In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring tether connectivity for demanding applications. The deployment of the fifth generation of telecommunications technologies (5G) has set the basis for XR offloading solutions with the goal of enabling lighter and fully wearable XR devices. In this paper, we present a traffic dataset for two demanding XR offloading scenarios that substantially extend those available in the current state of the art, captured using a fully developed end-to-end XR offloading solution. We also propose a set of accurate traffic models for the proposed scenarios based on the captured data, accompanied by a simple and consistent method to generate synthetic data from the fitted models. Finally, using an open-source 5G radio access network (RAN) emulator, we validate the models both at the application and resource allocation layers. Overall, this work aims to provide a valuable contribution to the field with data and tools for designing, testing, improving, and extending XR offloading solutions in academia and industry.

The endeavours to interpret the acquired data are combined with the efforts to strengthen the smart city's multidimensional framework. As the name implies, smart cities are built atop more intelligent data. However, it is a significant challenge because Big Data needs to be evaluated to provide interpretation for a posterior evolution of the current technology. Therefore, using the right building blocks is vital, aligned with clear and convincing guidelines on best practices. To achieve a scale of evaluation, we need standards. The intertwining development drivers need to define how we see and measure the world around us and how this Big Data in the era of IoT informs the decision-making processes. Hence, we are introducing an evaluation model for Big Data obtained from the assessment of Quality of Service (QoS) and Quality of Experience (QoE) delivery in an urban environment. Using the Best-Worst Method (BWM) combined with the orientation of Intuitionistic fuzzy sets. We obtained intuitive preference information based on various criteria. Thus, by prioritizing these end-user predilections and transmitting them into adaptable technological improvements, we achieved a significant step toward sustainable Smart Cities.

The upcoming IEEE 802.11be standard aims to provide extremely high bitrates to support next generation use cases. Among the proposed features, multi-link operation (MLO) is probably the one contributing most towards this goal. MLO enables new types of devices, i.e., multi-link devices (MLDs), to transmit simultaneously over multiple frequency bands to achieve massive bitrates (reaching 40 Gbps) and, consequently, lower latency. However, the coexistence of MLDs with legacy devices in existing and future wireless local area network (WLAN) deployments has not yet been explicitly investigated. In this work, we investigate different band management policies over a three-band densely populated WLAN, allowing MLDs to use one or more bands for the access procedure and data transfer. We evaluate, via extensive simulations, the access delay of the devices and the network throughput with respect to the ratio of legacy devices and MLDs. We show that by using different band allocation policies for MLDs, several trade-offs regarding throughput and access delay arise that need careful consideration to avoid performance degradation.

Recently, the Industrial Internet of Things (IIoT) and Industry 4.0 paradigms have attracted considerable relevance by leading all manufacturing companies to invest in IIoT solutions in order to be competitive on the market. Despite the huge number of IIoT solutions on the market, all platforms refer to a common architectural model and share the same functionalities. Among these functionalities, the most relevant is the capability of deploy and run custom containerized applications. This feature has a pivotal role of customizing the IIoT platform and tailoring it to each business scenario. In this paper, we define the general architectural model and common functionalities of the IIoT platforms. Then, we analyze and compare two of the most used IIoT solutions on the market, Siemens Industrial Edge, and EdgeX Foundry. We also define three metrics for evaluating the functionality of deploying custom services on the edge, and we test these two platforms. Finally, we present their comparative performance, advantages, and limitations.

More and more devices connect to the network leading to the emergence of Internet of Things (IoT). One of the most popular use cases is the Industrial IoT (IIoT), in which ultra-high end-to-end network reliability and low latency are the main requirements. In order to fulfill these features, the Institute of Electrical and Electronics Engineers (IEEE) has published the 802.15.4-2015 standard and in particular, its Time Slotted Channel Hopping (TSCH) mode more specifically. Furthermore, to enable transmission of Internet Protocol version 6 (IPv6) packets, the IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) adaptation layer was introduced in RFC 4944 standard, which defines three functions, fragmentation, reassembly and fragment forwarding. However, RFC 4944 does not cope well with lossy links since it requires reception of all fragments in order to reassemble the original IPv6 packet. In order to improve network reliability, Forward Error Correction (FEC) techniques were proposed that enable recovery of lost fragments using redundancy. In this paper, we thoroughly present the state of the art approaches that use FEC techniques. We then introduce our proposal, namely E-XORFEC, which deploys the ability of recovering different fragments based on redundancy factors. Finally, we implement the considered methods in the 6TiSCH simulator, evaluate their performance and finally show that our proposed method outperforms all of them.

