The Internet traditionally connected primarily desktop computers and mainframes among themselves and to servers. However, over time, laptops joined the networks, followed by cellular phones, and, increasingly, smaller and more numerous devices are also joining the Internet. Sensors, cameras, vehicles, drones, light-bulbs, appliances, medical devices, personal sensors and many more will enable scores of applications impossible otherwise.

The Internet of Things (IoT) offers a variety of research problems at all stages of things identification, data collection, transportation, storage and processing. IoT systems often produce extremely large quantities of data, requiring big data analytics specific to the application. Energy efficiency is many times a requirement for long term monitoring in many applications. Cyber-physical systems are closely related to the internet of things, sharing much of the same research challenges and potential impact to our lives.

Each network is individually tailored to the needs of the user. Yet how do you find out if the network is meeting those needs and if not, what needs to be done to fix it? The answer is performance evaluation. After the network has been set up, several methods may be used to determine how effective the network is at its given task. Data is collected about use and performance, then analyzed statistically to identify key problems. After outlining a solution, optimization of the network will occur until the performance is deemed satisfactory.

Each network is individually tailored to the various needs of the users, including throughput, delay requirement, fairness, resilience/robustness of the network, cost-effectiveness, to name a few. Yet how do you find out if the network is meeting those needs and if not, what needs to be done to fix it? The answer lies in the network design and performance evaluation.

Once the network has been set up, several methods may be used to determine how effective the network is at its given task under various conditions. Data is collected about network usage and performance, and then statistically analyzed to identify key problems. After outlining a solution, optimization of the network will then follow until the performance is deemed satisfactory.

Experts in network design and performance evaluation must have critical understanding of network protocols across all layers and their interactions, simulation skills, analytical modeling techniques for user traffic and protocols, as well as network measurement and statistical analysis of data, toward optimizing existing networks for best performance. This skill set is required also for developing new protocols for networks in unconventional/challenging environments.

Network design and performance evaluation is central to almost all kinds of existing networks and protocols ranging from connection-oriented networks, packet-switched networks or Internet, various protocols in transport layer and MAC layer, up to wireless networks and mobile ad-hoc/sensor networks.

The services sector develops technological applications that help businesses, governments and other organizations improve what they do and tap into completely new areas. It currently represents over 75 percent of the U.S. economy and is growing rapidly as companies seize new business opportunities by building more efficient IT systems, streamlining business processes and embracing the Internet. At IBM and similar companies like HP, services now account for 50 percent or more of the company’s revenue.

Networking services,in particular, are taking off as telecom companies are introducing triple play services, third and fourth generation cellular services, e-business applications, and advanced multimedia messaging.

Network services experts must have a sophisticated understanding of business strategy, business processes, information technology, systems engineering and the management of individuals and teams. Combining the strengths of computer science, computer engineering, system engineering, and management brings together all of these necessary components in an interdisciplinary setting.

Network management, typically applied to large-scale networks such as computer networks and telecommunications networks, refers to the maintenance and administration of such networks at the top level.

Network management is the execution of the set of functions required for controlling, planning, allocating, deploying, coordinating, and monitoring the resources of a network.

Some of these functions are initial network planning, frequency allocation, predetermined traffic routing to support load balancing, cryptographic key distribution authorization, configuration management, fault management, security management, performance management, bandwidth management, and accounting management.

Online social networks (OSNs), such as Facebook, Twitter, have become a vital platform for exchanging/sharing information and spreading news and/or even malware/virus. New forms of online social networks specializing on different type of services and contents constantly emerge. With the enormous number of users carrying mobile devices connected to the Internet via WiFi or cellular links almost all the time, this trend will continue to persist for years to come.

As an overlay network, the OSN poses unique challenges as well as golden opportunities. There still remain a number of challenging research issues around OSNs, such as how to estimate the social connectivity of users; how to efficiently spread rumor/virus/information over a given OSN; or how to identify which users to compromise for maximal viral marketing, to list a few. To address these issues, we should be able to estimate/sample how users are interconnected, to quickly sample certain network-wide statistics, properly model and generate realistic graph topologies for OSNs, and deal with possibly multi-layered OSNs over which information and/or virus spreads out. These problems are further exacerbated by the fact that such graphs (OSNs) are very large, typically unknown as a whole, and evolving over time, along with millions of users producing big networked data in real time, out of which certain meaningful decisions and actions need to be taken. Along with the recent breakthrough in device-to-device communications, such as Bluetooth and WiFi direct, mobile social networks (MSNs) are becoming an indispensable part of online social networks, opening a new paradigm for network design and analysis.

A wireless network is a computer network that uses radio signals at the physical layer. This results in a fundamentally different paradigms for wireless networking, as the links, the foundation of traditional networking, become a blurred notion. In terms of classification, there are single-hop and multi-hop as well as infrastructure-based and infrastructure-less wireless networks. Examples on the single hop side include cellular networks, wireless local area networks (e.g., 802.11), wireless personal area networks (e.g., Bluetooth), etc. On the multi-hop side, wireless mesh networks, wireless sensor networks and vehicular networks are popular examples.

Research topics in wireless networks stem from the wide variety of problems caused by the wireless nature of the links, including the always broadcast nature of the medium at the physical layer, followed by the inevitable interference and the resulting high error rates and reduce throughput. Additionally, localization problems specific to wireless devices are still confounding the community despite significant research efforts in the past decades.

Recently, significant increase in wireless data demands for a variety of applications also prompted the need for significant research toward efficient spectrum usage – many different solutions exist to increase this efficiency, but none come even close to meeting the ever increasing demand. The future of 5G wireless systems and high-speed WiFi technologies also brings new applications for content sharing, online social networks, real-time gaming, and intelligent transportation systems.