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The virtualization of network resources provides unique flexibility in service provisioning in most levels of the network stack. Softwarization of the network control and operation (SDN) is a key enabler of that development. Starting from the network core, SDN is a dominant trend in the evolution of network architectures with increased emphasis recently on the network edge. I will present some recent results in this area starting with a study on migration from legacy networking to SDN enabled network modules. The trade-off between the benefits of SDN upgrades and the cost of deployment is addressed and captured by an appropriate sub-modular function that allows to optimize the penetration pace of the technology. Validation on some real world network topologies and traffic matrices will be presented as well. Then we move our attention to the network periphery. A wireless multi-hop extension at the network edge is considered and the problem of enabling SDN is addressed via replication of SDN controllers. The delay constraints of the controlled data-path elements is appropriately modeled and the problem of locating the controllers is addressed via optimization and a proof-of concept implementation. An alternate approach is considered then for the wireless network where we assume coexistence of SDN enabled components with network islands operating under distributed adhoc routing protocols. The trade-off of the coexistence is studied and the impact of SDN penetration is evaluated. Finally we shift attention to the joint optimization of service placement and request routing in dense multi-cell wireless networks. The storage requirements of the different services as well as the processing and communication requirements of the service requests by the users need to be addressed. An algorithm that achieves close-to-optimal performance using a randomized rounding technique is presented. Extensive evaluation demonstrates that our approach can effectively utilize the available resources to maximize the number of requests served by the edge servers.

Biography

Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering atYale University. His research interests are in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, architectures and protocols of wireless systems, sensor networks, novel internet architectures and experimental platforms for network research. His mostnotable contributions include the max-weight scheduling algorithm and the back pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the ACM SIGMETRICS achievement award 2020, the inaugural INFOCOM 2007 Achievement Award "for fundamental contributions to resource allocation in communication networks," several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991). He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park, University of Ioannina and University of Thessaly, Greece.

Cyber-physical systems (CPSs) are frequently subjected to multiple and simultaneous attacks; however, defenders are often unaware of what kind of attack strategy has been adopted by the attackers. By simulating an attacker's behavior, we can predict and propose three possible attack strategies that can be combined with interdependence features and a knowledge of smart grid, i.e. Topology Structure Attack Strategy(TSAS), Socio-economic Attack Strategy(SEAS) and Power Flow Attack Strategy(PFAS). TSASs focus on node loss, SEASs concentrate on economic loss, and PFASs utilize the mechanism of power flow to trigger load-redistribution, inducing more node failures. Malicious attackers may choose different attack strategies. However, the defense mechanisms portrayed in many studies may only deal with one type of attack strategy.As such,we will divide cyber and physical nodes into four protection levels and design a coupling mode between different levels in order to establish an integrated defense mechanism against TSAS, SEAS or PFAS. Experimental results show that cyber and physical attack goals (AGs) that can achieve the expected attack effects have been explored and exploited by the attackers. When the same proportional AGs are successfully attacked, a coordinated cyber-physical attack (CCPA) has a better attack effect than cyber-attacks or physical attacks. Finally, experimental results show that an integrated defense mechanism can not only mislead the attackers and cause them to select insignificant nodes as AGs, but that it can also improve the robustness of CPSs, enhancing their ability to deal with CCPAs.

Autonomous Underwater Vehicles (AUVs) have several fundamental civilian and military applications, and Denial of Service (DoS) attacks against their communications are a serious threat. In this work, we analyze such an attack using game theory in an asymmetric scenario, in which the node under attack does not know the position of the jammer that blocks its signals. The jammer has a dual objective, disrupting communications and forcing the legitimate transmitter to spend more energy protecting its transmissions. Our model shows that, if both nodes act rationally, the transmitter is able to quickly reduce its disadvantage, estimating the location of the jammer and responding optimally to the attack.

THz-band communication is a key technology to achieve ultra-high-data-rate wireless links in beyond-5G wireless networks. A main challenge with this frequency band is that the wireless links can be easily disconnected because of beam misalignment in mobile environments. This paper focuses on beam control in THz-band wireless UAV networks, where perfect beam alignment is hard to achieve in the presence of multi-scale mobility uncertainties of the flying UAVs. We propose a learning-based stochastic beam control scheme called LeTera to reduce the outage probability of the THz-band wireless links. The scheme dynamically predicts through echo state learning the best beam width based on statistical information of the UAV mobility pattern. The scheme is evaluated using mobility traces collected through a series of UAV flight field experiments in different weather. Results show that LeTera can predict the optimal beamwidth with 99% accuracy and nearly optimal link capacity can be achieved in the presence of beam alignment latency.

Deep space communications have characteristics such as long delay, easily broken link and high code error rate. In this paper, a Raptor-like rateless spinal coding scheme is proposed by concatenating the outer systematic polar code (SPC), so as to promote the error-correction performance for the deep space communications. An interleaving operation is used to arrange the inner coding order of SPC bits. Moreover, a segmented cyclic redundancy check (CRC)-aided decoding approach is exploited to reduce the complexity of the concatenating decoding. The rateless transmission control is designed accordingly. Numerical simulation results demonstrate the performance advantage of the proposed polar-spinal codes compared to the existing rateless codes for the reliability acquisition of deep space communications.

There is a general understanding in the Wireless sensor network (WSN) community that the protocol developed solely based on theoretical/simulation considerations do not perform reliably in real-time deployments. This further motivates to carry out experimental link characterization of WSNs deployed in real-time. In addition, this is more crucial for underground mines, where the reliable operation of a wireless communication system is a concern. While received signal strength indicator (RSSI) and link quality indicator (LQI) -a hardware metric of sensor node platform such as CC2420 have been studied in the past for normal terrestrial environment; there has so far been no detailed evaluation reported in Underground mines covering different deployment scenarios. In this work, we present a detailed empirical evaluation of RSSI and LQI to estimate the packet reception rate (PRR) in several underground mine workings. We report our experimental findings from straight, near-face, and curved mine galleries carried out in real-time operational underground coal mine. From our experimental observations, we further provide a discussion on the selection of better link estimator for such high-stress environment. We find that LQI serves as a better candidate over RSSI in all deployment settings. Hence, LQI could be utilized to develop and design communication protocols to be deployed in underground mines. Furthermore, our results also indicate that a LQI value equal or above 100 and RSSI value equal or above -82 dBm has PRR at least 85% in underground mines.

In vehicular networks many applications and services require the use of vehicle position, which motivates the research on high-precision vehicle localization schemes. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet new accuracy requirements in some special scenarios. On the other hand, radio frequency identification (RFID) has become an efficient booster for internet of things (IOT) due to its low cost, battery-free, and unique identification. In this paper, we propose an RFID-based vehicle localization scheme in GPS-less environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we therefore adopt single antenna multi-frequency-based range estimation, in which the integer ambiguity problem is solved by a closed-form robust Chinese remainder theorem (CRT) algorithm. Then, Levenberg-Marquardt (LM) algorithm is exploited to obtain the location of the vehicle. Experimental results demonstrate that the proposed scheme can achieve accurate vehicle localization with error lower than 33cm at the probability of 90%.

Terahertz based in-vivo Wireless NanoSensor Networks (iWNSNs) implant nano-scale sensor nodes into the body, and uses the human body as the network center to remotely monitor vital signs. Due to the characteristics of the terahertz channel in the body and the limited resources of nano-devices, the MAC protocol of the traditional Wireless Sensor Network is not suitable for iWNSNs. In this paper, a MAC protocol based on data priority, called DPB-MAC, is proposed. This protocol considers the difference of node data services in the network and prioritizes the services of the source node according to the timeliness of the data and the energy consumption of the node itself. Subsequently, a queuing mechanism is introduced on the basis of TDMA, and the time slots of the transmission phase are allocated according to the number of nodes in the network and the priority of the node data service, thereby avoiding conflicts between nodes. Simulations results show that the protocol can effectively reduce the average end-to-end delay and improve the average throughput.

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