This paper proposes an optimal admission and placement stochastic controller that inserts security and depend-ability in the operational aspects of edge-cloud system under a 5G deployment. The proposed mechanism uses the frame-work of Semi-Markov Decision Making Process (SMDP) and seeks for an optimal policy that efficiently allocates the virtual resources to secure and run the services across the cloudified infrastructure. Driven by a new latency-oriented cost structure, the optimal controller achieves a dependable and secure operation by optimally balancing the service requests between the edge and the cloud system taking into account the service profile, the workload, and the traffic load. A structural analysis of the optimal policy reveals its implementation friendliness while a cloudnomics analysis shows that the optimal cost can be further optimized by fine tuning the parameters of the proposed cost structure.

Fire-fighting drones will play a key role in aiding fire-fighting efforts, not only in forest surveillance but also in adopting an agile aid in wildfire fire extinguishing. One of the key challenges in scaling-up the use of fire-fighting drones is their ability to autonomously carry out their tasks, so that swarms of them could be rapidly dispersed. However, in certain situations, it is inevitable that a remote controller needs to be involved in steering/adjusting their operation due to the highly volatile operation environment, especially with heat columns. We build on the potential of the Tactile Internet (TI) in delivering near real-time haptic interaction with drones. We build on the IEEE P1918.1 standard TI architecture and present a deterministic state machine following detailing remote interaction with such drones, and present the message exchanges to support their remote operation. Furthermore, we present a fire-fighting drone use case focused on flight autonomy, remote TI control, and identifying the characteristics of drone in service.

Fire-fighting drones will play a key role in aiding fire-fighting efforts, not only in forest surveillance but also in adopting an agile aid in wildfire fire extinguishing. One of the key challenges in scaling-up the use of fire-fighting drones is their ability to autonomously carry out their tasks, so that swarms of them could be rapidly dispersed. However, in certain situations, it is inevitable that a remote controller needs to be involved in steering/adjusting their operation due to the highly volatile operation environment, especially with heat columns. We build on the potential of the Tactile Internet (TI) in delivering near real-time haptic interaction with drones. We build on the IEEE P1918.1 standard TI architecture and present a deterministic state machine following detailing remote interaction with such drones, and present the message exchanges to support their remote operation. Furthermore, we present a fire-fighting drone use case focused on flight autonomy, remote TI control, and identifying the characteristics of drone in service.

In this paper, soft clustering on network nodes using Multivariate Gaussian Mixture Models (MGMM) is applied to design a machine learning-based routing protocol for Opportunistic Networks (OppNets). The proposed protocol, called Multivariate Gaussian Mixture-based Prediction routing (MGMP), involves sending messages in concentrated bursts to a group of comparable devices detected using a clustering technique. The network features are utilized for training the MGMM-based clustering model to help identify the relay nodes as the best hop for message transmission. The performance of the proposed MGMP protocol is evaluated using the Haggle Infocom-2006 real dataset and compared against two benchmark protocols KNNR and MLPROPH. The considered performance metrics are average latency, delivery probability and, messages dropped. It has been found that when the TTL is increased, the delivery probability increases and then decreases. Indeed, MGMP outperforms MLPROPH and KNNR in terms of delivery probability by 18.10% and 21.30%, respectively.

The Ever-Increasing evolution of technologies in communication, artificial intelligence (AI), manufacturing, etc., is promoting a new wave of industrial revolution. Industrial Internet of Things (IIoT) has been considered as a critical stimulator for both science and economics by amounts of countries.

Remote interaction and manipulation of physical objects is one of the promising directions in networked services across the connected world. In formalizing an enabling architecture, the IEEE P1918.1 Standard for the Tactile Internet (TI) is being developed to outline tactile interactions and democratizing skills. For the Tactile Internet to become a reality, advancements in network technologies, orchestrated with haptic-technologies and artificial intelligence, are necessary. The standardization of 5G, rolling out as the next generation of mobile communications, is paving the way for supporting the Tactile Internet vision. The IEEE P1918.1 Working Group (WG) has been developing the architecture, objectives, reasoning and future use cases of the Tactile Internet. This paper focuses on the potential for enabling TI-based remote surgery, and introduces a specific use case (cholecystectomy) that is mapped to the IEEE P1918.1 standard reference architecture. Challenging issues such as network design, Quality of Service (QoS), and multiplexing are taken into account and discussed. This work also emphasizes the role of deterministic message exchanges that would support Tactile Internet in carrying out a remote operation.

This paper proposes a novel routing protocol for Opportunistic networks called Energy Efficient MultiObjectives Optimized Routing (E2MOOR), which uses a multi-objectives weight function for efficient routing. The proposed protocol is energy efficient and predicts the next optimal forwarder based on four objectives, which consists of hop encounter, distance between the source/intermediate and destination, delivery probability, and node’s energy consumption, as context information. The pareto optimal solutions set is extracted through the Naive and Slow algorithm. The nodes in the set are further used for forwarding the data packets to the destination node. The proposed protocol is evaluated considering the double, triple, and quadruple objective functions, when the predefined threshold values are varied. Simulations results show that the proposed E2MOOR scheme outperforms the E-Epidemic, E-PRoPHET, and E-EDR chosen as benchmarks routing protocols.

Continuous service provision, minimization of energy consumption and improvement of the provided security are the three fields that are tormenting sensors in a smart home. In the current work we propose and evaluate a model that continue to provide its services in a smart home environment even in the case of a fault. Our proposed solution achieves high energy efficiency and guarantees minimum security specifications that describe a reliable smart home model by utilizing blockchain.

The Industrial Internet of Things (IIoT) is expected to be a key enabler for the Industry 4.0. However, networked control automation often requires high reliability and bounded latency to react properly. Thus, modern wireless protocols for industrial networks, such as IEEE 802.15.4-2015 time-slotted channel hopping (TSCH), rely on a strict schedule of the transmissions to avoid collisions and to make the end-to-end traffic deterministic. Unfortunately, guaranteeing a bounded end-to-end latency is particularly challenging since transmissions have to be temporally chained. Even worse, potential degradation of the link quality may result in reconstructing the whole TSCH schedule along the path. In this article, we propose the low-latency distributed scheduling function (LDSF) that relies on the organization of the slotframe in smaller parts, called blocks. Each transmitter selects the right set of blocks, depending on its hop distance from the border router, so that retransmission opportunities are automatically scheduled. To save energy, a node can still turn off its radio as soon as its packet is correctly acknowledged. Our mathematical analysis as well as our simulation evaluation show the efficiency of the proposed LDSF algorithm compared to three state-of-the-art scheduling functions (SFs): 1) the minimal SF (MSF); 2) low-latency SF (LLSF); and 3) stratum.

In this article, we propose a spatial sampling approach to reduce energy consumption for over-the-air (function) computation (AirComp) scheme by utilizing the cross-correlations among sensor readings. Since the conventional AirComp scheme leads to a reduction in total transmission time and latency thanks to the joint communication and computation processes, it is especially well-suited to Internet of Things (IoT) monitoring systems. AirComp lets simultaneous transmissions of all nodes by exploiting the superposition property of wireless channel; however, it does not overcome the high energy consumption paradigm, which is a fundamental problem of densely deployed IoT monitoring systems. We present a minimum mean square error (MMSE) estimation scheme while a small number of observations are available for densely deployed networks. The proposed MMSE estimator provides a significant mean squared error improvement with reducing energy consumption compared to the conventional estimator. Since the network lifetime of IoT monitoring systems can be almost doubled, the proposed estimator provides flexibility for the dense deployment of nodes. The simulation results verify the theoretical expressions.

Efficient image transfer in the Internet of Things (IoT) Era, has many requirements that need to be satisfied based on the nature of each underlying application. Internet of Things is a research paradigm that empowers data, data fusion and encompasses embedded images, which need to be transmitted to numerous interconnected devices. The risks of contravention of owner's rights are increasing, while data transfer creates new demands for securing the Internet of Things. In this respect, watermarking schemes can be used to save those rights from illegal usage and copying of digital image data. For IoT applications digital watermarking can be used to guarantee that the data used is protected and the rights of the owner are secured (i.e. both user and machine generated). This work proposes a novel watermarking scheme based on the biorthogonal family, (biorthogonal 2.2, biorthogonal 3.5 and biorthogonal 5.5) wavelet transform, while it uses a convolution for symlets wavelet transform and coiflets wavelet transform. These wavelet family approaches are highly robust against various types of attacks (both passive and active), for the prevention of the piracy and authentication of the data over IoT ecosystems. The proposed framework is thoroughly evaluated showing great robustness against attacks and allowing a higher level of protection compared to other available frameworks and schemes.

Drone networks offer rapid network deployment to areas that can pose access difficulty. This paper investigates the deployment of multi-hop drone-based unmanned aerial vehicles networks with a focus on the self-organization aspect. When rescue drones carry out their rescue operations which may fly faraway from the gateway, relay drones are autonomously deployed to maintain connectivity. We study the multiple dedicated connections where each rescue drone is connected to the gateway via dedicated relay drones. We show that this approach lacks sharing of relay drones and thus requires more relay drones. We then propose a centralized greedy algorithm and a distributed solution to significantly reduce the number of relay drones. We show that while the distributed self-organized drones (DSOD) solution requires a slightly higher number of relay drones than the greedy algorithm, it eliminates the need for global message exchange which makes it attractive for practical use.

Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our architectural proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.

Industrial applications require more and more low-power operation and high-reliability (close to 100%). Since traditional low-power radio technologies are sensitive to external interference, many recent standards implement frequency hopping schemes. For instance, IEEE 802.15.4-2015 time slotted channel hopping (TSCH) relies on a deterministic schedule of data transmissions combined with a pseudo-random frequency hopping scheme to improve the reliability. Unfortunately, specific radio channels keep on increasing the average number of retransmissions. Using a subset of the best radio channels (whitelisting) helps to improve the reliability, but may create collisions when used improperly. We here investigate the most accurate techniques to use only the best radio channels while still providing deterministic performance. We propose to group the links per timeslot, allocating them either to the same whitelist or even appropriately reordering them to avoid collisions. Finally, we evaluate the performance of the different whitelisting schemes using an experimental dataset from FIT IoT-LAB platform, proving the relevance of such approach to improve the reliability.

The papers in this special section focus on mobile cloud computing. The papers address variety of interesting topics covering different aspects of the Mobile Cloud, such as process offloading, work sharing, performance enhancement of Mobile Clouds, security issues in Mobile Clouds, and applications of Mobile Clouds.

The new generation of LTE (Long Term Evolution) networks provides ubiquitous broadband access to mobile devices matching land communications in quality and speed. However, to optimize network resource usage in a dynamic environment network operators need models and strategies to constantly assess and manage the end-user's Quality of Experience (QoE). Given the importance of these activities, in the current paper, we focus on quality monitoring and the usage of QoE-agents in an LTE-Advanced Pro network. Specifically, we identify the location and the operation of the QoE-Agents based on the accuracy of the measurements and the load in the network considering the frequency of the measurements and the running applications. Emulations have been also carried out to evaluate two scenarios with different network conditions and we made experiments with different quality sampling rates and different application configurations. The preliminary results have shown that the proposed strategy brings to acceptable errors from our measurements, low CPU utilization and acceptable memory utilization.

With the large growth of the Internet of Things (IoT), a strong focus has been put on designing and developing energy efficient and high performance protocols. Industrial-type wireless networks require strict and on-time delivery guarantees, such as close to 100% network reliability and ultra low delay. To this aim, standards such as IEEE 802.15.4-TSCH or Wireless HART, aim to guarantee high-level network reliability by keeping nodes time-synchronized and by employing a slow channel hopping pattern to combat noisy environments and external interference. In wireless networks, since all the radio channels are not impacted in a similar manner, blacklisting bad channels may improve performance of the whole wireless infrastructure. In this paper, we perform a thorough experimental study to characterize the radio (for all IEEE 802.15.4 channels) and connectivity among the nodes of an indoor testbed. More precisely, we investigate the locality of these blacklisting techniques and we highlighted: the fact that some channels perform poorly only in a small set of locations, for certain radio links. Our study tends to justify the need for local blacklisting techniques, demanding more control packets, but dealing more efficiently with spectral re-use.

The utilization of social parameters, such as the centrality principle, is vital to be considered, in order to optimally exploit the behaviour of Mobile Cloud devices. This work elaborates on the design and exploitation of social and device-centric parameters, as a typical prediction-based method in Mobile Cloud Systems with delay constraints. Based on this consideration, this work introduces a Content-oriented Social Offloading (CSO) scheme. The proposed CSO scheme is based on utilizing the complex characteristics of social communities, in order to optimally choose the mobile node for applying the offloading process, resulting in minimized energy consumption for each mobile device. The proposed scheme aims at saving resources on the user's device, thus minimizing energy consumption and prolonging the device's lifetime. A thorough assessment has been performed, in order to evaluate the performance of the proposed scheme, while delay-sensitive “critical-process executions” are taking place. Furthermore, we carry out a comparison against other similar schemes under the same conditions, in order to validate the efficiency of the proposed framework, with respect to the energy conservation of the mobile nodes.

Advance clustering techniques have been widely used in Wireless Sensor Networks (WSNs) since they can potentially reduce latency, improve scheduling, decrease end-to-end delay and optimise energy consumption within a dense network topology. In this paper, we present a novel clustering algorithm for high density IEEE 802.15.4-2015 Time-Slotted Channel Hopping (TSCH). In particular, the proposed methodology merges a variety of solutions into an integrated clustering design. Assuming an homogeneous network distribution, the proposed configuration deploys a hierarchical down-top approach of equally numbered sub-groups, in which the formation of the separate sub-groups is adapted to the network density and the node selection metric is based on the link quality indicator. The presented algorithm is implemented in Contiki Operating System (OS) and several test vectors have been designed in order to evaluate the performance of the proposed algorithm in a COOJA simulation environment. Performance results demonstrate the capability of the clustering structure since compared to the default scheme it significantly improves the energy efficiency up to 35%, packet drops more than 40% as well the packet retransmission rate. Last but not least, the outcome of this study indicates a major increase in the network lifetime, i.e., up to 50%.

Industrial applications require more and more low-power operations, low-delay, deterministic communications as well as end-to-end reliability close to 100%. However, traditional radio technologies are sensitive to external interference, which degrades the reliability and introduces unpredictable delays due to collision detection and retransmissions. Therefore, recent standardization efforts focus on slow channel hopping strategies to provide strict Quality of Service (QoS) for the Industrial Internet of Things (IIoT). By keeping nodes time-synchronized and by employing a channel hopping approach, IEEE 802.15.4-TSCH (Time-Slotted Channel Hoping) aims at providing high-level network reliability. However, some radio channels still suffer from high external interference and need to be blacklisted. Since the interference pattern is rather dynamic, unpredictable and highly localized, we here propose heuristics to decide which channels to blacklist. To avoid deafness, the transmitter and the receiver must also agree on a consistent blacklist. Furthermore, since the external interference may be time-dependent as well, we also propose mechanisms to decide when a channel has to be blacklisted or on the contrary recovered. Our thorough experimental evaluation based on OpenWSN and FIT IoT-LAB highlight the relevance of this approach: with a localized blacklisting strategy, we increase by 20% packet delivery rate for the worst links.

The emergence of microgeneration systems steadily increases, and it raises concerns regarding their impact on the power grid. It is, therefore, crucial to efficiently integrate them into future smart grid architectures, as there is not any standard way to monitor production units. Moreover, current data collection systems are simple and do not consider their impact on local area networks. This paper presents a set of proposed mechanisms that reduces the monitoring traffic, while offering management flexibility on large-scale systems. This study is illustrated with measurements performed on a small grid, and it shows that, for monitoring a photovoltaic production, both 1-min and 1-s intervals provide the same production estimation, while significantly decreasing the associated traffic. It can be reduced even more by aggregating several measurements during a given period before sending them and by using specific mechanisms to ensure reliability. This experiment also helps authors identify best practices for monitoring different equipment based on their behaviors.

Recent boom of mobile applications has become an essential class of mobile Internet of Things (IoT), whereby large amounts of sensed data are collected and shared by mobile sensing devices for observing phenomena such as traffic or the environmental. Currently, most of the proposed Medium Access Control (MAC) protocols mainly focus on static networks. However, mobile sensor nodes may pose many communication challenges during the design and development of a MAC protocol. These difficulties first require an efficient connection establishment between a mobile and static node, and then an efficient data packet transmissions. In this study, we propose MobIQ, an advanced mobility-handling MAC scheme for low-power MAC protocols, which achieves for efficient neighbour(hood) discovery and low-delay communication. Our thorough performance evaluation, conducted on top of Contiki OS, shows that MobIQ outperforms state-of-the-art solutions such as MoX-MAC, MOBINET and ME-ContikiMAC, in terms of significantly reducing delay, contention to the medium and energy consumption.

Abstract Wireless Mesh Networks are considered as a promising solution for offering low‐cost access to broadband services. However, one of the main challenges in the design of these networks is their vulnerability to security attacks. In this paper, we analyze the fundamental security challenges and constraints of these networks, classify several possible attacks, and survey several intrusion prevention, detection, and response mechanisms found in the literature. Copyright © 2013 John Wiley & Sons, Ltd.

Cyber-physical systems (CPS) are emerging research areas that involve multiple disciplines. Two critical components in CPS are networking technologies and security. Because of the multi-disciplinary nature, the networking and security of CPS expand beyond traditional computing domains and have to consider the impact of applied physical systems. Therefore, new innovations are required such as novel transmission technologies, networking protocols, architectures, and security solutions. As a result, a significant amount of research work is expected for new models, performance analysis as well as evaluation, prototypes, and testbeds. This special issue focuses on research interests and activities related to networking and security in SmartGrid, Transportation and Medical Systems, with an emphasis on original analytical, experimental, and systems-related papers in these target domains.

In traditional routing protocols designed for Wireless Sensor Networks, each sensor node is related to one or more neighbors that will forward its readings up to the sink. This technique performs well for static topologies with homogeneous configurations, but usually fails to cope with network dynamics such as mobility and node failures. Opportunistic routing is an approach to address this particular problem. In this context, the data packets are addressed to a set of potential forwarders and then forwarded by the neighbor that first acknowledges the message. Yet, several former studies demonstrated that in some cases, a single packet may be forwarded by multiple neighbors simultaneously. This situation leads to packet duplication and consequently to increased channel occupancy and energy consumption in the network. In this paper, we study to what extent the previously reported phenomenon depends on both the topology density and the nodes MAC configuration. We then introduce a mechanism that handles the potential deafness in the network through heterogeneous configuration among the nodes in the network. We do so through local, dynamic and automatic MAC parameters adaptation, in order to reduce unnecessary traffic, channel occupancy and energy consumption due to packet duplication in opportunistic networks. Finally, we provide both theoretical analysis and experimental campaign to detail the benefits of our approach.

IEEE 802.15.4 Medium Access Control (MAC) layer does not include the Request-To-Send/Clear-To-Send (RTS/CTS) handshake mechanism, in order to overcome the hidden node problem that affects Wireless Sensor Networks (WSNs). In this paper we propose and analyse the use of RTS/CTS in IEEE 802.15.4 for the nonbeacon-enable mode. The proposed solution shows that by considering the RTS/CTS mechanism combined with packet concatenation we improve the network performance in terms of maximum throughput, minimum delay and bandwidth effciency. In IEEE 802.15.4 with RTS/CTS, the backoff procedure process is not repeated for each data packet sent unlike the basic access mode of IEEE 802.15.4, but only for each RTS/CTS set. Therefore, the channel utilization is maximized by decreasing the deferral time period before transmitting a data packet. Our work introduces an analytical model capable of accounting the retransmission delay and the maximum number of backoff stages. The successful validation of our analytical model is carried out by comparison against simulation results by using the OMNeT++ simulator.

Dynamic routing is considered an attractive solution for many network applications such as monitoring and tracking. In dynamic routing the path between the source and the destination can change dynamically following the network conditions. Next node selection criteria can have a great impact on the selection of each path and as a consequence on the network performance. Moreover, each network application may have different Quality of Service (QoS) requirements. In this paper, we examine the impact of the next node selection criteria on the network performance. Three routing protocols with different approaches along with their routing algorithms are presented. To examine the performance of each approach a discrete event simulator is used. It is shown that the selection criteria should be carefully designed following the QoS requirements of the network application. Furthermore, the obtained performance results can be used as an indicator of the appropriateness of a dynamic protocol for a variety of applications.

The Internet is a highly distributed and complex system consisting of billion devices and has become the field of various kinds of conflicts during the last two decades. As a matter of fact, various actors utilise the Internet for illicit purposes, such as for performing Distributed Denial of Service Attacks (DDoS) and for spreading various types of aggressive malware. Despite the fact that numerous services provide information regarding the threat level of the Internet, they are mostly based on information acquired by their sensors or on offline statistical sampling of various security applications (antivirus software, intrusion detection systems etc.). This paper presents PROTOS (PROactive Threat Observatory System), an early warning system which is capable of estimating the threat level across the Internet, using both a global and a local approach. The proposed system is therefore able to determine whether a specific host is under an imminent threat, as well as to provide an estimation of the malicious activity across the Internet.

Under the cognitive networking architecture, this paper presents an opportunistic routing protocol for cognitive radio in Wireless Sensor Networks (WSNs), which can deliver higher performance and efficiency in multihop wireless communications. Cognitive Networking with Opportunistic Routing, (CNOR), opportunistically routes traffic across paths over all available spectrum. A discrete event simulator is applied to evaluate and compare the proposed scheme against three other routing protocols: traditional routing with single channel, traditional routing with multiple channels and opportunistic routing with single channel. It is shown that by integrating opportunistic routing with cognitive radio much better results can be obtained, with respect to energy consumption, throughput and latency.

One of the fundamental reasons for the IEEE 802.15.4 standard Medium Access Control (MAC) inefficiency is overhead. The current paper proposes and analyses the Sensor Block Acknowledgment MAC (SBACK-MAC) protocol, a new innovative protocol that allows the aggregation of several acknowledgment responses in one special BACK Response packet. Two different solutions are addressed. The first one considers the SBACK-MAC protocol in the presence of BACK Request (concatenation) while the second one considers the SBACK-MAC in the absence of BACK Request (piggyback). The proposed solutions address a distributed scenario with single-destination and single-rate frame aggregation. The throughput and delay performance is mathematically derived under ideal conditions (a channel environment with no transmission errors). The proposed schemes are compared against the basic access mode of IEEE 802.15.4 through extensive simulations by employing the OM-NET++ simulator. We demonstrate that the network performance is significantly improved in terms of throughput and end-to-end delay.

Survivability is crucial in Wireless Sensor Networks (WSNs) especially when they are used for monitoring and tracking applications with limited available resources. In this paper we are proposing the use of an energy Efficient Cognitive Unicast Routing (ECUR) protocol that tries to keep a balance between the energy consumption and the packet delay in a WSN. The proposed routing protocol has a next node selection criterion to change the routing path dynamically following the network conditions and the channel availability while the energy consumption per node is also considered. Simulation results are presented that show an increase in network lifetime of up to 30% compared with geographic opportunistic routing while the packet delay remains similar.

Internet Protocol Television or IPTV is a multimedia content delivery service that is increasingly proliferating in the consumers lives and a hot-topic for telecommunication companies to gain a share on consumers' entertainment budget. This alternate method of distributing television content over IP requires a high level of network performance and guaranteed Quality of Service (QoS) in order for the end user to have an assured and high-quality viewing experience. Yet, the quality margins of IPTV are commonly put to test in almost all cases where customers re-distribute the service within their premisses. In this paper, our main motivation is to investigate how such real-life networks perform in terms of distributing this inelastic and high-bandwidth type of service utilizing traffic measurements. Furthermore, our goal is to verify and analyze whether these studied networks are well suited for multicast IPTV traffic.

The scope of this research paper is the proposal of voice quality prediction models, based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS), for Adaptive Multi Rate (AMR) telephony service provided by GSM and UMTS rollout public land mobile networks. For the scope of our research we performed an experimental drive-test measurement campaign in order to evaluate objectively the End-to-End Service QoS (ESQoS) as well as radio System QoS (SQoS) parameters. Subsequently, the collected measurement data are used to train ANFIS empirical models. We then assess the prediction performance by depicting 2D/3D FIS surfaces and the impacts of SQoS to ESQoS. The prediction methodology can be successfully applied in Quality of Experience centric radio network planning, fine tuning and optimization processes by mobile operators.

This paper focuses on the presentation of a simulation framework, prototyping environment and demonstration scenario developed in the context of the Self-NET Future Internet mechanisms. Self management mechanisms of Future Internet elements are introduced, which incorporate the Monitoring, Decision making and Execution cognitive cycles. Such functionality enables the envisaged elements to sense their environment and adapt to it dynamically. The scenario described here is the one related to coverage and capacity optimization. The cognitive framework of the network elements and the implemented Future Internet mechanisms are presented, along with the findings originated by the performance measurements on the developed prototypes. In order to enable the evaluation of the proposed schemes, a set of assessment metrics are identified and the scenario is fragmented in simpler test cases.

Transmitting multimedia streams over IEEE 802.11 Wireless Local Area Networks (WLANs) with high performance and reliability is a challenging task. In the current paper, we study the basic problems that video transmission encounters in wireless LANs and we present some of the proposed solutions. In particular, we will outline the solutions that were selected for the 802.11aa amendment to the IEEE 802.11 standard in order to provide reliable and robust transport of video streams in IEEE 802.11 wireless LANs.

In future distributed systems, the appearance of mechanisms that should be able to realize robust, predictable and self-adaptive behaviour for existing networked systems can be seen as a critical enabler for further growth. If, in addition, such mechanisms are related to options for achieving cooperation of several (heterogeneous) network elements and/or domains, with the aim of reducing energy wastage and minimizing energy consumption, then the challenge becomes critical. To this aim, the present paper discusses an innovative approach, focusing upon the development of energy-aware distributed and cooperating systems for monitoring and control, centred on appropriate wireless sensor networks for the provision of radio and environmental context information. In particular, we discuss the concept of a “Self-Growing Network” that is able to efficiently incorporate autonomic and collaborative capabilities, intending to increase the level of the global network energy efficiency by using cognition-based features. Self-growing capabilities are enablers for a novel type of network flexibility. They allow optimising a heterogeneous collection of network nodes or sub-networks to be dedicated to a specific optimisation “purpose” temporarily and on-demand.

IEEE 802.11e Enhanced Distributed Channel Access (EDCA) is developed to provide Wireless Local Area Networks (WLANs) with Quality of Service (QoS) support. Several solutions have been proposed in order to provide a fair channel access for all competing stations. This paper first studies the QoS capabilities of the Adaptive Weighted and Prioritized Polling (AWPP) protocol that adopts the frame structure and the basic polling scheme of the Priority Oriented Adaptive Polling (POAP) protocol. Our analytical approach is validated by plotting analytical results against simulation outcome. We then explore the fairness provision of the AWPP protocol utilizing Jain's fairness index. Finally, we provide a comparative performance analysis between EDCA, POAP and AWPP protocols and we demonstrate that AWPP outperforms the other two.

Internet Protocol Television or IPTV is defined as an alternate method of distributing television content over IP by a telecom carrier or an Internet service provider (ISP) and it is increasingly gaining market share in modern communication networks. IPTV poses specific requirements on the network infrastructure and at the same time it requires that several network performance characteristics are maintained under defined levels in order for the end user to have an assured viewing experience. In this paper, our main motivation is to investigate how a real life network performs in terms of distributing this inelastic and high-bandwidth type of service utilizing traffic measurements. Furthermore, our goal is to verify and analyze whether the studied network is suited for multicast IPTV traffic.

Multihop networks are used in a variety of ways, that include ad-hoc networks, wireless sensor networks, and mesh networks. These networks consist of nodes that connect over a wireless channel, and communicate over multiple links by forwarding each other's messages. The throughput is determined by the contention among the nodes for the wireless resources. Thus routing algorithms, influence the level of contention at each location of the network, and play an important role in the network throughput. Additionally, the sequence of transmission opportunities of each node, which is determined by the scheduling algorithm, should also be carefully designed, since it affects the available resources of each node. In this paper we study the effect of routing and scheduling algorithms on the throughput of multihop networks. We propose a per-flow joint routing/scheduling algorithm that has the property of routing the flows in a way that avoids congested areas with limited availability, and also schedules links, in a way that a) slots are assigned to flows, instead of links or nodes, b) the assignment is fair c) fairness does not lead to underutilization. Simulation results showed that the proposed algorithm increases the average throughput, and the degree of satisfaction of each node, while at the same time it decreases the average delay.

