalability. ARCA-IoT is such a system which not only identifies the attributes (including quality of service) essential for trust but also presents a user-centric model that is robust enough to tackle the attacks made by dishonest entities to manipulate the trustworthiness. For scalability and interoperability, a cloud-assisted environment is introduced in the ARCA-IoT. An intuitive Naive Bayes approach is used to train the ARCA-IoT in a way that it calculates the probabilities of the trustworthiness of the entities and then identifies various types of attacks with the support of three proposed algorithms. The approach is validated with a specifically designed simulated environment. Based on our simulation results, the ARCA-IoT demonstrates the effectiveness in term of performance metrics such as accuracy, sensitivity, specificity, and precision. Besides this, the system outperforms the existing related approaches in terms of a qualitative analysis based on different parametric metrics such as interoperability, scalability, context-awareness, and a human-like decision.

In RPL routing protocol, the destination advertisement object (DAO) control messages are announced by the child nodes to their parents to build downward routes. A malicious insider node can exploit this feature to send fake DAOs to its parents periodically, triggering those parents, in turn, to forward the fake messages upward to the root node. In this letter, we show how this behavior can have a detrimental side effect on the performance of the network, increasing power consumption, latency, and reducing reliability. To address this problem, a new scheme is introduced to mitigate significantly the effect of the DAO attack on network performance.

Simultaneous localization and environment mapping (SLAM) is the core to robotic mapping and navigation as it constructs simultaneously the unknown environment and localizes the agent within. However, in millimeter wave (mmWave) research, SLAM is still at its infancy. This paper consists a first of its kind in mapping an indoor environment based on the RSS, Time-Difference-of-Arrival, and Angle-of-Arrival measurements. We introduce MOSAIC as a new approach for SLAM in indoor environment by exploiting the map-based channel model. More precisely, we perform localization and environment inference through obstacle detection and dimensioning. The concept of virtual anchor nodes (VANs), known in literature as the mirrors of the real anchors with respect to the obstacles in the environment, is explored. Then, based on these VANs, the obstacles positions and dimensions are estimated by detecting the zone of paths obstruction, points of reflection, and obstacle vertices. Then, extended Kalman filter is adapted to the studied environment to improve the estimation of the points of reflection hence the mapping accuracy. Cramer-Rao lower bounds are also derived to find the optimal number of anchor nodes. Simulation results have shown high localization accuracy and obstacle detection using mmWave technology.

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model.

Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions.