WORLD population growth is facing three major challenges: demographic peak of baby boomers, increase of life expectancy leading to aging population and rise in health care costs. In Australia, life expectancy has increased significantly from 70.8 years in 1960 to 81.7 Wireless Body Area Networks: A Survey years in 2010 and in the United States from 69.8 years in 1960 to 78.2 years in 2010, an average increase of 13.5%1. Given the U.S. age pyramid2 shown in Fig. 1, the number of adults ranging from 60 to 80 years old in 2050 is expected to be double that of the year 2000 (from 33 million to 81 million Wireless Body Area Networks: A Survey people) due to retirement of baby boomers3. It is expected that this increase will overload health care systems, significantly affecting the quality of life. Wireless Body Area Networks: A Survey Further, current trends in total health care expenditure are expected to reach 20% of the Gross Domestic Product (GDP) in 2022, which is a big threat to the US economy. Moreover, the overall health care expenditures in the U.S. has significantly increased from 250 billion in 1980 to 1.85 trillion in 2004, even though 45 million Americans were uninsured4. These statistics necessitate a dramatic shift in current health care systems towards more affordable and scalable solutions. On the other hand, Wireless Body Area Networks: A Survey millions of people die from cancer, cardiovascular disease, Parkinson’s, asthma, obesity, diabetes and many more chronic or fatal diseases every year. The common problem with all current fatal diseases is that many people experience the symptoms and have disease diagnosed when it is too late. Research has shown that most diseases can be prevented if they are detected in their early stages. Wireless Body Area Networks: A Survey Therefore, future health care systems should provide proactive wellness management and concentrate on early detection and prevention of diseases. One key solution to more affordable and proactive health care systems is through wearable monitoring systems capable of early detection of abnormal conditions resulting in major improvements in the quality of life. Wireless Body Area Networks: A Survey In this case, even monitoring vital signals such as the heart rate allows patients to engage in their normal activities instead of staying at home or close to a specialized medical service .

A SENSOR network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. The purpose of a sensor network is to provide users with the information of interest from data gathered by spatially distributed sensors. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods Thus, it is not surprising that signal estimation has been one of the most fundamental collaborative information processing problems in sensor networks and has found wide applications in military and civilian fields, such as target tracking and localization, air traffic Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods control, guidance, and navigation. Such signal estimations in a sensor network could be done under the end-to-end information flow paradigm by communicating all the relevant data to a central collector node, e.g., a sink node. This, however, is a highly inefficient solution in sensor networks, because it may cause long packet delay, and it has the potential for a critical failure point at the central collector node, and most of all, the sensor networks are usually severely constrained in energy and bandwidth,. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods To avoid these problems, an alternative solution is for the estimation to be performed in-network every sensor with both sensing and computation capabilities performs not only as a sensor but also as an estimator, and it collects measurements from its neighbors to generate estimates. This is known as the distributed estimation and has attracted increasing attention during the past few years . In sensor networks,measurements aresampled and Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods transmitted to estimators via unreliable communication networks. lthoughfrequent measurement sampling and transmission may improve estimation performance, it, however, consumes much energy and is thus not desirable in sensor networks with constrained energy. In other words, estimation should be performed in an energyefficient way in sensor networks, and one straightforward yet efficient way is to increase measurement sampling periods. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods However, thismayin turn degrade estimation erformance. Thus, one has to tradeoff between estimation performance and energy consumption in sensor network based estimations and the tradeoff can be intuitively realized by adopting a nonuniform sampling strategy. Such a strategy brings much design flexibility, e.g., onemayincrease thesampling period to save energies during some periods while decrease it to improve estimation performance during some other time intervals when necessary.

The widespread use of mobile devices using third-generation (3G) and Long-Term Evolution (LTE) networks has led to the development of various applications that take advantage of the always-on Internet connectivity provided by these networks. Instant messenger (IM) or social network services (SNSs) like Facebook and The Impact of Application Signaling Traffic on Public Land Mobile Networks Twitter are some examples of this class of new mobile applications. Traditional Internet applications, such as web surfing and file transfer, are characterized by a usage pattern that has distinct active and inactive phases. An active phase is a period in which several bursts of packets are transmitted, while an inactive phase is characterized by no data transmission during a sustained time period. The traffic pattern of recent and emerging applications that rely on always-on connectivity is quite different. Since the emerging mobile applications support real-time communications services, they are often constantly running in background mode to receive status updates or messages from other parties. The Impact of Application Signaling Traffic on Public Land Mobile Networks Thus, the applications continuously generate short signaling messages such as keep-alive and ping requests to maintain the always-on connectivity. Although the traffic volume of keep-alive messages is not large, frequent short messages can incur a large amount of related signaling traffic in the mobile network. In 3G or LTE networks, the user equipment (UE) and radio access networks keep the radio resource control (RRC) states. The Impact of Application Signaling Traffic on Public Land Mobile Networks The UE stays in RRC Connected mode when it transmits or receives data during active periods and stays in RRC Idle mode during inactive periods. To send even a small data packet, the UE changes the RRC state to the RRC Connected mode prior to transmission. This RRC radio state change generates a lot of signaling messages, resulting in a rapid increase in traffic loading. The Impact of Application Signaling Traffic on Public Land Mobile Networks The amount of signaling traffic leads to two major problems: rapid drainage of the mobile device’s battery and a signaling traffic surge in the mobile network. In , the authors focused on the issues of the energy impact on the mobile device. In this article, we focus on the signaling impact of these applications on public land mobile networks (PLMNs). The signaling traffic surge, or so-called signaling storm, due to the rapid growth in use of The Impact of Application Signaling Traffic on Public Land Mobile Networks these applications is having a serious impact on mobile network performance. The frequent RRC state change leads to increased signaling overhead over the air interface and through the core elements of a mobile network. The effect of signaling traffic loading gets more severe for the core network as the number of UE devices connected to the core network elements increases.Several mobile network operators (MNOs) have experienced severe service outage or degraded network performance due to the increase of application signaling traffic . Furthermore, the stability of the network can also be impacted by signaling traffic when there is an application server failure or outage.

The recent, and strong, orientation of the Internet towards services has led to a closer coupling between the transport/network and service/application layers aiming to increase the overall Management Driven Hybrid Multicast Framework for Content Aware Networks efficiency through cross layer optimization. This can be achieved by making networks more aware of the transported content — content aware networks (CANs), or making applications more aware of network conditions — network aware applications (NAA). Management Driven Hybrid Multicast Framework for Content Aware Networks In parallel, recent developments of multimedia and content oriented services (e.g,. IPTV, video streaming, video on demand, and Internet TV) have reinforced the interest in multicast technologies. IP multicast has not been globally deployed due to problems related to group management, router capabilities, inter-domain transport, and lack of quality of service (QoS) support Management Driven Hybrid Multicast Framework for Content Aware Networks. Overlay multicast, despite its lower efficiency, has emerged as an alternative . In a complex scenario, a hybrid multicast, combining IP multicast with overlay multicast, can be attractive in terms of scalability, efficiency, and flexibility . Another trend, aiming to overcome the current Internet ossification by creating customized flexible networks, is to use network virtualization . Management Driven Hybrid Multicast Framework for Content Aware Networks New business entities (Fig. 1), named virtual network providers, can offer customized virtual networks. In particular, services providers (SP) can deploy their services on top of some hired virtual networks without the burden of performing connectivity control. Such virtualized transport service can be deployed by network providers (NPs), either enhanced to become virtual NPs, or by cooperating with separate new Management Driven Hybrid Multicast Framework for Content Aware Networks entities that offer network virtualization. However, each NP still manages its own infrastructure. While full network virtualization is challenging in terms of seamless deployment, more “light” solutions can be attractive by being deployed as parallel data planes , logically separated but under the coordination of a single management and control plane. In [9], several research challenges Management Driven Hybrid Multicast Framework for Content Aware Networks related to the management and control planes are identified. The proposed solution addresses some of them. In particular, the guaranteeing of service availability in accordance to a pre-established service level agreement (SLA), guaranteeing QoS, supporting large-scale service provisioning and deployment, enabling higher integration between services and networks, and the capability of accepting new activated ondemand services.