Due to the unique characteristics of long signal propagation, high error rate and low bandwidth in the underwater environment, the design of the medium access control (MAC) protocol for underwater acoustic networks poses significant challenges. The previous MAC protocols designed for flexible communication models have limited achievements in performance. In this paper, we consider a practical application and propose a data-centric multi-hop MAC protocol, called DC-MAC, to enhance the performance on throughput and average end-to-end packet transmission delay. Our design uses multi-channel strategy to limit transmission interference by creating multiple collision domains, and dynamic collision-free polling strategy to offer efficient protocol handshake. We analyze the saturation throughput performance and conduct extensive simulation experiments to study the throughput and delay performance. Comparing to slotted FAMA which is a potential MAC protocol candidate for the same environment, our results show that DC-MAC outperforms its peer.

IEEE 802.11 is world widely established as the most popular protocol for Wireless Local Area Networks (WLANs). Voice over IP (VoIP) applications are widely spread due to their relatively low cost. Applying VoIP traffic on IEEE 802.11 WLANs introduces significant delays in the wireless medium due to the small size of voice packets. In this paper, we develop an analytical model for the IEEE 802.11 Distributed Coordination Function (DCF) considering voice and data stations transmitting simultaneously. Our new model calculates IEEE 802.11 DCF performance in terms of average delay, delay variation (jitter) and drop probability for voice packets. Metric values that constitute good voice quality are identified and based on these we develop a methodology for assessing the network's capacity in terms of good quality voice calls. The proposed analytical model is validated by comparing analytical results against simulation outcome. Finally, we study the impact of data transmissions on voice capacity and show that for every data session (a data station and a TCP-ACK station) added to a 802.11b WLAN, its voice capacity drops by 2 voice sessions (4 voice stations).

Ubiquitous service delivery requires the selection of the optimal access network in a heterogeneous wireless environment. However, since the selection of an access network in such an environment depends on several parameters with different relative importance (such as the network and the application characteristics, the user preferences, the service cost), it is a difficult task to be achieved. In this paper, an effective access network selection algorithm for heterogeneous wireless networks is proposed that combines two Multi Attribute Decision Making (MADM) methods, the Analytic Hierarchy Process (AHP) method and the Total Order Preference by Similarity to the Ideal Solution (TOPSIS) method. More specifically, the AHP method is used to determine weights of the criteria and the TOPSIS method is used to obtain the final access network ranking.

A simple and accurate packet delay distribution analysis is presented for the RTS/CTS access scheme of the IEEE 802.11 distributed coordination function (DCF). The packet delay distribution is effectively studied by developing an analytical model that calculates the probability that a packet is successfully transmitted after experiencing a delay time equal to a given value. The model uses simple mathematical relations and provides accurate delay distribution curves. The accuracy of the analytical model is verified by simulations

IEEE 802.11 is worldwide implemented being the most widely deployed protocol for wireless local area networks (WLANs). In a realistic environment, WLANs encounter channel fading which causes independent and burst transmission errors which in addition with packet collisions degrade overall performance. In this paper, we present a simple and effective contention window-resetting scheme, named double increment double decrement (DIDD), to improve the performance of the binary exponential backoff (BEB) scheme utilized in IEEE 802.11. Our work becomes important and meaningful since it analyzes and models the error patterns in order to evaluate their potential impact on throughput efficiency and average packet delay performance. In particular, we provide useful insights by exploring the effect of independent and burst transmission errors, packet size and network size on the performance of the DIDD scheme

The paper studies the packet delay distribution of the IEEE 802.11 distributed coordination function (DCF) protocol. DCF treats packets in an unfair manner. Results indicate that, in large networks, most packets have very low time delays, some packets have delays close to the average value and a small number of packets experience extremely high delays. We study the DCF delay distribution by developing a mathematical model that calculates the important properties of the constituent curves of the delay distribution curve, namely the probability that a packet is successfully transmitted from a particular backoff stage and the average delay of the successfully transmitted packets from this backoff stage. The model is simple, gives an insight into the internal mechanisms of DCF and applies to both basic and RTS/CTS access mechanisms. The accuracy of the analytical model is verified by simulations. Analytical results are presented that explore the effect of network size and of the initial contention window size on the fairness of DCF regarding the distribution of packet delays.

IEEE 802.11 is worldwide established and the most used protocol for wireless local area networks (WLANs). In this paper, we propose an improved analytical model that calculates IEEE 802.11 DCF performance taking into account both the packet retry limits and transmission errors for the IEEE 802.11a protocol. Validation of our new analytical performance model results is carried out by comparison with the simulation results using the OPNET/sup TM/ simulation package. We explore the effect of transmission errors, packet retry limits, data rate and network size on the performance of the basic access scheme, in terms of throughput, packet delay, packet drop time and drop probability.

The contribution of this paper is on the study of packet delays for the IEEE 802.11 wireless local area network DCF MAC protocol. A method is presented capable of taking into account retransmission delays with or without retry limits. We present an analytical model based on a Markov chain which allows us to derive closed form expressions for the packet delays, the probability of a packet being discarded when it reaches the maximum retransmission limit and the average time to drop such a packet for the basic and RTS/CTS access mechanisms. The results presented are for standard protocol parameters versus the number of contention stations. Finally, the accuracy of the analytical model is verified by simulations.