With the emergence of next generation networks, Network Traffic Classification (NTC) has seen greater importance in network management and security. Recently, Channel State Information (CSI) based WiFi sensing techniques have shown their potential for NTC applications [1], [2] as a privacy-preserving yet effective tool. As CSI could be prone to interference, this paper examines the performance of CSI-based NTC models under interference-induced adversarial attacks. Specifically, the impact of spectral allocation of the interference, underlying interfering network traffic type, and physical location of the interference are studied and quantified. We conducted experiments using off-the-shelf devices to test the NTC performance, with and without the adversarial interference attack of ping, buffered video streaming, and live video streaming network traffics. Subsequently, we found that spectral allocation of the attacking interference and the underlying traffic types of interference could be used to deceive the established NTC model, and different network traffic types show different robustness across the interference cases. Namely, ping suffers the most in the spectrally manipulated attack, with the classification accuracy down to as low as 23.2%, whereas Twitch might be completely misidentified as other traffic in underlying traffic type controlled attack.

As wireless networks continue to evolve into 5G and beyond, the deployment of networks will be increasingly heterogeneous comprising of base-stations or access points or distributed units serving users in different spectra utilizing different technologies. A quasi-quantum random graph coloring technique is suggested in this paper using virtual cell identifiers to coordinate across such access nodes and to enable such self-organizing networks to be optimized utilizing a heterogeneous intelligent controller across such access networks. The paper also discusses additional possibilities for future exploration.

This paper explores and suggests possibilities for the design of quantum blockchain systems that are inspired by quantum processing techniques. Quantum states are defined that can be processed either by a physical quantum computer or virtually by emulation on a classical computer where such states can be entangled across different nodes in the system. The collapse of quantum state variables are explored utilizing non-deterministic smart contract processing. A quantum blockchain network is realized with different nodes in the system interacting with each other though a communication network. Different networking use-cases are explored such as to determine which user is given access to a network at a given time, or to select the best access node for a given user.

Doubly-near-far problem in RF-powered networks can be mitigated by choosing appropriate device-to-device (D2D) communication mode and implementing energy-efficient information transfer (IT). In this work, we present a novel RF energy harvesting architecture where each transmitting-receiving user pair is allocated a disjoint channel for its communication which is fully powered by downlink energy transfer (ET) from hybrid access point (HAP). Considering that each user pair can select either D2D or cellular mode of communication, we propose an optimized transmission protocol controlled by the HAP that involves harvested energy-aware jointly optimal mode selection (MS) and time allocation (TA) for ET and IT to maximize the sum-throughput. Jointly global optimal solutions are derived by efficiently resolving the combinatorial issue with the help of optimal MS strategy for a given TA for ET. Closed-form expressions for the optimal TA in D2D and cellular modes are also derived to gain further analytical insights. Numerical results show that the joint optimal MS and TA, which significantly outperforms the benchmark schemes in terms of achievable RF-powered sum-throughput, is closely followed by the optimal TA scheme for D2D users. In fact, about 2/3 fraction of the total user pairs prefer to follow the D2D mode for efficient RF-powered IT.

Performance of RF powered communication network is bottlenecked by short downlink energy transfer range and doubly-near-far problem faced in uplink information transfer to Hybrid Access Point (HAP). Theses problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma of R by proposing a novel integrated information and energy relaying (i2ER) protocol. It also provides insights on the relay positions suited for either energy relaying (ER), or information relaying (IR), or where neither ER nor IR will be useful. In this regard, while considering Rician fading channels, we derive the expression for mean harvested dc power at S via energy transfer from HAP and ER from R. We also derive the closed-form outage probability expression for decode-and-forward relaying with maximal-ratio-combining at HAP over Rician channels. Finally numerical results validating the analytical results and providing insights on the relaying role (ER and/or IR) of RF harvesting helping node are presented.

The performance of RF powered communication networks is bottlenecked by the short downlink energy transfer range and the doubly near-far problem faced in uplink information transfer to hybrid access point (HAP). These problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma at R by providing insights into its optimal positions suited for either energy relaying (ER) or information relaying (IR). It also investigates the possibilities of integrated ER and IR along with the regions where neither ER nor IR will be useful. In this regard, while considering Rician fading channels and practical nonlinear RF energy harvesting model, the expression for mean harvested dc power at S via energy transfer from HAP and ER from R is first derived. The closedform outage probability expression is also derived for decodeand-forward relaying with maximal-ratio-combining at HAP over Rician channels. Using these expressions, insights into optimal relaying mode is obtained along with global-optimal utilization of harvested energy at R for ER and IR to maximize the delaylimited RF-powered throughput. Numerical results validate the analysis and provide insights into the optimal relaying mode.