Wireless sensor networking (WSN) is a continuously evolving technology for various applications, such as environment monitoring, patient monitoring, and many industrial applications. Wireless sensors can potentially be deployed in a large geographical area via multihop Scheduling Multi-Channel and Multi-Timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization communications. Unlike delay-tolerant applications, patient monitoring, disaster warning, intruder detection, and many industrial applications require timely responses. However, it is challenging to provide timely and reliable communication in WSNs, mainly due to the fact that conventional WSNs operate on a single channel. Sensor nodes must Scheduling Multi-Channel and Multi-Timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization compete with other nodes to access a single channel medium of limited bandwidth. If a transceiver operates on multiple channels, multiple simultaneous transmissions and receptions are feasible on wireless media without interfering with each other, and the bandwidth limitation can be relieved. Therefore, using multiple channels and time slots facilitates timely communication. In IEEE Std 802.15.4 for WSNs, a superframe structure consists of a contention access period (CAP) and a guaranteed time slot (GTS). Our proposal utilizes this superframe structure, but each time slot is extended to accommodate multiple channels as in IEEE Std 802.15.4e to guarantee end-to-end delay. The channels and time slots available to a node vary because each Scheduling Multi-Channel and Multi-Timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization node’s selection of channels and time slots imposes a set of constraints on the channels and time slots available to its neighbors. Our proposal affords each node the freedom to choose the optimal time slot and channel in establishing communication links to its neighbors, resulting in high throughput and low delay. Scheduling Multi-Channel and Multi-Timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization Scheduling is a critical process for virtually all resource-allocation problems, especially to meet quality of service (QoS) requirements. Scheduling channels and time slots for all nodes constituting an end-to-end (e2e) path to meet certain delay bounds is challenging because each node has a different remaining path length to the destination and encounters dissimilar channel environments. Assuming the channels and time slots are integer-numbered from 1 to some arbitrary number, a simple approach would be to schedule them in a sequenced and Scheduling Multi-Channel and Multi-Timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization staggered fashion from the ource to the destination; that is, each node chooses the smallest number out of the available time slots and channels, and this channel-time slot combination becomes unavailable to its children, parent, and their neighbors.

The multihop (mobile) ad hoc networking paradigm emerged, in the civilian field, in the 1990s with the availability of off-the-shelf wireless technologies able to provide direct network connections among users devices: Bluetooth for personal area networks, and the 802.11 Mobile Ad Hoc Networking: Milestones, Challenges, and New Research Directions standards family for high-speed wireless LAN . Specifically, these wireless standards allow direct communications among network devices within the transmission range of their wireless interfaces, thus making the single-hop ad hoc network a reality, that is, infrastructureless Mobile Ad Hoc Networking: Milestones, Challenges, and New Research Directions WLAN/WPAN where devices communicate without the need for any network infrastructure (Fig. 1). The multihop paradigm was then conceived to extend the possibility to communicate with any couple of network nodes, without the need to develop any ubiquitous network infrastructure. In the ’90s, we assisted in the usage of the multihop paradigm in mobile ad hoc networks, Mobile Ad Hoc Networking: Milestones, Challenges, and New Research Directions where nearby users directly communicate (by exploiting the wireless-network interfaces of their devices in ad hoc mode) not only to exchange their own data but also to relay the traffic of other network nodes that cannot directly communicate, thus operating as routers do in the legacy Internet. For this reason, in a MANET, the users’ devices cooperatively provide the Internet services, usually provided by the network infrastructure. At its birth, the MANET was seen as one of the most innovative and challenging wireless networking paradigms , and was promising to become one of the major technologies, increasingly present in the everyday life of everybody. We help you to formulate mobile ad hoc network project topics. The potentialities of this networking paradigm made Mobile Ad Hoc Networking: Milestones, Challenges, and New Research Directions ad hoc networking an attractive option for building fourth-generation (4G) wireless networks, and hence MANET immediately gained momentum, and this produced tremendous research efforts in the mobile network community . The Internet model was central to the MANET Internet Engineering Task Force (IETF) working group, which, inheriting the TCP/IP protocols stack layering, assumed an IPcentric view of a MANET; see “Mobile Ad Hoc Networks” by J. P. Macker and M. Mobile Ad Hoc Networking: Milestones, Challenges, and New Research Directions S. Scott Corson in . The MANET research community focused on what we call pure generalpurpose MANETs, where pure indicates that no infrastructure is assumed to implement the network functions, and no authority is in charge of managing and controlling the network. Generalpurpose denotes that these networks are not designed with any specific application in mind, but rather to support any legacy TCP/IP application .

The continuous growth of consumers’ IP traffic fed by the generalization of broadband access (through digital subscriber line and fiber to the home) and the emerging rich-content high-rate and bursty applications, such as video on demand, HDTV, and cloud computing, can only be met with the abundant capacity Planning and Operating Flexible Optical Networks: Algorithmic Issues and Tools provided by optical transport networks. For the future, it is expected that the traffic will not only increase in volume traffic increase of 34 percent on average per year but will also exhibit high burstiness, resulting in large variations over time and direction. Recent research efforts on optical networks have focused on architectures that support variable spectrum connections as a way to increase spectral efficiency and reduce costs. Flexible or elastic optical networks appear as a promising technology for meeting the requirements of next generation networks that will span across both the core and metro segments, Planning and Operating Flexible Optical Networks: Algorithmic Issues and Tools and potentially also across the access, all the way to the end user. A flexible network is based on the flex-grid technology, which migrates from the fixed 100 or 50 GHz grid that traditional wavelengthdivision multiplexing, (D)WDM, networks utilize . Flex-grid has granularity of 12.5 GHz, standardized by the International Telecommunication Union and can combine the spectrum units, referred to as slots, to create wider channels on an as needed basis. Planning and Operating Flexible Optical Networks: Algorithmic Issues and Tools Flexible networks are built using bandwidth variable optical switches that are configured to create optical paths of sufficient spectrum slots. We refer to such a connection as a flexpath, a variation of the word lightpath used in standard WDM networks. Bandwidth variable switches operate in a transparent manner for transit traffic that is switched while remaining in the optical domain. Flexible networks in addition to flex-grid switches assume the use of bandwidth variable transponders . Planning and Operating Flexible Optical Networks: Algorithmic Issues and Tools Various BVT implementations exist , employing single- or multicarrier transmission schemes, and usually having some sort of digital signal processing (DSP) capabilities at the receiver but also at the transmitter side. Several transmission parameters can be controlled in a BVT, including the baud rate, the modulation format (number of bits encoded per symbol), the forward error correction (FEC) used, the spectrum slots employed, and the useful bit rate. Since transmission parameters are controllable, the term software defined optics has also recently been used, implying that optical networks, which currently rely on the slowly changing circuit switching paradigm, become more dynamic. Planning and Operating Flexible Optical Networks: Algorithmic Issues and Tools Deciding the transmission parameters is quite complicated since physical layer impairments (PLIs) such as noise, dispersion, interference, and nonlinear effects accumulate and deteriorate the quality of transmission (QoT) of the flexpaths. In particular, the QoT of a flexpath depends on its BVT transmission parameters, the guardband used from its spectrum-adjacent flexpaths, and their transmission parameters.

Multi-layer IP-optical networking promises significant cost reductions for the same availability as today’s networks However, to realize these savings, it is necessary to Multi-Layer Capacity Planning for IP-Optical Networks change the planning process such that it is aware of the behavior of both layers. This article explains today’s nonintegrated planning process and its deficiencies. It then suggests a multi-layer router bypass optimization process. Next, it explains how different multi-layer restoration schemes work, and the required changes to the planning process to efficiently design the network in an optimized way for such schemes. The process is demonstrated on a small four-node example, and the resulting savings are compared to savings achieved for real-world networks that approximate the Multi-Layer Capacity Planning for IP-Optical Networks Deutsche Telekom and Telefonica backbone networks. The resulting IP layer links drive the demands of the optical layer. The optical layer design phase ensures that each of the links is feasible from a transmission perspective. Based on the output of this phase, it is possible to acquire transponders and regenerators and implement the lightpaths defined.   At the same time, the required additional IP ports can be acquired and connected to the transponders. Once the IP layer is connected over these lightpaths, the topology is extended to include the new links. Multi-Layer Capacity Planning for IP-Optical Networks The network now enters an operations phase, where traffic data and IP and optical performance is collected This data is used to drive the next planning phase. bviously, this is an idealized iew of the process. In reality, different phases happen in parallel; for example, the network continues to operate during the next planning phase. The steps are sometimes not as distinct; for example, the IP planning team may interact with the optical planning team to ensure that the optical paths provided for the IP layer are sufficiently diverse. Multi-Layer Capacity Planning for IP-Optical Networks But overall, the interaction is manual and error-prone. One of the basic capabilities needed in a multilayer tool is the ability to optimize the IP layer given knowledge of the optical layer topology. This process typically starts with a basic IP topology, in which traffic has to go through many IP Multi-Layer Capacity Planning for IP-Optical Networks hops to reach its destination. The links are well utilized since the IP layer can re-groom traffic at every hop. In order to save router ports and transponders, thereby reducing network cost, the algorithm considers the traffic demands in the IP layer and identifies intermediate routers that can be bypassed. Only links that contribute to reduction of the overall IP+optical network cost are selected to ensure that expensive optical resources are not wasted. We call this process multi-layer bypass optimization or MLBO.