One of the key goals of Network Functions Virtualization (NFV) is achieving energy efficiency through workload consolidation. A good example for maximizing energy savings is the Virtualization of Content Delivery Networks (vCDNs) NFV use case where the video streaming workloads exhibit significant difference between prime-time and non-prime-time usage of the infrastructure. This paper examines the practical challenges in maximizing energy efficiency for vCDN workloads. This paper proposes an open NFV architectural framework for conveying content virality information from Cloud applications such as YouTube, Twitter and mechanisms for leveraging it to maximize the energy efficiency for vCDN workloads. This paper also proposes a more general architecture for any Cloud/NFV application that may experience virality.

With increasing popularity of Netflix, Yahoo! Video, etc., interactive multimedia services such as video-on-demand (VoD) provide an interesting and rich field of research. The advent of smarter wireless devices has surged the need for such services through wireless connectivity. However, personalization of individual user needs, reducing latency, coupled with maintaining low operational costs provides a challenging problem. In this paper, we propose an efficient VoD system, for wireless mobile devices, based on a novel caching algorithm, Intelligent Network Caching Algorithm (INCA) using analytics-driven look ahead scheme for both prefetch and replacement policies to deliver higher performance. This enables enhanced Quality Of Experience (QoE) of users with limited infrastructural changes and low operational cost. Alongside, we develop theoretical formulation of the QoE optimization problem that lies at the intersection of MPC (Markov Predictive Control) and MDP (Markov Decision Process). Empirical analysis over realistic user video query logs demonstrate better cache hit rate and QoE with low prefetch bandwidth, compared to existing caching schemes.

It is well-known that the performance of Web-browsing as well as mobile applications (or apps) suffers on today's cellular networks. In this work, we perform a systematic measurement study of more than 50 popular apps and 2 cellular networks, and discover that while cellular networks have predictable latency, it is the path between exit points of cellular networks (e.g., GGSN) and cloud-servers that degrades apps performance. High latency and unpredictability over this path affects browsing and activity completion times of apps, worsening the performance by several magnitudes. Furthermore, we find that as the number of apps on mobile devices increases, cellular networks in turn suffer due to large number of active connections, primarily used for push notifications, experiencing heavy signaling overhead in the network. Towards accelerating the performance of apps and improving their operational efficiency, we envision an easy to deploy operator-managed platform, and study two architectural optimizations that sit at vantage points inside cellular networks: virtual app-server (vApp) and network-assisted, virtual push-notification server (vPNS). vApps improve apps' browsing experience while vPNSs take the burden of carrying periodic message off cellular networks. Using trace-driven simulations, we find that vApps can improve activity completion times by more than 3-fold, whereas vPNS can reduce the signaling load by a factor of 6 in cellular networks and reduce energy consumption by a factor of 2 on mobile devices.

The articles in this special issue focus on the technologies and applications that support molecular, biological, and multiscale communication.

In this paper, we propose PROMETHEUS, a framework for proactive temperature aware scheduling of embedded workloads on single instruction set architecture heterogeneous multiprocessor systems-on-chip. It systematically combines temperature aware task assignment, task migration, and dynamic voltage and frequency scaling. PROMETHEUS is based on our novel low overhead temperature prediction technique, Tempo. In contrast to previous work, Tempo allows accurate estimation of potential thermal effects of future scheduling decisions without requiring any runtime adaptation. It reduces the maximum prediction error by up to an order of magnitude. Using Tempo, PROMETHEUS framework provides two temperature aware scheduling techniques that proactively avoid power states leading to future thermal emergencies while matching the performance needs to the workload requirements. The first technique, TempoMP, integrates Tempo with an online multiparametric optimization method to guide decisions on task assignment, migration, and setting core power states in a temperature aware fashion. Our second scheduling technique, TemPrompt uses Tempo in a heuristic algorithm that provides comparable efficiency at lower overhead. On average, these two techniques reduce the lateness of the tasks by 2.5× and energy-lateness product (ELP) by 5× compared to the previous work.

Current approaches in combating cancer employ nanoparticle-based therapies that harness biological and chemical phenomena to deliver anticancer drugs. Successful studies have shown the utility of leveraging biochemical cues to recruit nanoparticles to the tumor microenvironment. In this paper, we explore the possibility of using collaborative wireless nanobots or WiNoBots to efficiently target and accumulate at the site of tumor tissues. Unlike biological and chemical approaches, nanobots directly communicate with each other via wireless signals. Signaling nanobots emit a wireless signal to receiving nanobots, which move in the direction of the electric field gradient. Together these nanobots concert a collaborative action to home to the site of a signal. Using computer simulations, we show that collaborative wireless signaling produces an aggregate wavefront that reduces the mean path length that nanobots travel to reach the site of a diseased tissue. Our results motivate further studies of these unique nanobot wireless systems, which may be able to confine and concentrate a therapeutic response.

While the connectivity, sensing, and computational capabilities of today's smartphones have increased, congestion in wireless channels and energy consumption remain major issues. We present a technique for model-driven adaptive environmental sensing, designed to reduce the amount of data that is communicated over the cellular network. In simulations of an exposure monitoring system, our technique reduced the number of messages sent by 85%, obtained power savings of 80% while generating a global model of pollution with error of maximum 0.5 ppm, a negligible amount for the application of interest.

This paper proposes a hierarchical wireless system for real-time tracking of moving assets. There are multiple layers of wirelessly connected assets that need to be tracked. The small items, such as bottles, packed in a box are monitored by a sensor module contained within each box. All boxes shipped in a cargo truck form an ad hoc network that adapts to the battery level and link quality. A small subset of boxes are dynamically selected to report sensor information to a backend server. A wireless networking protocol is proposed to minimize the power consumption based on available energy and wireless link conditions in the distributed hierarchical system. The system is validated by simulation and prototype implementations.

Energy consumption is a critical parameter in wireless healthcare systems which consist of battery operated devices such as sensors and local aggregators. The system battery lifetime depends on the allocation of processing, sensing, and communication tasks to devices of the system. In this paper, we optimize the battery life of a wireless healthcare system by efficiently assigning tasks to the available resources. There are several dynamically changing characteristics in the system, such as task parameters (processing complexity, arrival rate, and output data), each device's available battery capacity, varying wireless channel conditions, and network load. Our dynamic task assignment algorithm, ¿DynAHeal¿ adapts to such changing conditions, and improves the battery life. Our experiments show that the task assignment given by DynAHeal improves the overall system lifetime under varying dynamic conditions on an average 60% relative to sending all the data for processing to the base station, and 35% with respect to an optimal static design time assignment.

This paper discusses three current and emerging heterogeneous wireless wide area network architectures, namely, relay-based, direct-access-based, and whitespace-based, architectures that can support incremental wireless data services. For such heterogeneous architectures, the paper suggests a method to estimate the pricing for an incremental wireless data services around an established mean state of utilization of available wireless capacity in a wireless network. A 6-tuple (μ, σ, λ, A, B, K) is defined as the network state, and the cost, revenue, and profit from incremental data services are provided. The modeling is general and is applicable to different operator networks.

Joint routing-and-scheduling has been considered in wireless mesh networks for its significant performance improvement. While existing work assumes it, accurate traffic information is usually not available due to traffic dynamics, as well as inaccuracy and delay in its measurement and dissemination. In addition, the joint routing and scheduling usually requires a centralized controller to calculate the optimal routing and scheduling and distribute such policies to all the nodes. Thus, even if the accurate traffic information is always available, the central controller has to compute the routing and scheduling repeatedly because the traffic demands change continuously. This leads to prohibitive computation and distribution overhead. Therefore, in this paper, we propose a joint routing-scheduling scheme that achieves robust performance under traffic information uncertainty. In particular, it achieves worst-case optimal performance under a range of traffic conditions. This unique feature validates the use of centralized routing and scheduling in wireless mesh networks. As long as the traffic variation is within the estimation range, the routing and scheduling do not need to be recomputed and redistributed. Through extensive simulations, we show that our proposed scheme meets the objective (i.e., optimizes the worst-case performance). Moreover, although it only guarantees the worst-case performance in theory, its average performance is also good. For example, our proposed scheme can perform better than a fixed optimal routing and scheduling scheme in more than 80 percent of 500 random traffic instances. Our scheme provides insights on the desired properties of multipath routing, namely, spatial reuse and load balancing.

This paper suggests a system architecture for wireless wide-area-networking access using adhoc networking between a mobile client node without direct connectivity to a wireless-wide-area-network and a mobile service provider node with connectivity to a wireless-wide-area-network. It provides a means for securely providing such adhoc wireless networking services using a server for tunneling and routing, registration and authentication. The architecture also provides support for handoff of a client node from one service provider to another with persistence of a tunnel between the client and the server enabling a soft-handoff Different wireless protocols may be used for adhoc networking, with filtered interconnection of authenticated clients implemented at a service provider node. The architecture is applicable across different wide-area-network protocols, and provides simultaneous support for multiple wide-area-network protocols.

We propose an integrated cross-layer optimization algorithm for maximizing the decoded video quality of delay-constrained streaming in a quality-of-service (QoS) enabled multi-hop wireless mesh network. The key to our algorithm is the synergistic optimization of control parameters at each node of the multi-hop network, across the protocol layers - application, network, medium access control (MAC) and physical (PHY) layers, as well as end-to-end, i.e. across the various network nodes. To drive this optimization, we assume an overlay network infrastructure, which conveys information on the conditions of each link. Quantitative results are presented that demonstrate the merits and the need for cross-layer optimization in an efficient solution for real-time video transmission using existing protocols and infrastructures

This paper proposes weighted combining of retransmitted vectors for improved data recovery in single and multiple-antenna wireless LAN transceivers. Combining is suggested at multiple levels in the receiver design: at the post-equalizer, post-demapper and post-decoder stages. The best performance is obtained by exploiting diversity in time, equivalent to performing maximal-ratio combining of the information from repeated transmissions of the same data packet, separated in time (MRC-In-Time), over a SISO or MIMO WLAN channel. The proposed schemes can also be applied to a cooperative-diversity scenario where a receiver combines transmissions of the same message from multiple sources.

The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and co-existence. By combining the continuous scalability of MPEG-4 fine-granularity-scalability (FGS) video coding with the robustness of variable modulation, a powerful method of video transmission is achieved that allows a video stream to be viewed with imperceptible degradation over a continuously changing wireless channel. In addition, sigmoid functions for throughput modelling for 802.11a networks are presented, and a game theoretic approach is suggested for further adapting the modulation and channel coding strategy to improve the video transmission performance under different wireless channel conditions.

Edge detection and shape detection in digital images are very computationally intensive problems. Parallel algorithms can potentially provide significant speedups while preserving the quality of the result obtained. Hough and Radon Transforms are projection-based transforms which are commonly used for edge detection and shape detection respectively. We propose in this paper various new parallel algorithms which exploit both task and data parallelism available in Hough and Radon transforms algorithms. A memory scalable aggressive task parallel algorithm is shown to be the most optimal algorithm in terms of memory scalability and performance on an IBM SP2.

We propose three synchronous parallel algorithms for scalable parallel test set partitioned fault simulation. The algorithms are based on a new two-stage approach to parallelizing fault simulation for sequential VLSI circuits in which the test set is partitioned among the available processors, The test set partitioning inherent in the algorithms overcomes the good circuit logic simulation bottleneck that exists in traditional fault partitioned approaches to parallel fault simulation. The implementations were done on a shared memory multiprocessor and on a network of workstations. Two of the algorithms show a small degree of pessimism in a few cases, with respect to the fault coverage as compared with a uniprocessor run, while the third algorithm provides the same results as in a uniprocessor run. All algorithms provide excellent speedups and perform much better than a traditional fault partitioned approach, on both shared and distributed memory parallel platforms.

The Boundary Element Method is a widely-used discretization technique for solving boundary-value problems in engineering analysis. The solution of large problems by this method is limited by the storage and computational requirements for the generation and solution of large matrix systems resulting from the discretization. We discuss the implementation of these computations on the IBM SP-2 distributed-memory parallel computer, for applications involving the 3DD Laplace and Helmholtz equations.

Linear predictive models seek to optimally extract information about a sample of a signal based on some subset of its causal past. Very little work has been done in investigating the importance and choice of this subset (support) in the prediction process. The paper addresses the problem of finding the optimal support for use by a linear predictive model. The authors derive a general result relating the distortion incurred in predicting a sample of a stationary signal based on a causal support in terms of the Wiener coefficients of a larger support and the autocorrelation matrix. Based on the above result, they derive an algorithm which optimally reduces the size of the support by one at each stage. The algorithm is tested on the Barbara image for image estimation and on the football image sequence for pel-recursive motion compensation and is shown to outperform (by large margins in some cases) conventionally chosen supports.>

We present in this paper two parallel algorithms for the two-dimensional discrete wavelet transform. A mathematical model for the computation vs. communication tradeoff for these algorithms is presented and the scalability of the algorithms is analyzed. The wormhole routing model was used to model the communication costs involved. The algorithms were implemented on the Connection Machine-5 (CM-5^{a}). The theoretical results obtained were validated by the simulations on the CM-5.