WIRELESS sensor networks (WSNs) are vulnerable to several types of attacks including passive eavesdropping, jamming, compromising of the sensor nodes, and insertion of malicious nodes into the network . Widespread adoption of WSNs, particularity for mission-critical tasks, hinges on the development of strong protection mechanisms against such attacks . Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks Due to the scarcity of resources, traditional wireless network security solutions are not viable for WSNs. The life span of a sensor node is usually determined by its energy supply which is mostly expended for data processing and communication . Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks Moreover, size and cost constraints of the nodes limit their memory size and processing power. Therefore, security solutions which demand excessive processing, storage or communication overhead are not practical. In particular, due to their high computational complexity, public key ciphers are not suitable for WSNs Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks. An important application of WSNs, which has been extensively studied in recent years, involves decentralized detection whereby the sensors send their (quantized) measurements to an ally fusion center (AFC) which attempts to detect the state of nature using the data received from all the sensors. Due to the broadcast nature of the wireless media, the sensors’ Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks data are prone to interception by unauthorized parties . In this paper we are concerned with data confidentiality in the presence of passive eavesdropping. In particular, weassume that the transmissions of the nodes are over insecure channels. An eavesdropping fusion center (EFC) is attempting to intercept the sensor’s messages and to detect the state of nature. Since the sensors’ data are used for hypothesis testing, security can be provided by degrading the detection performance of EFC. Optimal Probabilistic Encryption for Secure Detection in Wireless Sensor Networks The communication between the sensors and AFC (or EFC) is assumed to be over a parallel access channel where the sensors are connected to AFC (or EFC) by a dedicated channel. The dedicated channels are assumed to be independent and identical and are modeled by (noisy) discrete memoryless channels .

As mobile networking and services are entering a new communication era offering smart phones to users with higher capabilities and more diverse applications, emerging service requirements create new challenges for the current mobile network architecture. Supporting Highly Mobile Users in Cost-Effective Decentralized Mobile Operator Networks Such new requirements partly reflect the popularity of several new services and the emerging content rich and bandwidth-intensive mobile applications. In addition, they capture the operator’s desire to offer flat rate tariffs to attract more users encouraging the adoption Supporting Highly Mobile Users in Cost-Effective Decentralized Mobile Operator Networks of new services. Such business paradigm may work great in an early phase assisting the success of new technologies, i.e. Long Term Evolution (LTE), but at a later stage it may create a rebound effect with serious revenue problems for operators. Indeed mobile operators are facing a challenging task to accommodate huge traffic volumes, far beyond the original network capacity Effectively, such new requirements challenge the current mobile network architecture, which is highly centralized, not optimized for high-volume data applications. Supporting Highly Mobile Users in Cost-Effective Decentralized Mobile Operator Networks The main problem relates to the fact that central gateways handle all mobile traffic, acting as a data and mobility anchor for several radio access points without any complementary caching or data offload support at the network edge. A straightforward solution for mobile operators is to invest in upgrading their network infrastructure in terms of backhaul speed and core network resources with the objective to always be capable to accommodate peak hour traffic demands. Supporting Highly Mobile Users in Cost-Effective Decentralized Mobile Operator Networks Whilst these are technical-wise feasible solutions, financially they are challenging, particularly due to the modest Average Revenues per Users (ARPU), given, in turn, the trend towards flat rate business models. Operators are thus interested in cost-effective methods for accommodating the ever-increasing mobile network traffic ensuring minimal investment into the current infrastructure. Supporting Highly Mobile Users in Cost-Effective Decentralized Mobile Operator Networks Network decentralization is a key enabler, which allows operators to be equipped with economically competitive solutions against increased traffic demands and flat rate charges. The basis for realizing network decentralization is to place small-scale network nodes with mobility and IP access functionalities, similar to those provided by the currently centralized gateways, towards the network edge. Such local data anchor gateways allow operators to employ solutions that can selectively offload traffic as close to the Radio Access Network (RAN) as possible.